diff --git a/CLAUDE.md b/CLAUDE.md index 5e0589a..df17515 100644 --- a/CLAUDE.md +++ b/CLAUDE.md @@ -31,7 +31,7 @@ rm -rf book/_build pytest # Run tests with coverage -pytest --cov=src/BetterCodeBetterScience --cov-report term-missing +pytest --cov=src/bettercode --cov-report term-missing # Run specific test modules pytest tests/textmining/ @@ -62,7 +62,7 @@ pre-commit run --all-files ## Project Structure - `book/` - MyST markdown chapters (configured in myst.yml) -- `src/BetterCodeBetterScience/` - Example Python code referenced in book chapters +- `src/bettercode/` - Example Python code referenced in book chapters - `tests/` - Test examples demonstrating testing concepts from the book - `data/` - Data files for examples - `scripts/` - Utility scripts diff --git a/book/AI_coding_assistants.md b/book/AI_coding_assistants.md index 8644328..770d05a 100644 --- a/book/AI_coding_assistants.md +++ b/book/AI_coding_assistants.md @@ -96,7 +96,7 @@ def linear_regression_normal_eq(X: np.ndarray, y: np.ndarray) -> np.ndarray: ``` Unlike the previous examples, the code now includes type hints. -It's always a bad idea to generalize from a single result, so we ran these prompts through ChatGPT 10 times each (using the Openai API to generate them programmatically; see the [notebook](../src/BetterCodeBetterScience/incontext_learning_example.ipynb)). +It's always a bad idea to generalize from a single result, so we ran these prompts through ChatGPT 10 times each (using the Openai API to generate them programmatically; see the [notebook](../src/bettercode/incontext_learning_example.ipynb)). Here are the function signatures generated for each of the 10 runs without mentioning type hints: ``` @@ -272,7 +272,7 @@ In addition to the time and labor of running things by hand, it is also a recipe You might be asking at this point, "What's an API"? The acronym stands for "Application Programming Interface", which is a method by which one can programmatically send commands to and receive responses from a computer system, which could be local or remote[^1]. To understand this better, let's see how to send a chat command and receive a response from the Claude language model. -The full outline is in [the notebook](https://github.com/poldrack/BetterCodeBetterScience/blob/main/src/BetterCodeBetterScience/language_model_api_prompting.ipynb). +The full outline is in [the notebook](https://github.com/poldrack/BetterCodeBetterScience/blob/main/src/bettercode/language_model_api_prompting.ipynb). Coding agents are very good at generating code to perform API calls, so I used Claude Sonnet 4 to generate the example code in the notebook: ```python @@ -358,7 +358,7 @@ Let's see how we could get the previous example to return a JSON object containi Here we will use a function called `send_prompt_to_claude()` that wraps the call to the model object and returns the text from the result: ```python -from BetterCodeBetterScience.llm_utils import send_prompt_to_claude +from bettercode.llm_utils import send_prompt_to_claude json_prompt = """ What is the capital of France? diff --git a/book/data_management.md b/book/data_management.md index ad85f22..3f71afa 100644 --- a/book/data_management.md +++ b/book/data_management.md @@ -471,7 +471,7 @@ df_merged = pd.concat([df1, df2, df3], ignore_index=True) The most common file formats are *comma-separated value* (CSV) or *tab-separated value* (TSV) files. Both of these have the benefit of being represented in plain text, so their contents can be easily examined without any special software. I generally prefer to use tabs rather than commas as the separator (or *delimiter*), primarily because they can more easily naturally represent longer pieces of text that may include commas. These can also be represented using CSV, but they require additional processing in order to *escape* the commas within the text so that they are not interpreted as delimiters. -Text file formats like CSV and TSV are nice for their ease of interpretability, but they are highly inefficient for large data compared to optimized file formats, such as the *Parquet* format. To see this in action, I loaded a brain image and saved all of the non-zero data points (857,785 to be exact) to a data frame, which I then saved to CSV and Parquet formats; see [the management notebook](src/BetterCodeBetterScience/data_management.ipynb) for details. Looking at the resulting files, we can see that the Parquet file is only about 20% the size of the CSV file: +Text file formats like CSV and TSV are nice for their ease of interpretability, but they are highly inefficient for large data compared to optimized file formats, such as the *Parquet* format. To see this in action, I loaded a brain image and saved all of the non-zero data points (857,785 to be exact) to a data frame, which I then saved to CSV and Parquet formats; see [the management notebook](src/bettercode/data_management.ipynb) for details. Looking at the resulting files, we can see that the Parquet file is only about 20% the size of the CSV file: ```bash ➤ du -sk /tmp/brain_tabular.* @@ -718,7 +718,7 @@ In this section we discuss data organization. The most important principle of da ### File granularity -One common decision that we need to make when managing data is to save data in more smaller files versus fewer larger files. The right answer to this question depends in part on how we will have to access the data. If we only need to access a small portion of the data and we can easily determine which file to open to obtain those data, then it probably makes sense to save many small files. However, if we need to combine data across many small files, then it likely makes sense to save the data as one large file. For example, in the [data management notebook](src/BetterCodeBetterScience/data_management.ipynb) there is an example where we create a large (10000 x 100000) matrix of random numbers, and save them either to a single file or to a separate file for each row. When loading these data, the loading of the single file is about 5 times faster than loading the individual files. +One common decision that we need to make when managing data is to save data in more smaller files versus fewer larger files. The right answer to this question depends in part on how we will have to access the data. If we only need to access a small portion of the data and we can easily determine which file to open to obtain those data, then it probably makes sense to save many small files. However, if we need to combine data across many small files, then it likely makes sense to save the data as one large file. For example, in the [data management notebook](src/bettercode/data_management.ipynb) there is an example where we create a large (10000 x 100000) matrix of random numbers, and save them either to a single file or to a separate file for each row. When loading these data, the loading of the single file is about 5 times faster than loading the individual files. Another consideration about the number of files has to do with storage systems that are commonly used on high-performance computing systems. On these systems, it is common to have separate quotas for total space used (e.g., in terabytes) as well as for the number of *inodes*, which are structures that store information about files and folders on a UNIX filesystem. Thus, generating many small files (e.g., millions) can sometimes cause problems on these systems. For this reason, we generally err on the side of generating fewer larger files versus more smaller files when working on high-performance computing systems. @@ -1038,7 +1038,7 @@ unlock(ok): my_datalad_repo/data/demographics.csv (file) We then use a Python script to make the change, which in this case is removing some columns from the dataset: ```bash -➤ python src/BetterCodeBetterScience/modify_data.py my_datalad_repo/data/demographics.csv +➤ python src/bettercode/modify_data.py my_datalad_repo/data/demographics.csv ``` @@ -1074,7 +1074,7 @@ nothing to save, working tree clean Although the previous example was meant to provide background on how DataLad works, in practice there is actually a much easier way to accomplish these steps, which is by using the [`datalad run`](https://docs.datalad.org/en/stable/generated/man/datalad-run.html) command. This command will automatically take care of fetching and unlocking the relevant files, running the command, and then committing the files back in, generating a commit message that tracks the specific command that was used: ```bash -➤ datalad run -i my_datalad_repo/data/demographics.csv -o my_datalad_repo/data/demographics.csv -- uv run src/BetterCodeBetterScience/modify_data.py my_datalad_repo/data/demographics.csv +➤ datalad run -i my_datalad_repo/data/demographics.csv -o my_datalad_repo/data/demographics.csv -- uv run src/bettercode/modify_data.py my_datalad_repo/data/demographics.csv [INFO ] Making sure inputs are available (this may take some time) unlock(ok): my_datalad_repo/data/demographics.csv (file) [INFO ] == Command start (output follows) ===== @@ -1082,7 +1082,7 @@ unlock(ok): my_datalad_repo/data/demographics.csv (file) Uninstalled 1 package in 1ms Installed 1 package in 1ms [INFO ] == Command exit (modification check follows) ===== -run(ok): /Users/poldrack/Dropbox/code/BetterCodeBetterScience (dataset) [uv run src/BetterCodeBetterScience/modif...] +run(ok): /Users/poldrack/Dropbox/code/BetterCodeBetterScience (dataset) [uv run src/bettercode/modif...] add(ok): data/demographics.csv (file) save(ok): my_datalad_repo (dataset) add(ok): my_datalad_repo (dataset) @@ -1095,12 +1095,12 @@ commit 3ef3b94a0abffec6a8db7570a97339f48ee728ed (HEAD -> text/datamgmt-Nov3) Author: Russell Poldrack Date: Mon Dec 15 13:28:06 2025 -0800 - [DATALAD RUNCMD] uv run src/BetterCodeBetterScience/modif... + [DATALAD RUNCMD] uv run src/bettercode/modif... === Do not change lines below === { "chain": [], - "cmd": "uv run src/BetterCodeBetterScience/modify_data.py my_datalad_repo/data/demographics.csv", + "cmd": "uv run src/bettercode/modify_data.py my_datalad_repo/data/demographics.csv", "exit": 0, "extra_inputs": [], "inputs": [ @@ -1220,7 +1220,7 @@ The question that I will ask is as follows: How well can the biological similari - A dataset of genome-wise association study (GWAS) results for specific traits obtained from [here](https://www.ebi.ac.uk/gwas/docs/file-downloads). - Abstracts that refer to each of the traits identified in the GWAS result, obtained from the [PubMed](https://pubmed.ncbi.nlm.nih.gov/) database. -I will not present all of the code for each step; this can be found [here](src/BetterCodeBetterScience/database_example_funcs.py) and [here](src/BetterCodeBetterScience/database.py). Rather, I will show portions that are particularly relevant to the databases being used. +I will not present all of the code for each step; this can be found [here](src/bettercode/database_example_funcs.py) and [here](src/bettercode/database.py). Rather, I will show portions that are particularly relevant to the databases being used. ### Adding GWAS data to a document store @@ -1236,7 +1236,7 @@ In this case, looking at the data we see that several columns contain multiple v gwas_data = get_exploded_gwas_data() ``` -We can now import the data from this data frame into a MongoDB collection, mapping each unique trait to the genes that are reported as being associated with it. First I generated a separate function that sets up a MongoDB collection (see `setup_mongo_collection` [here](src/BetterCodeBetterScience/database.py)). We can then use that function to set up our gene set collection: +We can now import the data from this data frame into a MongoDB collection, mapping each unique trait to the genes that are reported as being associated with it. First I generated a separate function that sets up a MongoDB collection (see `setup_mongo_collection` [here](src/bettercode/database.py)). We can then use that function to set up our gene set collection: ```python diff --git a/book/project_organization.md b/book/project_organization.md index b8dc513..e5d5cde 100644 --- a/book/project_organization.md +++ b/book/project_organization.md @@ -237,7 +237,7 @@ A final way that one might use notebooks is as a way to create standalone progra It's very common for researchers to use different coding languages to solve different problems. A common use case is the Python user who wishes to take advantage of the much wider range of statistical methods that are implemented in R. There is a package called `rpy2` that allows this within pure Python code, but it can be cumbersome to work with, particularly due to the need to convert complex data types. Fortunately, Jupyter notebooks provide a convenient solution to this problem, via [*magic* commands](https://scipy-ipython.readthedocs.io/en/latest/interactive/magics.html). These are commands that start with either a `%` (for line commands) or `%%` for cell commands, which enable additional functionality. -An example of this can be seen in the [mixing_languages.ipynb](src/BetterCodeBetterScience/notebooks/mixing_languages.ipynb) notebook, in which we load and preprocess some data using Python and then use R magic commands to analyze the data using a package only available within R. In this example, we will work with data from a study published by our laboratory (Eisenberg et al., 2019), in which 522 people completed a large battery of psychological tests and surveys. We will focus here on the responses to a survey known as the "Barratt Impulsiveness Scale" which includes 30 questions related to different aspects of the psychological construct of "impulsiveness"; for example, "I say things without thinking" or "I plan tasks carefully". Each participant rated each of these statements on a four-point scale from 'Rarely/Never' to 'Almost Always/Always'; the scores were coded so that the number 1 always represented the most impulsive choice and 4 represented the most self-controlled choice. +An example of this can be seen in the [mixing_languages.ipynb](src/bettercode/notebooks/mixing_languages.ipynb) notebook, in which we load and preprocess some data using Python and then use R magic commands to analyze the data using a package only available within R. In this example, we will work with data from a study published by our laboratory (Eisenberg et al., 2019), in which 522 people completed a large battery of psychological tests and surveys. We will focus here on the responses to a survey known as the "Barratt Impulsiveness Scale" which includes 30 questions related to different aspects of the psychological construct of "impulsiveness"; for example, "I say things without thinking" or "I plan tasks carefully". Each participant rated each of these statements on a four-point scale from 'Rarely/Never' to 'Almost Always/Always'; the scores were coded so that the number 1 always represented the most impulsive choice and 4 represented the most self-controlled choice. In order to enable the R magic commands, we first need to load the rpy2 extension for Jupyter: @@ -526,7 +526,7 @@ test output from container To create a reproducible software execution environment, we will often need to create our own new Docker image that contains the necessary dependencies and application code. AI coding tools are generally quite good at creating the required `Dockerfile` that defines the image. We use the following prompt to Claude Sonnet 4: ``` -I would like to generate a Dockerfile to define a Docker image based on the python:3.13.9 image. The Python package wonderwords should be installed from PyPi. A local Python script should be created that creates a random sentence using wonderwords.RandomSentence() and prints it. This script should be the entrypoint for the Docker container. Create this within src/BetterCodeBetterScience/docker-example inside the current project. Do not create a new workspace - use the existing workspace for this project. +I would like to generate a Dockerfile to define a Docker image based on the python:3.13.9 image. The Python package wonderwords should be installed from PyPi. A local Python script should be created that creates a random sentence using wonderwords.RandomSentence() and prints it. This script should be the entrypoint for the Docker container. Create this within src/bettercode/docker-example inside the current project. Do not create a new workspace - use the existing workspace for this project. ``` Here is the content of the resulting `Dockerfile`: diff --git a/book/software_engineering.md b/book/software_engineering.md index b6b1575..09c1074 100644 --- a/book/software_engineering.md +++ b/book/software_engineering.md @@ -758,7 +758,7 @@ C = 299792458 We could then import this from our module within the iPython shell: ``` -In: from BetterCodeBetterScience.constants import C +In: from bettercode.constants import C In: C Out: 299792458 @@ -793,7 +793,7 @@ class Constants: Then within our iPython shell, we generate an instance of the Constants class, and see what happens if we try to change the value once it's instantiated: ``` -In: from BetterCodeBetterScience.constants import Constants +In: from bettercode.constants import Constants In: constants = Constants() @@ -806,7 +806,7 @@ AttributeError Traceback (most recent call last) Cell In[4], line 1 ----> 1 constants.C = 42 -File ~/Dropbox/code/BetterCodeBetterScience/src/BetterCodeBetterScience/constants.py:11, in Constants.__setattr__(self, name, value) +File ~/Dropbox/code/BetterCodeBetterScience/src/bettercode/constants.py:11, in Constants.__setattr__(self, name, value) 10 def __setattr__(self, name, value): ---> 11 raise AttributeError("Constants cannot be modified") @@ -847,8 +847,8 @@ We see that `ruff` detects both formatting problems (such as the lack of spaces We can also use `ruff` from the command line to detect and fix code problems: ```bash -❯ ruff check src/BetterCodeBetterScience/formatting_example.py -src/BetterCodeBetterScience/formatting_example.py:6:1: F403 `from numpy.random import *` used; unable to detect undefined names +❯ ruff check src/bettercode/formatting_example.py +src/bettercode/formatting_example.py:6:1: F403 `from numpy.random import *` used; unable to detect undefined names | 4 | # Poorly formatted code for linting example 5 | @@ -858,7 +858,7 @@ src/BetterCodeBetterScience/formatting_example.py:6:1: F403 `from numpy.random i 8 | mynum=randint(0,100) | -src/BetterCodeBetterScience/formatting_example.py:8:7: F405 `randint` may be undefined, or defined from star imports +src/bettercode/formatting_example.py:8:7: F405 `randint` may be undefined, or defined from star imports | 6 | from numpy.random import * 7 | @@ -872,12 +872,12 @@ Found 2 errors. Most linters can also automatically fix the issues that they detect in the code. `ruff` modifies the file in place, so we will first create a copy (so that our original remains intact) and then run the formatter on that copy: ```bash -❯ cp src/BetterCodeBetterScience/formatting_example.py src/BetterCodeBetterScience/formatting_example_ruff.py +❯ cp src/bettercode/formatting_example.py src/bettercode/formatting_example_ruff.py -❯ ruff format src/BetterCodeBetterScience/formatting_example_ruff.py +❯ ruff format src/bettercode/formatting_example_ruff.py 1 file reformatted -❯ diff src/BetterCodeBetterScience/formatting_example.py src/BetterCodeBetterScience/formatting_example_ruff.py +❯ diff src/bettercode/formatting_example.py src/bettercode/formatting_example_ruff.py 1,3d0 < < diff --git a/book/testing.md b/book/testing.md index f1f7059..2841392 100644 --- a/book/testing.md +++ b/book/testing.md @@ -103,10 +103,10 @@ def test_escape_velocity(): We can run this using `pytest` (more about this later), which tells us that the test passes: ```bash -❯ pytest src/BetterCodeBetterScience/escape_velocity.py +❯ pytest src/bettercode/escape_velocity.py ====================== test session starts ====================== -src/BetterCodeBetterScience/escape_velocity.py .. [100%] +src/bettercode/escape_velocity.py .. [100%] ======================= 1 passed in 0.10s ======================= ``` @@ -116,10 +116,10 @@ If the returned value didn't match the known value (within a given level of tole For example, if we had mis-specified the expected value as 1186.0, we would have seen an error like this: ```bash -❯ pytest src/BetterCodeBetterScience/escape_velocity.py +❯ pytest src/bettercode/escape_velocity.py ====================== test session starts ====================== -src/BetterCodeBetterScience/escape_velocity.py F [100%] +src/bettercode/escape_velocity.py F [100%] =========================== FAILURES =========================== _____________________ test_escape_velocity _____________________ @@ -138,9 +138,9 @@ E assert False E + where False = (1186.0, 11185.97789184991) E + where = np.allclose -src/BetterCodeBetterScience/escape_velocity.py:26: AssertionError +src/bettercode/escape_velocity.py:26: AssertionError ===================== short test summary info ===================== -FAILED src/BetterCodeBetterScience/escape_velocity.py::test_escape_velocity - AssertionError: Test failed! +FAILED src/bettercode/escape_velocity.py::test_escape_velocity - AssertionError: Test failed! ======================== 1 failed in 0.11s ======================== ``` @@ -280,11 +280,11 @@ def test_find_outliers_identical_values(): Running this with the original function definition, we see that it fails: ```python -❯ pytest src/BetterCodeBetterScience/bug_driven_testing.py +❯ pytest src/bettercode/bug_driven_testing.py =========================== test session starts =========================== collected 2 items -src/BetterCodeBetterScience/bug_driven_testing.py .F [100%] +src/bettercode/bug_driven_testing.py .F [100%] ================================ FAILURES ================================= ___________________ test_find_outliers_identical_values ___________________ @@ -294,7 +294,7 @@ ___________________ test_find_outliers_identical_values ___________________ > outliers = find_outliers(data, threshold=2.0) -src/BetterCodeBetterScience/bug_driven_testing.py:50: +src/bettercode/bug_driven_testing.py:50: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ data = [5, 5, 5, 5, 5], threshold = 2.0 @@ -327,9 +327,9 @@ data = [5, 5, 5, 5, 5], threshold = 2.0 > z_score = abs(value - mean) / std # Bug: std can be 0! E ZeroDivisionError: float division by zero -src/BetterCodeBetterScience/bug_driven_testing.py:31: ZeroDivisionError +src/bettercode/bug_driven_testing.py:31: ZeroDivisionError ========================= short test summary info ========================= -FAILED src/BetterCodeBetterScience/bug_driven_testing.py::test_find_outliers_identical_values +FAILED src/bettercode/bug_driven_testing.py::test_find_outliers_identical_values - ZeroDivisionError: float division by zero ======================= 1 failed, 1 passed in 0.10s ======================= ``` @@ -347,11 +347,11 @@ Here we add a comment to explain the intention of the statement. Running the tests now will show that the problem is fixed: ```python -❯ pytest src/BetterCodeBetterScience/bug_driven_testing.py +❯ pytest src/bettercode/bug_driven_testing.py =========================== test session starts =========================== collected 2 items -src/BetterCodeBetterScience/bug_driven_testing.py .. [100%] +src/bettercode/bug_driven_testing.py .. [100%] ============================ 2 passed in 0.08s ============================ @@ -546,7 +546,7 @@ def test_distance_same_y(): Now that we have our tests, we can run them using the `pytest` command: ```bash -pytest src/BetterCodeBetterScience/distance_testing +pytest src/bettercode/distance_testing ``` This command will cause pytest to search (by default) for any files named `test_*.py` or `*_test.py` in the relevant path, and the select any functions whose name starts with the prefix "test". @@ -567,7 +567,7 @@ In our research, it was not uncommon for ChatGPT to generate incorrect test valu Once we fix the expected value for that test (the square root of 89), then we can rerun the tests and see that they have passed: ```bash -python -m pytest pytest src/BetterCodeBetterScience/distance_testing +python -m pytest pytest src/bettercode/distance_testing ==================== test session starts ===================== src/codingforscience/simple_testing/test_distance.py . [ 16%] @@ -608,7 +608,7 @@ def test_escape_velocity(): When we run this test (renaming it `test_escape_velocity_gpt4`), we see that one of the tests fails: ```bash -❯ pytest src/BetterCodeBetterScience/escape_velocity.py::test_escape_velocity_gpt4 +❯ pytest src/bettercode/escape_velocity.py::test_escape_velocity_gpt4 ==================================== test session starts ==================================== platform darwin -- Python 3.12.0, pytest-8.4.1, pluggy-1.5.0 rootdir: /Users/poldrack/Dropbox/code/BetterCodeBetterScience @@ -616,7 +616,7 @@ configfile: pyproject.toml plugins: cov-5.0.0, anyio-4.6.0, hypothesis-6.115.3, mock-3.14.0 collected 1 item -src/BetterCodeBetterScience/escape_velocity.py F [100%] +src/bettercode/escape_velocity.py F [100%] ========================================= FAILURES ========================================== _________________________________ test_escape_velocity_gpt4 _________________________________ @@ -643,9 +643,9 @@ E comparison failed E Obtained: 59564.97 E Expected: 60202.716344497014 ± 60.2027 -src/BetterCodeBetterScience/escape_velocity.py:52: AssertionError +src/bettercode/escape_velocity.py:52: AssertionError ================================== short test summary info ================================== -FAILED src/BetterCodeBetterScience/escape_velocity.py::test_escape_velocity_gpt4 - assert 60202.716344497014 ± 60.2027 == 59564.97 +FAILED src/bettercode/escape_velocity.py::test_escape_velocity_gpt4 - assert 60202.716344497014 ± 60.2027 == 59564.97 ===================================== 1 failed in 0.12s ===================================== ``` @@ -814,7 +814,7 @@ The `pytest-cov` extension for the `pytest` testing package can provide us with ---------- coverage: platform darwin, python 3.12.0-final-0 ---------- Name Stmts Miss Cover Missing ------------------------------------------------------------------------------------ -src/BetterCodeBetterScience/textmining/textmining.py 30 1 97% 70 +src/bettercode/textmining/textmining.py 30 1 97% 70 ------------------------------------------------------------------------------------ TOTAL 30 1 97% ``` @@ -1207,7 +1207,7 @@ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ tests/property_based_testing/test_propertybased_smoke.py:19: in test_linear_regression_without_validation params = linear_regression(X, y, validate=False) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -src/BetterCodeBetterScience/my_linear_regression.py:61: in linear_regression +src/bettercode/my_linear_regression.py:61: in linear_regression return np.linalg.inv(X.T @ X) @ X.T @ y ^^^^^^^^^^^^^^^^^^^^^^ .venv/lib/python3.12/site-packages/numpy/linalg/_linalg.py:615: in inv diff --git a/book/workflows.md b/book/workflows.md index 4fea77a..a3c7591 100644 --- a/book/workflows.md +++ b/book/workflows.md @@ -303,7 +303,7 @@ In order for Snakemake to execute each of our modules, we need to wrap those mod ```python from pathlib import Path import pandas as pd -from BetterCodeBetterScience.simple_workflow.visualization import ( +from bettercode.simple_workflow.visualization import ( generate_clustered_heatmap, ) @@ -574,7 +574,7 @@ Another important feature of a workflow related to statelessness is *idempotency I asked Claude Code to help with this: -> I would like to modify the workflow described in src/BetterCodeBetterScience/rnaseq/modular_workflow/run_workflow.py to make it execute in a stateless way through the use of checkpointing. Please analyze the code and suggest the best way to accomplish this. +> I would like to modify the workflow described in src/bettercode/rnaseq/modular_workflow/run_workflow.py to make it execute in a stateless way through the use of checkpointing. Please analyze the code and suggest the best way to accomplish this. After analyzing the codebase Claude came up with three proposed solutions to the problem: @@ -588,7 +588,7 @@ Here we will examine the first (recommended) option and the third solution; whil We start with a custom approach in order to get a better view of the details of workflow orchestration. It's important to note that I generally would not recommend building one's one custom workflow manager, at least not before trying a general-purpose workflow engine, but I will show an example of a custom workflow engine in order to provide a better understanding of the detailed process of workflow management. We start with a prompt: -> let's implement the recommended Stateless Workflow with Checkpointing. Please generate new code within src/BetterCodeBetterScience/rnaseq/stateless_workflow. +> let's implement the recommended Stateless Workflow with Checkpointing. Please generate new code within src/bettercode/rnaseq/stateless_workflow. The resulting code worked straight out of the box, but it didn't maintain any sort of log of its processing, which can be very useful. In particular, I wanted to log the time required to execute each step in the workflow, for use in optimization that I will discuss further below. I asked Claude to add this: diff --git a/problems_to_solve.md b/problems_to_solve.md index f45c496..b0e0a9f 100644 --- a/problems_to_solve.md +++ b/problems_to_solve.md @@ -3,7 +3,7 @@ Open problems marked with [ ] Fixed problems marked with [x] -[x] I would like to generate a new example of a very simple pandas-based data analysis workflow for demonstrating the features of Prefect and snakemake. Put the new code into src/BetterCodeBetterScience/simple_workflow. The example should include separate modules that implement each of the following functions: +[x] I would like to generate a new example of a very simple pandas-based data analysis workflow for demonstrating the features of Prefect and snakemake. Put the new code into src/bettercode/simple_workflow. The example should include separate modules that implement each of the following functions: - load these two files (using the first column as the index for each): - https://raw.githubusercontent.com/IanEisenberg/Self_Regulation_Ontology/refs/heads/master/Data/Complete_02-16-2019/meaningful_variables_clean.csv - https://raw.githubusercontent.com/IanEisenberg/Self_Regulation_Ontology/refs/heads/master/Data/Complete_02-16-2019/demographics.csv @@ -58,7 +58,7 @@ Fixed problems marked with [x] - Updated Snakefile to use `wf_snakemake/` for CHECKPOINT_DIR, RESULTS_DIR, FIGURE_DIR, LOG_DIR - Updated WORKFLOW_OVERVIEW.md to reflect new output structure -[x] I would now like to add another workflow, with code saved to src/BetterCodeBetterScience/rnaseq/snakemake_workflow. This workflow will use the Snakemake workflow manager (https://snakemake.readthedocs.io/en/stable/index.html); otherwise it should be functionally equivalent to the other workflows already developed. +[x] I would now like to add another workflow, with code saved to src/bettercode/rnaseq/snakemake_workflow. This workflow will use the Snakemake workflow manager (https://snakemake.readthedocs.io/en/stable/index.html); otherwise it should be functionally equivalent to the other workflows already developed. - Created `snakemake_workflow/` directory with: - `Snakefile`: Main workflow entry point - `config/config.yaml`: All workflow parameters with defaults @@ -73,7 +73,7 @@ Fixed problems marked with [x] - Added `snakemake>=8.0` dependency to pyproject.toml - Usage: `snakemake --cores 8 --config datadir=/path/to/data` -[x] I would like to add a new workflow, with code saved to src/BetterCodeBetterScience/rnaseq/prefect_workflow. This workflow will use the Prefect workflow manager (https://github.com/PrefectHQ/prefect) to manage the workflow that was previously developed in src/BetterCodeBetterScience/rnaseq/stateless_workflow. The one new feature that I would like to add here is to perform steps 8-11 separately on each different cell type that survives the initial filtering. +[x] I would like to add a new workflow, with code saved to src/bettercode/rnaseq/prefect_workflow. This workflow will use the Prefect workflow manager (https://github.com/PrefectHQ/prefect) to manage the workflow that was previously developed in src/bettercode/rnaseq/stateless_workflow. The one new feature that I would like to add here is to perform steps 8-11 separately on each different cell type that survives the initial filtering. - Created `prefect_workflow/` directory with: - `tasks.py`: Prefect task definitions wrapping modular workflow functions - `flows.py`: Main workflow flow with parallel per-cell-type analysis diff --git a/prompts/refactor_monolithic_to_modular.md b/prompts/refactor_monolithic_to_modular.md index a6ea614..9cdd35e 100644 --- a/prompts/refactor_monolithic_to_modular.md +++ b/prompts/refactor_monolithic_to_modular.md @@ -2,7 +2,7 @@ Prompt: please read CLAUDE.md for guidelines, and then read refactor_monolithic_ # Goal -src/BetterCodeBetterScience/rnaseq/immune_scrnaseq_monolithic.py is currently a single monolithic script for a data analysis workflow. I would like to refactor it into a modular script based on the following decomposition of the workflow: +src/bettercode/rnaseq/immune_scrnaseq_monolithic.py is currently a single monolithic script for a data analysis workflow. I would like to refactor it into a modular script based on the following decomposition of the workflow: - Data (down)loading - Data filtering (removing subjects or cell types with insufficient observations) @@ -23,5 +23,5 @@ src/BetterCodeBetterScience/rnaseq/immune_scrnaseq_monolithic.py is currently a - Overrepresentation analysis (Enrichr) - Predictive modeling -Please generate a new set of scripts within a new directory called `src/BetterCodeBetterScience/rnaseq/modular_workflow` that implements the same workflow in a modular way. +Please generate a new set of scripts within a new directory called `src/bettercode/rnaseq/modular_workflow` that implements the same workflow in a modular way. diff --git a/pyproject.toml b/pyproject.toml index 310d3b8..1054d6c 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,5 +1,5 @@ [project] -name = "BetterCodeBetterScience" +name = "bettercode" version = "0.1.0" description = "Code for BetterCodeBetterScience book" readme = "README.md" @@ -89,7 +89,7 @@ build-backend = "hatchling.build" # add script entry points here [project.scripts] -check-links = "BetterCodeBetterScience.check_links:main" +check-links = "bettercode.check_links:main" [tool.codespell] # Ref: https://github.com/codespell-project/codespell#using-a-config-file diff --git a/scripts/datalad_test.sh b/scripts/datalad_test.sh index 334a4fc..417de84 100644 --- a/scripts/datalad_test.sh +++ b/scripts/datalad_test.sh @@ -8,7 +8,7 @@ datalad download-url -d . -O my_datalad_repo/data/ https://raw.githubusercontent datalad unlock my_datalad_repo/data/demographics.csv -python src/BetterCodeBetterScience/modify_data.py my_datalad_repo/data/demographics.csv +python src/bettercode/modify_data.py my_datalad_repo/data/demographics.csv datalad status diff --git a/src/BetterCodeBetterScience/LifeSnaps_example.ipynb b/src/bettercode/LifeSnaps_example.ipynb similarity index 99% rename from src/BetterCodeBetterScience/LifeSnaps_example.ipynb rename to src/bettercode/LifeSnaps_example.ipynb index 9d75202..bf846ac 100644 --- a/src/BetterCodeBetterScience/LifeSnaps_example.ipynb +++ b/src/bettercode/LifeSnaps_example.ipynb @@ -617,7 +617,7 @@ ], "metadata": { "kernelspec": { - "display_name": "BetterCodeBetterScience", + "display_name": "bettercode", "language": "python", "name": "python3" }, diff --git a/src/BetterCodeBetterScience/__init__.py b/src/bettercode/__init__.py similarity index 100% rename from src/BetterCodeBetterScience/__init__.py rename to src/bettercode/__init__.py diff --git a/src/bettercode/bci_workflow_example.ipynb b/src/bettercode/bci_workflow_example.ipynb new file mode 100644 index 0000000..05a2315 --- /dev/null +++ b/src/bettercode/bci_workflow_example.ipynb @@ -0,0 +1,467 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "66907fd0", + "metadata": {}, + "source": [ + "data from https://physionet.org/content/bigp3bci/1.0.0/bigP3BCI-data/StudyA/A_01/SE001/Test/CB/#files-panel" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "5ef78db4", + "metadata": {}, + "outputs": [], + "source": [ + "import mne\n", + "from pathlib import Path\n", + "\n", + "\n", + "basedir = Path('/Users/poldrack/data_unsynced/bigp3bci/bigp3bci')" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "8b648456", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Extracting EDF parameters from /Users/poldrack/data_unsynced/bigp3bci/bigp3bci/Train/CB/A_01_SE001_CB_Train01.edf...\n", + "Setting channel info structure...\n", + "Creating raw.info structure...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/var/folders/r2/f85nyfr1785fj4257wkdj7480000gn/T/ipykernel_1600/3149368029.py:3: RuntimeWarning: Channels contain different highpass filters. Highest filter setting will be stored.\n", + " raw = mne.io.read_raw_edf(datafile, preload=True)\n", + "/var/folders/r2/f85nyfr1785fj4257wkdj7480000gn/T/ipykernel_1600/3149368029.py:3: RuntimeWarning: Channels contain different lowpass filters. Lowest filter setting will be stored.\n", + " raw = mne.io.read_raw_edf(datafile, preload=True)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Reading 0 ... 35207 = 0.000 ... 137.527 secs...\n" + ] + } + ], + "source": [ + "datafile = basedir / 'Train/CB/A_01_SE001_CB_Train01.edf'\n", + "\n", + "raw = mne.io.read_raw_edf(datafile, preload=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "b46a3de0", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + " \n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + " \n", + " \n", + "\n", + "\n", + "\n", + "\n", + " \n", + " \n", + " \n", + "\n", + "\n", + "\n", + " \n", + " \n", + " \n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + "\n", + " \n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + " \n", + " \n", + "\n", + "\n", + "\n", + "\n", + " \n", + " \n", + " \n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + " \n", + " \n", + " \n", + "\n", + "\n", + "\n", + "\n", + " \n", + " \n", + " \n", + "\n", + "\n", + "\n", + " \n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + " \n", + " \n", + "\n", + "\n", + "\n", + " \n", + "\n", + " \n", + " \n", + " \n", + "\n", + "\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + "\n", + " \n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + " \n", + " \n", + "\n", + "\n", + "\n", + "\n", + " \n", + " \n", + " \n", + "\n", + "\n", + "\n", + "\n", + " \n", + " \n", + " \n", + "\n", + "\n", + "\n", + "
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Measurement date2020-01-01 at 00:00:00 UTC
ParticipantA_01
ExperimenterUnknown
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" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "raw" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "f9330424", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "raw.annotations" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "aa369b64", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "bettercode", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.0" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/src/BetterCodeBetterScience/bug_driven_testing.py b/src/bettercode/bug_driven_testing.py similarity index 100% rename from src/BetterCodeBetterScience/bug_driven_testing.py rename to src/bettercode/bug_driven_testing.py diff --git a/src/bettercode/check_links.py b/src/bettercode/check_links.py new file mode 100644 index 0000000..342c93b --- /dev/null +++ b/src/bettercode/check_links.py @@ -0,0 +1,16 @@ +import os +from pathlib import Path + +def main(): + # get all markdown files in the ./book directory + book_dir = './book' + md_files = list(Path(book_dir).rglob('*.md')) + for md_file in md_files: + cmd = f'python -m linkcheckmd {md_file.as_posix()}' + print(f"Checking links in {md_file.as_posix()}") + # run command and print stdout and stderr + result = os.popen(cmd).read() + print(result) + +if __name__ == "__main__": + main() \ No newline at end of file diff --git a/src/BetterCodeBetterScience/constants.py b/src/bettercode/constants.py similarity index 100% rename from src/BetterCodeBetterScience/constants.py rename to src/bettercode/constants.py diff --git a/src/BetterCodeBetterScience/data_management.ipynb b/src/bettercode/data_management.ipynb similarity index 99% rename from src/BetterCodeBetterScience/data_management.ipynb rename to src/bettercode/data_management.ipynb index 9c276c1..780cd87 100644 --- a/src/BetterCodeBetterScience/data_management.ipynb +++ b/src/bettercode/data_management.ipynb @@ -931,7 +931,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "/Users/poldrack/Dropbox/code/BetterCodeBetterScience\n" + "/Users/poldrack/Dropbox/code/bettercode\n" ] } ], @@ -966,7 +966,7 @@ "\n", "datalad unlock data/demographics.csv\n", "\n", - "python ../src/BetterCodeBetterScience/modify_data.py data/demographics.csv\n", + "python ../src/bettercode/modify_data.py data/demographics.csv\n", "datalad save -m \"removed Motivation variables from demographics.csv\"\n", "datalad status\n", "\n" @@ -995,8 +995,8 @@ "name": "stdout", "output_type": "stream", "text": [ - " \u001b[1;31mmodified\u001b[0m: /Users/poldrack/Dropbox/code/BetterCodeBetterScience/my_datalad_repo/data/demographics.csv (\u001b[1;35mfile\u001b[0m)\n", - " \u001b[1;31mmodified\u001b[0m: /Users/poldrack/Dropbox/code/BetterCodeBetterScience/my_datalad_repo/data/meaningful_variables_clean.csv (\u001b[1;35mfile\u001b[0m)\n", + " \u001b[1;31mmodified\u001b[0m: /Users/poldrack/Dropbox/code/bettercode/my_datalad_repo/data/demographics.csv (\u001b[1;35mfile\u001b[0m)\n", + " \u001b[1;31mmodified\u001b[0m: /Users/poldrack/Dropbox/code/bettercode/my_datalad_repo/data/meaningful_variables_clean.csv (\u001b[1;35mfile\u001b[0m)\n", "\u001b[0m" ] } @@ -1008,7 +1008,7 @@ ], "metadata": { "kernelspec": { - "display_name": "BetterCodeBetterScience", + "display_name": "bettercode", "language": "python", "name": "python3" }, diff --git a/src/BetterCodeBetterScience/database.py b/src/bettercode/database.py similarity index 100% rename from src/BetterCodeBetterScience/database.py rename to src/bettercode/database.py diff --git a/src/BetterCodeBetterScience/database_example_funcs.py b/src/bettercode/database_example_funcs.py similarity index 100% rename from src/BetterCodeBetterScience/database_example_funcs.py rename to src/bettercode/database_example_funcs.py diff --git a/src/BetterCodeBetterScience/database_examples.ipynb b/src/bettercode/database_examples.ipynb similarity index 99% rename from src/BetterCodeBetterScience/database_examples.ipynb rename to src/bettercode/database_examples.ipynb index 98fb270..bcee4d2 100644 --- a/src/BetterCodeBetterScience/database_examples.ipynb +++ b/src/bettercode/database_examples.ipynb @@ -41,7 +41,7 @@ " get_chromadb_collection,\n", " get_neo4j_session\n", ")\n", - "from BetterCodeBetterScience.database_example_funcs import (\n", + "from bettercode.database_example_funcs import (\n", " get_exploded_gwas_data,\n", " import_genesets_by_trait, \n", " get_trait_info_from_ols,\n", @@ -54,7 +54,7 @@ " add_pubmed_abstracts_to_chromadb,\n", " build_neo4j_graph\n", ")\n", - "from BetterCodeBetterScience.database import get_mongo_client\n", + "from bettercode.database import get_mongo_client\n", "import seaborn as sns\n", "import matplotlib.pyplot as plt\n", "\n", @@ -8069,7 +8069,7 @@ ], "metadata": { "kernelspec": { - "display_name": "BetterCodeBetterScience", + "display_name": "bettercode", "language": "python", "name": "python3" }, diff --git a/src/BetterCodeBetterScience/database_examples.py b/src/bettercode/database_examples.py similarity index 97% rename from src/BetterCodeBetterScience/database_examples.py rename to src/bettercode/database_examples.py index bc0c7c8..4bee1fa 100644 --- a/src/BetterCodeBetterScience/database_examples.py +++ b/src/bettercode/database_examples.py @@ -7,7 +7,7 @@ # format_version: '1.3' # jupytext_version: 1.16.4 # kernelspec: -# display_name: BetterCodeBetterScience +# display_name: bettercode # language: python # name: python3 # --- @@ -33,7 +33,7 @@ get_chromadb_collection, get_neo4j_session, ) -from BetterCodeBetterScience.database_example_funcs import ( +from bettercode.database_example_funcs import ( get_exploded_gwas_data, import_geneset_annotations_by_trait, get_trait_info_from_ols, @@ -42,7 +42,7 @@ compute_phenotype_similarities, compute_text_similarities, ) -from BetterCodeBetterScience.database import get_mongo_client +from bettercode.database import get_mongo_client import seaborn as sns import matplotlib.pyplot as plt diff --git a/src/BetterCodeBetterScience/distance.py b/src/bettercode/distance.py similarity index 100% rename from src/BetterCodeBetterScience/distance.py rename to src/bettercode/distance.py diff --git a/src/BetterCodeBetterScience/distance_testing/test_distance.py b/src/bettercode/distance_testing/test_distance.py similarity index 91% rename from src/BetterCodeBetterScience/distance_testing/test_distance.py rename to src/bettercode/distance_testing/test_distance.py index 8daf7bc..28bf874 100644 --- a/src/BetterCodeBetterScience/distance_testing/test_distance.py +++ b/src/bettercode/distance_testing/test_distance.py @@ -1,7 +1,7 @@ # generate a function that calculates the distance between two points # where each point is defined as a tuple of two numbers -from BetterCodeBetterScience.distance import distance +from bettercode.distance import distance import math def test_distance_zero(): diff --git a/src/BetterCodeBetterScience/docker-example/Dockerfile b/src/bettercode/docker-example/Dockerfile similarity index 100% rename from src/BetterCodeBetterScience/docker-example/Dockerfile rename to src/bettercode/docker-example/Dockerfile diff --git a/src/BetterCodeBetterScience/docker-example/Makefile b/src/bettercode/docker-example/Makefile similarity index 100% rename from src/BetterCodeBetterScience/docker-example/Makefile rename to src/bettercode/docker-example/Makefile diff --git a/src/BetterCodeBetterScience/docker-example/README.md b/src/bettercode/docker-example/README.md similarity index 100% rename from src/BetterCodeBetterScience/docker-example/README.md rename to src/bettercode/docker-example/README.md diff --git a/src/BetterCodeBetterScience/docker-example/random_sentence.py b/src/bettercode/docker-example/random_sentence.py similarity index 100% rename from src/BetterCodeBetterScience/docker-example/random_sentence.py rename to src/bettercode/docker-example/random_sentence.py diff --git a/src/BetterCodeBetterScience/escape_velocity.py b/src/bettercode/escape_velocity.py similarity index 100% rename from src/BetterCodeBetterScience/escape_velocity.py rename to src/bettercode/escape_velocity.py diff --git a/src/BetterCodeBetterScience/formatting_example.py b/src/bettercode/formatting_example.py similarity index 100% rename from src/BetterCodeBetterScience/formatting_example.py rename to src/bettercode/formatting_example.py diff --git a/src/BetterCodeBetterScience/formatting_example_ai.py b/src/bettercode/formatting_example_ai.py similarity index 100% rename from src/BetterCodeBetterScience/formatting_example_ai.py rename to src/bettercode/formatting_example_ai.py diff --git a/src/BetterCodeBetterScience/formatting_example_ruff.py b/src/bettercode/formatting_example_ruff.py similarity index 100% rename from src/BetterCodeBetterScience/formatting_example_ruff.py rename to src/bettercode/formatting_example_ruff.py diff --git a/src/BetterCodeBetterScience/incontext_learning_example.ipynb b/src/bettercode/incontext_learning_example.ipynb similarity index 100% rename from src/BetterCodeBetterScience/incontext_learning_example.ipynb rename to src/bettercode/incontext_learning_example.ipynb diff --git a/src/BetterCodeBetterScience/language_model_api_prompting.ipynb b/src/bettercode/language_model_api_prompting.ipynb similarity index 98% rename from src/BetterCodeBetterScience/language_model_api_prompting.ipynb rename to src/bettercode/language_model_api_prompting.ipynb index 8c06173..5105c15 100644 --- a/src/BetterCodeBetterScience/language_model_api_prompting.ipynb +++ b/src/bettercode/language_model_api_prompting.ipynb @@ -114,7 +114,7 @@ } ], "source": [ - "from BetterCodeBetterScience.llm_utils import send_prompt_to_claude\n", + "from bettercode.llm_utils import send_prompt_to_claude\n", "\n", "json_prompt = \"\"\"\n", "What is the capital of France? \n", @@ -265,7 +265,7 @@ ], "metadata": { "kernelspec": { - "display_name": "BetterCodeBetterScience", + "display_name": "bettercode", "language": "python", "name": "python3" }, diff --git a/src/BetterCodeBetterScience/llm_utils.py b/src/bettercode/llm_utils.py similarity index 100% rename from src/BetterCodeBetterScience/llm_utils.py rename to src/bettercode/llm_utils.py diff --git a/src/bettercode/method_chaining_example.ipynb b/src/bettercode/method_chaining_example.ipynb new file mode 100644 index 0000000..5840ef3 --- /dev/null +++ b/src/bettercode/method_chaining_example.ipynb @@ -0,0 +1,333 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "ba8b711f", + "metadata": {}, + "source": [ + "### Example of method chaining\n", + "\n", + "for workflow chapter" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "7adf8124", + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "61364300", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['Sex',\n", + " 'Age',\n", + " 'Race',\n", + " 'OtherRace',\n", + " 'HispanicLatino',\n", + " 'HighestEducation',\n", + " 'HeightInches',\n", + " 'WeightPounds',\n", + " 'RelationshipStatus',\n", + " 'DivorceCount',\n", + " 'LongestRelationship',\n", + " 'RelationshipNumber',\n", + " 'ChildrenNumber',\n", + " 'HouseholdIncome',\n", + " 'RetirementAccount',\n", + " 'RetirementPercentStocks',\n", + " 'RentOwn',\n", + " 'MortgageDebt',\n", + " 'CarDebt',\n", + " 'EducationDebt',\n", + " 'CreditCardDebt',\n", + " 'OtherDebtSources',\n", + " 'OtherDebtAmount',\n", + " 'CoffeeCupsPerDay',\n", + " 'TeaCupsPerDay',\n", + " 'CaffienatedSodaCansPerDay',\n", + " 'CaffieneOtherSourcesDayMG',\n", + " 'GamblingProblem',\n", + " 'TrafficTicketsLastYearCount',\n", + " 'TrafficAccidentsLifeCount',\n", + " 'ArrestedChargedLifeCount',\n", + " 'MotivationForParticipation',\n", + " 'MotivationOther',\n", + " 'adaptive_n_back.hddm_drift',\n", + " 'adaptive_n_back.hddm_drift_load',\n", + " 'adaptive_n_back.hddm_non_decision',\n", + " 'adaptive_n_back.hddm_thresh',\n", + " 'adaptive_n_back.mean_load.logTr',\n", + " 'angling_risk_task_always_sunny.keep_adjusted_clicks',\n", + " 'angling_risk_task_always_sunny.keep_coef_of_variation',\n", + " 'angling_risk_task_always_sunny.release_adjusted_clicks',\n", + " 'angling_risk_task_always_sunny.release_coef_of_variation.logTr',\n", + " 'attention_network_task.alerting_hddm_drift',\n", + " 'attention_network_task.conflict_hddm_drift.ReflogTr',\n", + " 'attention_network_task.hddm_drift',\n", + " 'attention_network_task.hddm_non_decision.ReflogTr',\n", + " 'attention_network_task.hddm_thresh',\n", + " 'attention_network_task.orienting_hddm_drift',\n", + " 'bickel_titrator.hyp_discount_rate_large.logTr',\n", + " 'bickel_titrator.hyp_discount_rate_medium.logTr',\n", + " 'bickel_titrator.hyp_discount_rate_small.logTr',\n", + " 'bis11_survey.Attentional',\n", + " 'bis11_survey.Motor.logTr',\n", + " 'bis11_survey.Nonplanning',\n", + " 'bis_bas_survey.BAS_drive',\n", + " 'bis_bas_survey.BAS_fun_seeking',\n", + " 'bis_bas_survey.BAS_reward_responsiveness',\n", + " 'bis_bas_survey.BIS',\n", + " 'brief_self_control_survey.self_control',\n", + " 'choice_reaction_time.hddm_drift',\n", + " 'choice_reaction_time.hddm_non_decision.logTr',\n", + " 'choice_reaction_time.hddm_thresh',\n", + " 'cognitive_reflection_survey.correct_proportion',\n", + " 'cognitive_reflection_survey.intuitive_proportion',\n", + " 'columbia_card_task_cold.avg_cards_chosen',\n", + " 'columbia_card_task_cold.gain_sensitivity.logTr',\n", + " 'columbia_card_task_cold.information_use',\n", + " 'columbia_card_task_cold.loss_sensitivity',\n", + " 'columbia_card_task_cold.probability_sensitivity',\n", + " 'columbia_card_task_hot.avg_cards_chosen',\n", + " 'columbia_card_task_hot.gain_sensitivity.logTr',\n", + " 'columbia_card_task_hot.information_use',\n", + " 'columbia_card_task_hot.loss_sensitivity.ReflogTr',\n", + " 'columbia_card_task_hot.probability_sensitivity',\n", + " 'dickman_survey.functional',\n", + " 'dietary_decision.health_sensitivity',\n", + " 'dietary_decision.taste_sensitivity',\n", + " 'digit_span.forward_span',\n", + " 'digit_span.reverse_span',\n", + " 'directed_forgetting.hddm_drift',\n", + " 'directed_forgetting.hddm_non_decision',\n", + " 'directed_forgetting.hddm_thresh',\n", + " 'directed_forgetting.proactive_interference_hddm_drift.logTr',\n", + " 'discount_titrate.percent_patient',\n", + " 'dospert_eb_survey.ethical',\n", + " 'dospert_eb_survey.financial',\n", + " 'dospert_eb_survey.health_safety.logTr',\n", + " 'dospert_eb_survey.recreational',\n", + " 'dospert_eb_survey.social',\n", + " 'dospert_rp_survey.ethical',\n", + " 'dospert_rp_survey.financial',\n", + " 'dospert_rp_survey.health_safety',\n", + " 'dospert_rp_survey.recreational',\n", + " 'dospert_rp_survey.social',\n", + " 'dospert_rt_survey.ethical',\n", + " 'dospert_rt_survey.financial',\n", + " 'dospert_rt_survey.health_safety',\n", + " 'dospert_rt_survey.recreational',\n", + " 'dospert_rt_survey.social',\n", + " 'dot_pattern_expectancy.AY-BY_hddm_drift',\n", + " 'dot_pattern_expectancy.BX-BY_hddm_drift',\n", + " 'dot_pattern_expectancy.bias',\n", + " 'dot_pattern_expectancy.dprime',\n", + " 'dot_pattern_expectancy.hddm_drift',\n", + " 'dot_pattern_expectancy.hddm_non_decision',\n", + " 'dot_pattern_expectancy.hddm_thresh.logTr',\n", + " 'eating_survey.cognitive_restraint',\n", + " 'eating_survey.emotional_eating',\n", + " 'eating_survey.uncontrolled_eating',\n", + " 'erq_survey.reappraisal',\n", + " 'erq_survey.suppression',\n", + " 'five_facet_mindfulness_survey.act_with_awareness',\n", + " 'five_facet_mindfulness_survey.describe',\n", + " 'five_facet_mindfulness_survey.nonjudge',\n", + " 'five_facet_mindfulness_survey.nonreact',\n", + " 'five_facet_mindfulness_survey.observe',\n", + " 'future_time_perspective_survey.future_time_perspective',\n", + " 'go_nogo.bias',\n", + " 'go_nogo.dprime',\n", + " 'grit_scale_survey.grit',\n", + " 'hierarchical_rule.score',\n", + " 'holt_laury_survey.beta.logTr',\n", + " 'holt_laury_survey.risk_aversion',\n", + " 'holt_laury_survey.safe_choices',\n", + " 'impulsive_venture_survey.venturesomeness',\n", + " 'information_sampling_task.Decreasing_Win_P_correct',\n", + " 'information_sampling_task.Decreasing_Win_motivation',\n", + " 'information_sampling_task.Fixed_Win_P_correct',\n", + " 'information_sampling_task.Fixed_Win_motivation',\n", + " 'keep_track.score',\n", + " 'kirby.hyp_discount_rate_large.logTr',\n", + " 'kirby.hyp_discount_rate_small.logTr',\n", + " 'local_global_letter.conflict_hddm_drift',\n", + " 'local_global_letter.global_bias_hddm_drift',\n", + " 'local_global_letter.hddm_drift',\n", + " 'local_global_letter.hddm_non_decision',\n", + " 'local_global_letter.hddm_thresh',\n", + " 'local_global_letter.switch_cost_hddm_drift',\n", + " 'mindful_attention_awareness_survey.mindfulness',\n", + " 'motor_selective_stop_signal.SSRT',\n", + " 'motor_selective_stop_signal.hddm_drift',\n", + " 'motor_selective_stop_signal.hddm_non_decision.ReflogTr',\n", + " 'motor_selective_stop_signal.hddm_thresh.logTr',\n", + " 'motor_selective_stop_signal.proactive_control_hddm_drift',\n", + " 'motor_selective_stop_signal.reactive_control_hddm_drift',\n", + " 'mpq_control_survey.control.ReflogTr',\n", + " 'probabilistic_selection.positive_learning_bias',\n", + " 'psychological_refractory_period_two_choices.PRP_slope',\n", + " 'ravens.score',\n", + " 'recent_probes.hddm_drift',\n", + " 'recent_probes.hddm_non_decision',\n", + " 'recent_probes.hddm_thresh',\n", + " 'recent_probes.proactive_interference_hddm_drift',\n", + " 'selection_optimization_compensation_survey.compensation',\n", + " 'selection_optimization_compensation_survey.elective_selection',\n", + " 'selection_optimization_compensation_survey.loss_based_selection',\n", + " 'selection_optimization_compensation_survey.optimization.ReflogTr',\n", + " 'self_regulation_survey.control',\n", + " 'sensation_seeking_survey.boredom_susceptibility',\n", + " 'sensation_seeking_survey.disinhibition',\n", + " 'sensation_seeking_survey.experience_seeking',\n", + " 'sensation_seeking_survey.thrill_adventure_seeking',\n", + " 'shape_matching.hddm_drift',\n", + " 'shape_matching.hddm_non_decision.ReflogTr',\n", + " 'shape_matching.hddm_thresh.logTr',\n", + " 'shape_matching.stimulus_interference_hddm_drift',\n", + " 'shift_task.acc',\n", + " 'shift_task.learning_rate',\n", + " 'shift_task.learning_to_learn',\n", + " 'shift_task.model_beta.logTr',\n", + " 'shift_task.model_decay.ReflogTr',\n", + " 'shift_task.model_learning_rate',\n", + " 'simon.hddm_drift',\n", + " 'simon.hddm_non_decision',\n", + " 'simon.hddm_thresh',\n", + " 'simon.simon_hddm_drift',\n", + " 'simple_reaction_time.avg_rt.logTr',\n", + " 'spatial_span.forward_span',\n", + " 'spatial_span.reverse_span',\n", + " 'stim_selective_stop_signal.SSRT',\n", + " 'stim_selective_stop_signal.hddm_drift',\n", + " 'stim_selective_stop_signal.hddm_non_decision.ReflogTr',\n", + " 'stim_selective_stop_signal.hddm_thresh.logTr',\n", + " 'stim_selective_stop_signal.reactive_control_hddm_drift',\n", + " 'stop_signal.SSRT_high.logTr',\n", + " 'stop_signal.SSRT_low',\n", + " 'stop_signal.hddm_drift',\n", + " 'stop_signal.hddm_non_decision.ReflogTr',\n", + " 'stop_signal.hddm_thresh.logTr',\n", + " 'stop_signal.proactive_SSRT_speeding',\n", + " 'stop_signal.proactive_slowing_hddm_drift',\n", + " 'stop_signal.proactive_slowing_hddm_thresh',\n", + " 'stroop.hddm_drift',\n", + " 'stroop.hddm_non_decision',\n", + " 'stroop.hddm_thresh.logTr',\n", + " 'stroop.stroop_hddm_drift',\n", + " 'ten_item_personality_survey.agreeableness',\n", + " 'ten_item_personality_survey.conscientiousness.ReflogTr',\n", + " 'ten_item_personality_survey.emotional_stability',\n", + " 'ten_item_personality_survey.extraversion',\n", + " 'ten_item_personality_survey.openness',\n", + " 'theories_of_willpower_survey.endorse_limited_resource',\n", + " 'threebytwo.cue_switch_cost_hddm_drift',\n", + " 'threebytwo.hddm_drift',\n", + " 'threebytwo.hddm_non_decision',\n", + " 'threebytwo.hddm_thresh.logTr',\n", + " 'threebytwo.task_switch_cost_hddm_drift',\n", + " 'time_perspective_survey.future',\n", + " 'time_perspective_survey.past_negative',\n", + " 'time_perspective_survey.past_positive',\n", + " 'time_perspective_survey.present_fatalistic',\n", + " 'time_perspective_survey.present_hedonistic',\n", + " 'tower_of_london.avg_move_time.logTr',\n", + " 'tower_of_london.num_extra_moves',\n", + " 'tower_of_london.num_optimal_solutions',\n", + " 'tower_of_london.planning_time',\n", + " 'two_stage_decision.model_based',\n", + " 'two_stage_decision.model_free',\n", + " 'two_stage_decision.perseverance',\n", + " 'upps_impulsivity_survey.lack_of_perseverance',\n", + " 'upps_impulsivity_survey.lack_of_premeditation',\n", + " 'upps_impulsivity_survey.negative_urgency',\n", + " 'upps_impulsivity_survey.positive_urgency',\n", + " 'upps_impulsivity_survey.sensation_seeking',\n", + " 'writing_task.neutral_probability',\n", + " 'writing_task.positive_probability']" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_demographics = pd.read_csv('https://raw.githubusercontent.com/IanEisenberg/Self_Regulation_Ontology/refs/heads/master/Data/Complete_02-16-2019/demographics.csv', index_col=0)\n", + "df_measures = pd.read_csv('https://raw.githubusercontent.com/IanEisenberg/Self_Regulation_Ontology/refs/heads/master/Data/Complete_02-16-2019/meaningful_variables_clean.csv', index_col=0)\n", + "\n", + "df = df_demographics.join(df_measures)\n", + "list(df.columns)" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "6a6a576a", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Sex\n", + "Female 0.156489\n", + "Male 0.274131\n", + "Name: EverArrested, dtype: float64\n" + ] + } + ], + "source": [ + "arrest_stats_by_sex = (df\n", + " .dropna(subset=['Sex', 'ArrestedChargedLifeCount'])\n", + " .replace({'Sex': {0: 'Male', 1: 'Female'}})\n", + " .assign(EverArrested=lambda x: (x['ArrestedChargedLifeCount'] > 0).astype(int))\n", + " .groupby('Sex')\n", + " ['EverArrested']\n", + " .mean()\n", + ")\n", + "\n", + "print(arrest_stats_by_sex)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ed7103cc", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "bettercode", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.0" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/src/BetterCodeBetterScience/modify_data.py b/src/bettercode/modify_data.py similarity index 100% rename from src/BetterCodeBetterScience/modify_data.py rename to src/bettercode/modify_data.py diff --git a/src/BetterCodeBetterScience/my_linear_regression.py b/src/bettercode/my_linear_regression.py similarity index 100% rename from src/BetterCodeBetterScience/my_linear_regression.py rename to src/bettercode/my_linear_regression.py diff --git a/src/BetterCodeBetterScience/narps/bids_utils.py b/src/bettercode/narps/bids_utils.py similarity index 100% rename from src/BetterCodeBetterScience/narps/bids_utils.py rename to src/bettercode/narps/bids_utils.py diff --git a/src/BetterCodeBetterScience/narps/image_utils.py b/src/bettercode/narps/image_utils.py similarity index 100% rename from src/BetterCodeBetterScience/narps/image_utils.py rename to src/bettercode/narps/image_utils.py diff --git a/src/BetterCodeBetterScience/narps/narps_megascript.py b/src/bettercode/narps/narps_megascript.py similarity index 99% rename from src/BetterCodeBetterScience/narps/narps_megascript.py rename to src/bettercode/narps/narps_megascript.py index b7e6ddf..2300935 100644 --- a/src/BetterCodeBetterScience/narps/narps_megascript.py +++ b/src/bettercode/narps/narps_megascript.py @@ -7,7 +7,7 @@ import tarfile import urllib.request import shutil -from BetterCodeBetterScience.narps.bids_utils import ( +from bettercode.narps.bids_utils import ( parse_bids_filename, find_bids_files, modify_bids_filename, diff --git a/src/BetterCodeBetterScience/pubmed.py b/src/bettercode/pubmed.py similarity index 100% rename from src/BetterCodeBetterScience/pubmed.py rename to src/bettercode/pubmed.py diff --git a/src/BetterCodeBetterScience/rnaseq/RUNNING_WORKFLOWS.md b/src/bettercode/rnaseq/RUNNING_WORKFLOWS.md similarity index 91% rename from src/BetterCodeBetterScience/rnaseq/RUNNING_WORKFLOWS.md rename to src/bettercode/rnaseq/RUNNING_WORKFLOWS.md index 64b9c96..f9261f3 100644 --- a/src/BetterCodeBetterScience/rnaseq/RUNNING_WORKFLOWS.md +++ b/src/bettercode/rnaseq/RUNNING_WORKFLOWS.md @@ -50,7 +50,7 @@ The monolithic workflow is a single Python script that runs all analysis steps s ```bash # Edit the datadir path in the script first, then run: -python src/BetterCodeBetterScience/rnaseq/immune_scrnaseq_monolithic.py +python src/bettercode/rnaseq/immune_scrnaseq_monolithic.py ``` ### Running as a Jupyter Notebook @@ -58,7 +58,7 @@ python src/BetterCodeBetterScience/rnaseq/immune_scrnaseq_monolithic.py The script uses jupytext format and can be opened directly in Jupyter: ```bash -jupyter notebook src/BetterCodeBetterScience/rnaseq/immune_scrnaseq_monolithic.py +jupyter notebook src/bettercode/rnaseq/immune_scrnaseq_monolithic.py ``` ### Output Location @@ -86,12 +86,12 @@ The modular workflow uses reusable pipeline functions organized by analysis step ```bash # Using environment variable export DATADIR=/path/to/your/data -python -m BetterCodeBetterScience.rnaseq.modular_workflow.run_workflow +python -m bettercode.rnaseq.modular_workflow.run_workflow # Or import and run programmatically python -c " from pathlib import Path -from BetterCodeBetterScience.rnaseq.modular_workflow.run_workflow import run_full_workflow +from bettercode.rnaseq.modular_workflow.run_workflow import run_full_workflow datadir = Path('/path/to/your/data/immune_aging') results = run_full_workflow(datadir) @@ -137,14 +137,14 @@ The stateless workflow adds robust checkpointing using BIDS-compliant naming. It ```bash # Basic run (resumes from last checkpoint automatically) export DATADIR=/path/to/your/data -python -m BetterCodeBetterScience.rnaseq.stateless_workflow.run_workflow +python -m bettercode.rnaseq.stateless_workflow.run_workflow ``` ### Force Re-run from a Specific Step ```python from pathlib import Path -from BetterCodeBetterScience.rnaseq.stateless_workflow.run_workflow import ( +from bettercode.rnaseq.stateless_workflow.run_workflow import ( run_stateless_workflow, print_checkpoint_status, ) @@ -172,7 +172,7 @@ results = run_stateless_workflow(datadir, force_from_step=5) ### Utility Functions ```python -from BetterCodeBetterScience.rnaseq.stateless_workflow.run_workflow import ( +from bettercode.rnaseq.stateless_workflow.run_workflow import ( list_checkpoints, print_checkpoint_status, list_execution_logs, @@ -220,15 +220,15 @@ The Prefect workflow uses the Prefect orchestration framework and analyzes all c ```bash # Basic run with default config -python -m BetterCodeBetterScience.rnaseq.prefect_workflow.run_workflow --datadir /path/to/data/immune_aging +python -m bettercode.rnaseq.prefect_workflow.run_workflow --datadir /path/to/data/immune_aging # With custom config file -python -m BetterCodeBetterScience.rnaseq.prefect_workflow.run_workflow \ +python -m bettercode.rnaseq.prefect_workflow.run_workflow \ --datadir /path/to/data/immune_aging \ --config /path/to/custom_config.yaml # Force re-run from step 8 -python -m BetterCodeBetterScience.rnaseq.prefect_workflow.run_workflow \ +python -m bettercode.rnaseq.prefect_workflow.run_workflow \ --datadir /path/to/data/immune_aging \ --force-from 8 ``` @@ -236,7 +236,7 @@ python -m BetterCodeBetterScience.rnaseq.prefect_workflow.run_workflow \ ### List Available Cell Types ```bash -python -m BetterCodeBetterScience.rnaseq.prefect_workflow.run_workflow \ +python -m bettercode.rnaseq.prefect_workflow.run_workflow \ --datadir /path/to/data/immune_aging \ --list-cell-types ``` @@ -244,7 +244,7 @@ python -m BetterCodeBetterScience.rnaseq.prefect_workflow.run_workflow \ ### Analyze a Single Cell Type ```bash -python -m BetterCodeBetterScience.rnaseq.prefect_workflow.run_workflow \ +python -m bettercode.rnaseq.prefect_workflow.run_workflow \ --datadir /path/to/data/immune_aging \ --cell-type "central memory CD4-positive, alpha-beta T cell" ``` @@ -317,7 +317,7 @@ The Snakemake workflow uses the Snakemake workflow management system with dynami ### Running the Workflow ```bash -cd src/BetterCodeBetterScience/rnaseq/snakemake_workflow +cd src/bettercode/rnaseq/snakemake_workflow # Run full workflow snakemake --cores 16 --config datadir=/path/to/data/immune_aging diff --git a/src/bettercode/rnaseq/ad_scrnaseq_1_dataprep.ipynb b/src/bettercode/rnaseq/ad_scrnaseq_1_dataprep.ipynb new file mode 100644 index 0000000..0f85be7 --- /dev/null +++ b/src/bettercode/rnaseq/ad_scrnaseq_1_dataprep.ipynb @@ -0,0 +1,253 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 20, + "id": "0d3385e0", + "metadata": {}, + "outputs": [], + "source": [ + "import anndata as ad\n", + "from anndata.experimental import read_lazy\n", + "import dask.array as da\n", + "import h5py\n", + "import numpy as np\n", + "import scanpy as sc\n", + "from pathlib import Path\n", + "\n", + "datadir = Path('/Users/poldrack/data_unsynced/BCBS/ad_scrnaseq/')" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "7b67c0b6", + "metadata": {}, + "outputs": [], + "source": [ + "datafile = datadir / 'dad4819b-4c14-439c-b32a-2c8d68bd22e1.h5ad'\n", + "\n", + "load_annotation_index = True\n", + "adata = read_lazy(h5py.File(datafile, 'r'),\n", + " load_annotation_index=load_annotation_index)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "40d53939", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "AnnData object with n_obs × n_vars = 1395601 × 35483\n", + " obs: 'assay_ontology_term_id', 'suspension_type', 'cell_type_ontology_term_id', 'development_stage_ontology_term_id', 'disease_ontology_term_id', 'self_reported_ethnicity_ontology_term_id', 'sex_ontology_term_id', 'tissue_ontology_term_id', 'is_primary_data', 'donor_id', 'Neurotypical reference', 'Class', 'Subclass', 'Supertype', 'Age at death', 'Years of education', 'Cognitive status', 'ADNC', 'Braak stage', 'Thal phase', 'CERAD score', 'APOE4 status', 'Lewy body disease pathology', 'LATE-NC stage', 'Microinfarct pathology', 'Specimen ID', 'PMI', 'Number of UMIs', 'Genes detected', 'Fraction mitochrondrial UMIs', 'tissue_type', 'cell_type', 'assay', 'disease', 'sex', 'tissue', 'self_reported_ethnicity', 'development_stage', 'observation_joinid'\n", + " var: 'feature_is_filtered', 'feature_name', 'feature_reference', 'feature_biotype', 'feature_length', 'feature_type'\n", + " uns: 'ADNC_colors', 'APOE4 status_colors', 'Age at death_colors', 'Braak stage_colors', 'CERAD score_colors', 'Cognitive status_colors', 'Great Apes Metadata', 'LATE-NC stage_colors', 'Lewy body disease pathology_colors', 'Microinfarct pathology_colors', 'PMI_colors', 'Subclass_colors', 'Supertype_colors', 'Thal phase_colors', 'UW Clinical Metadata', 'Years of education_colors', 'batch_condition', 'citation', 'default_embedding', 'neighbors', 'organism', 'organism_ontology_term_id', 'schema_reference', 'schema_version', 'sex_ontology_term_id_colors', 'title', 'umap'\n", + " obsm: 'X_scVI', 'X_umap'\n", + " obsp: 'connectivities', 'distances'\n" + ] + } + ], + "source": [ + "print(adata)" + ] + }, + { + "cell_type": "markdown", + "id": "96e66a8f", + "metadata": {}, + "source": [ + "Filter to a smaller set of glial cell types" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "7eeca179", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['L2/3-6 intratelencephalic projecting glutamatergic neuron'\n", + " 'L5 extratelencephalic projecting glutamatergic cortical neuron'\n", + " 'L6b glutamatergic cortical neuron' 'VIP GABAergic cortical interneuron'\n", + " 'astrocyte of the cerebral cortex'\n", + " 'caudal ganglionic eminence derived cortical interneuron'\n", + " 'cerebral cortex endothelial cell'\n", + " 'chandelier pvalb GABAergic cortical interneuron'\n", + " 'corticothalamic-projecting glutamatergic cortical neuron'\n", + " 'lamp5 GABAergic cortical interneuron' 'microglial cell'\n", + " 'near-projecting glutamatergic cortical neuron' 'oligodendrocyte'\n", + " 'oligodendrocyte precursor cell' 'pvalb GABAergic cortical interneuron'\n", + " 'sncg GABAergic cortical interneuron'\n", + " 'sst GABAergic cortical interneuron' 'vascular leptomeningeal cell']\n" + ] + } + ], + "source": [ + "unique_cell_types = np.unique(adata.obs['cell_type'])\n", + "print(unique_cell_types)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "308f00cc", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "np.int64(129930)" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "selected_types = [\n", + " 'astrocyte of the cerebral cortex',\n", + " 'microglial cell'\n", + "]\n", + "mask = np.isin(adata.obs['cell_type'].values, selected_types)\n", + "np.sum(mask)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "f741845e", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Subsetting data...\n", + "Loading data into memory...\n", + "Filtering genes with zero counts...\n", + "View of AnnData object with n_obs × n_vars = 129930 × 35483\n", + " obs: 'assay_ontology_term_id', 'suspension_type', 'cell_type_ontology_term_id', 'development_stage_ontology_term_id', 'disease_ontology_term_id', 'self_reported_ethnicity_ontology_term_id', 'sex_ontology_term_id', 'tissue_ontology_term_id', 'is_primary_data', 'donor_id', 'Neurotypical reference', 'Class', 'Subclass', 'Supertype', 'Age at death', 'Years of education', 'Cognitive status', 'ADNC', 'Braak stage', 'Thal phase', 'CERAD score', 'APOE4 status', 'Lewy body disease pathology', 'LATE-NC stage', 'Microinfarct pathology', 'Specimen ID', 'PMI', 'Number of UMIs', 'Genes detected', 'Fraction mitochrondrial UMIs', 'tissue_type', 'cell_type', 'assay', 'disease', 'sex', 'tissue', 'self_reported_ethnicity', 'development_stage', 'observation_joinid'\n", + " var: 'feature_is_filtered', 'feature_name', 'feature_reference', 'feature_biotype', 'feature_length', 'feature_type'\n", + " uns: 'ADNC_colors', 'APOE4 status_colors', 'Age at death_colors', 'Braak stage_colors', 'CERAD score_colors', 'Cognitive status_colors', 'Great Apes Metadata', 'LATE-NC stage_colors', 'Lewy body disease pathology_colors', 'Microinfarct pathology_colors', 'PMI_colors', 'Subclass_colors', 'Supertype_colors', 'Thal phase_colors', 'UW Clinical Metadata', 'Years of education_colors', 'batch_condition', 'citation', 'default_embedding', 'neighbors', 'organism', 'organism_ontology_term_id', 'schema_reference', 'schema_version', 'sex_ontology_term_id_colors', 'title', 'umap'\n", + " obsm: 'X_scVI', 'X_umap'\n", + " obsp: 'connectivities', 'distances'\n" + ] + } + ], + "source": [ + "\n", + "print(\"Subsetting data...\")\n", + "subset_adata = adata[mask, :]\n", + "\n", + "print(\"Loading data into memory (this can take a few minutes)...\")\n", + "subset_loaded = subset_adata.to_memory()\n", + "\n", + "# filter out genes with zero counts across all selected cells\n", + "print(\"Filtering genes with zero counts...\")\n", + "sc.pp.filter_genes(subset_loaded, min_counts=1)\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "310f8343", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "AnnData object with n_obs × n_vars = 129930 × 33389\n", + " obs: 'assay_ontology_term_id', 'suspension_type', 'cell_type_ontology_term_id', 'development_stage_ontology_term_id', 'disease_ontology_term_id', 'self_reported_ethnicity_ontology_term_id', 'sex_ontology_term_id', 'tissue_ontology_term_id', 'is_primary_data', 'donor_id', 'Neurotypical reference', 'Class', 'Subclass', 'Supertype', 'Age at death', 'Years of education', 'Cognitive status', 'ADNC', 'Braak stage', 'Thal phase', 'CERAD score', 'APOE4 status', 'Lewy body disease pathology', 'LATE-NC stage', 'Microinfarct pathology', 'Specimen ID', 'PMI', 'Number of UMIs', 'Genes detected', 'Fraction mitochrondrial UMIs', 'tissue_type', 'cell_type', 'assay', 'disease', 'sex', 'tissue', 'self_reported_ethnicity', 'development_stage', 'observation_joinid'\n", + " var: 'feature_is_filtered', 'feature_name', 'feature_reference', 'feature_biotype', 'feature_length', 'feature_type', 'n_counts'\n", + " uns: 'ADNC_colors', 'APOE4 status_colors', 'Age at death_colors', 'Braak stage_colors', 'CERAD score_colors', 'Cognitive status_colors', 'Great Apes Metadata', 'LATE-NC stage_colors', 'Lewy body disease pathology_colors', 'Microinfarct pathology_colors', 'PMI_colors', 'Thal phase_colors', 'UW Clinical Metadata', 'Years of education_colors', 'batch_condition', 'citation', 'default_embedding', 'neighbors', 'organism', 'organism_ontology_term_id', 'schema_reference', 'schema_version', 'sex_ontology_term_id_colors', 'title', 'umap'\n", + " obsm: 'X_scVI', 'X_umap'\n", + " obsp: 'connectivities', 'distances'\n" + ] + } + ], + "source": [ + "print(subset_loaded)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "69a5552a", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(129930, 33389)\n" + ] + } + ], + "source": [ + "subset_df = subset_loaded.to_df()\n", + "print(subset_df.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "ecb61446", + "metadata": {}, + "outputs": [], + "source": [ + "subset_loaded.write(datadir / 'glia_subset.h5ad')" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "10b6c9e6", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "total 117720632\n", + "-rw-r--r-- 1 poldrack staff 50G Dec 17 13:09 dad4819b-4c14-439c-b32a-2c8d68bd22e1.h5ad\n", + "-rw-r--r--@ 1 poldrack staff 6.6G Dec 17 16:18 glia_subset.h5ad\n" + ] + } + ], + "source": [ + "!ls -lh /Users/poldrack/data_unsynced/BCBS/ad_scrnaseq" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "bettercode", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.0" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/src/bettercode/rnaseq/ad_scrnaseq_2_preprocess.ipynb b/src/bettercode/rnaseq/ad_scrnaseq_2_preprocess.ipynb new file mode 100644 index 0000000..f96fd04 --- /dev/null +++ b/src/bettercode/rnaseq/ad_scrnaseq_2_preprocess.ipynb @@ -0,0 +1,2452 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "c3333c8c", + "metadata": {}, + "source": [ + "Preprocessing based on suggestions from Google Gemini\n", + "\n", + "based on https://www.sc-best-practices.org/preprocessing_visualization/quality_control.html\n", + "\n", + "and https://www.10xgenomics.com/analysis-guides/common-considerations-for-quality-control-filters-for-single-cell-rna-seq-data\n", + "\n", + "Code in this notebook primarily generated using Gemini 3.0" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "5e94c5f6", + "metadata": {}, + "outputs": [], + "source": [ + "import anndata as ad\n", + "import dask.array as da\n", + "import h5py\n", + "import numpy as np\n", + "import scanpy as sc\n", + "from pathlib import Path\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "\n", + "datadir = Path('/Users/poldrack/data_unsynced/BCBS/ad_scrnaseq/')" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "3c5b35d2", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "AnnData object with n_obs × n_vars = 129930 × 33389\n", + " obs: 'assay_ontology_term_id', 'suspension_type', 'cell_type_ontology_term_id', 'development_stage_ontology_term_id', 'disease_ontology_term_id', 'self_reported_ethnicity_ontology_term_id', 'sex_ontology_term_id', 'tissue_ontology_term_id', 'is_primary_data', 'donor_id', 'Neurotypical reference', 'Class', 'Subclass', 'Supertype', 'Age at death', 'Years of education', 'Cognitive status', 'ADNC', 'Braak stage', 'Thal phase', 'CERAD score', 'APOE4 status', 'Lewy body disease pathology', 'LATE-NC stage', 'Microinfarct pathology', 'Specimen ID', 'PMI', 'Number of UMIs', 'Genes detected', 'Fraction mitochrondrial UMIs', 'tissue_type', 'cell_type', 'assay', 'disease', 'sex', 'tissue', 'self_reported_ethnicity', 'development_stage', 'observation_joinid'\n", + " var: 'feature_is_filtered', 'feature_name', 'feature_reference', 'feature_biotype', 'feature_length', 'feature_type', 'n_counts'\n", + " uns: 'ADNC_colors', 'APOE4 status_colors', 'Age at death_colors', 'Braak stage_colors', 'CERAD score_colors', 'Cognitive status_colors', 'Great Apes Metadata', 'LATE-NC stage_colors', 'Lewy body disease pathology_colors', 'Microinfarct pathology_colors', 'PMI_colors', 'Thal phase_colors', 'UW Clinical Metadata', 'Years of education_colors', 'batch_condition', 'citation', 'default_embedding', 'neighbors', 'organism', 'organism_ontology_term_id', 'schema_reference', 'schema_version', 'sex_ontology_term_id_colors', 'title', 'umap'\n", + " obsm: 'X_scVI', 'X_umap'\n", + " obsp: 'connectivities', 'distances'\n" + ] + } + ], + "source": [ + "adata = ad.read_h5ad(datadir / 'glia_subset.h5ad')\n", + "print(adata)" + ] + }, + { + "cell_type": "code", + "execution_count": 106, + "id": "e775c5d1", + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.microsoft.datawrangler.viewer.v0+json": { + "columns": [ + { + "name": "Braak stage", + "rawType": "category", + "type": "unknown" + }, + { + "name": "count", + "rawType": "int64", + "type": "integer" + } + ], + "ref": "8838e5a9-618e-4593-bb8e-d977c34b4e25", + "rows": [ + [ + "Braak V", + "51332" + ], + [ + "Braak IV", + "30920" + ], + [ + "Braak VI", + "22304" + ], + [ + "Braak III", + "7803" + ], + [ + "Braak II", + "5855" + ], + [ + "Reference", + "4725" + ], + [ + "Braak 0", + "3815" + ] + ], + "shape": { + "columns": 1, + "rows": 7 + } + }, + "text/plain": [ + "Braak stage\n", + "Braak V 51332\n", + "Braak IV 30920\n", + "Braak VI 22304\n", + "Braak III 7803\n", + "Braak II 5855\n", + "Reference 4725\n", + "Braak 0 3815\n", + "Name: count, dtype: int64" + ] + }, + "execution_count": 106, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "adata.obs['Braak stage'].value_counts()" + ] + }, + { + "cell_type": "markdown", + "id": "ca1edf40", + "metadata": {}, + "source": [ + "### Quality control\n", + "\n", + "based on https://www.sc-best-practices.org/preprocessing_visualization/quality_control.html\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "a95e8baa", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Number of mitochondrial genes: 13\n", + "Number of ribosomal genes: 106\n", + "Number of hemoglobin genes: 12\n" + ] + } + ], + "source": [ + "# mitochondrial genes\n", + "adata.var[\"mt\"] = adata.var['feature_name'].str.startswith(\"MT-\")\n", + "print(f\"Number of mitochondrial genes: {adata.var['mt'].sum()}\")\n", + "\n", + "# ribosomal genes\n", + "adata.var[\"ribo\"] = adata.var['feature_name'].str.startswith((\"RPS\", \"RPL\"))\n", + "print(f\"Number of ribosomal genes: {adata.var['ribo'].sum()}\")\n", + "\n", + "# hemoglobin genes.\n", + "adata.var[\"hb\"] = adata.var['feature_name'].str.contains(\"^HB[^(P)]\")\n", + "print(f\"Number of hemoglobin genes: {adata.var['hb'].sum()}\")\n", + "\n", + "sc.pp.calculate_qc_metrics(\n", + " adata, qc_vars=[\"mt\", \"ribo\", \"hb\"], inplace=True, percent_top=[20], log1p=True\n", + ")\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "c79181f1", + "metadata": {}, + "source": [ + "Visualization of distributions " + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "a4819733", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "\n", + "# 1. Violin plots to see the distribution of QC metrics\n", + "# Note: I am using the exact column names from your adata output\n", + "p1 = sc.pl.violin(adata, ['total_counts', 'n_genes_by_counts', 'pct_counts_mt'],\n", + " jitter=0.4, multi_panel=True)\n", + "\n", + "# 2. Scatter plot to spot doublets and dying cells\n", + "# High mito + low genes = dying cell\n", + "# High counts + high genes = potential doublet\n", + "sc.pl.scatter(adata, x='total_counts', y='n_genes_by_counts', color='pct_counts_mt')" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "be603387", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Dropped 0 cells based on mitochondrial fraction\n", + "Dropped 326 cells based on gene detection\n", + "Dropped 1096 cells based on UMI counts\n", + "Original cells: 129930\n", + "Cells after QC: 128508\n" + ] + } + ], + "source": [ + "# Create a copy or view to avoid modifying the original if needed\n", + "adata_qc = adata.copy()\n", + "\n", + "# 1. Filter based on mitochondrial fraction - 10% doesn't seem to lose any cells\n", + "adata_qc = adata_qc[adata_qc.obs['Fraction mitochrondrial UMIs'] < 0.1, :]\n", + "n_obs_after_mito_filter = adata_qc.n_obs\n", + "print(f'Dropped {adata.n_obs - adata_qc.n_obs} cells based on mitochondrial fraction')\n", + "\n", + "# 2. Filter based on Genes detected (Min 200, Max 7000)\n", + "adata_qc = adata_qc[adata_qc.obs['Genes detected'] > 200, :]\n", + "adata_qc = adata_qc[adata_qc.obs['Genes detected'] < 7000, :]\n", + "n_obs_after_gene_filter = adata_qc.n_obs\n", + "print(f'Dropped {n_obs_after_mito_filter - n_obs_after_gene_filter} cells based on gene detection')\n", + "\n", + "# 3. (Optional) Filter based on UMI counts\n", + "# Usually gene filtering covers this, but you can remove extreme outliers\n", + "adata_qc = adata_qc[adata_qc.obs['Number of UMIs'] < 30000, :]\n", + "n_obs_after_umi_filter = adata_qc.n_obs\n", + "print(f'Dropped {n_obs_after_gene_filter - n_obs_after_umi_filter} cells based on UMI counts')\n", + "\n", + "print(f\"Original cells: {adata.n_obs}\")\n", + "print(f\"Cells after QC: {adata_qc.n_obs}\")" + ] + }, + { + "cell_type": "markdown", + "id": "faa4a504", + "metadata": {}, + "source": [ + "perform doublet detection\n", + "- first need to filter out small batches" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "7c89ced5", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Smallest batches:\n", + "Specimen ID\n", + "M2XM_220302_222-R_A01 160\n", + "M1TX_211012_151_A01 158\n", + "M2XM_220207_216-R_B01 151\n", + "M2XM_220221_211-R_C01 144\n", + "M2XM_220222_206-R_C01 144\n", + "M2XM_220221_211-R_B01 140\n", + "M1TX_211012_151_B01 120\n", + "M2XM_220222_206-R_A01 75\n", + "M2TX_210511_203_G01 7\n", + "M2TX_210824_431_A01 1\n", + "Name: count, dtype: int64\n" + ] + } + ], + "source": [ + "# Check the number of cells per Specimen ID\n", + "batch_counts = adata.obs['Specimen ID'].value_counts()\n", + "\n", + "# Look at the smallest batches\n", + "print(\"Smallest batches:\")\n", + "print(batch_counts.tail(10))" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "904c9a6a", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Original cells: 129930\n", + "Cells after removing small batches: 129922\n" + ] + } + ], + "source": [ + "# Define a minimum threshold (e.g., 50 cells)\n", + "min_cells = 50\n", + "\n", + "# Identify batches to keep\n", + "valid_batches = batch_counts[batch_counts >= min_cells].index\n", + "\n", + "# Filter the AnnData object\n", + "adata_filtered = adata[adata.obs['Specimen ID'].isin(valid_batches)].copy()\n", + "\n", + "print(f\"Original cells: {adata.n_obs}\")\n", + "print(f\"Cells after removing small batches: {adata_filtered.n_obs}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "f77921c1", + "metadata": {}, + "outputs": [], + "source": [ + "# this just adds the doublet scores to the adata.obs\n", + "sc.pp.scrublet(adata_filtered, batch_key='Specimen ID', expected_doublet_rate=0.06)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "69ff081a", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CCTCACAGTTTGTTGG-L8TX_211124_01_D03-1144726144 0.038068\n", + "TAGCACACAGGACAGT-L8TX_210812_01_A11-1124416554 0.019699\n", + "CTAAGTGTCACCTGTC-L8TX_210617_01_C12-1113634358 0.046377\n", + "GTCACTCGTTGTCTAG-L8TX_210902_01_E09-1129993892 0.134638\n", + "ATCAGGTAGCACACAG-L8TX_210826_01_H06-1127603900 0.093248\n", + " ... \n", + "GTAGTACAGAGCCCAA-L8TX_210826_01_G06-1127603907 0.097889\n", + "ATCGATGTCGCCAACG-L8TX_210923_01_A08-1131758239 0.044068\n", + "CCTCCAATCCGATCTC-L8TX_210916_01_E04-1131593674 0.051873\n", + "GGTCACGTCTCGAGTA-L8TX_211028_01_C05-1140206518 0.069965\n", + "GTCGCGATCGTGCATA-L8TX_210826_01_H06-1127603900 0.057826\n", + "Name: doublet_score, Length: 129922, dtype: float64\n" + ] + } + ], + "source": [ + "print(adata_filtered.obs['doublet_score'])" + ] + }, + { + "cell_type": "markdown", + "id": "04d2984a", + "metadata": {}, + "source": [ + "visualize doublets\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "5882e417", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "sc.pl.umap(adata_filtered, color=['doublet_score', 'predicted_doublet'], size=20)\n" + ] + }, + { + "cell_type": "markdown", + "id": "25f6e3fb", + "metadata": {}, + "source": [ + "filter doublets\n", + "- how consistent are these results with other methods for doublet detection? https://www.sc-best-practices.org/preprocessing_visualization/quality_control.html#doublet-detection" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "dd9b6443", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "found 3168 predicted doublets\n", + "Remaining cells: 126754\n" + ] + } + ], + "source": [ + "# Check how many doublets were found\n", + "print(f'found {adata_filtered.obs[\"predicted_doublet\"].sum()} predicted doublets')\n", + "\n", + "# Filter the data to keep only singlets (False)\n", + "# write back to adata for simplicity\n", + "adata = adata_filtered[adata_filtered.obs['predicted_doublet'] == False, :]\n", + "\n", + "print(f\"Remaining cells: {adata.n_obs}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "00da60cb", + "metadata": {}, + "outputs": [], + "source": [ + "# set the .raw attribute (standard Scanpy convention)\n", + "adata.raw = adata" + ] + }, + { + "cell_type": "markdown", + "id": "d97842a3", + "metadata": {}, + "source": [ + "#### Total Count Normalization\n", + "This scales each cell so that they all have the same total number of counts (default is often 10,000, known as \"CP10k\")." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "2d2d9b0c", + "metadata": {}, + "outputs": [], + "source": [ + "# Normalize to 10,000 reads per cell\n", + "# target_sum=1e4 is the standard for 10x data\n", + "sc.pp.normalize_total(adata, target_sum=1e4)" + ] + }, + { + "cell_type": "markdown", + "id": "0efc2045", + "metadata": {}, + "source": [ + "#### Log Transformation (Log1p)\n", + "This applies a natural logarithm to the data: log(X+1). This reduces the skewness of the data (since gene expression follows a power law) and stabilizes the variance." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "d412ddd8", + "metadata": {}, + "outputs": [], + "source": [ + "# Logarithmically transform the data\n", + "sc.pp.log1p(adata)" + ] + }, + { + "cell_type": "markdown", + "id": "bd5a1cde", + "metadata": {}, + "source": [ + "#### select high-variance features\n", + "\n", + "First let's plot variablility across genes" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "0283b027", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Identify highly variable genes\n", + "# 'seurat' flavor is the standard default\n", + "sc.pp.highly_variable_genes(adata, min_mean=0.0125, max_mean=3, min_disp=0.5)\n", + "\n", + "# Plot to see the dispersion\n", + "sc.pl.highly_variable_genes(adata)\n", + "\n", + "# (Optional) Retain only the variable genes for downstream analysis (PCA/UMAP)\n", + "# This creates a smaller view of the data for speed\n", + "adata_hvg = adata[:, adata.var['highly_variable']]" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "b22a4d73", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "View of AnnData object with n_obs × n_vars = 126754 × 4587\n", + " obs: 'assay_ontology_term_id', 'suspension_type', 'cell_type_ontology_term_id', 'development_stage_ontology_term_id', 'disease_ontology_term_id', 'self_reported_ethnicity_ontology_term_id', 'sex_ontology_term_id', 'tissue_ontology_term_id', 'is_primary_data', 'donor_id', 'Neurotypical reference', 'Class', 'Subclass', 'Supertype', 'Age at death', 'Years of education', 'Cognitive status', 'ADNC', 'Braak stage', 'Thal phase', 'CERAD score', 'APOE4 status', 'Lewy body disease pathology', 'LATE-NC stage', 'Microinfarct pathology', 'Specimen ID', 'PMI', 'Number of UMIs', 'Genes detected', 'Fraction mitochrondrial UMIs', 'tissue_type', 'cell_type', 'assay', 'disease', 'sex', 'tissue', 'self_reported_ethnicity', 'development_stage', 'observation_joinid', 'n_genes_by_counts', 'log1p_n_genes_by_counts', 'total_counts', 'log1p_total_counts', 'pct_counts_in_top_20_genes', 'total_counts_mt', 'log1p_total_counts_mt', 'pct_counts_mt', 'total_counts_ribo', 'log1p_total_counts_ribo', 'pct_counts_ribo', 'total_counts_hb', 'log1p_total_counts_hb', 'pct_counts_hb', 'n_genes', 'doublet_score', 'predicted_doublet'\n", + " var: 'feature_is_filtered', 'feature_name', 'feature_reference', 'feature_biotype', 'feature_length', 'feature_type', 'n_counts', 'mt', 'ribo', 'hb', 'n_cells_by_counts', 'mean_counts', 'log1p_mean_counts', 'pct_dropout_by_counts', 'total_counts', 'log1p_total_counts', 'highly_variable', 'means', 'dispersions', 'dispersions_norm'\n", + " uns: 'ADNC_colors', 'APOE4 status_colors', 'Age at death_colors', 'Braak stage_colors', 'CERAD score_colors', 'Cognitive status_colors', 'Great Apes Metadata', 'LATE-NC stage_colors', 'Lewy body disease pathology_colors', 'Microinfarct pathology_colors', 'PMI_colors', 'Thal phase_colors', 'UW Clinical Metadata', 'Years of education_colors', 'batch_condition', 'citation', 'default_embedding', 'neighbors', 'organism', 'organism_ontology_term_id', 'schema_reference', 'schema_version', 'sex_ontology_term_id_colors', 'title', 'umap', 'scrublet', 'predicted_doublet_colors', 'log1p', 'hvg'\n", + " obsm: 'X_scVI', 'X_umap'\n", + " obsp: 'connectivities', 'distances'" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "adata_hvg\n" + ] + }, + { + "cell_type": "markdown", + "id": "2055120b", + "metadata": {}, + "source": [ + "### Dimensionality reduction" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "93802dfb", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/poldrack/Dropbox/code/bettercode/.venv/lib/python3.12/site-packages/scanpy/neighbors/__init__.py:577: UserWarning: You’re trying to run this on 4587 dimensions of `.X`, if you really want this, set `use_rep='X'`.\n", + " Falling back to preprocessing with `sc.pp.pca` and default params.\n", + " x = _choose_representation(self._adata, use_rep=use_rep, n_pcs=n_pcs)\n" + ] + } + ], + "source": [ + "sc.pp.neighbors(adata_hvg)\n", + "sc.tl.umap(adata_hvg)" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "ce2bc327", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "sc.pl.umap(adata_hvg, color=\"total_counts\")\n" + ] + }, + { + "cell_type": "markdown", + "id": "2209ee21", + "metadata": {}, + "source": [ + "### Clustering\n" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "60b9ce78", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Compute PCA\n", + "sc.tl.pca(adata_hvg, svd_solver='arpack')\n", + "\n", + "# (Optional) Visualize variance explained by each PC\n", + "# This helps decide how many PCs to use. The \"elbow\" usually flattens around 10-20.\n", + "sc.pl.pca_variance_ratio(adata_hvg, log=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "35247ba6", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/var/folders/r2/f85nyfr1785fj4257wkdj7480000gn/T/ipykernel_70584/1212141890.py:3: FutureWarning: In the future, the default backend for leiden will be igraph instead of leidenalg.\n", + "\n", + " To achieve the future defaults please pass: flavor=\"igraph\" and n_iterations=2. directed must also be False to work with igraph's implementation.\n", + " sc.tl.leiden(adata_hvg, resolution=0.5)\n" + ] + } + ], + "source": [ + "# Run Leiden clustering\n", + "# This adds a 'leiden' column to adata.obs\n", + "sc.tl.leiden(adata_hvg, resolution=0.5)" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "e9562ecd", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plot UMAP with Leiden clusters\n", + "sc.pl.umap(adata_hvg, color=['leiden', 'cell_type'], legend_loc='on data')" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "25b45de3", + "metadata": {}, + "outputs": [], + "source": [ + "# drop individuals with development stage of \"80 year-old and over stage\"\n", + "\n", + "adata_hvg = adata_hvg[adata_hvg.obs['development_stage'] != \"80 year-old and over stage\"].copy()" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "89cd89cd", + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.microsoft.datawrangler.viewer.v0+json": { + "columns": [ + { + "name": "development_stage", + "rawType": "category", + "type": "unknown" + }, + { + "name": "count", + "rawType": "int64", + "type": "integer" + } + ], + "ref": "41ca4c70-21a3-4854-8b21-2df970179f20", + "rows": [ + [ + "81-year-old stage", + "6163" + ], + [ + "83-year-old stage", + "5758" + ], + [ + "88-year-old stage", + "5203" + ], + [ + "82-year-old stage", + "5133" + ], + [ + "75-year-old stage", + "4368" + ], + [ + "89-year-old stage", + "3946" + ], + [ + "86-year-old stage", + "3835" + ], + [ + "87-year-old stage", + "3226" + ], + [ + "80-year-old stage", + "3161" + ], + [ + "69-year-old stage", + "2736" + ], + [ + "78-year-old stage", + "2427" + ], + [ + "72-year-old stage", + "2047" + ], + [ + "84-year-old stage", + "1847" + ], + [ + "50-year-old stage", + "1707" + ], + [ + "29-year-old stage", + "1602" + ], + [ + "68-year-old stage", + "1435" + ], + [ + "42-year-old stage", + "1416" + ], + [ + "70-year-old stage", + "1109" + ], + [ + "85-year-old stage", + "811" + ], + [ + "77-year-old stage", + "724" + ] + ], + "shape": { + "columns": 1, + "rows": 20 + } + }, + "text/plain": [ + "development_stage\n", + "81-year-old stage 6163\n", + "83-year-old stage 5758\n", + "88-year-old stage 5203\n", + "82-year-old stage 5133\n", + "75-year-old stage 4368\n", + "89-year-old stage 3946\n", + "86-year-old stage 3835\n", + "87-year-old stage 3226\n", + "80-year-old stage 3161\n", + "69-year-old stage 2736\n", + "78-year-old stage 2427\n", + "72-year-old stage 2047\n", + "84-year-old stage 1847\n", + "50-year-old stage 1707\n", + "29-year-old stage 1602\n", + "68-year-old stage 1435\n", + "42-year-old stage 1416\n", + "70-year-old stage 1109\n", + "85-year-old stage 811\n", + "77-year-old stage 724\n", + "Name: count, dtype: int64" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "adata_hvg.obs['development_stage'].value_counts()" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "c6acbfb3", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "<>:1: SyntaxWarning: invalid escape sequence '\\d'\n", + "<>:1: SyntaxWarning: invalid escape sequence '\\d'\n", + "/var/folders/r2/f85nyfr1785fj4257wkdj7480000gn/T/ipykernel_70584/2428158716.py:1: SyntaxWarning: invalid escape sequence '\\d'\n", + " adata_hvg.obs['age_group'] = adata_hvg.obs['development_stage'].astype(str).str.extract('(\\d+)').astype(int)\n" + ] + }, + { + "data": { + "application/vnd.microsoft.datawrangler.viewer.v0+json": { + "columns": [ + { + "name": "age_group", + "rawType": "int64", + "type": "integer" + }, + { + "name": "count", + "rawType": "int64", + "type": "integer" + } + ], + "ref": "38d132b3-9860-4db9-b35e-14f7bfb98a76", + "rows": [ + [ + "81", + "6163" + ], + [ + "83", + "5758" + ], + [ + "88", + "5203" + ], + [ + "82", + "5133" + ], + [ + "75", + "4368" + ], + [ + "89", + "3946" + ], + [ + "86", + "3835" + ], + [ + "87", + "3226" + ], + [ + "80", + "3161" + ], + [ + "69", + "2736" + ], + [ + "78", + "2427" + ], + [ + "72", + "2047" + ], + [ + "84", + "1847" + ], + [ + "50", + "1707" + ], + [ + "29", + "1602" + ], + [ + "68", + "1435" + ], + [ + "42", + "1416" + ], + [ + "70", + "1109" + ], + [ + "85", + "811" + ], + [ + "77", + "724" + ] + ], + "shape": { + "columns": 1, + "rows": 20 + } + }, + "text/plain": [ + "age_group\n", + "81 6163\n", + "83 5758\n", + "88 5203\n", + "82 5133\n", + "75 4368\n", + "89 3946\n", + "86 3835\n", + "87 3226\n", + "80 3161\n", + "69 2736\n", + "78 2427\n", + "72 2047\n", + "84 1847\n", + "50 1707\n", + "29 1602\n", + "68 1435\n", + "42 1416\n", + "70 1109\n", + "85 811\n", + "77 724\n", + "Name: count, dtype: int64" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "adata_hvg.obs['age_group'] = adata_hvg.obs['development_stage'].astype(str).str.extract('(\\d+)').astype(int)\n", + "adata_hvg.obs['age_group'].value_counts()" + ] + }, + { + "cell_type": "markdown", + "id": "01545b84", + "metadata": {}, + "source": [ + "Replace ENS labels with gene names for ease of use" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "924b09d9", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Index(['FIRRM', 'FGR', 'CFH', 'CFTR', 'LAP3'], dtype='object', name='feature_name')\n" + ] + } + ], + "source": [ + "\n", + "# 1. (Optional) Save the current Ensembl IDs to a new column so you don't lose them\n", + "adata_hvg.var['ensembl_id'] = adata_hvg.var_names\n", + "\n", + "# 2. Set the gene symbols as the new index\n", + "# We use .astype(str) to ensure they are treated as text\n", + "adata_hvg.var_names = adata_hvg.var['feature_name'].astype(str)\n", + "\n", + "# 3. Ensure the new names are unique\n", + "# This appends a suffix (e.g., '-1') to any duplicate gene names\n", + "adata_hvg.var_names_make_unique()\n", + "\n", + "# Verify the change\n", + "print(adata_hvg.var_names[:5])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2907db1a", + "metadata": {}, + "outputs": [], + "source": [ + "### Differential expression\n" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "0e30e949", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " names scores logfoldchanges pvals pvals_adj \\\n", + "0 ENSG00000148655 21.945333 1.418953 9.596347e-107 1.281650e-103 \n", + "1 ENSG00000151012 21.826395 0.553919 1.303032e-105 1.611368e-102 \n", + "2 ENSG00000245532 20.493479 0.121720 2.461613e-93 2.162915e-90 \n", + "3 ENSG00000102547 19.357334 0.823570 1.767992e-83 1.311811e-80 \n", + "4 ENSG00000251562 18.953377 0.088232 4.141383e-80 2.711306e-77 \n", + "5 ENSG00000158186 18.834421 0.627369 3.944100e-79 2.438696e-76 \n", + "6 ENSG00000259481 18.718952 0.895761 3.469127e-78 2.106012e-75 \n", + "7 ENSG00000083067 17.528713 0.462992 8.650611e-69 4.376292e-66 \n", + "8 ENSG00000198743 17.149014 0.990561 6.392797e-66 3.006325e-63 \n", + "9 ENSG00000235823 16.967937 0.933872 1.418281e-64 6.399321e-62 \n", + "\n", + " ensembl_id \n", + "0 NaN \n", + "1 NaN \n", + "2 NaN \n", + "3 NaN \n", + "4 NaN \n", + "5 NaN \n", + "6 ENSG00000259481 \n", + "7 NaN \n", + "8 NaN \n", + "9 NaN \n" + ] + } + ], + "source": [ + "# differnetial expression\n", + "\n", + "# 1. Subset to the cluster of interest\n", + "subset = adata_hvg[adata_hvg.obs['leiden'] == '3'].copy()\n", + "\n", + "# 2. Run Rank Genes Groups (Differential Expression)\n", + "# Compare 'Dementia' vs 'No dementia' (adjust exact labels based on your Step 1 output)\n", + "sc.tl.rank_genes_groups(subset, groupby='Cognitive status', reference='No dementia', method='wilcoxon')\n", + "\n", + "# 3. View the top upregulated genes in Dementia for this cluster\n", + "sc.pl.rank_genes_groups(subset, n_genes=20, sharey=False,\n", + " gene_symbols='feature_name')\n", + "\n", + "# 4. Get the table of results\n", + "de_results = sc.get.rank_genes_groups_df(subset, group='Dementia', gene_symbols='ensembl_id')\n", + "print(de_results.head(10))" + ] + }, + { + "cell_type": "markdown", + "id": "c68688e2", + "metadata": {}, + "source": [ + "### pathway enrichment\n", + "\n", + "- what pathways are enriched for healthy vs demented?" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "7f64589e", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "AnnData object with n_obs × n_vars = 58654 × 4587\n", + " obs: 'assay_ontology_term_id', 'suspension_type', 'cell_type_ontology_term_id', 'development_stage_ontology_term_id', 'disease_ontology_term_id', 'self_reported_ethnicity_ontology_term_id', 'sex_ontology_term_id', 'tissue_ontology_term_id', 'is_primary_data', 'donor_id', 'Neurotypical reference', 'Class', 'Subclass', 'Supertype', 'Age at death', 'Years of education', 'Cognitive status', 'ADNC', 'Braak stage', 'Thal phase', 'CERAD score', 'APOE4 status', 'Lewy body disease pathology', 'LATE-NC stage', 'Microinfarct pathology', 'Specimen ID', 'PMI', 'Number of UMIs', 'Genes detected', 'Fraction mitochrondrial UMIs', 'tissue_type', 'cell_type', 'assay', 'disease', 'sex', 'tissue', 'self_reported_ethnicity', 'development_stage', 'observation_joinid', 'n_genes_by_counts', 'log1p_n_genes_by_counts', 'total_counts', 'log1p_total_counts', 'pct_counts_in_top_20_genes', 'total_counts_mt', 'log1p_total_counts_mt', 'pct_counts_mt', 'total_counts_ribo', 'log1p_total_counts_ribo', 'pct_counts_ribo', 'total_counts_hb', 'log1p_total_counts_hb', 'pct_counts_hb', 'n_genes', 'doublet_score', 'predicted_doublet', 'leiden', 'age_group'\n", + " var: 'feature_is_filtered', 'feature_name', 'feature_reference', 'feature_biotype', 'feature_length', 'feature_type', 'n_counts', 'mt', 'ribo', 'hb', 'n_cells_by_counts', 'mean_counts', 'log1p_mean_counts', 'pct_dropout_by_counts', 'total_counts', 'log1p_total_counts', 'highly_variable', 'means', 'dispersions', 'dispersions_norm', 'ensembl_id'\n", + " uns: 'ADNC_colors', 'APOE4 status_colors', 'Age at death_colors', 'Braak stage_colors', 'CERAD score_colors', 'Cognitive status_colors', 'Great Apes Metadata', 'LATE-NC stage_colors', 'Lewy body disease pathology_colors', 'Microinfarct pathology_colors', 'PMI_colors', 'Thal phase_colors', 'UW Clinical Metadata', 'Years of education_colors', 'batch_condition', 'citation', 'default_embedding', 'neighbors', 'organism', 'organism_ontology_term_id', 'schema_reference', 'schema_version', 'sex_ontology_term_id_colors', 'title', 'umap', 'scrublet', 'predicted_doublet_colors', 'log1p', 'hvg', 'pca', 'leiden', 'leiden_colors', 'cell_type_colors'\n", + " obsm: 'X_scVI', 'X_umap', 'X_pca'\n", + " varm: 'PCs'\n", + " obsp: 'connectivities', 'distances'\n" + ] + } + ], + "source": [ + "print(adata_hvg)" + ] + }, + { + "cell_type": "markdown", + "id": "ef06e1bf", + "metadata": {}, + "source": [ + "### Clustering genes\n", + "\n", + "find sets of genes that move together across samples\n", + "\n", + "see: https://aistudio.google.com/app/prompts?state=%7B%22ids%22:%5B%221aA_hN3GkkcSFqzGJESR5YAv6cp0bJXMe%22%5D,%22action%22:%22open%22,%22userId%22:%22102042762597626922038%22,%22resourceKeys%22:%7B%7D%7D&usp=sharing\n" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "id": "009761d2", + "metadata": {}, + "outputs": [], + "source": [ + "from scipy import sparse\n", + "\n", + "# we need this because scanpy uses sparse matrices\n", + "def sparse_corr(X):\n", + " \"\"\"\n", + " Compute Pearson correlation matrix for a sparse matrix X.\n", + " X: sparse matrix of shape (n_cells, n_genes)\n", + " Returns: dense correlation matrix (n_genes, n_genes)\n", + " \"\"\"\n", + " n = X.shape[0]\n", + "\n", + " # 1. Calculate Mean and Standard Deviation for each gene\n", + " # .A1 converts matrix result to 1D numpy array\n", + " sums = np.array(X.sum(axis=0)).flatten() \n", + " sum_sq = np.array(X.power(2).sum(axis=0)).flatten()\n", + " \n", + " # Calculate Variance: E[x^2] - (E[x])^2\n", + " # We multiply by n to match the denominator of covariance later\n", + " # var = (sum_sq - (sums^2)/n) / (n-1)\n", + " # But we just need the numerator part for normalization first\n", + " \n", + " # Calculate Stds (sample std dev)\n", + " var = (sum_sq - (sums**2) / n) / (n - 1)\n", + " \n", + " # Handle constant genes (variance = 0) to avoid division by zero\n", + " var[var <= 0] = 1e-9\n", + " stds = np.sqrt(var)\n", + "\n", + " # 2. Calculate Covariance Numerator\n", + " # Formula: sum(x*y) - n * mean_x * mean_y\n", + " # Efficient sparse matrix multiplication\n", + " XtX = (X.T @ X).toarray() \n", + " \n", + " # Calculate the correction term (n * mean_x * mean_y)\n", + " # equivalent to: (sum_x * sum_y) / n\n", + " correction = np.outer(sums, sums) / n\n", + " \n", + " cov_numerator = XtX - correction\n", + "\n", + " # 3. Calculate Correlation\n", + " # corr = cov / (std_x * std_y * (n-1))\n", + " denominator = np.outer(stds, stds) * (n - 1)\n", + " \n", + " corr = cov_numerator / denominator\n", + "\n", + " # Clip values to [-1, 1] to handle floating point errors\n", + " return np.clip(corr, -1, 1)" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "id": "9c7d61ac", + "metadata": {}, + "outputs": [], + "source": [ + "import scipy.cluster.hierarchy as sch\n", + "\n", + "# compute correlation across columns of the data matrix\n", + "gene_corr = sparse_corr(adata_hvg.X)\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "id": "1f09da4b", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 56, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# plot a histogram of the correlations\n", + "plt.figure(figsize=(8, 5))\n", + "sns.histplot(gene_corr[np.triu_indices_from(gene_corr, k=1)].flatten(), bins=100, kde=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 58, + "id": "a6725aaf", + "metadata": {}, + "outputs": [], + "source": [ + "def create_adjmtx_pernode(m, density, use_abs_corr=True,\n", + " zero_tril=True, zero_diag=True):\n", + " \"\"\"\n", + " Create adjacency matrix for specified graph density based on functional connectivity.\n", + " density is computed per node to prevent isolated nodes.\n", + "\n", + " Parameters:\n", + " -----------\n", + " m : numpy.ndarray\n", + " Functional connectivity matrix (n_nodes x n_nodes)\n", + " NOTE: this must be a symmetric matrix\n", + " density : float\n", + " Desired graph density (between 0 and 1)\n", + " use_abs_corr : bool, optional\n", + " Whether to use absolute correlation values. Default is True.\n", + " zero_tril : bool, optional\n", + " Whether to zero out the lower triangular part of the matrix. Default is True.\n", + " zero_diag : bool, optional\n", + " Whether to zero out the diagonal of the matrix. Default is True. \n", + "\n", + " Returns:\n", + " --------\n", + " connection_matrix : numpy.ndarray\n", + " Binary connection matrix (n_nodes x n_nodes) with specified density for each node\n", + " \"\"\"\n", + " n_nodes = m.shape[0]\n", + " # Process each density\n", + "\n", + " if use_abs_corr:\n", + " m = np.abs(m)\n", + " \n", + " # Preallocate connection matrix\n", + " connection_matrix = np.zeros(m.shape).astype(int)\n", + "\n", + " # Process each node\n", + " for i in range(n_nodes):\n", + " # Check if node has any nonzero connections\n", + " if np.any(m[:, i]):\n", + " # Sort node connections from strongest to weakest\n", + " sorted_indices = np.argsort(m[:, i])[::-1]\n", + " # Calculate number of connections to keep\n", + " n_connections = int(np.ceil(n_nodes * density))\n", + " # Set connections in both directions (symmetric matrix)\n", + " connection_matrix[sorted_indices[:n_connections], i] = 1\n", + " connection_matrix[i, sorted_indices[:n_connections]] = 1\n", + " if zero_tril:\n", + " connection_matrix[np.tril_indices_from(connection_matrix)] = 0\n", + " if zero_diag:\n", + " connection_matrix[np.diag_indices_from(connection_matrix)] = 0\n", + "\n", + " return connection_matrix\n", + "\n", + "from infomap import Infomap\n", + "import networkx as nx\n", + "\n", + "def run_infomap(corrmtx, density=0.01,verbosity_level=0, normalize=False, seed=42, num_trials=50):\n", + "\n", + " adjmtx = create_adjmtx_pernode(corrmtx, density=density, use_abs_corr=True)\n", + " G = nx.from_numpy_array(adjmtx)\n", + "\n", + " im = Infomap(\n", + " silent=True,\n", + " seed=seed,\n", + " num_trials=num_trials,\n", + " clu=True,\n", + " no_self_links=True,\n", + " two_level=True,\n", + " verbosity_level=verbosity_level,\n", + " )\n", + "\n", + " _ = im.add_networkx_graph(G)\n", + " im.run()\n", + " if verbosity_level > 0:\n", + " print(f\"Found {im.num_top_modules} modules with codelength: {im.codelength}\")\n", + "\n", + " module_id_dict = {node.node_id: node.module_id for node in im.tree if node.is_leaf}\n", + " module_list = np.array([module_id_dict[i] for i in range(len(G.nodes()))])\n", + "\n", + " return module_list, im\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "id": "97fa3c44", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Found 101 modules with codelength: 8.544726150786348\n" + ] + } + ], + "source": [ + "im_modules, im = run_infomap(gene_corr, density=0.001, verbosity_level=1, seed=42, num_trials=100)" + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "id": "4cd6d0c6", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Counter({np.int64(1): 1363, np.int64(2): 630, np.int64(3): 239, np.int64(4): 194, np.int64(5): 185, np.int64(7): 160, np.int64(6): 159, np.int64(8): 100, np.int64(9): 95, np.int64(10): 89, np.int64(11): 74, np.int64(12): 69, np.int64(13): 51, np.int64(16): 48, np.int64(15): 47, np.int64(18): 45, np.int64(17): 44, np.int64(14): 44, np.int64(19): 42, np.int64(20): 37, np.int64(21): 32, np.int64(25): 28, np.int64(24): 27, np.int64(22): 25, np.int64(23): 25, np.int64(26): 24, np.int64(30): 22, np.int64(28): 21, np.int64(27): 21, np.int64(29): 21, np.int64(31): 21, np.int64(35): 18, np.int64(34): 17, np.int64(33): 17, np.int64(36): 17, np.int64(32): 15, np.int64(38): 14, np.int64(41): 14, np.int64(39): 14, np.int64(48): 13, np.int64(50): 13, np.int64(42): 13, np.int64(45): 13, np.int64(49): 13, np.int64(43): 13, np.int64(40): 12, np.int64(44): 12, np.int64(46): 12, np.int64(47): 12, np.int64(53): 12, np.int64(52): 12, np.int64(37): 11, np.int64(51): 11, np.int64(54): 10, np.int64(58): 10, np.int64(56): 10, np.int64(55): 10, np.int64(59): 10, np.int64(63): 9, np.int64(57): 9, np.int64(62): 9, np.int64(61): 9, np.int64(64): 9, np.int64(67): 9, np.int64(65): 9, np.int64(66): 9, np.int64(69): 8, np.int64(60): 8, np.int64(68): 8, np.int64(71): 8, np.int64(70): 8, np.int64(79): 7, np.int64(74): 7, np.int64(80): 7, np.int64(77): 7, np.int64(72): 7, np.int64(73): 7, np.int64(78): 7, np.int64(85): 6, np.int64(84): 6, np.int64(76): 6, np.int64(87): 6, np.int64(75): 6, np.int64(82): 6, np.int64(83): 6, np.int64(81): 6, np.int64(86): 6, np.int64(88): 5, np.int64(92): 4, np.int64(91): 4, np.int64(89): 4, np.int64(90): 4, np.int64(94): 3, np.int64(93): 3, np.int64(95): 3, np.int64(96): 2, np.int64(97): 2, np.int64(98): 2, np.int64(99): 2, np.int64(100): 2, np.int64(101): 2})\n" + ] + } + ], + "source": [ + "# compute the size of each module\n", + "from collections import Counter\n", + "module_sizes = Counter(im_modules)\n", + "print(module_sizes)" + ] + }, + { + "cell_type": "code", + "execution_count": 63, + "id": "503ab870", + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "\n", + "# 1. Create AnnData from the correlation matrix\n", + "# Rows = Genes, Cols = Correlation with every other gene\n", + "adata_genes = ad.AnnData(X=gene_corr.copy())\n", + "\n", + "# 2. Set names\n", + "adata_genes.obs_names = adata_hvg.var['feature_name'].astype(str)\n", + "adata_genes.var_names = adata_hvg.var['feature_name'].astype(str)\n", + "\n", + "# 3. Add the Infomap Module info\n", + "# Convert to string for categorical coloring\n", + "adata_genes.obs['Infomap_Module'] = im_modules.astype(str)\n", + "adata_genes.obs['Infomap_Module'] = adata_genes.obs['Infomap_Module'].fillna('Unassigned')\n", + "\n", + "\n", + "\n", + "# 4. Filter out 'Unassigned' genes (optional, cleans up the plot)\n", + "adata_genes = adata_genes[adata_genes.obs['Infomap_Module'] != 'Unassigned']" + ] + }, + { + "cell_type": "code", + "execution_count": 64, + "id": "e120b452", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/poldrack/Dropbox/code/bettercode/.venv/lib/python3.12/site-packages/scanpy/preprocessing/_pca/__init__.py:384: ImplicitModificationWarning: Setting element `.obsm['X_pca']` of view, initializing view as actual.\n", + " adata.obsm[key_obsm] = x_pca\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# 1. PCA\n", + "# We use arpack solver. If you have many genes, this reduces noise.\n", + "sc.tl.pca(adata_genes, svd_solver='arpack')\n", + "\n", + "# 2. Neighbors\n", + "# Identify genes with similar correlation profiles\n", + "sc.pp.neighbors(adata_genes, n_neighbors=15, n_pcs=20)\n", + "\n", + "# 3. UMAP\n", + "sc.tl.umap(adata_genes)\n", + "\n", + "# 4. Plot\n", + "# legend_loc='on data' puts the label on top of the cluster\n", + "sc.pl.umap(adata_genes, \n", + " color='Infomap_Module', \n", + " title='Gene Network UMAP (Based on Correlation)',\n", + " legend_loc='on data',\n", + " frameon=False)" + ] + }, + { + "cell_type": "code", + "execution_count": 65, + "id": "44a434c8", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Module size: 1363 genes\n", + "Ribosomal genes: 3\n", + "Mitochondrial genes: 0\n", + "['FIRRM', 'HS3ST1', 'ICA1', 'CASP10', 'PLXND1', 'CALCR', 'SKAP2', 'MSL3', 'ABCB4', 'ITGAL', 'CEACAM21', 'GAS7', 'CYTH3', 'TRAF3IP3', 'MLXIPL', 'STAB1', 'CD4', 'BTK', 'LDAF1', 'ERCC1']\n" + ] + } + ], + "source": [ + "# Assuming 'junk_id' is the ID of your large module\n", + "junk_id = 1 \n", + "junk_genes = adata_genes.var_names[im_modules == junk_id]\n", + "\n", + "# 1. Check size\n", + "print(f\"Module size: {len(junk_genes)} genes\")\n", + "\n", + "# 2. Check for Ribosomal (RPS/RPL) or Mitochondrial (MT-) genes\n", + "ribo_count = junk_genes.str.startswith(('RPS', 'RPL')).sum()\n", + "mito_count = junk_genes.str.startswith('MT-').sum()\n", + "\n", + "print(f\"Ribosomal genes: {ribo_count}\")\n", + "print(f\"Mitochondrial genes: {mito_count}\")\n", + "\n", + "# 3. Print the top 20 genes to eyeball\n", + "print(junk_genes[:20].tolist())" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c5b6f570", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Found 101 unique modules\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/poldrack/Dropbox/code/bettercode/.venv/lib/python3.12/site-packages/scanpy/preprocessing/_scale.py:309: UserWarning: Received a view of an AnnData. Making a copy.\n", + " view_to_actual(adata)\n", + "/Users/poldrack/.local/share/uv/python/cpython-3.12.0-macos-aarch64-none/lib/python3.12/functools.py:909: UserWarning: zero-centering a sparse array/matrix densifies it.\n", + " return dispatch(args[0].__class__)(*args, **kw)\n", + "/Users/poldrack/Dropbox/code/bettercode/.venv/lib/python3.12/site-packages/scanpy/preprocessing/_scale.py:309: UserWarning: Received a view of an AnnData. Making a copy.\n", + " view_to_actual(adata)\n", + "/Users/poldrack/.local/share/uv/python/cpython-3.12.0-macos-aarch64-none/lib/python3.12/functools.py:909: UserWarning: zero-centering a sparse array/matrix densifies it.\n", + " return dispatch(args[0].__class__)(*args, **kw)\n", + "/Users/poldrack/Dropbox/code/bettercode/.venv/lib/python3.12/site-packages/scanpy/preprocessing/_scale.py:309: UserWarning: Received a view of an AnnData. Making a copy.\n", + " view_to_actual(adata)\n", + "/Users/poldrack/.local/share/uv/python/cpython-3.12.0-macos-aarch64-none/lib/python3.12/functools.py:909: UserWarning: zero-centering a sparse array/matrix densifies it.\n", + " return dispatch(args[0].__class__)(*args, **kw)\n", + "/Users/poldrack/Dropbox/code/bettercode/.venv/lib/python3.12/site-packages/scanpy/preprocessing/_scale.py:309: UserWarning: Received a view of an AnnData. Making a copy.\n", + " view_to_actual(adata)\n", + "/Users/poldrack/.local/share/uv/python/cpython-3.12.0-macos-aarch64-none/lib/python3.12/functools.py:909: UserWarning: zero-centering a sparse array/matrix densifies it.\n", + " return dispatch(args[0].__class__)(*args, **kw)\n", + "/Users/poldrack/Dropbox/code/bettercode/.venv/lib/python3.12/site-packages/scanpy/preprocessing/_scale.py:309: UserWarning: Received a view of an AnnData. Making a copy.\n", + " view_to_actual(adata)\n", + "/Users/poldrack/.local/share/uv/python/cpython-3.12.0-macos-aarch64-none/lib/python3.12/functools.py:909: UserWarning: zero-centering a sparse array/matrix densifies it.\n", + " return dispatch(args[0].__class__)(*args, **kw)\n", + "/Users/poldrack/Dropbox/code/bettercode/.venv/lib/python3.12/site-packages/scanpy/preprocessing/_scale.py:309: UserWarning: Received a view of an AnnData. Making a copy.\n", + " view_to_actual(adata)\n", + "/Users/poldrack/.local/share/uv/python/cpython-3.12.0-macos-aarch64-none/lib/python3.12/functools.py:909: UserWarning: zero-centering a sparse array/matrix densifies it.\n", + " return dispatch(args[0].__class__)(*args, **kw)\n", + "/Users/poldrack/Dropbox/code/bettercode/.venv/lib/python3.12/site-packages/scanpy/preprocessing/_scale.py:309: UserWarning: Received a view of an AnnData. Making a copy.\n", + " view_to_actual(adata)\n", + "/Users/poldrack/.local/share/uv/python/cpython-3.12.0-macos-aarch64-none/lib/python3.12/functools.py:909: UserWarning: zero-centering a sparse array/matrix densifies it.\n", + " return dispatch(args[0].__class__)(*args, **kw)\n", + "/Users/poldrack/Dropbox/code/bettercode/.venv/lib/python3.12/site-packages/scanpy/preprocessing/_scale.py:309: UserWarning: Received a view of an AnnData. Making a copy.\n", + " view_to_actual(adata)\n", + "/Users/poldrack/.local/share/uv/python/cpython-3.12.0-macos-aarch64-none/lib/python3.12/functools.py:909: UserWarning: zero-centering a sparse array/matrix densifies it.\n", + " return dispatch(args[0].__class__)(*args, **kw)\n", + "/Users/poldrack/Dropbox/code/bettercode/.venv/lib/python3.12/site-packages/scanpy/preprocessing/_scale.py:309: UserWarning: Received a view of an AnnData. Making a copy.\n", + " view_to_actual(adata)\n", + "/Users/poldrack/.local/share/uv/python/cpython-3.12.0-macos-aarch64-none/lib/python3.12/functools.py:909: UserWarning: zero-centering a sparse array/matrix densifies it.\n", + " return dispatch(args[0].__class__)(*args, **kw)\n", + "/Users/poldrack/Dropbox/code/bettercode/.venv/lib/python3.12/site-packages/scanpy/preprocessing/_scale.py:309: UserWarning: Received a view of an AnnData. Making a copy.\n", + " view_to_actual(adata)\n", + "/Users/poldrack/.local/share/uv/python/cpython-3.12.0-macos-aarch64-none/lib/python3.12/functools.py:909: UserWarning: zero-centering a sparse array/matrix densifies it.\n", + " return dispatch(args[0].__class__)(*args, **kw)\n", + "/Users/poldrack/Dropbox/code/bettercode/.venv/lib/python3.12/site-packages/scanpy/preprocessing/_scale.py:309: UserWarning: Received a view of an AnnData. Making a copy.\n", + " view_to_actual(adata)\n", + "/Users/poldrack/.local/share/uv/python/cpython-3.12.0-macos-aarch64-none/lib/python3.12/functools.py:909: UserWarning: zero-centering a sparse array/matrix densifies it.\n", + " return dispatch(args[0].__class__)(*args, **kw)\n", + "/Users/poldrack/Dropbox/code/bettercode/.venv/lib/python3.12/site-packages/scanpy/preprocessing/_scale.py:309: UserWarning: Received a view of an AnnData. Making a copy.\n", + " view_to_actual(adata)\n", + "/Users/poldrack/.local/share/uv/python/cpython-3.12.0-macos-aarch64-none/lib/python3.12/functools.py:909: UserWarning: zero-centering a sparse array/matrix densifies it.\n", + " return dispatch(args[0].__class__)(*args, **kw)\n", + "/Users/poldrack/Dropbox/code/bettercode/.venv/lib/python3.12/site-packages/scanpy/preprocessing/_scale.py:309: UserWarning: Received a view of an AnnData. Making a copy.\n", + " view_to_actual(adata)\n", + "/Users/poldrack/.local/share/uv/python/cpython-3.12.0-macos-aarch64-none/lib/python3.12/functools.py:909: UserWarning: zero-centering a sparse array/matrix densifies it.\n", + " return dispatch(args[0].__class__)(*args, **kw)\n", + "/Users/poldrack/Dropbox/code/bettercode/.venv/lib/python3.12/site-packages/scanpy/preprocessing/_scale.py:309: UserWarning: Received a view of an AnnData. Making a copy.\n", + " view_to_actual(adata)\n", + "/Users/poldrack/.local/share/uv/python/cpython-3.12.0-macos-aarch64-none/lib/python3.12/functools.py:909: UserWarning: zero-centering a sparse array/matrix densifies it.\n", + " return dispatch(args[0].__class__)(*args, **kw)\n", + "/Users/poldrack/Dropbox/code/bettercode/.venv/lib/python3.12/site-packages/scanpy/preprocessing/_scale.py:309: UserWarning: Received a view of an AnnData. Making a copy.\n", + " view_to_actual(adata)\n", + "/Users/poldrack/.local/share/uv/python/cpython-3.12.0-macos-aarch64-none/lib/python3.12/functools.py:909: UserWarning: zero-centering a sparse array/matrix densifies it.\n", + " return dispatch(args[0].__class__)(*args, **kw)\n", + "/Users/poldrack/Dropbox/code/bettercode/.venv/lib/python3.12/site-packages/scanpy/preprocessing/_scale.py:309: UserWarning: Received a view of an AnnData. Making a copy.\n", + " view_to_actual(adata)\n", + "/Users/poldrack/.local/share/uv/python/cpython-3.12.0-macos-aarch64-none/lib/python3.12/functools.py:909: UserWarning: zero-centering a sparse array/matrix densifies it.\n", + " return dispatch(args[0].__class__)(*args, **kw)\n", + "/Users/poldrack/Dropbox/code/bettercode/.venv/lib/python3.12/site-packages/scanpy/preprocessing/_scale.py:309: UserWarning: Received a view of an AnnData. Making a copy.\n", + " view_to_actual(adata)\n", + "/Users/poldrack/.local/share/uv/python/cpython-3.12.0-macos-aarch64-none/lib/python3.12/functools.py:909: UserWarning: zero-centering a sparse array/matrix densifies it.\n", + " return dispatch(args[0].__class__)(*args, **kw)\n", + "/Users/poldrack/Dropbox/code/bettercode/.venv/lib/python3.12/site-packages/scanpy/preprocessing/_scale.py:309: UserWarning: Received a view of an AnnData. Making a copy.\n", + " view_to_actual(adata)\n", + "/Users/poldrack/.local/share/uv/python/cpython-3.12.0-macos-aarch64-none/lib/python3.12/functools.py:909: UserWarning: zero-centering a sparse array/matrix densifies it.\n", + " return dispatch(args[0].__class__)(*args, **kw)\n", + "/Users/poldrack/Dropbox/code/bettercode/.venv/lib/python3.12/site-packages/scanpy/preprocessing/_scale.py:309: UserWarning: Received a view of an AnnData. Making a copy.\n", + " view_to_actual(adata)\n", + "/Users/poldrack/.local/share/uv/python/cpython-3.12.0-macos-aarch64-none/lib/python3.12/functools.py:909: UserWarning: zero-centering a sparse array/matrix densifies it.\n", + " return dispatch(args[0].__class__)(*args, **kw)\n", + "/Users/poldrack/Dropbox/code/bettercode/.venv/lib/python3.12/site-packages/scanpy/preprocessing/_scale.py:309: UserWarning: Received a view of an AnnData. Making a copy.\n", + " view_to_actual(adata)\n", + "/Users/poldrack/.local/share/uv/python/cpython-3.12.0-macos-aarch64-none/lib/python3.12/functools.py:909: UserWarning: zero-centering a sparse array/matrix densifies it.\n", + " return dispatch(args[0].__class__)(*args, **kw)\n", + "/Users/poldrack/Dropbox/code/bettercode/.venv/lib/python3.12/site-packages/scanpy/preprocessing/_scale.py:309: UserWarning: Received a view of an AnnData. Making a copy.\n", + " view_to_actual(adata)\n", + "/Users/poldrack/.local/share/uv/python/cpython-3.12.0-macos-aarch64-none/lib/python3.12/functools.py:909: UserWarning: zero-centering a sparse array/matrix densifies it.\n", + " return dispatch(args[0].__class__)(*args, **kw)\n", + "/Users/poldrack/Dropbox/code/bettercode/.venv/lib/python3.12/site-packages/scanpy/preprocessing/_scale.py:309: UserWarning: Received a view of an AnnData. Making a copy.\n", + " view_to_actual(adata)\n", + "/Users/poldrack/.local/share/uv/python/cpython-3.12.0-macos-aarch64-none/lib/python3.12/functools.py:909: UserWarning: zero-centering a sparse array/matrix densifies it.\n", + " return dispatch(args[0].__class__)(*args, **kw)\n", + "/Users/poldrack/Dropbox/code/bettercode/.venv/lib/python3.12/site-packages/scanpy/preprocessing/_scale.py:309: UserWarning: Received a view of an AnnData. Making a copy.\n", + " view_to_actual(adata)\n", + "/Users/poldrack/.local/share/uv/python/cpython-3.12.0-macos-aarch64-none/lib/python3.12/functools.py:909: UserWarning: zero-centering a sparse array/matrix densifies it.\n", + " return dispatch(args[0].__class__)(*args, **kw)\n", + "/Users/poldrack/Dropbox/code/bettercode/.venv/lib/python3.12/site-packages/scanpy/preprocessing/_scale.py:309: UserWarning: Received a view of an AnnData. Making a copy.\n", + " view_to_actual(adata)\n", + "/Users/poldrack/.local/share/uv/python/cpython-3.12.0-macos-aarch64-none/lib/python3.12/functools.py:909: UserWarning: zero-centering a sparse array/matrix densifies it.\n", + " return dispatch(args[0].__class__)(*args, **kw)\n", + "/Users/poldrack/Dropbox/code/bettercode/.venv/lib/python3.12/site-packages/scanpy/preprocessing/_scale.py:309: UserWarning: Received a view of an AnnData. Making a copy.\n", + " view_to_actual(adata)\n", + "/Users/poldrack/.local/share/uv/python/cpython-3.12.0-macos-aarch64-none/lib/python3.12/functools.py:909: UserWarning: zero-centering a sparse array/matrix densifies it.\n", + " return dispatch(args[0].__class__)(*args, **kw)\n", + "/Users/poldrack/Dropbox/code/bettercode/.venv/lib/python3.12/site-packages/scanpy/preprocessing/_scale.py:309: UserWarning: Received a view of an AnnData. Making a copy.\n", + " view_to_actual(adata)\n", + "/Users/poldrack/.local/share/uv/python/cpython-3.12.0-macos-aarch64-none/lib/python3.12/functools.py:909: UserWarning: zero-centering a sparse array/matrix densifies it.\n", + " return dispatch(args[0].__class__)(*args, **kw)\n", + "/Users/poldrack/Dropbox/code/bettercode/.venv/lib/python3.12/site-packages/scanpy/preprocessing/_scale.py:309: UserWarning: Received a view of an AnnData. Making a copy.\n", + " view_to_actual(adata)\n", + "/Users/poldrack/.local/share/uv/python/cpython-3.12.0-macos-aarch64-none/lib/python3.12/functools.py:909: UserWarning: zero-centering a sparse array/matrix densifies it.\n", + " return dispatch(args[0].__class__)(*args, **kw)\n", + "/Users/poldrack/Dropbox/code/bettercode/.venv/lib/python3.12/site-packages/scanpy/preprocessing/_scale.py:309: UserWarning: Received a view of an AnnData. Making a copy.\n", + " view_to_actual(adata)\n", + "/Users/poldrack/.local/share/uv/python/cpython-3.12.0-macos-aarch64-none/lib/python3.12/functools.py:909: UserWarning: zero-centering a sparse array/matrix densifies it.\n", + " return dispatch(args[0].__class__)(*args, **kw)\n", + "/Users/poldrack/Dropbox/code/bettercode/.venv/lib/python3.12/site-packages/scanpy/preprocessing/_scale.py:309: UserWarning: Received a view of an AnnData. Making a copy.\n", + " view_to_actual(adata)\n", + "/Users/poldrack/.local/share/uv/python/cpython-3.12.0-macos-aarch64-none/lib/python3.12/functools.py:909: UserWarning: zero-centering a sparse array/matrix densifies it.\n", + " return dispatch(args[0].__class__)(*args, **kw)\n", + "/Users/poldrack/Dropbox/code/bettercode/.venv/lib/python3.12/site-packages/scanpy/preprocessing/_scale.py:309: UserWarning: Received a view of an AnnData. Making a copy.\n", + " view_to_actual(adata)\n", + "/Users/poldrack/.local/share/uv/python/cpython-3.12.0-macos-aarch64-none/lib/python3.12/functools.py:909: UserWarning: zero-centering a sparse array/matrix densifies it.\n", + " return dispatch(args[0].__class__)(*args, **kw)\n", + "/Users/poldrack/Dropbox/code/bettercode/.venv/lib/python3.12/site-packages/scanpy/preprocessing/_scale.py:309: UserWarning: Received a view of an AnnData. Making a copy.\n", + " view_to_actual(adata)\n", + "/Users/poldrack/.local/share/uv/python/cpython-3.12.0-macos-aarch64-none/lib/python3.12/functools.py:909: UserWarning: zero-centering a sparse array/matrix densifies it.\n", + " return dispatch(args[0].__class__)(*args, **kw)\n", + "/Users/poldrack/Dropbox/code/bettercode/.venv/lib/python3.12/site-packages/scanpy/preprocessing/_scale.py:309: UserWarning: Received a view of an AnnData. Making a copy.\n", + " view_to_actual(adata)\n", + "/Users/poldrack/.local/share/uv/python/cpython-3.12.0-macos-aarch64-none/lib/python3.12/functools.py:909: UserWarning: zero-centering a sparse array/matrix densifies it.\n", + " return dispatch(args[0].__class__)(*args, **kw)\n", + "/Users/poldrack/Dropbox/code/bettercode/.venv/lib/python3.12/site-packages/scanpy/preprocessing/_scale.py:309: UserWarning: Received a view of an AnnData. Making a copy.\n", + " view_to_actual(adata)\n", + "/Users/poldrack/.local/share/uv/python/cpython-3.12.0-macos-aarch64-none/lib/python3.12/functools.py:909: UserWarning: zero-centering a sparse array/matrix densifies it.\n", + " return dispatch(args[0].__class__)(*args, **kw)\n", + "/Users/poldrack/Dropbox/code/bettercode/.venv/lib/python3.12/site-packages/scanpy/preprocessing/_scale.py:309: UserWarning: Received a view of an AnnData. Making a copy.\n", + " view_to_actual(adata)\n", + "/Users/poldrack/.local/share/uv/python/cpython-3.12.0-macos-aarch64-none/lib/python3.12/functools.py:909: UserWarning: zero-centering a sparse array/matrix densifies it.\n", + " return dispatch(args[0].__class__)(*args, **kw)\n", + "/Users/poldrack/Dropbox/code/bettercode/.venv/lib/python3.12/site-packages/scanpy/preprocessing/_scale.py:309: UserWarning: Received a view of an AnnData. Making a copy.\n", + " view_to_actual(adata)\n", + "/Users/poldrack/.local/share/uv/python/cpython-3.12.0-macos-aarch64-none/lib/python3.12/functools.py:909: UserWarning: zero-centering a sparse array/matrix densifies it.\n", + " return dispatch(args[0].__class__)(*args, **kw)\n", + "/Users/poldrack/Dropbox/code/bettercode/.venv/lib/python3.12/site-packages/scanpy/preprocessing/_scale.py:309: UserWarning: Received a view of an AnnData. Making a copy.\n", + " view_to_actual(adata)\n", + "/Users/poldrack/.local/share/uv/python/cpython-3.12.0-macos-aarch64-none/lib/python3.12/functools.py:909: UserWarning: zero-centering a sparse array/matrix densifies it.\n", + " return dispatch(args[0].__class__)(*args, **kw)\n", + "/Users/poldrack/Dropbox/code/bettercode/.venv/lib/python3.12/site-packages/scanpy/preprocessing/_scale.py:309: UserWarning: Received a view of an AnnData. Making a copy.\n", + " view_to_actual(adata)\n", + "/Users/poldrack/.local/share/uv/python/cpython-3.12.0-macos-aarch64-none/lib/python3.12/functools.py:909: UserWarning: zero-centering a sparse array/matrix densifies it.\n", + " return dispatch(args[0].__class__)(*args, **kw)\n", + "/Users/poldrack/Dropbox/code/bettercode/.venv/lib/python3.12/site-packages/scanpy/preprocessing/_scale.py:309: UserWarning: Received a view of an AnnData. Making a copy.\n", + " view_to_actual(adata)\n", + "/Users/poldrack/.local/share/uv/python/cpython-3.12.0-macos-aarch64-none/lib/python3.12/functools.py:909: UserWarning: zero-centering a sparse array/matrix densifies it.\n", + " return dispatch(args[0].__class__)(*args, **kw)\n", + "/Users/poldrack/Dropbox/code/bettercode/.venv/lib/python3.12/site-packages/scanpy/preprocessing/_scale.py:309: UserWarning: Received a view of an AnnData. Making a copy.\n", + " view_to_actual(adata)\n", + "/Users/poldrack/.local/share/uv/python/cpython-3.12.0-macos-aarch64-none/lib/python3.12/functools.py:909: UserWarning: zero-centering a sparse array/matrix densifies it.\n", + " return dispatch(args[0].__class__)(*args, **kw)\n", + "/Users/poldrack/Dropbox/code/bettercode/.venv/lib/python3.12/site-packages/scanpy/preprocessing/_scale.py:309: UserWarning: Received a view of an AnnData. Making a copy.\n", + " view_to_actual(adata)\n", + "/Users/poldrack/.local/share/uv/python/cpython-3.12.0-macos-aarch64-none/lib/python3.12/functools.py:909: UserWarning: zero-centering a sparse array/matrix densifies it.\n", + " return dispatch(args[0].__class__)(*args, **kw)\n", + "/Users/poldrack/Dropbox/code/bettercode/.venv/lib/python3.12/site-packages/scanpy/preprocessing/_scale.py:309: UserWarning: Received a view of an AnnData. Making a copy.\n", + " view_to_actual(adata)\n", + "/Users/poldrack/.local/share/uv/python/cpython-3.12.0-macos-aarch64-none/lib/python3.12/functools.py:909: UserWarning: zero-centering a sparse array/matrix densifies it.\n", + " return dispatch(args[0].__class__)(*args, **kw)\n", + "/Users/poldrack/Dropbox/code/bettercode/.venv/lib/python3.12/site-packages/scanpy/preprocessing/_scale.py:309: UserWarning: Received a view of an AnnData. Making a copy.\n", + " view_to_actual(adata)\n", + "/Users/poldrack/.local/share/uv/python/cpython-3.12.0-macos-aarch64-none/lib/python3.12/functools.py:909: UserWarning: zero-centering a sparse array/matrix densifies it.\n", + " return dispatch(args[0].__class__)(*args, **kw)\n", + "/Users/poldrack/Dropbox/code/bettercode/.venv/lib/python3.12/site-packages/scanpy/preprocessing/_scale.py:309: UserWarning: Received a view of an AnnData. Making a copy.\n", + " view_to_actual(adata)\n", + "/Users/poldrack/.local/share/uv/python/cpython-3.12.0-macos-aarch64-none/lib/python3.12/functools.py:909: UserWarning: zero-centering a sparse array/matrix densifies it.\n", + " return dispatch(args[0].__class__)(*args, **kw)\n", + "/Users/poldrack/Dropbox/code/bettercode/.venv/lib/python3.12/site-packages/scanpy/preprocessing/_scale.py:309: UserWarning: Received a view of an AnnData. Making a copy.\n", + " view_to_actual(adata)\n", + "/Users/poldrack/.local/share/uv/python/cpython-3.12.0-macos-aarch64-none/lib/python3.12/functools.py:909: UserWarning: zero-centering a sparse array/matrix densifies it.\n", + " return dispatch(args[0].__class__)(*args, **kw)\n", + "/Users/poldrack/Dropbox/code/bettercode/.venv/lib/python3.12/site-packages/scanpy/preprocessing/_scale.py:309: UserWarning: Received a view of an AnnData. Making a copy.\n", + " view_to_actual(adata)\n", + "/Users/poldrack/.local/share/uv/python/cpython-3.12.0-macos-aarch64-none/lib/python3.12/functools.py:909: UserWarning: zero-centering a sparse array/matrix densifies it.\n", + " return dispatch(args[0].__class__)(*args, **kw)\n", + "/Users/poldrack/Dropbox/code/bettercode/.venv/lib/python3.12/site-packages/scanpy/preprocessing/_scale.py:309: UserWarning: Received a view of an AnnData. Making a copy.\n", + " view_to_actual(adata)\n", + "/Users/poldrack/.local/share/uv/python/cpython-3.12.0-macos-aarch64-none/lib/python3.12/functools.py:909: UserWarning: zero-centering a sparse array/matrix densifies it.\n", + " return dispatch(args[0].__class__)(*args, **kw)\n", + "/Users/poldrack/Dropbox/code/bettercode/.venv/lib/python3.12/site-packages/scanpy/preprocessing/_scale.py:309: UserWarning: Received a view of an AnnData. Making a copy.\n", + " view_to_actual(adata)\n", + "/Users/poldrack/.local/share/uv/python/cpython-3.12.0-macos-aarch64-none/lib/python3.12/functools.py:909: UserWarning: zero-centering a sparse array/matrix densifies it.\n", + " return dispatch(args[0].__class__)(*args, **kw)\n", + "/Users/poldrack/Dropbox/code/bettercode/.venv/lib/python3.12/site-packages/scanpy/preprocessing/_scale.py:309: UserWarning: Received a view of an AnnData. Making a copy.\n", + " view_to_actual(adata)\n", + "/Users/poldrack/.local/share/uv/python/cpython-3.12.0-macos-aarch64-none/lib/python3.12/functools.py:909: UserWarning: zero-centering a sparse array/matrix densifies it.\n", + " return dispatch(args[0].__class__)(*args, **kw)\n", + "/Users/poldrack/Dropbox/code/bettercode/.venv/lib/python3.12/site-packages/scanpy/preprocessing/_scale.py:309: UserWarning: Received a view of an AnnData. Making a copy.\n", + " view_to_actual(adata)\n", + "/Users/poldrack/.local/share/uv/python/cpython-3.12.0-macos-aarch64-none/lib/python3.12/functools.py:909: UserWarning: zero-centering a sparse array/matrix densifies it.\n", + " return dispatch(args[0].__class__)(*args, **kw)\n", + "/Users/poldrack/Dropbox/code/bettercode/.venv/lib/python3.12/site-packages/scanpy/preprocessing/_scale.py:309: UserWarning: Received a view of an AnnData. Making a copy.\n", + " view_to_actual(adata)\n", + "/Users/poldrack/.local/share/uv/python/cpython-3.12.0-macos-aarch64-none/lib/python3.12/functools.py:909: UserWarning: zero-centering a sparse array/matrix densifies it.\n", + " return dispatch(args[0].__class__)(*args, **kw)\n", + "/Users/poldrack/Dropbox/code/bettercode/.venv/lib/python3.12/site-packages/scanpy/preprocessing/_scale.py:309: UserWarning: Received a view of an AnnData. Making a copy.\n", + " view_to_actual(adata)\n", + "/Users/poldrack/.local/share/uv/python/cpython-3.12.0-macos-aarch64-none/lib/python3.12/functools.py:909: UserWarning: zero-centering a sparse array/matrix densifies it.\n", + " return dispatch(args[0].__class__)(*args, **kw)\n", + "/Users/poldrack/Dropbox/code/bettercode/.venv/lib/python3.12/site-packages/scanpy/preprocessing/_scale.py:309: UserWarning: Received a view of an AnnData. Making a copy.\n", + " view_to_actual(adata)\n", + "/Users/poldrack/.local/share/uv/python/cpython-3.12.0-macos-aarch64-none/lib/python3.12/functools.py:909: UserWarning: zero-centering a sparse array/matrix densifies it.\n", + " return dispatch(args[0].__class__)(*args, **kw)\n", + "/Users/poldrack/Dropbox/code/bettercode/.venv/lib/python3.12/site-packages/scanpy/preprocessing/_scale.py:309: UserWarning: Received a view of an AnnData. Making a copy.\n", + " view_to_actual(adata)\n", + "/Users/poldrack/.local/share/uv/python/cpython-3.12.0-macos-aarch64-none/lib/python3.12/functools.py:909: UserWarning: zero-centering a sparse array/matrix densifies it.\n", + " return dispatch(args[0].__class__)(*args, **kw)\n", + "/Users/poldrack/Dropbox/code/bettercode/.venv/lib/python3.12/site-packages/scanpy/preprocessing/_scale.py:309: UserWarning: Received a view of an AnnData. Making a copy.\n", + " view_to_actual(adata)\n", + "/Users/poldrack/.local/share/uv/python/cpython-3.12.0-macos-aarch64-none/lib/python3.12/functools.py:909: UserWarning: zero-centering a sparse array/matrix densifies it.\n", + " return dispatch(args[0].__class__)(*args, **kw)\n", + "/Users/poldrack/Dropbox/code/bettercode/.venv/lib/python3.12/site-packages/scanpy/preprocessing/_scale.py:309: UserWarning: Received a view of an AnnData. Making a copy.\n", + " view_to_actual(adata)\n", + "/Users/poldrack/.local/share/uv/python/cpython-3.12.0-macos-aarch64-none/lib/python3.12/functools.py:909: UserWarning: zero-centering a sparse array/matrix densifies it.\n", + " return dispatch(args[0].__class__)(*args, **kw)\n", + "/Users/poldrack/Dropbox/code/bettercode/.venv/lib/python3.12/site-packages/scanpy/preprocessing/_scale.py:309: UserWarning: Received a view of an AnnData. Making a copy.\n", + " view_to_actual(adata)\n", + "/Users/poldrack/.local/share/uv/python/cpython-3.12.0-macos-aarch64-none/lib/python3.12/functools.py:909: UserWarning: zero-centering a sparse array/matrix densifies it.\n", + " return dispatch(args[0].__class__)(*args, **kw)\n", + "/Users/poldrack/Dropbox/code/bettercode/.venv/lib/python3.12/site-packages/scanpy/preprocessing/_scale.py:309: UserWarning: Received a view of an AnnData. Making a copy.\n", + " view_to_actual(adata)\n", + "/Users/poldrack/.local/share/uv/python/cpython-3.12.0-macos-aarch64-none/lib/python3.12/functools.py:909: UserWarning: zero-centering a sparse array/matrix densifies it.\n", + " return dispatch(args[0].__class__)(*args, **kw)\n", + "/Users/poldrack/Dropbox/code/bettercode/.venv/lib/python3.12/site-packages/scanpy/preprocessing/_scale.py:309: UserWarning: Received a view of an AnnData. Making a copy.\n", + " view_to_actual(adata)\n", + "/Users/poldrack/.local/share/uv/python/cpython-3.12.0-macos-aarch64-none/lib/python3.12/functools.py:909: UserWarning: zero-centering a sparse array/matrix densifies it.\n", + " return dispatch(args[0].__class__)(*args, **kw)\n" + ] + }, + { + "ename": "NameError", + "evalue": "name 'unique_modules_str' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[31m---------------------------------------------------------------------------\u001b[39m", + "\u001b[31mNameError\u001b[39m Traceback (most recent call last)", + "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[73]\u001b[39m\u001b[32m, line 62\u001b[39m\n\u001b[32m 54\u001b[39m homogeneity_scores[mod_id] = var_explained\n\u001b[32m 58\u001b[39m module_means_df = pd.DataFrame(module_means, index=adata_hvg.obs_names)\n\u001b[32m 60\u001b[39m adata_hvg.obs[\u001b[33m'\u001b[39m\u001b[33mInfomap_Module\u001b[39m\u001b[33m'\u001b[39m] = pd.Categorical(\n\u001b[32m 61\u001b[39m [\u001b[38;5;28mstr\u001b[39m(mod) \u001b[38;5;28;01mfor\u001b[39;00m mod \u001b[38;5;129;01min\u001b[39;00m modules],\n\u001b[32m---> \u001b[39m\u001b[32m62\u001b[39m categories=\u001b[43munique_modules_str\u001b[49m,\n\u001b[32m 63\u001b[39m ordered=\u001b[38;5;28;01mTrue\u001b[39;00m\n\u001b[32m 64\u001b[39m )\n\u001b[32m 66\u001b[39m \u001b[38;5;66;03m# Check the results\u001b[39;00m\n\u001b[32m 67\u001b[39m module_means_df.head()\n", + "\u001b[31mNameError\u001b[39m: name 'unique_modules_str' is not defined" + ] + } + ], + "source": [ + "import numpy as np\n", + "from scipy import sparse\n", + "import pandas as pd\n", + "\n", + "# Get the list of unique modules from your dictionary\n", + "unique_modules = sorted(np.unique(adata_genes.obs['Infomap_Module'].astype(int)))\n", + "print(f'Found {len(unique_modules)} unique modules')\n", + "homogeneity_scores = {}\n", + "\n", + "size_thresh = 10\n", + "\n", + "module_means = {}\n", + "modules = im_modules.astype(str)\n", + "\n", + "for mod_id in unique_modules:\n", + " # 1. Get the list of genes for this module\n", + " # Filter to ensure these genes exist in your current adata object\n", + " module_mask = im_modules == mod_id\n", + "\n", + " if np.sum(module_mask) < size_thresh:\n", + " # set label to Unassigned\n", + " modules[module_mask] = 'Unassigned'\n", + " continue\n", + "\n", + "\n", + " # 2. Subset the data to these genes\n", + " # This creates a View (lightweight)\n", + " gene_subset = adata_hvg[:, module_mask]\n", + " \n", + " # 3. Calculate the mean expression across genes (axis=1)\n", + " # Handle sparse matrix explicitly to be safe\n", + " if sparse.issparse(gene_subset.X):\n", + " # .mean() on sparse returns a matrix, .A1 flattens it to a 1D array\n", + " mean_vals = gene_subset.X.mean(axis=1).A1\n", + " else:\n", + " mean_vals = np.mean(gene_subset.X, axis=1)\n", + " \n", + " module_means[mod_id] = mean_vals\n", + " \n", + " # compute homogeneity - how much variance is accounted for\n", + " # by the first principal component of the genes in the module\n", + " \n", + " # First scale the data (Z-score)\n", + " # PCA requires unit variance so high-expression genes don't dominate\n", + " # This densifies the matrix, which is why we subsampled first\n", + " sc.pp.scale(gene_subset, max_value=10)\n", + " \n", + " # 3. Run PCA (we only need the 1st component)\n", + " sc.tl.pca(gene_subset, n_comps=1)\n", + " \n", + " # 4. Extract the variance ratio\n", + " # 'variance_ratio' sums to 1.0 across all PCs\n", + " var_explained = gene_subset.uns['pca']['variance_ratio'][0]\n", + " homogeneity_scores[mod_id] = var_explained\n", + "\n", + "\n", + "\n", + " module_means_df = pd.DataFrame(module_means, index=adata_hvg.obs_names)\n", + "\n", + "unique_modules_str = [str(mod) for mod in np.unique(modules)]\n", + "adata_hvg.var['Infomap_Module'] = pd.Categorical(\n", + " [str(mod) for mod in modules],\n", + " categories=unique_modules_str,\n", + " ordered=True\n", + ")\n", + "\n", + "\n", + "# Check the results\n", + "module_means_df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 78, + "id": "2d3e4158", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{np.int64(1): np.float64(0.153287518918392),\n", + " np.int64(2): np.float64(0.028535903271219524),\n", + " np.int64(3): np.float64(0.10507818167472564),\n", + " np.int64(4): np.float64(0.18204036861133976),\n", + " np.int64(5): 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+ "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "homogeneity_scores" + ] + }, + { + "cell_type": "code", + "execution_count": 85, + "id": "0d0f53f8", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array(['Dementia', 'No dementia', 'Reference'], dtype=object)" + ] + }, + "execution_count": 85, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.unique(adata_hvg.obs['Cognitive status'])" + ] + }, + { + "cell_type": "code", + "execution_count": 91, + "id": "cb7c8a28", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Optimization terminated successfully.\n", + " Current function value: 0.568460\n", + " Iterations 6\n", + "Prediction accuracy: 0.715\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/poldrack/Dropbox/code/bettercode/.venv/lib/python3.12/site-packages/statsmodels/base/model.py:130: ValueWarning: unknown kwargs ['data']\n", + " warnings.warn(msg, ValueWarning)\n", + "/Users/poldrack/Dropbox/code/bettercode/.venv/lib/python3.12/site-packages/statsmodels/base/model.py:130: ValueWarning: unknown kwargs ['data']\n", + " warnings.warn(msg, ValueWarning)\n" + ] + } + ], + "source": [ + "# create a statmodels model predicting adata_hvg.obs['Cognitive status'] from the module means\n", + "\n", + "import statsmodels.api as sm\n", + "\n", + "X = module_means_df.values\n", + "X = sm.add_constant(X)\n", + "y = adata_hvg.obs['Cognitive status'].astype('category').cat.codes.values\n", + "\n", + "# drop values with cognitive status = \"Reference\"\n", + "mask = adata_hvg.obs['Cognitive status'] != 'Reference'\n", + "X = X[mask, :]\n", + "y = y[mask]\n", + "\n", + "model = sm.Logit(\n", + " endog=y,\n", + " exog=X,\n", + " data=pd.concat([adata_hvg.obs, module_means_df], axis=1)\n", + ").fit() \n", + "model.summary()\n", + "\n", + "# compute prediction accuracy\n", + "preds = model.predict(X)\n", + "pred_labels = (preds > 0.5).astype(int)\n", + "accuracy = (pred_labels == y).mean()\n", + "print(f'Prediction accuracy: {accuracy:.3f}')\n" + ] + }, + { + "cell_type": "code", + "execution_count": 99, + "id": "c5889bb2", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(38,)" + ] + }, + "execution_count": 99, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.unique(adata_hvg.obs['donor_id']).shape" + ] + }, + { + "cell_type": "code", + "execution_count": 109, + "id": "16547d3e", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "
Model: MixedLM Dependent Variable: y
No. Observations: 58654 Method: REML
No. Groups: 38 Scale: 0.0000
Min. group size: 668 Log-Likelihood: 660473.4969
Max. group size: 2980 Converged: Yes
Mean group size: 1543.5
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Coef. Std.Err. z P>|z| [0.025 0.975]
const 85.542 0.083 1030.907 0.000 85.380 85.705
x1 0.000 0.000 0.000 1.000 -0.000 0.000
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x4 -0.000 0.000 -0.000 1.000 -0.000 0.000
x5 -0.000 0.000 -0.000 1.000 -0.000 0.000
x6 0.000 0.000 0.000 1.000 -0.000 0.000
x7 0.000 0.000 0.000 1.000 -0.000 0.000
x8 0.000 0.000 0.000 1.000 -0.000 0.000
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x10 0.000 0.000 0.000 1.000 -0.000 0.000
x11 0.000 0.000 0.000 1.000 -0.000 0.000
x12 -0.000 0.000 -0.000 1.000 -0.000 0.000
x13 0.000 0.000 0.000 1.000 -0.000 0.000
x14 -0.000 0.000 -0.000 1.000 -0.000 0.000
x15 0.000 0.000 0.000 1.000 -0.000 0.000
x16 -0.000 0.000 -0.000 1.000 -0.000 0.000
x17 -0.000 0.000 -0.000 1.000 -0.000 0.000
x18 -0.000 0.000 -0.000 1.000 -0.000 0.000
x19 0.000 0.000 0.000 1.000 -0.000 0.000
x20 -0.000 0.000 -0.000 1.000 -0.000 0.000
x21 0.000 0.000 0.000 1.000 -0.000 0.000
x22 0.000 0.000 0.000 1.000 -0.000 0.000
x23 -0.000 0.000 -0.000 1.000 -0.000 0.000
x24 0.000 0.000 0.000 1.000 -0.000 0.000
x25 -0.000 0.000 -0.000 1.000 -0.000 0.000
x26 0.000 0.000 0.000 1.000 -0.000 0.000
x27 -0.000 0.000 -0.000 1.000 -0.000 0.000
x28 0.000 0.000 0.000 1.000 -0.000 0.000
x29 -0.000 0.000 -0.000 1.000 -0.000 0.000
x30 -0.000 0.000 -0.000 1.000 -0.000 0.000
x31 0.000 0.000 0.000 1.000 -0.000 0.000
x32 0.000 0.000 0.000 1.000 -0.000 0.000
x33 0.000 0.000 0.000 1.000 -0.000 0.000
x34 0.000 0.000 0.000 1.000 -0.000 0.000
x35 -0.000 0.000 -0.000 1.000 -0.000 0.000
x36 -0.000 0.000 -0.000 1.000 -0.000 0.000
x37 0.000 0.000 0.000 1.000 -0.000 0.000
x38 0.000 0.000 0.000 1.000 -0.000 0.000
x39 -0.000 0.000 -0.000 1.000 -0.000 0.000
x40 -0.000 0.000 -0.000 1.000 -0.000 0.000
x41 -0.000 0.000 -0.000 1.000 -0.000 0.000
x42 -0.000 0.000 -0.000 1.000 -0.000 0.000
x43 0.000 0.000 0.000 1.000 -0.000 0.000
x44 -0.000 0.000 -0.000 1.000 -0.000 0.000
x45 -0.000 0.000 -0.000 1.000 -0.000 0.000
x46 0.000 0.000 0.000 1.000 -0.000 0.000
x47 0.000 0.000 0.000 1.000 -0.000 0.000
x48 0.000 0.000 0.000 1.000 -0.000 0.000
x49 -0.000 0.000 -0.000 1.000 -0.000 0.000
x50 -0.000 0.000 -0.000 1.000 -0.000 0.000
x51 0.000 0.000 0.000 1.000 -0.000 0.000
x52 -0.000 0.000 -0.000 1.000 -0.000 0.000
x53 0.000 0.000 0.000 1.000 -0.000 0.000
x54 -0.000 0.000 -0.000 1.000 -0.000 0.000
x55 0.000 0.000 0.000 1.000 -0.000 0.000
x56 0.000 0.000 0.000 1.000 -0.000 0.000
x57 0.000 0.000 0.000 1.000 -0.000 0.000
x58 0.000 0.000 0.000 1.000 -0.000 0.000
Group Var 0.136 358.854

\n" + ], + "text/latex": [ + "\\begin{table}\n", + "\\caption{Mixed Linear Model Regression Results}\n", + "\\label{}\n", + "\\begin{center}\n", + "\\begin{tabular}{llll}\n", + "\\hline\n", + "Model: & MixedLM & Dependent Variable: & y \\\\\n", + "No. Observations: & 58654 & Method: & REML \\\\\n", + "No. Groups: & 38 & Scale: & 0.0000 \\\\\n", + "Min. group size: & 668 & Log-Likelihood: & 660473.4969 \\\\\n", + "Max. group size: & 2980 & Converged: & Yes \\\\\n", + "Mean group size: & 1543.5 & & \\\\\n", + "\\hline\n", + "\\end{tabular}\n", + "\\end{center}\n", + "\n", + "\\begin{center}\n", + "\\begin{tabular}{lrrrrrr}\n", + "\\hline\n", + " & Coef. & Std.Err. & z & P$> |$z$|$ & [0.025 & 0.975] \\\\\n", + "\\hline\n", + "const & 85.542 & 0.083 & 1030.907 & 0.000 & 85.380 & 85.705 \\\\\n", + "x1 & 0.000 & 0.000 & 0.000 & 1.000 & -0.000 & 0.000 \\\\\n", + "x2 & 0.000 & 0.000 & 0.000 & 1.000 & -0.000 & 0.000 \\\\\n", + "x3 & -0.000 & 0.000 & -0.000 & 1.000 & -0.000 & 0.000 \\\\\n", + "x4 & -0.000 & 0.000 & -0.000 & 1.000 & -0.000 & 0.000 \\\\\n", + "x5 & -0.000 & 0.000 & -0.000 & 1.000 & -0.000 & 0.000 \\\\\n", + "x6 & 0.000 & 0.000 & 0.000 & 1.000 & -0.000 & 0.000 \\\\\n", + "x7 & 0.000 & 0.000 & 0.000 & 1.000 & -0.000 & 0.000 \\\\\n", + "x8 & 0.000 & 0.000 & 0.000 & 1.000 & -0.000 & 0.000 \\\\\n", + "x9 & -0.000 & 0.000 & -0.000 & 1.000 & -0.000 & 0.000 \\\\\n", + "x10 & 0.000 & 0.000 & 0.000 & 1.000 & -0.000 & 0.000 \\\\\n", + "x11 & 0.000 & 0.000 & 0.000 & 1.000 & -0.000 & 0.000 \\\\\n", + "x12 & -0.000 & 0.000 & -0.000 & 1.000 & -0.000 & 0.000 \\\\\n", + "x13 & 0.000 & 0.000 & 0.000 & 1.000 & -0.000 & 0.000 \\\\\n", + "x14 & -0.000 & 0.000 & -0.000 & 1.000 & -0.000 & 0.000 \\\\\n", + "x15 & 0.000 & 0.000 & 0.000 & 1.000 & -0.000 & 0.000 \\\\\n", + "x16 & -0.000 & 0.000 & -0.000 & 1.000 & -0.000 & 0.000 \\\\\n", + "x17 & -0.000 & 0.000 & -0.000 & 1.000 & -0.000 & 0.000 \\\\\n", + "x18 & -0.000 & 0.000 & -0.000 & 1.000 & -0.000 & 0.000 \\\\\n", + "x19 & 0.000 & 0.000 & 0.000 & 1.000 & -0.000 & 0.000 \\\\\n", + "x20 & -0.000 & 0.000 & -0.000 & 1.000 & -0.000 & 0.000 \\\\\n", + "x21 & 0.000 & 0.000 & 0.000 & 1.000 & -0.000 & 0.000 \\\\\n", + "x22 & 0.000 & 0.000 & 0.000 & 1.000 & -0.000 & 0.000 \\\\\n", + "x23 & -0.000 & 0.000 & -0.000 & 1.000 & -0.000 & 0.000 \\\\\n", + "x24 & 0.000 & 0.000 & 0.000 & 1.000 & -0.000 & 0.000 \\\\\n", + "x25 & -0.000 & 0.000 & -0.000 & 1.000 & -0.000 & 0.000 \\\\\n", + "x26 & 0.000 & 0.000 & 0.000 & 1.000 & -0.000 & 0.000 \\\\\n", + "x27 & -0.000 & 0.000 & -0.000 & 1.000 & -0.000 & 0.000 \\\\\n", + "x28 & 0.000 & 0.000 & 0.000 & 1.000 & -0.000 & 0.000 \\\\\n", + "x29 & -0.000 & 0.000 & -0.000 & 1.000 & -0.000 & 0.000 \\\\\n", + "x30 & -0.000 & 0.000 & -0.000 & 1.000 & -0.000 & 0.000 \\\\\n", + "x31 & 0.000 & 0.000 & 0.000 & 1.000 & -0.000 & 0.000 \\\\\n", + "x32 & 0.000 & 0.000 & 0.000 & 1.000 & -0.000 & 0.000 \\\\\n", + "x33 & 0.000 & 0.000 & 0.000 & 1.000 & -0.000 & 0.000 \\\\\n", + "x34 & 0.000 & 0.000 & 0.000 & 1.000 & -0.000 & 0.000 \\\\\n", + "x35 & -0.000 & 0.000 & -0.000 & 1.000 & -0.000 & 0.000 \\\\\n", + "x36 & -0.000 & 0.000 & -0.000 & 1.000 & -0.000 & 0.000 \\\\\n", + "x37 & 0.000 & 0.000 & 0.000 & 1.000 & -0.000 & 0.000 \\\\\n", + "x38 & 0.000 & 0.000 & 0.000 & 1.000 & -0.000 & 0.000 \\\\\n", + "x39 & -0.000 & 0.000 & -0.000 & 1.000 & -0.000 & 0.000 \\\\\n", + "x40 & -0.000 & 0.000 & -0.000 & 1.000 & -0.000 & 0.000 \\\\\n", + "x41 & -0.000 & 0.000 & -0.000 & 1.000 & -0.000 & 0.000 \\\\\n", + "x42 & -0.000 & 0.000 & -0.000 & 1.000 & -0.000 & 0.000 \\\\\n", + "x43 & 0.000 & 0.000 & 0.000 & 1.000 & -0.000 & 0.000 \\\\\n", + "x44 & -0.000 & 0.000 & -0.000 & 1.000 & -0.000 & 0.000 \\\\\n", + "x45 & -0.000 & 0.000 & -0.000 & 1.000 & -0.000 & 0.000 \\\\\n", + "x46 & 0.000 & 0.000 & 0.000 & 1.000 & -0.000 & 0.000 \\\\\n", + "x47 & 0.000 & 0.000 & 0.000 & 1.000 & -0.000 & 0.000 \\\\\n", + "x48 & 0.000 & 0.000 & 0.000 & 1.000 & -0.000 & 0.000 \\\\\n", + "x49 & -0.000 & 0.000 & -0.000 & 1.000 & -0.000 & 0.000 \\\\\n", + "x50 & -0.000 & 0.000 & -0.000 & 1.000 & -0.000 & 0.000 \\\\\n", + "x51 & 0.000 & 0.000 & 0.000 & 1.000 & -0.000 & 0.000 \\\\\n", + "x52 & -0.000 & 0.000 & -0.000 & 1.000 & -0.000 & 0.000 \\\\\n", + "x53 & 0.000 & 0.000 & 0.000 & 1.000 & -0.000 & 0.000 \\\\\n", + "x54 & -0.000 & 0.000 & -0.000 & 1.000 & -0.000 & 0.000 \\\\\n", + "x55 & 0.000 & 0.000 & 0.000 & 1.000 & -0.000 & 0.000 \\\\\n", + "x56 & 0.000 & 0.000 & 0.000 & 1.000 & -0.000 & 0.000 \\\\\n", + "x57 & 0.000 & 0.000 & 0.000 & 1.000 & -0.000 & 0.000 \\\\\n", + "x58 & 0.000 & 0.000 & 0.000 & 1.000 & -0.000 & 0.000 \\\\\n", + "Group Var & 0.136 & 358.854 & & & & \\\\\n", + "\\hline\n", + "\\end{tabular}\n", + "\\end{center}\n", + "\\end{table}\n", + "\\bigskip\n" + ], + "text/plain": [ + "\n", + "\"\"\"\n", + " Mixed Linear Model Regression Results\n", + "=========================================================\n", + "Model: MixedLM Dependent Variable: y \n", + "No. Observations: 58654 Method: REML \n", + "No. Groups: 38 Scale: 0.0000 \n", + "Min. group size: 668 Log-Likelihood: 660473.4969\n", + "Max. group size: 2980 Converged: Yes \n", + "Mean group size: 1543.5 \n", + "---------------------------------------------------------\n", + " Coef. Std.Err. z P>|z| [0.025 0.975]\n", + "---------------------------------------------------------\n", + "const 85.542 0.083 1030.907 0.000 85.380 85.705\n", + "x1 0.000 0.000 0.000 1.000 -0.000 0.000\n", + "x2 0.000 0.000 0.000 1.000 -0.000 0.000\n", + "x3 -0.000 0.000 -0.000 1.000 -0.000 0.000\n", + "x4 -0.000 0.000 -0.000 1.000 -0.000 0.000\n", + "x5 -0.000 0.000 -0.000 1.000 -0.000 0.000\n", + "x6 0.000 0.000 0.000 1.000 -0.000 0.000\n", + "x7 0.000 0.000 0.000 1.000 -0.000 0.000\n", + "x8 0.000 0.000 0.000 1.000 -0.000 0.000\n", + "x9 -0.000 0.000 -0.000 1.000 -0.000 0.000\n", + "x10 0.000 0.000 0.000 1.000 -0.000 0.000\n", + "x11 0.000 0.000 0.000 1.000 -0.000 0.000\n", + "x12 -0.000 0.000 -0.000 1.000 -0.000 0.000\n", + "x13 0.000 0.000 0.000 1.000 -0.000 0.000\n", + "x14 -0.000 0.000 -0.000 1.000 -0.000 0.000\n", + "x15 0.000 0.000 0.000 1.000 -0.000 0.000\n", + "x16 -0.000 0.000 -0.000 1.000 -0.000 0.000\n", + "x17 -0.000 0.000 -0.000 1.000 -0.000 0.000\n", + "x18 -0.000 0.000 -0.000 1.000 -0.000 0.000\n", + "x19 0.000 0.000 0.000 1.000 -0.000 0.000\n", + "x20 -0.000 0.000 -0.000 1.000 -0.000 0.000\n", + "x21 0.000 0.000 0.000 1.000 -0.000 0.000\n", + "x22 0.000 0.000 0.000 1.000 -0.000 0.000\n", + "x23 -0.000 0.000 -0.000 1.000 -0.000 0.000\n", + "x24 0.000 0.000 0.000 1.000 -0.000 0.000\n", + "x25 -0.000 0.000 -0.000 1.000 -0.000 0.000\n", + "x26 0.000 0.000 0.000 1.000 -0.000 0.000\n", + "x27 -0.000 0.000 -0.000 1.000 -0.000 0.000\n", + "x28 0.000 0.000 0.000 1.000 -0.000 0.000\n", + "x29 -0.000 0.000 -0.000 1.000 -0.000 0.000\n", + "x30 -0.000 0.000 -0.000 1.000 -0.000 0.000\n", + "x31 0.000 0.000 0.000 1.000 -0.000 0.000\n", + "x32 0.000 0.000 0.000 1.000 -0.000 0.000\n", + "x33 0.000 0.000 0.000 1.000 -0.000 0.000\n", + "x34 0.000 0.000 0.000 1.000 -0.000 0.000\n", + "x35 -0.000 0.000 -0.000 1.000 -0.000 0.000\n", + "x36 -0.000 0.000 -0.000 1.000 -0.000 0.000\n", + "x37 0.000 0.000 0.000 1.000 -0.000 0.000\n", + "x38 0.000 0.000 0.000 1.000 -0.000 0.000\n", + "x39 -0.000 0.000 -0.000 1.000 -0.000 0.000\n", + "x40 -0.000 0.000 -0.000 1.000 -0.000 0.000\n", + "x41 -0.000 0.000 -0.000 1.000 -0.000 0.000\n", + "x42 -0.000 0.000 -0.000 1.000 -0.000 0.000\n", + "x43 0.000 0.000 0.000 1.000 -0.000 0.000\n", + "x44 -0.000 0.000 -0.000 1.000 -0.000 0.000\n", + "x45 -0.000 0.000 -0.000 1.000 -0.000 0.000\n", + "x46 0.000 0.000 0.000 1.000 -0.000 0.000\n", + "x47 0.000 0.000 0.000 1.000 -0.000 0.000\n", + "x48 0.000 0.000 0.000 1.000 -0.000 0.000\n", + "x49 -0.000 0.000 -0.000 1.000 -0.000 0.000\n", + "x50 -0.000 0.000 -0.000 1.000 -0.000 0.000\n", + "x51 0.000 0.000 0.000 1.000 -0.000 0.000\n", + "x52 -0.000 0.000 -0.000 1.000 -0.000 0.000\n", + "x53 0.000 0.000 0.000 1.000 -0.000 0.000\n", + "x54 -0.000 0.000 -0.000 1.000 -0.000 0.000\n", + "x55 0.000 0.000 0.000 1.000 -0.000 0.000\n", + "x56 0.000 0.000 0.000 1.000 -0.000 0.000\n", + "x57 0.000 0.000 0.000 1.000 -0.000 0.000\n", + "x58 0.000 0.000 0.000 1.000 -0.000 0.000\n", + "Group Var 0.136 358.854 \n", + "=========================================================\n", + "\n", + "\"\"\"" + ] + }, + "execution_count": 109, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# create a model to predict age_group from the module means\n", + "\n", + "y = adata_hvg.obs['age_group'].values\n", + "\n", + "X = module_means_df.values\n", + "X = sm.add_constant(X)\n", + "\n", + "model = sm.MixedLM(\n", + " endog=y,\n", + " exog=X,\n", + " groups=adata_hvg.obs['donor_id']\n", + ").fit()\n", + "model.summary()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "43b22401", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "bettercode", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.0" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/src/bettercode/rnaseq/compression_timing_test.ipynb b/src/bettercode/rnaseq/compression_timing_test.ipynb new file mode 100644 index 0000000..5cc2d70 --- /dev/null +++ b/src/bettercode/rnaseq/compression_timing_test.ipynb @@ -0,0 +1,157 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "b8feb8b7", + "metadata": {}, + "source": [ + "Compare timing of compressed vs uncompressed h5ad files for reading and writing" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "f6de78f4", + "metadata": {}, + "outputs": [], + "source": [ + "import anndata as ad\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "6dcd2026", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CPU times: user 581 ms, sys: 748 ms, total: 1.33 s\n", + "Wall time: 1.33 s\n" + ] + } + ], + "source": [ + "# get original data\n", + "%time d_orig = ad.read_h5ad('/Users/poldrack/data_unsynced/BCBS/immune_aging/workflow/checkpoints/step02_filtered.h5ad')" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "9b156226", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CPU times: user 1.13 s, sys: 804 ms, total: 1.93 s\n", + "Wall time: 2.89 s\n" + ] + } + ], + "source": [ + "# save uncompressed version to get timing\n", + "%time d_orig.write_h5ad('/Users/poldrack/data_unsynced/BCBS/immune_aging/workflow/checkpoints/step02_filtered_uncompressed.h5ad', compression=None)" + ] + }, + { + "cell_type": "markdown", + "id": "88313de1", + "metadata": {}, + "source": [] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "231a276c", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CPU times: user 1min 44s, sys: 876 ms, total: 1min 45s\n", + "Wall time: 1min 45s\n" + ] + } + ], + "source": [ + "# save compressed version to get timing\n", + "%time d_orig.write_h5ad('/Users/poldrack/data_unsynced/BCBS/immune_aging/workflow/checkpoints/step02_filtered_compressed.h5ad', compression='gzip')" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "84ca79f8", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CPU times: user 18.9 s, sys: 763 ms, total: 19.7 s\n", + "Wall time: 19.8 s\n" + ] + } + ], + "source": [ + "# load compressed version to get timing\n", + "%time d_comp = ad.read_h5ad('/Users/poldrack/data_unsynced/BCBS/immune_aging/workflow/checkpoints/step02_filtered_compressed.h5ad')" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "6ac5aae9", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2.9G\t/Users/poldrack/data_unsynced/BCBS/immune_aging/workflow/checkpoints/step02_filtered_compressed.h5ad\n", + "7.3G\t/Users/poldrack/data_unsynced/BCBS/immune_aging/workflow/checkpoints/step02_filtered_uncompressed.h5ad\n", + "7.3G\t/Users/poldrack/data_unsynced/BCBS/immune_aging/workflow/checkpoints/step02_filtered.h5ad\n" + ] + } + ], + "source": [ + "!du -sh /Users/poldrack/data_unsynced/BCBS/immune_aging/workflow/checkpoints/step02_filtered*.h5ad" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "38a604b7", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "bettercode", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.0" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/src/BetterCodeBetterScience/rnaseq/immune_scrnaseq_1_dataprep.ipynb b/src/bettercode/rnaseq/immune_scrnaseq_1_dataprep.ipynb similarity index 99% rename from src/BetterCodeBetterScience/rnaseq/immune_scrnaseq_1_dataprep.ipynb rename to src/bettercode/rnaseq/immune_scrnaseq_1_dataprep.ipynb index 3fbe4cb..296bd08 100644 --- a/src/BetterCodeBetterScience/rnaseq/immune_scrnaseq_1_dataprep.ipynb +++ b/src/bettercode/rnaseq/immune_scrnaseq_1_dataprep.ipynb @@ -384,7 +384,7 @@ ], "metadata": { "kernelspec": { - "display_name": "BetterCodeBetterScience", + "display_name": "bettercode", "language": "python", "name": "python3" }, diff --git a/src/bettercode/rnaseq/immune_scrnaseq_1_dataprep.py b/src/bettercode/rnaseq/immune_scrnaseq_1_dataprep.py new file mode 100644 index 0000000..b3407c5 --- /dev/null +++ b/src/bettercode/rnaseq/immune_scrnaseq_1_dataprep.py @@ -0,0 +1,157 @@ +# --- +# jupyter: +# jupytext: +# text_representation: +# extension: .py +# format_name: percent +# format_version: '1.3' +# jupytext_version: 1.18.1 +# kernelspec: +# display_name: bettercode +# language: python +# name: python3 +# --- + +# %% [markdown] +# ### Immune system gene expression and aging +# +# We will use a dataset distributed by the [OneK1K](https://onek1k.org/) project, which includes single-cell RNA-seq data from peripheral blood mononuclear cells (PBMCs) obtained from 982 donors, comprising more than 1.2 million cells in total. These data are released under a Creative Commons Zero Public Domain Dedication and are thus free to reuse, with the restriction that users agree not to attempt to reidentify the participants. +# +# The flagship paper for this study is: +# +# Yazar S., Alquicira-Hernández J., Wing K., Senabouth A., Gordon G., Andersen S., Lu Q., Rowson A., Taylor T., Clarke L., Maccora L., Chen C., Cook A., Ye J., Fairfax K., Hewitt A., Powell J. Single cell eQTL mapping identified cell type specific control of autoimmune disease. Science, 376, 6589 (2022) +# +# We will use the data to ask a simple question: how does gene expression in PBMCs change with age? + +# %% +import anndata as ad +from anndata.experimental import read_lazy +import dask.array as da +import h5py +import numpy as np +import scanpy as sc +from pathlib import Path +import os + +datadir = Path('/Users/poldrack/data_unsynced/BCBS/immune_aging/') + +# %% +datafile = datadir / 'a3f5651f-cd1a-4d26-8165-74964b79b4f2.h5ad' +url = 'https://datasets.cellxgene.cziscience.com/a3f5651f-cd1a-4d26-8165-74964b79b4f2.h5ad' +dataset_name = 'OneK1K' + +if not datafile.exists(): + cmd = f'wget -O {datafile.as_posix()} {url}' + print(f'Downloading data from {url} to {datafile.as_posix()}') + os.system(cmd) + +load_annotation_index = True +adata = read_lazy(h5py.File(datafile, 'r'), + load_annotation_index=load_annotation_index) + +# %% +print(adata) + +# %% +unique_cell_types = np.unique(adata.obs['cell_type']) +print(unique_cell_types) + +# %% [markdown] +# ### Filtering out bad donors + +# %% +import matplotlib.pyplot as plt +import pandas as pd +from scipy.stats import scoreatpercentile + +# 1. Calculate how many cells each donor has +donor_cell_counts = pd.Series(adata.obs['donor_id']).value_counts() + +# Print some basic statistics to read the exact numbers +print("Donor Cell Count Statistics:") +print(donor_cell_counts.describe()) + +# 2. Plot the histogram +plt.figure(figsize=(10, 6)) +# Bins set to 'auto' or a fixed number depending on your N of donors +plt.hist(donor_cell_counts.values, bins=50, color='skyblue', edgecolor='black') + +plt.title('Distribution of Total Cells per Donor') +plt.xlabel('Number of Cells Captured') +plt.ylabel('Number of Donors') +plt.grid(axis='y', alpha=0.5) + +# Optional: Draw a vertical line at the propsoed cutoff +# This helps you visualize how many donors you would lose. +cutoff_percentile = 10 # e.g., 10th percentile +min_cells_per_donor = int(scoreatpercentile(donor_cell_counts.values, cutoff_percentile)) +print(f'cutoff of {min_cells_per_donor} would exclude {(donor_cell_counts < min_cells_per_donor).sum()} donors') +plt.axvline(min_cells_per_donor, color='red', linestyle='dashed', linewidth=1, label=f'Cutoff ({min_cells_per_donor} cells)') +plt.legend() + +plt.show() + +# %% +print(f"Filtering to keep only donors with at least {min_cells_per_donor} cells.") +print(f"Number of donors excluded: {(donor_cell_counts < min_cells_per_donor).sum()}") +valid_donors = donor_cell_counts[donor_cell_counts >= min_cells_per_donor].index +adata = adata[adata.obs['donor_id'].isin(valid_donors)] + +# %% +print(f'Number of donors after filtering: {len(valid_donors)}') + +# %% [markdown] +# ### Filtering cell types by frequency +# +# Drop cell types that don't have at least 10 cells for at least 95% of people + +# %% +import pandas as pd + +# 1. Calculate the count of cells for each 'cell_type' within each 'donor_id' +# We use pandas crosstab on adata.obs, which is loaded in memory. +counts_per_donor = pd.crosstab(adata.obs['donor_id'], adata.obs['cell_type']) + +# 2. Identify cell types to keep +# Keep if >= 10 cells in at least 90% of donors + +min_cells = 10 +percent_donors = 0.9 +donor_count = counts_per_donor.shape[0] +cell_types_to_keep = counts_per_donor.columns[ + (counts_per_donor >= min_cells).sum(axis=0) >= (donor_count * percent_donors)] + +print(f"Keeping {len(cell_types_to_keep)} cell types out of {len(counts_per_donor.columns)}") +print(f"Cell types to keep: {cell_types_to_keep.tolist()}") + +# 3. Filter the AnnData object +# We subset the AnnData to include only observations belonging to the valid cell types. +adata_filtered = adata[adata.obs['cell_type'].isin(cell_types_to_keep)] + +# %% +# now drop subjects who have any zeros in these cell types +donor_celltype_counts = pd.crosstab(adata_filtered.obs['donor_id'], adata_filtered.obs['cell_type']) +valid_donors_final = donor_celltype_counts.index[ + (donor_celltype_counts >= min_cells).all(axis=1)] +adata_filtered = adata_filtered[adata_filtered.obs['donor_id'].isin(valid_donors_final)] +print(f"Final number of donors after filtering: {len(valid_donors_final)}") + +# %% + +print("Loading data into memory (this can take a few minutes)...") +adata_loaded = adata_filtered.to_memory() + +# filter out genes with zero counts across all selected cells +print("Filtering genes with zero counts...") +sc.pp.filter_genes(adata_loaded, min_counts=1) + + +# %% +print(adata_loaded) + + +# %% +adata_loaded.write(datadir / f'dataset-{dataset_name}_subset-immune_filtered.h5ad') + +# %% +# !ls -lh /Users/poldrack/data_unsynced/BCBS/immune_aging diff --git a/src/BetterCodeBetterScience/rnaseq/immune_scrnaseq_2_preprocess.ipynb b/src/bettercode/rnaseq/immune_scrnaseq_2_preprocess.ipynb similarity index 99% rename from src/BetterCodeBetterScience/rnaseq/immune_scrnaseq_2_preprocess.ipynb rename to src/bettercode/rnaseq/immune_scrnaseq_2_preprocess.ipynb index 02a3b5e..69c6c11 100644 --- a/src/BetterCodeBetterScience/rnaseq/immune_scrnaseq_2_preprocess.ipynb +++ b/src/bettercode/rnaseq/immune_scrnaseq_2_preprocess.ipynb @@ -520,7 +520,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/Users/poldrack/Dropbox/code/BetterCodeBetterScience/.venv/lib/python3.12/site-packages/scanpy/preprocessing/_pca/__init__.py:226: FutureWarning: Argument `use_highly_variable` is deprecated, consider using the mask argument. Use_highly_variable=True can be called through mask_var=\"highly_variable\". Use_highly_variable=False can be called through mask_var=None\n", + "/Users/poldrack/Dropbox/code/bettercode/.venv/lib/python3.12/site-packages/scanpy/preprocessing/_pca/__init__.py:226: FutureWarning: Argument `use_highly_variable` is deprecated, consider using the mask argument. Use_highly_variable=True can be called through mask_var=\"highly_variable\". Use_highly_variable=False can be called through mask_var=None\n", " mask_var_param, mask_var = _handle_mask_var(\n" ] } @@ -1962,7 +1962,7 @@ ], "metadata": { "kernelspec": { - "display_name": "BetterCodeBetterScience", + "display_name": "bettercode", "language": "python", "name": "python3" }, diff --git a/src/BetterCodeBetterScience/rnaseq/immune_scrnaseq_monolithic.py b/src/bettercode/rnaseq/immune_scrnaseq_monolithic.py similarity index 99% rename from src/BetterCodeBetterScience/rnaseq/immune_scrnaseq_monolithic.py rename to src/bettercode/rnaseq/immune_scrnaseq_monolithic.py index cac638e..d3a4eac 100644 --- a/src/BetterCodeBetterScience/rnaseq/immune_scrnaseq_monolithic.py +++ b/src/bettercode/rnaseq/immune_scrnaseq_monolithic.py @@ -7,7 +7,7 @@ # format_version: '1.3' # jupytext_version: 1.18.1 # kernelspec: -# display_name: BetterCodeBetterScience +# display_name: bettercode # language: python # name: python3 # --- diff --git a/src/BetterCodeBetterScience/rnaseq/modular_workflow/__init__.py b/src/bettercode/rnaseq/modular_workflow/__init__.py similarity index 100% rename from src/BetterCodeBetterScience/rnaseq/modular_workflow/__init__.py rename to src/bettercode/rnaseq/modular_workflow/__init__.py diff --git a/src/BetterCodeBetterScience/rnaseq/modular_workflow/clustering.py b/src/bettercode/rnaseq/modular_workflow/clustering.py similarity index 100% rename from src/BetterCodeBetterScience/rnaseq/modular_workflow/clustering.py rename to src/bettercode/rnaseq/modular_workflow/clustering.py diff --git a/src/BetterCodeBetterScience/rnaseq/modular_workflow/data_filtering.py b/src/bettercode/rnaseq/modular_workflow/data_filtering.py similarity index 100% rename from src/BetterCodeBetterScience/rnaseq/modular_workflow/data_filtering.py rename to src/bettercode/rnaseq/modular_workflow/data_filtering.py diff --git a/src/BetterCodeBetterScience/rnaseq/modular_workflow/data_loading.py b/src/bettercode/rnaseq/modular_workflow/data_loading.py similarity index 100% rename from src/BetterCodeBetterScience/rnaseq/modular_workflow/data_loading.py rename to src/bettercode/rnaseq/modular_workflow/data_loading.py diff --git a/src/BetterCodeBetterScience/rnaseq/modular_workflow/differential_expression.py b/src/bettercode/rnaseq/modular_workflow/differential_expression.py similarity index 100% rename from src/BetterCodeBetterScience/rnaseq/modular_workflow/differential_expression.py rename to src/bettercode/rnaseq/modular_workflow/differential_expression.py diff --git a/src/BetterCodeBetterScience/rnaseq/modular_workflow/dimensionality_reduction.py b/src/bettercode/rnaseq/modular_workflow/dimensionality_reduction.py similarity index 100% rename from src/BetterCodeBetterScience/rnaseq/modular_workflow/dimensionality_reduction.py rename to src/bettercode/rnaseq/modular_workflow/dimensionality_reduction.py diff --git a/src/BetterCodeBetterScience/rnaseq/modular_workflow/overrepresentation_analysis.py b/src/bettercode/rnaseq/modular_workflow/overrepresentation_analysis.py similarity index 100% rename from src/BetterCodeBetterScience/rnaseq/modular_workflow/overrepresentation_analysis.py rename to src/bettercode/rnaseq/modular_workflow/overrepresentation_analysis.py diff --git a/src/BetterCodeBetterScience/rnaseq/modular_workflow/pathway_analysis.py b/src/bettercode/rnaseq/modular_workflow/pathway_analysis.py similarity index 100% rename from src/BetterCodeBetterScience/rnaseq/modular_workflow/pathway_analysis.py rename to src/bettercode/rnaseq/modular_workflow/pathway_analysis.py diff --git a/src/BetterCodeBetterScience/rnaseq/modular_workflow/predictive_modeling.py b/src/bettercode/rnaseq/modular_workflow/predictive_modeling.py similarity index 100% rename from src/BetterCodeBetterScience/rnaseq/modular_workflow/predictive_modeling.py rename to src/bettercode/rnaseq/modular_workflow/predictive_modeling.py diff --git a/src/BetterCodeBetterScience/rnaseq/modular_workflow/preprocessing.py b/src/bettercode/rnaseq/modular_workflow/preprocessing.py similarity index 100% rename from src/BetterCodeBetterScience/rnaseq/modular_workflow/preprocessing.py rename to src/bettercode/rnaseq/modular_workflow/preprocessing.py diff --git a/src/BetterCodeBetterScience/rnaseq/modular_workflow/pseudobulk.py b/src/bettercode/rnaseq/modular_workflow/pseudobulk.py similarity index 100% rename from src/BetterCodeBetterScience/rnaseq/modular_workflow/pseudobulk.py rename to src/bettercode/rnaseq/modular_workflow/pseudobulk.py diff --git a/src/BetterCodeBetterScience/rnaseq/modular_workflow/quality_control.py b/src/bettercode/rnaseq/modular_workflow/quality_control.py similarity index 100% rename from src/BetterCodeBetterScience/rnaseq/modular_workflow/quality_control.py rename to src/bettercode/rnaseq/modular_workflow/quality_control.py diff --git a/src/BetterCodeBetterScience/rnaseq/modular_workflow/run_workflow.py b/src/bettercode/rnaseq/modular_workflow/run_workflow.py similarity index 90% rename from src/BetterCodeBetterScience/rnaseq/modular_workflow/run_workflow.py rename to src/bettercode/rnaseq/modular_workflow/run_workflow.py index f88d965..703eaed 100644 --- a/src/BetterCodeBetterScience/rnaseq/modular_workflow/run_workflow.py +++ b/src/bettercode/rnaseq/modular_workflow/run_workflow.py @@ -8,40 +8,40 @@ from dotenv import load_dotenv -from BetterCodeBetterScience.rnaseq.modular_workflow.clustering import ( +from bettercode.rnaseq.modular_workflow.clustering import ( run_clustering_pipeline, ) -from BetterCodeBetterScience.rnaseq.modular_workflow.data_filtering import ( +from bettercode.rnaseq.modular_workflow.data_filtering import ( run_filtering_pipeline, ) -from BetterCodeBetterScience.rnaseq.modular_workflow.data_loading import ( +from bettercode.rnaseq.modular_workflow.data_loading import ( download_data, load_anndata, load_lazy_anndata, save_anndata, ) -from BetterCodeBetterScience.rnaseq.modular_workflow.differential_expression import ( +from bettercode.rnaseq.modular_workflow.differential_expression import ( run_differential_expression_pipeline, ) -from BetterCodeBetterScience.rnaseq.modular_workflow.dimensionality_reduction import ( +from bettercode.rnaseq.modular_workflow.dimensionality_reduction import ( run_dimensionality_reduction_pipeline, ) -from BetterCodeBetterScience.rnaseq.modular_workflow.overrepresentation_analysis import ( +from bettercode.rnaseq.modular_workflow.overrepresentation_analysis import ( run_overrepresentation_pipeline, ) -from BetterCodeBetterScience.rnaseq.modular_workflow.pathway_analysis import ( +from bettercode.rnaseq.modular_workflow.pathway_analysis import ( run_gsea_pipeline, ) -from BetterCodeBetterScience.rnaseq.modular_workflow.predictive_modeling import ( +from bettercode.rnaseq.modular_workflow.predictive_modeling import ( run_predictive_modeling_pipeline, ) -from BetterCodeBetterScience.rnaseq.modular_workflow.preprocessing import ( +from bettercode.rnaseq.modular_workflow.preprocessing import ( run_preprocessing_pipeline, ) -from BetterCodeBetterScience.rnaseq.modular_workflow.pseudobulk import ( +from bettercode.rnaseq.modular_workflow.pseudobulk import ( run_pseudobulk_pipeline, ) -from BetterCodeBetterScience.rnaseq.modular_workflow.quality_control import ( +from bettercode.rnaseq.modular_workflow.quality_control import ( run_qc_pipeline, ) diff --git a/src/BetterCodeBetterScience/rnaseq/prefect_workflow/__init__.py b/src/bettercode/rnaseq/prefect_workflow/__init__.py similarity index 100% rename from src/BetterCodeBetterScience/rnaseq/prefect_workflow/__init__.py rename to src/bettercode/rnaseq/prefect_workflow/__init__.py diff --git a/src/BetterCodeBetterScience/rnaseq/prefect_workflow/config/config.yaml b/src/bettercode/rnaseq/prefect_workflow/config/config.yaml similarity index 93% rename from src/BetterCodeBetterScience/rnaseq/prefect_workflow/config/config.yaml rename to src/bettercode/rnaseq/prefect_workflow/config/config.yaml index e4043e2..7858904 100644 --- a/src/BetterCodeBetterScience/rnaseq/prefect_workflow/config/config.yaml +++ b/src/bettercode/rnaseq/prefect_workflow/config/config.yaml @@ -1,7 +1,7 @@ # Configuration for Prefect scRNA-seq immune aging workflow # # Usage: -# python -m BetterCodeBetterScience.rnaseq.prefect_workflow.run_workflow --config /path/to/config.yaml +# python -m bettercode.rnaseq.prefect_workflow.run_workflow --config /path/to/config.yaml # # Override any parameter via CLI: # python ... --dataset-name MyDataset --force-from 5 diff --git a/src/BetterCodeBetterScience/rnaseq/prefect_workflow/flows.py b/src/bettercode/rnaseq/prefect_workflow/flows.py similarity index 99% rename from src/BetterCodeBetterScience/rnaseq/prefect_workflow/flows.py rename to src/bettercode/rnaseq/prefect_workflow/flows.py index ef40fdc..9947c72 100644 --- a/src/BetterCodeBetterScience/rnaseq/prefect_workflow/flows.py +++ b/src/bettercode/rnaseq/prefect_workflow/flows.py @@ -11,7 +11,7 @@ import yaml from prefect import flow, get_run_logger -from BetterCodeBetterScience.rnaseq.prefect_workflow.tasks import ( +from bettercode.rnaseq.prefect_workflow.tasks import ( clustering_task, differential_expression_task, dimensionality_reduction_task, @@ -24,11 +24,11 @@ pseudobulk_task, quality_control_task, ) -from BetterCodeBetterScience.rnaseq.stateless_workflow.checkpoint import ( +from bettercode.rnaseq.stateless_workflow.checkpoint import ( bids_checkpoint_name, load_checkpoint, ) -from BetterCodeBetterScience.rnaseq.stateless_workflow.execution_log import ( +from bettercode.rnaseq.stateless_workflow.execution_log import ( create_execution_log, serialize_parameters, ) diff --git a/src/BetterCodeBetterScience/rnaseq/prefect_workflow/run_workflow.py b/src/bettercode/rnaseq/prefect_workflow/run_workflow.py similarity index 92% rename from src/BetterCodeBetterScience/rnaseq/prefect_workflow/run_workflow.py rename to src/bettercode/rnaseq/prefect_workflow/run_workflow.py index 6247b55..70153e1 100644 --- a/src/BetterCodeBetterScience/rnaseq/prefect_workflow/run_workflow.py +++ b/src/bettercode/rnaseq/prefect_workflow/run_workflow.py @@ -1,13 +1,13 @@ """Entry point for running the Prefect-based scRNA-seq workflow. Usage: - python -m BetterCodeBetterScience.rnaseq.prefect_workflow.run_workflow + python -m bettercode.rnaseq.prefect_workflow.run_workflow Or with arguments: - python -m BetterCodeBetterScience.rnaseq.prefect_workflow.run_workflow --force-from 8 + python -m bettercode.rnaseq.prefect_workflow.run_workflow --force-from 8 With custom config: - python -m BetterCodeBetterScience.rnaseq.prefect_workflow.run_workflow --config /path/to/config.yaml + python -m bettercode.rnaseq.prefect_workflow.run_workflow --config /path/to/config.yaml """ import argparse @@ -16,7 +16,7 @@ from dotenv import load_dotenv -from BetterCodeBetterScience.rnaseq.prefect_workflow.flows import ( +from bettercode.rnaseq.prefect_workflow.flows import ( analyze_single_cell_type, load_config, run_workflow, @@ -93,7 +93,7 @@ def main(): # List cell types if requested if args.list_cell_types: - from BetterCodeBetterScience.rnaseq.stateless_workflow.checkpoint import ( + from bettercode.rnaseq.stateless_workflow.checkpoint import ( bids_checkpoint_name, load_checkpoint, ) diff --git a/src/BetterCodeBetterScience/rnaseq/prefect_workflow/tasks.py b/src/bettercode/rnaseq/prefect_workflow/tasks.py similarity index 91% rename from src/BetterCodeBetterScience/rnaseq/prefect_workflow/tasks.py rename to src/bettercode/rnaseq/prefect_workflow/tasks.py index b0c7c83..9946680 100644 --- a/src/BetterCodeBetterScience/rnaseq/prefect_workflow/tasks.py +++ b/src/bettercode/rnaseq/prefect_workflow/tasks.py @@ -10,41 +10,41 @@ import pandas as pd from prefect import task -from BetterCodeBetterScience.rnaseq.modular_workflow.clustering import ( +from bettercode.rnaseq.modular_workflow.clustering import ( run_clustering_pipeline, ) -from BetterCodeBetterScience.rnaseq.modular_workflow.data_filtering import ( +from bettercode.rnaseq.modular_workflow.data_filtering import ( run_filtering_pipeline, ) -from BetterCodeBetterScience.rnaseq.modular_workflow.data_loading import ( +from bettercode.rnaseq.modular_workflow.data_loading import ( download_data, load_lazy_anndata, ) -from BetterCodeBetterScience.rnaseq.modular_workflow.differential_expression import ( +from bettercode.rnaseq.modular_workflow.differential_expression import ( run_differential_expression_pipeline, ) -from BetterCodeBetterScience.rnaseq.modular_workflow.dimensionality_reduction import ( +from bettercode.rnaseq.modular_workflow.dimensionality_reduction import ( run_dimensionality_reduction_pipeline, ) -from BetterCodeBetterScience.rnaseq.modular_workflow.overrepresentation_analysis import ( +from bettercode.rnaseq.modular_workflow.overrepresentation_analysis import ( run_overrepresentation_pipeline, ) -from BetterCodeBetterScience.rnaseq.modular_workflow.pathway_analysis import ( +from bettercode.rnaseq.modular_workflow.pathway_analysis import ( run_gsea_pipeline, ) -from BetterCodeBetterScience.rnaseq.modular_workflow.predictive_modeling import ( +from bettercode.rnaseq.modular_workflow.predictive_modeling import ( run_predictive_modeling_pipeline, ) -from BetterCodeBetterScience.rnaseq.modular_workflow.preprocessing import ( +from bettercode.rnaseq.modular_workflow.preprocessing import ( run_preprocessing_pipeline, ) -from BetterCodeBetterScience.rnaseq.modular_workflow.pseudobulk import ( +from bettercode.rnaseq.modular_workflow.pseudobulk import ( run_pseudobulk_pipeline, ) -from BetterCodeBetterScience.rnaseq.modular_workflow.quality_control import ( +from bettercode.rnaseq.modular_workflow.quality_control import ( run_qc_pipeline, ) -from BetterCodeBetterScience.rnaseq.stateless_workflow.checkpoint import ( +from bettercode.rnaseq.stateless_workflow.checkpoint import ( load_checkpoint, save_checkpoint, ) diff --git a/src/BetterCodeBetterScience/rnaseq/snakemake_workflow/Makefile b/src/bettercode/rnaseq/snakemake_workflow/Makefile similarity index 100% rename from src/BetterCodeBetterScience/rnaseq/snakemake_workflow/Makefile rename to src/bettercode/rnaseq/snakemake_workflow/Makefile diff --git a/src/BetterCodeBetterScience/rnaseq/snakemake_workflow/Snakefile b/src/bettercode/rnaseq/snakemake_workflow/Snakefile similarity index 100% rename from src/BetterCodeBetterScience/rnaseq/snakemake_workflow/Snakefile rename to src/bettercode/rnaseq/snakemake_workflow/Snakefile diff --git a/src/BetterCodeBetterScience/rnaseq/snakemake_workflow/WORKFLOW_OVERVIEW.md b/src/bettercode/rnaseq/snakemake_workflow/WORKFLOW_OVERVIEW.md similarity index 100% rename from src/BetterCodeBetterScience/rnaseq/snakemake_workflow/WORKFLOW_OVERVIEW.md rename to src/bettercode/rnaseq/snakemake_workflow/WORKFLOW_OVERVIEW.md diff --git a/src/BetterCodeBetterScience/rnaseq/snakemake_workflow/__init__.py b/src/bettercode/rnaseq/snakemake_workflow/__init__.py similarity index 100% rename from src/BetterCodeBetterScience/rnaseq/snakemake_workflow/__init__.py rename to src/bettercode/rnaseq/snakemake_workflow/__init__.py diff --git a/src/BetterCodeBetterScience/rnaseq/snakemake_workflow/config/config.yaml b/src/bettercode/rnaseq/snakemake_workflow/config/config.yaml similarity index 100% rename from src/BetterCodeBetterScience/rnaseq/snakemake_workflow/config/config.yaml rename to src/bettercode/rnaseq/snakemake_workflow/config/config.yaml diff --git a/src/BetterCodeBetterScience/rnaseq/snakemake_workflow/report/clustering.rst b/src/bettercode/rnaseq/snakemake_workflow/report/clustering.rst similarity index 100% rename from src/BetterCodeBetterScience/rnaseq/snakemake_workflow/report/clustering.rst rename to src/bettercode/rnaseq/snakemake_workflow/report/clustering.rst diff --git a/src/BetterCodeBetterScience/rnaseq/snakemake_workflow/report/de_results.rst b/src/bettercode/rnaseq/snakemake_workflow/report/de_results.rst similarity index 100% rename from src/BetterCodeBetterScience/rnaseq/snakemake_workflow/report/de_results.rst rename to src/bettercode/rnaseq/snakemake_workflow/report/de_results.rst diff --git a/src/BetterCodeBetterScience/rnaseq/snakemake_workflow/report/doublet_umap.rst b/src/bettercode/rnaseq/snakemake_workflow/report/doublet_umap.rst similarity index 100% rename from src/BetterCodeBetterScience/rnaseq/snakemake_workflow/report/doublet_umap.rst rename to src/bettercode/rnaseq/snakemake_workflow/report/doublet_umap.rst diff --git a/src/BetterCodeBetterScience/rnaseq/snakemake_workflow/report/enrichr.rst b/src/bettercode/rnaseq/snakemake_workflow/report/enrichr.rst similarity index 100% rename from src/BetterCodeBetterScience/rnaseq/snakemake_workflow/report/enrichr.rst rename to src/bettercode/rnaseq/snakemake_workflow/report/enrichr.rst diff --git a/src/BetterCodeBetterScience/rnaseq/snakemake_workflow/report/filtering.rst b/src/bettercode/rnaseq/snakemake_workflow/report/filtering.rst similarity index 100% rename from src/BetterCodeBetterScience/rnaseq/snakemake_workflow/report/filtering.rst rename to src/bettercode/rnaseq/snakemake_workflow/report/filtering.rst diff --git a/src/BetterCodeBetterScience/rnaseq/snakemake_workflow/report/gsea.rst b/src/bettercode/rnaseq/snakemake_workflow/report/gsea.rst similarity index 100% rename from src/BetterCodeBetterScience/rnaseq/snakemake_workflow/report/gsea.rst rename to src/bettercode/rnaseq/snakemake_workflow/report/gsea.rst diff --git a/src/BetterCodeBetterScience/rnaseq/snakemake_workflow/report/hemoglobin.rst b/src/bettercode/rnaseq/snakemake_workflow/report/hemoglobin.rst similarity index 100% rename from src/BetterCodeBetterScience/rnaseq/snakemake_workflow/report/hemoglobin.rst rename to src/bettercode/rnaseq/snakemake_workflow/report/hemoglobin.rst diff --git a/src/BetterCodeBetterScience/rnaseq/snakemake_workflow/report/pca.rst b/src/bettercode/rnaseq/snakemake_workflow/report/pca.rst similarity index 100% rename from src/BetterCodeBetterScience/rnaseq/snakemake_workflow/report/pca.rst rename to src/bettercode/rnaseq/snakemake_workflow/report/pca.rst diff --git a/src/BetterCodeBetterScience/rnaseq/snakemake_workflow/report/prediction.rst b/src/bettercode/rnaseq/snakemake_workflow/report/prediction.rst similarity index 100% rename from src/BetterCodeBetterScience/rnaseq/snakemake_workflow/report/prediction.rst rename to src/bettercode/rnaseq/snakemake_workflow/report/prediction.rst diff --git a/src/BetterCodeBetterScience/rnaseq/snakemake_workflow/report/pseudobulk.rst b/src/bettercode/rnaseq/snakemake_workflow/report/pseudobulk.rst similarity index 100% rename from src/BetterCodeBetterScience/rnaseq/snakemake_workflow/report/pseudobulk.rst rename to src/bettercode/rnaseq/snakemake_workflow/report/pseudobulk.rst diff --git a/src/BetterCodeBetterScience/rnaseq/snakemake_workflow/report/qc_scatter.rst b/src/bettercode/rnaseq/snakemake_workflow/report/qc_scatter.rst similarity index 100% rename from src/BetterCodeBetterScience/rnaseq/snakemake_workflow/report/qc_scatter.rst rename to src/bettercode/rnaseq/snakemake_workflow/report/qc_scatter.rst diff --git a/src/BetterCodeBetterScience/rnaseq/snakemake_workflow/report/qc_violin.rst b/src/bettercode/rnaseq/snakemake_workflow/report/qc_violin.rst similarity index 100% rename from src/BetterCodeBetterScience/rnaseq/snakemake_workflow/report/qc_violin.rst rename to src/bettercode/rnaseq/snakemake_workflow/report/qc_violin.rst diff --git a/src/BetterCodeBetterScience/rnaseq/snakemake_workflow/report/umap.rst b/src/bettercode/rnaseq/snakemake_workflow/report/umap.rst similarity index 100% rename from src/BetterCodeBetterScience/rnaseq/snakemake_workflow/report/umap.rst rename to src/bettercode/rnaseq/snakemake_workflow/report/umap.rst diff --git a/src/BetterCodeBetterScience/rnaseq/snakemake_workflow/report/workflow.rst b/src/bettercode/rnaseq/snakemake_workflow/report/workflow.rst similarity index 100% rename from src/BetterCodeBetterScience/rnaseq/snakemake_workflow/report/workflow.rst rename to src/bettercode/rnaseq/snakemake_workflow/report/workflow.rst diff --git a/src/BetterCodeBetterScience/rnaseq/snakemake_workflow/rules/common.smk b/src/bettercode/rnaseq/snakemake_workflow/rules/common.smk similarity index 100% rename from src/BetterCodeBetterScience/rnaseq/snakemake_workflow/rules/common.smk rename to src/bettercode/rnaseq/snakemake_workflow/rules/common.smk diff --git a/src/BetterCodeBetterScience/rnaseq/snakemake_workflow/rules/per_cell_type.smk b/src/bettercode/rnaseq/snakemake_workflow/rules/per_cell_type.smk similarity index 100% rename from src/BetterCodeBetterScience/rnaseq/snakemake_workflow/rules/per_cell_type.smk rename to src/bettercode/rnaseq/snakemake_workflow/rules/per_cell_type.smk diff --git a/src/BetterCodeBetterScience/rnaseq/snakemake_workflow/rules/preprocessing.smk b/src/bettercode/rnaseq/snakemake_workflow/rules/preprocessing.smk similarity index 100% rename from src/BetterCodeBetterScience/rnaseq/snakemake_workflow/rules/preprocessing.smk rename to src/bettercode/rnaseq/snakemake_workflow/rules/preprocessing.smk diff --git a/src/BetterCodeBetterScience/rnaseq/snakemake_workflow/rules/pseudobulk.smk b/src/bettercode/rnaseq/snakemake_workflow/rules/pseudobulk.smk similarity index 100% rename from src/BetterCodeBetterScience/rnaseq/snakemake_workflow/rules/pseudobulk.smk rename to src/bettercode/rnaseq/snakemake_workflow/rules/pseudobulk.smk diff --git a/src/BetterCodeBetterScience/rnaseq/snakemake_workflow/scripts/aggregate_results.py b/src/bettercode/rnaseq/snakemake_workflow/scripts/aggregate_results.py similarity index 100% rename from src/BetterCodeBetterScience/rnaseq/snakemake_workflow/scripts/aggregate_results.py rename to src/bettercode/rnaseq/snakemake_workflow/scripts/aggregate_results.py diff --git a/src/BetterCodeBetterScience/rnaseq/snakemake_workflow/scripts/cluster.py b/src/bettercode/rnaseq/snakemake_workflow/scripts/cluster.py similarity index 88% rename from src/BetterCodeBetterScience/rnaseq/snakemake_workflow/scripts/cluster.py rename to src/bettercode/rnaseq/snakemake_workflow/scripts/cluster.py index ff8ed68..14662ba 100644 --- a/src/BetterCodeBetterScience/rnaseq/snakemake_workflow/scripts/cluster.py +++ b/src/bettercode/rnaseq/snakemake_workflow/scripts/cluster.py @@ -2,10 +2,10 @@ from pathlib import Path -from BetterCodeBetterScience.rnaseq.modular_workflow.clustering import ( +from bettercode.rnaseq.modular_workflow.clustering import ( run_clustering_pipeline, ) -from BetterCodeBetterScience.rnaseq.stateless_workflow.checkpoint import ( +from bettercode.rnaseq.stateless_workflow.checkpoint import ( load_checkpoint, save_checkpoint, ) diff --git a/src/BetterCodeBetterScience/rnaseq/snakemake_workflow/scripts/differential_expression.py b/src/bettercode/rnaseq/snakemake_workflow/scripts/differential_expression.py similarity index 93% rename from src/BetterCodeBetterScience/rnaseq/snakemake_workflow/scripts/differential_expression.py rename to src/bettercode/rnaseq/snakemake_workflow/scripts/differential_expression.py index 193b423..fea2de6 100644 --- a/src/BetterCodeBetterScience/rnaseq/snakemake_workflow/scripts/differential_expression.py +++ b/src/bettercode/rnaseq/snakemake_workflow/scripts/differential_expression.py @@ -3,10 +3,10 @@ import json from pathlib import Path -from BetterCodeBetterScience.rnaseq.modular_workflow.differential_expression import ( +from bettercode.rnaseq.modular_workflow.differential_expression import ( run_differential_expression_pipeline, ) -from BetterCodeBetterScience.rnaseq.stateless_workflow.checkpoint import ( +from bettercode.rnaseq.stateless_workflow.checkpoint import ( load_checkpoint, save_checkpoint, ) diff --git a/src/BetterCodeBetterScience/rnaseq/snakemake_workflow/scripts/dimred.py b/src/bettercode/rnaseq/snakemake_workflow/scripts/dimred.py similarity index 90% rename from src/BetterCodeBetterScience/rnaseq/snakemake_workflow/scripts/dimred.py rename to src/bettercode/rnaseq/snakemake_workflow/scripts/dimred.py index 4e72cb6..4ce0ad5 100644 --- a/src/BetterCodeBetterScience/rnaseq/snakemake_workflow/scripts/dimred.py +++ b/src/bettercode/rnaseq/snakemake_workflow/scripts/dimred.py @@ -8,10 +8,10 @@ os.environ["NUMBA_NUM_THREADS"] = str(snakemake.threads) os.environ["OMP_NUM_THREADS"] = str(snakemake.threads) -from BetterCodeBetterScience.rnaseq.modular_workflow.dimensionality_reduction import ( +from bettercode.rnaseq.modular_workflow.dimensionality_reduction import ( run_dimensionality_reduction_pipeline, ) -from BetterCodeBetterScience.rnaseq.stateless_workflow.checkpoint import ( +from bettercode.rnaseq.stateless_workflow.checkpoint import ( load_checkpoint, save_checkpoint, ) diff --git a/src/BetterCodeBetterScience/rnaseq/snakemake_workflow/scripts/download.py b/src/bettercode/rnaseq/snakemake_workflow/scripts/download.py similarity index 83% rename from src/BetterCodeBetterScience/rnaseq/snakemake_workflow/scripts/download.py rename to src/bettercode/rnaseq/snakemake_workflow/scripts/download.py index 659b04d..83513f7 100644 --- a/src/BetterCodeBetterScience/rnaseq/snakemake_workflow/scripts/download.py +++ b/src/bettercode/rnaseq/snakemake_workflow/scripts/download.py @@ -2,7 +2,7 @@ from pathlib import Path -from BetterCodeBetterScience.rnaseq.modular_workflow.data_loading import download_data +from bettercode.rnaseq.modular_workflow.data_loading import download_data def main(): diff --git a/src/BetterCodeBetterScience/rnaseq/snakemake_workflow/scripts/enrichr.py b/src/bettercode/rnaseq/snakemake_workflow/scripts/enrichr.py similarity index 89% rename from src/BetterCodeBetterScience/rnaseq/snakemake_workflow/scripts/enrichr.py rename to src/bettercode/rnaseq/snakemake_workflow/scripts/enrichr.py index a076199..3130698 100644 --- a/src/BetterCodeBetterScience/rnaseq/snakemake_workflow/scripts/enrichr.py +++ b/src/bettercode/rnaseq/snakemake_workflow/scripts/enrichr.py @@ -4,10 +4,10 @@ import pandas as pd -from BetterCodeBetterScience.rnaseq.modular_workflow.overrepresentation_analysis import ( +from bettercode.rnaseq.modular_workflow.overrepresentation_analysis import ( run_overrepresentation_pipeline, ) -from BetterCodeBetterScience.rnaseq.stateless_workflow.checkpoint import save_checkpoint +from bettercode.rnaseq.stateless_workflow.checkpoint import save_checkpoint def main(): diff --git a/src/BetterCodeBetterScience/rnaseq/snakemake_workflow/scripts/filter.py b/src/bettercode/rnaseq/snakemake_workflow/scripts/filter.py similarity index 85% rename from src/BetterCodeBetterScience/rnaseq/snakemake_workflow/scripts/filter.py rename to src/bettercode/rnaseq/snakemake_workflow/scripts/filter.py index 41a24bc..0221ef3 100644 --- a/src/BetterCodeBetterScience/rnaseq/snakemake_workflow/scripts/filter.py +++ b/src/bettercode/rnaseq/snakemake_workflow/scripts/filter.py @@ -2,13 +2,13 @@ from pathlib import Path -from BetterCodeBetterScience.rnaseq.modular_workflow.data_filtering import ( +from bettercode.rnaseq.modular_workflow.data_filtering import ( run_filtering_pipeline, ) -from BetterCodeBetterScience.rnaseq.modular_workflow.data_loading import ( +from bettercode.rnaseq.modular_workflow.data_loading import ( load_lazy_anndata, ) -from BetterCodeBetterScience.rnaseq.stateless_workflow.checkpoint import save_checkpoint +from bettercode.rnaseq.stateless_workflow.checkpoint import save_checkpoint def main(): diff --git a/src/BetterCodeBetterScience/rnaseq/snakemake_workflow/scripts/gsea.py b/src/bettercode/rnaseq/snakemake_workflow/scripts/gsea.py similarity index 86% rename from src/BetterCodeBetterScience/rnaseq/snakemake_workflow/scripts/gsea.py rename to src/bettercode/rnaseq/snakemake_workflow/scripts/gsea.py index 2d956c0..aace1e9 100644 --- a/src/BetterCodeBetterScience/rnaseq/snakemake_workflow/scripts/gsea.py +++ b/src/bettercode/rnaseq/snakemake_workflow/scripts/gsea.py @@ -4,10 +4,10 @@ import pandas as pd -from BetterCodeBetterScience.rnaseq.modular_workflow.pathway_analysis import ( +from bettercode.rnaseq.modular_workflow.pathway_analysis import ( run_gsea_pipeline, ) -from BetterCodeBetterScience.rnaseq.stateless_workflow.checkpoint import save_checkpoint +from bettercode.rnaseq.stateless_workflow.checkpoint import save_checkpoint def main(): diff --git a/src/BetterCodeBetterScience/rnaseq/snakemake_workflow/scripts/prediction.py b/src/bettercode/rnaseq/snakemake_workflow/scripts/prediction.py similarity index 93% rename from src/BetterCodeBetterScience/rnaseq/snakemake_workflow/scripts/prediction.py rename to src/bettercode/rnaseq/snakemake_workflow/scripts/prediction.py index 5ff128b..64461a7 100644 --- a/src/BetterCodeBetterScience/rnaseq/snakemake_workflow/scripts/prediction.py +++ b/src/bettercode/rnaseq/snakemake_workflow/scripts/prediction.py @@ -5,10 +5,10 @@ import pandas as pd -from BetterCodeBetterScience.rnaseq.modular_workflow.predictive_modeling import ( +from bettercode.rnaseq.modular_workflow.predictive_modeling import ( run_predictive_modeling_pipeline, ) -from BetterCodeBetterScience.rnaseq.stateless_workflow.checkpoint import ( +from bettercode.rnaseq.stateless_workflow.checkpoint import ( load_checkpoint, save_checkpoint, ) diff --git a/src/BetterCodeBetterScience/rnaseq/snakemake_workflow/scripts/preprocess.py b/src/bettercode/rnaseq/snakemake_workflow/scripts/preprocess.py similarity index 88% rename from src/BetterCodeBetterScience/rnaseq/snakemake_workflow/scripts/preprocess.py rename to src/bettercode/rnaseq/snakemake_workflow/scripts/preprocess.py index dfdee46..f00fb4a 100644 --- a/src/BetterCodeBetterScience/rnaseq/snakemake_workflow/scripts/preprocess.py +++ b/src/bettercode/rnaseq/snakemake_workflow/scripts/preprocess.py @@ -2,10 +2,10 @@ from pathlib import Path -from BetterCodeBetterScience.rnaseq.modular_workflow.preprocessing import ( +from bettercode.rnaseq.modular_workflow.preprocessing import ( run_preprocessing_pipeline, ) -from BetterCodeBetterScience.rnaseq.stateless_workflow.checkpoint import ( +from bettercode.rnaseq.stateless_workflow.checkpoint import ( load_checkpoint, save_checkpoint, ) diff --git a/src/BetterCodeBetterScience/rnaseq/snakemake_workflow/scripts/pseudobulk.py b/src/bettercode/rnaseq/snakemake_workflow/scripts/pseudobulk.py similarity index 96% rename from src/BetterCodeBetterScience/rnaseq/snakemake_workflow/scripts/pseudobulk.py rename to src/bettercode/rnaseq/snakemake_workflow/scripts/pseudobulk.py index d8a19bb..e4a112b 100644 --- a/src/BetterCodeBetterScience/rnaseq/snakemake_workflow/scripts/pseudobulk.py +++ b/src/bettercode/rnaseq/snakemake_workflow/scripts/pseudobulk.py @@ -8,10 +8,10 @@ import json from pathlib import Path -from BetterCodeBetterScience.rnaseq.modular_workflow.pseudobulk import ( +from bettercode.rnaseq.modular_workflow.pseudobulk import ( run_pseudobulk_pipeline, ) -from BetterCodeBetterScience.rnaseq.stateless_workflow.checkpoint import ( +from bettercode.rnaseq.stateless_workflow.checkpoint import ( load_checkpoint, save_checkpoint, ) diff --git a/src/BetterCodeBetterScience/rnaseq/snakemake_workflow/scripts/qc.py b/src/bettercode/rnaseq/snakemake_workflow/scripts/qc.py similarity index 90% rename from src/BetterCodeBetterScience/rnaseq/snakemake_workflow/scripts/qc.py rename to src/bettercode/rnaseq/snakemake_workflow/scripts/qc.py index abda90f..95d5fb0 100644 --- a/src/BetterCodeBetterScience/rnaseq/snakemake_workflow/scripts/qc.py +++ b/src/bettercode/rnaseq/snakemake_workflow/scripts/qc.py @@ -2,10 +2,10 @@ from pathlib import Path -from BetterCodeBetterScience.rnaseq.modular_workflow.quality_control import ( +from bettercode.rnaseq.modular_workflow.quality_control import ( run_qc_pipeline, ) -from BetterCodeBetterScience.rnaseq.stateless_workflow.checkpoint import ( +from bettercode.rnaseq.stateless_workflow.checkpoint import ( load_checkpoint, save_checkpoint, ) diff --git a/src/BetterCodeBetterScience/rnaseq/stateless_workflow/__init__.py b/src/bettercode/rnaseq/stateless_workflow/__init__.py similarity index 100% rename from src/BetterCodeBetterScience/rnaseq/stateless_workflow/__init__.py rename to src/bettercode/rnaseq/stateless_workflow/__init__.py diff --git a/src/BetterCodeBetterScience/rnaseq/stateless_workflow/checkpoint.py b/src/bettercode/rnaseq/stateless_workflow/checkpoint.py similarity index 99% rename from src/BetterCodeBetterScience/rnaseq/stateless_workflow/checkpoint.py rename to src/bettercode/rnaseq/stateless_workflow/checkpoint.py index 08459ba..45e6a7e 100644 --- a/src/BetterCodeBetterScience/rnaseq/stateless_workflow/checkpoint.py +++ b/src/bettercode/rnaseq/stateless_workflow/checkpoint.py @@ -17,7 +17,7 @@ import pandas as pd if TYPE_CHECKING: - from BetterCodeBetterScience.rnaseq.stateless_workflow.execution_log import ( + from bettercode.rnaseq.stateless_workflow.execution_log import ( ExecutionLog, ) diff --git a/src/BetterCodeBetterScience/rnaseq/stateless_workflow/execution_log.py b/src/bettercode/rnaseq/stateless_workflow/execution_log.py similarity index 100% rename from src/BetterCodeBetterScience/rnaseq/stateless_workflow/execution_log.py rename to src/bettercode/rnaseq/stateless_workflow/execution_log.py diff --git a/src/BetterCodeBetterScience/rnaseq/stateless_workflow/run_workflow.py b/src/bettercode/rnaseq/stateless_workflow/run_workflow.py similarity index 95% rename from src/BetterCodeBetterScience/rnaseq/stateless_workflow/run_workflow.py rename to src/bettercode/rnaseq/stateless_workflow/run_workflow.py index f43384a..bdafbc6 100644 --- a/src/BetterCodeBetterScience/rnaseq/stateless_workflow/run_workflow.py +++ b/src/bettercode/rnaseq/stateless_workflow/run_workflow.py @@ -9,41 +9,41 @@ from dotenv import load_dotenv -from BetterCodeBetterScience.rnaseq.modular_workflow.clustering import ( +from bettercode.rnaseq.modular_workflow.clustering import ( run_clustering_pipeline, ) -from BetterCodeBetterScience.rnaseq.modular_workflow.data_filtering import ( +from bettercode.rnaseq.modular_workflow.data_filtering import ( run_filtering_pipeline, ) -from BetterCodeBetterScience.rnaseq.modular_workflow.data_loading import ( +from bettercode.rnaseq.modular_workflow.data_loading import ( download_data, load_lazy_anndata, ) -from BetterCodeBetterScience.rnaseq.modular_workflow.differential_expression import ( +from bettercode.rnaseq.modular_workflow.differential_expression import ( run_differential_expression_pipeline, ) -from BetterCodeBetterScience.rnaseq.modular_workflow.dimensionality_reduction import ( +from bettercode.rnaseq.modular_workflow.dimensionality_reduction import ( run_dimensionality_reduction_pipeline, ) -from BetterCodeBetterScience.rnaseq.modular_workflow.overrepresentation_analysis import ( +from bettercode.rnaseq.modular_workflow.overrepresentation_analysis import ( run_overrepresentation_pipeline, ) -from BetterCodeBetterScience.rnaseq.modular_workflow.pathway_analysis import ( +from bettercode.rnaseq.modular_workflow.pathway_analysis import ( run_gsea_pipeline, ) -from BetterCodeBetterScience.rnaseq.modular_workflow.predictive_modeling import ( +from bettercode.rnaseq.modular_workflow.predictive_modeling import ( run_predictive_modeling_pipeline, ) -from BetterCodeBetterScience.rnaseq.modular_workflow.preprocessing import ( +from bettercode.rnaseq.modular_workflow.preprocessing import ( run_preprocessing_pipeline, ) -from BetterCodeBetterScience.rnaseq.modular_workflow.pseudobulk import ( +from bettercode.rnaseq.modular_workflow.pseudobulk import ( run_pseudobulk_pipeline, ) -from BetterCodeBetterScience.rnaseq.modular_workflow.quality_control import ( +from bettercode.rnaseq.modular_workflow.quality_control import ( run_qc_pipeline, ) -from BetterCodeBetterScience.rnaseq.stateless_workflow.checkpoint import ( +from bettercode.rnaseq.stateless_workflow.checkpoint import ( bids_checkpoint_name, clear_checkpoints_from_step, load_checkpoint, @@ -51,7 +51,7 @@ run_with_checkpoint, run_with_checkpoint_multi, ) -from BetterCodeBetterScience.rnaseq.stateless_workflow.execution_log import ( +from bettercode.rnaseq.stateless_workflow.execution_log import ( ExecutionLog, create_execution_log, serialize_parameters, @@ -664,7 +664,7 @@ def load_execution_log(log_file: Path) -> ExecutionLog: """ import json - from BetterCodeBetterScience.rnaseq.stateless_workflow.execution_log import ( + from bettercode.rnaseq.stateless_workflow.execution_log import ( StepRecord, ) diff --git a/src/BetterCodeBetterScience/simpleScaler.py b/src/bettercode/simpleScaler.py similarity index 100% rename from src/BetterCodeBetterScience/simpleScaler.py rename to src/bettercode/simpleScaler.py diff --git a/src/BetterCodeBetterScience/simple_testing.py b/src/bettercode/simple_testing.py similarity index 100% rename from src/BetterCodeBetterScience/simple_testing.py rename to src/bettercode/simple_testing.py diff --git a/src/BetterCodeBetterScience/simple_workflow/__init__.py b/src/bettercode/simple_workflow/__init__.py similarity index 100% rename from src/BetterCodeBetterScience/simple_workflow/__init__.py rename to src/bettercode/simple_workflow/__init__.py diff --git a/src/BetterCodeBetterScience/simple_workflow/correlation.py b/src/bettercode/simple_workflow/correlation.py similarity index 100% rename from src/BetterCodeBetterScience/simple_workflow/correlation.py rename to src/bettercode/simple_workflow/correlation.py diff --git a/src/BetterCodeBetterScience/simple_workflow/filter_data.py b/src/bettercode/simple_workflow/filter_data.py similarity index 100% rename from src/BetterCodeBetterScience/simple_workflow/filter_data.py rename to src/bettercode/simple_workflow/filter_data.py diff --git a/src/BetterCodeBetterScience/simple_workflow/join_data.py b/src/bettercode/simple_workflow/join_data.py similarity index 100% rename from src/BetterCodeBetterScience/simple_workflow/join_data.py rename to src/bettercode/simple_workflow/join_data.py diff --git a/src/BetterCodeBetterScience/simple_workflow/load_data.py b/src/bettercode/simple_workflow/load_data.py similarity index 100% rename from src/BetterCodeBetterScience/simple_workflow/load_data.py rename to src/bettercode/simple_workflow/load_data.py diff --git a/src/BetterCodeBetterScience/simple_workflow/make_workflow/Makefile b/src/bettercode/simple_workflow/make_workflow/Makefile similarity index 100% rename from src/BetterCodeBetterScience/simple_workflow/make_workflow/Makefile rename to src/bettercode/simple_workflow/make_workflow/Makefile diff --git a/src/BetterCodeBetterScience/simple_workflow/make_workflow/__init__.py b/src/bettercode/simple_workflow/make_workflow/__init__.py similarity index 100% rename from src/BetterCodeBetterScience/simple_workflow/make_workflow/__init__.py rename to src/bettercode/simple_workflow/make_workflow/__init__.py diff --git a/src/BetterCodeBetterScience/simple_workflow/make_workflow/scripts/compute_correlation.py b/src/bettercode/simple_workflow/make_workflow/scripts/compute_correlation.py similarity index 92% rename from src/BetterCodeBetterScience/simple_workflow/make_workflow/scripts/compute_correlation.py rename to src/bettercode/simple_workflow/make_workflow/scripts/compute_correlation.py index e09a55b..29988df 100644 --- a/src/BetterCodeBetterScience/simple_workflow/make_workflow/scripts/compute_correlation.py +++ b/src/bettercode/simple_workflow/make_workflow/scripts/compute_correlation.py @@ -10,7 +10,7 @@ import pandas as pd -from BetterCodeBetterScience.simple_workflow.correlation import ( +from bettercode.simple_workflow.correlation import ( compute_spearman_correlation, ) diff --git a/src/BetterCodeBetterScience/simple_workflow/make_workflow/scripts/download_data.py b/src/bettercode/simple_workflow/make_workflow/scripts/download_data.py similarity index 100% rename from src/BetterCodeBetterScience/simple_workflow/make_workflow/scripts/download_data.py rename to src/bettercode/simple_workflow/make_workflow/scripts/download_data.py diff --git a/src/BetterCodeBetterScience/simple_workflow/make_workflow/scripts/filter_data.py b/src/bettercode/simple_workflow/make_workflow/scripts/filter_data.py similarity index 94% rename from src/BetterCodeBetterScience/simple_workflow/make_workflow/scripts/filter_data.py rename to src/bettercode/simple_workflow/make_workflow/scripts/filter_data.py index 73c13bc..a7083f5 100644 --- a/src/BetterCodeBetterScience/simple_workflow/make_workflow/scripts/filter_data.py +++ b/src/bettercode/simple_workflow/make_workflow/scripts/filter_data.py @@ -10,7 +10,7 @@ import pandas as pd -from BetterCodeBetterScience.simple_workflow.filter_data import ( +from bettercode.simple_workflow.filter_data import ( filter_numerical_columns, ) diff --git a/src/BetterCodeBetterScience/simple_workflow/make_workflow/scripts/generate_heatmap.py b/src/bettercode/simple_workflow/make_workflow/scripts/generate_heatmap.py similarity index 93% rename from src/BetterCodeBetterScience/simple_workflow/make_workflow/scripts/generate_heatmap.py rename to src/bettercode/simple_workflow/make_workflow/scripts/generate_heatmap.py index 11b0415..b9ed155 100644 --- a/src/BetterCodeBetterScience/simple_workflow/make_workflow/scripts/generate_heatmap.py +++ b/src/bettercode/simple_workflow/make_workflow/scripts/generate_heatmap.py @@ -10,7 +10,7 @@ import pandas as pd -from BetterCodeBetterScience.simple_workflow.visualization import ( +from bettercode.simple_workflow.visualization import ( generate_clustered_heatmap, ) diff --git a/src/BetterCodeBetterScience/simple_workflow/make_workflow/scripts/join_data.py b/src/bettercode/simple_workflow/make_workflow/scripts/join_data.py similarity index 91% rename from src/BetterCodeBetterScience/simple_workflow/make_workflow/scripts/join_data.py rename to src/bettercode/simple_workflow/make_workflow/scripts/join_data.py index b8f09f4..7133b57 100644 --- a/src/BetterCodeBetterScience/simple_workflow/make_workflow/scripts/join_data.py +++ b/src/bettercode/simple_workflow/make_workflow/scripts/join_data.py @@ -10,7 +10,7 @@ import pandas as pd -from BetterCodeBetterScience.simple_workflow.join_data import join_dataframes +from bettercode.simple_workflow.join_data import join_dataframes def main(): diff --git a/src/BetterCodeBetterScience/simple_workflow/prefect_workflow/__init__.py b/src/bettercode/simple_workflow/prefect_workflow/__init__.py similarity index 100% rename from src/BetterCodeBetterScience/simple_workflow/prefect_workflow/__init__.py rename to src/bettercode/simple_workflow/prefect_workflow/__init__.py diff --git a/src/BetterCodeBetterScience/simple_workflow/prefect_workflow/flows.py b/src/bettercode/simple_workflow/prefect_workflow/flows.py similarity index 97% rename from src/BetterCodeBetterScience/simple_workflow/prefect_workflow/flows.py rename to src/bettercode/simple_workflow/prefect_workflow/flows.py index 498e670..7c5bbf2 100644 --- a/src/BetterCodeBetterScience/simple_workflow/prefect_workflow/flows.py +++ b/src/bettercode/simple_workflow/prefect_workflow/flows.py @@ -12,7 +12,7 @@ from prefect import flow, get_run_logger -from BetterCodeBetterScience.simple_workflow.prefect_workflow.tasks import ( +from bettercode.simple_workflow.prefect_workflow.tasks import ( compute_correlation_task, filter_numerical_task, generate_heatmap_task, diff --git a/src/BetterCodeBetterScience/simple_workflow/prefect_workflow/run_workflow.py b/src/bettercode/simple_workflow/prefect_workflow/run_workflow.py similarity index 91% rename from src/BetterCodeBetterScience/simple_workflow/prefect_workflow/run_workflow.py rename to src/bettercode/simple_workflow/prefect_workflow/run_workflow.py index e85c670..57e4a2f 100644 --- a/src/BetterCodeBetterScience/simple_workflow/prefect_workflow/run_workflow.py +++ b/src/bettercode/simple_workflow/prefect_workflow/run_workflow.py @@ -9,7 +9,7 @@ import argparse from pathlib import Path -from BetterCodeBetterScience.simple_workflow.prefect_workflow.flows import run_workflow +from bettercode.simple_workflow.prefect_workflow.flows import run_workflow def main(): diff --git a/src/BetterCodeBetterScience/simple_workflow/prefect_workflow/tasks.py b/src/bettercode/simple_workflow/prefect_workflow/tasks.py similarity index 89% rename from src/BetterCodeBetterScience/simple_workflow/prefect_workflow/tasks.py rename to src/bettercode/simple_workflow/prefect_workflow/tasks.py index 4d5193e..b8c986f 100644 --- a/src/BetterCodeBetterScience/simple_workflow/prefect_workflow/tasks.py +++ b/src/bettercode/simple_workflow/prefect_workflow/tasks.py @@ -8,18 +8,18 @@ import pandas as pd from prefect import task -from BetterCodeBetterScience.simple_workflow.correlation import ( +from bettercode.simple_workflow.correlation import ( compute_spearman_correlation, ) -from BetterCodeBetterScience.simple_workflow.filter_data import ( +from bettercode.simple_workflow.filter_data import ( filter_numerical_columns, ) -from BetterCodeBetterScience.simple_workflow.join_data import join_dataframes -from BetterCodeBetterScience.simple_workflow.load_data import ( +from bettercode.simple_workflow.join_data import join_dataframes +from bettercode.simple_workflow.load_data import ( load_demographics, load_meaningful_variables, ) -from BetterCodeBetterScience.simple_workflow.visualization import ( +from bettercode.simple_workflow.visualization import ( generate_clustered_heatmap, save_correlation_matrix, ) diff --git a/src/BetterCodeBetterScience/simple_workflow/snakemake_workflow/Makefile b/src/bettercode/simple_workflow/snakemake_workflow/Makefile similarity index 100% rename from src/BetterCodeBetterScience/simple_workflow/snakemake_workflow/Makefile rename to src/bettercode/simple_workflow/snakemake_workflow/Makefile diff --git a/src/BetterCodeBetterScience/simple_workflow/snakemake_workflow/Snakefile b/src/bettercode/simple_workflow/snakemake_workflow/Snakefile similarity index 100% rename from src/BetterCodeBetterScience/simple_workflow/snakemake_workflow/Snakefile rename to src/bettercode/simple_workflow/snakemake_workflow/Snakefile diff --git a/src/BetterCodeBetterScience/simple_workflow/snakemake_workflow/__init__.py b/src/bettercode/simple_workflow/snakemake_workflow/__init__.py similarity index 100% rename from src/BetterCodeBetterScience/simple_workflow/snakemake_workflow/__init__.py rename to src/bettercode/simple_workflow/snakemake_workflow/__init__.py diff --git a/src/BetterCodeBetterScience/simple_workflow/snakemake_workflow/config/config.yaml b/src/bettercode/simple_workflow/snakemake_workflow/config/config.yaml similarity index 100% rename from src/BetterCodeBetterScience/simple_workflow/snakemake_workflow/config/config.yaml rename to src/bettercode/simple_workflow/snakemake_workflow/config/config.yaml diff --git a/src/bettercode/simple_workflow/snakemake_workflow/envs/bettercode.yml b/src/bettercode/simple_workflow/snakemake_workflow/envs/bettercode.yml new file mode 100644 index 0000000..9b498ea --- /dev/null +++ b/src/bettercode/simple_workflow/snakemake_workflow/envs/bettercode.yml @@ -0,0 +1,552 @@ +name: bettercode +channels: + - bioconda + - conda-forge +dependencies: + - _openmp_mutex=4.5=7_kmp_llvm + - amply=0.1.6=pyhd8ed1ab_1 + - annotated-types=0.7.0=pyhd8ed1ab_1 + - appdirs=1.4.4=pyhd8ed1ab_1 + - argparse-dataclass=2.0.0=pyhd8ed1ab_1 + - attrs=25.4.0=pyhcf101f3_1 + - backports.zstd=1.2.0=py313hf42fe89_0 + - brotli-python=1.2.0=py313hde1f3bb_1 + - bzip2=1.0.8=hd037594_8 + - ca-certificates=2025.11.12=hbd8a1cb_0 + - certifi=2025.11.12=pyhd8ed1ab_0 + - charset-normalizer=3.4.4=pyhd8ed1ab_0 + - click=8.3.1=pyh8f84b5b_1 + - coin-or-cbc=2.10.12=h0c75da4_4 + - coin-or-cgl=0.60.9=h24d7dbf_6 + - coin-or-clp=1.17.10=ha5fe85a_3 + - coin-or-osi=0.108.11=ha2b0f8f_8 + - coin-or-utils=2.11.12=hbea9910_7 + - colorama=0.4.6=pyhd8ed1ab_1 + - coloredlogs=15.0.1=pyhd8ed1ab_4 + - conda-inject=1.3.2=pyhd8ed1ab_0 + - configargparse=1.7.1=pyhe01879c_0 + - connection_pool=0.0.3=pyhd3deb0d_0 + - docutils=0.22.4=pyhd8ed1ab_0 + - dpath=2.2.0=pyha770c72_1 + - eido=0.2.4=pyhd8ed1ab_0 + - exceptiongroup=1.3.1=pyhd8ed1ab_0 + - gitdb=4.0.12=pyhd8ed1ab_0 + - gitpython=3.1.45=pyhff2d567_0 + - h2=4.3.0=pyhcf101f3_0 + - hpack=4.1.0=pyhd8ed1ab_0 + - humanfriendly=10.0=pyh707e725_8 + - hyperframe=6.1.0=pyhd8ed1ab_0 + - idna=3.11=pyhd8ed1ab_0 + - immutables=0.21=py313hcdf3177_2 + - iniconfig=2.3.0=pyhd8ed1ab_0 + - jinja2=3.1.6=pyhcf101f3_1 + - jsonschema=4.25.1=pyhe01879c_0 + - jsonschema-specifications=2025.9.1=pyhcf101f3_0 + - jupyter_core=5.9.1=pyhc90fa1f_0 + - libblas=3.11.0=5_h51639a9_openblas + - libcblas=3.11.0=5_hb0561ab_openblas + - libcxx=21.1.8=hf598326_0 + - libexpat=2.7.3=haf25636_0 + - libffi=3.5.2=he5f378a_0 + - libgcc=15.2.0=hcbb3090_16 + - libgfortran=15.2.0=h07b0088_16 + - libgfortran5=15.2.0=hdae7583_16 + - liblapack=3.11.0=5_hd9741b5_openblas + - liblapacke=3.11.0=5_h1b118fd_openblas + - liblzma=5.8.1=h39f12f2_2 + - libmpdec=4.0.0=h5505292_0 + - libopenblas=0.3.30=openmp_ha158390_3 + - libsqlite=3.51.1=h1b79a29_1 + - libzlib=1.3.1=h8359307_2 + - llvm-openmp=21.1.8=h4a912ad_0 + - logmuse=0.2.8=pyhd8ed1ab_1 + - markdown-it-py=4.0.0=pyhd8ed1ab_0 + - markupsafe=3.0.3=py313h7d74516_0 + - mdurl=0.1.2=pyhd8ed1ab_1 + - nbformat=5.10.4=pyhd8ed1ab_1 + - ncurses=6.5=h5e97a16_3 + - numpy=2.4.0=py313h16eae64_0 + - openssl=3.6.0=h5503f6c_0 + - packaging=25.0=pyh29332c3_1 + - pandas=2.3.3=py313h7d16b84_2 + - pephubclient=0.4.4=pyhd8ed1ab_1 + - peppy=0.40.8=pyhd8ed1ab_0 + - pip=25.3=pyh145f28c_0 + - platformdirs=4.5.1=pyhcf101f3_0 + - pluggy=1.6.0=pyhf9edf01_1 + - psutil=7.2.0=py313h6688731_0 + - pulp=2.8.0=py313h02cf4f5_3 + - pydantic=2.12.5=pyhcf101f3_1 + - pydantic-core=2.41.5=py313h2c089d5_1 + - pygments=2.19.2=pyhd8ed1ab_0 + - pyparsing=3.3.1=pyhcf101f3_0 + - pysocks=1.7.1=pyha55dd90_7 + - pytest=9.0.2=pyhcf101f3_0 + - python=3.13.11=hfc2f54d_100_cp313 + - python-dateutil=2.9.0.post0=pyhe01879c_2 + - python-fastjsonschema=2.21.2=pyhe01879c_0 + - python-tzdata=2025.3=pyhd8ed1ab_0 + - python_abi=3.13=8_cp313 + - pytz=2025.2=pyhd8ed1ab_0 + - pyyaml=6.0.3=py313h7d74516_0 + - readline=8.3=h46df422_0 + - referencing=0.37.0=pyhcf101f3_0 + - requests=2.32.5=pyhd8ed1ab_0 + - reretry=0.11.8=pyhd8ed1ab_1 + - rich=14.2.0=pyhcf101f3_0 + - rpds-py=0.30.0=py313h2c089d5_0 + - shellingham=1.5.4=pyhd8ed1ab_2 + - six=1.17.0=pyhe01879c_1 + - slack-sdk=3.39.0=pyhd8ed1ab_0 + - slack_sdk=3.39.0=hd8ed1ab_0 + - smart_open=7.5.0=pyhcf101f3_0 + - smmap=5.0.2=pyhd8ed1ab_0 + - snakemake=9.14.5=hdfd78af_0 + - snakemake-interface-common=1.22.0=pyhd4c3c12_0 + - snakemake-interface-executor-plugins=9.3.9=pyhdfd78af_0 + - snakemake-interface-logger-plugins=2.0.0=pyhd4c3c12_0 + - snakemake-interface-report-plugins=1.3.0=pyhd4c3c12_0 + - snakemake-interface-scheduler-plugins=2.0.2=pyhd4c3c12_0 + - snakemake-interface-storage-plugins=4.3.2=pyhd4c3c12_0 + - snakemake-minimal=9.14.5=pyhdfd78af_0 + - tabulate=0.9.0=pyhcf101f3_3 + - throttler=1.2.2=pyhd8ed1ab_0 + - tk=8.6.13=h892fb3f_3 + - tomli=2.3.0=pyhcf101f3_0 + - traitlets=5.14.3=pyhd8ed1ab_1 + - typer=0.21.0=pyhbb89825_0 + - typer-slim=0.21.0=pyhcf101f3_0 + - typer-slim-standard=0.21.0=hd63da06_0 + - typing-extensions=4.15.0=h396c80c_0 + - typing-inspection=0.4.2=pyhd8ed1ab_1 + - typing_extensions=4.15.0=pyhcf101f3_0 + - tzdata=2025c=h8577fbf_0 + - ubiquerg=0.8.0=pyhd8ed1ab_0 + - urllib3=2.6.2=pyhd8ed1ab_0 + - veracitools=0.1.3=py_0 + - wrapt=1.17.3=py313hcdf3177_1 + - yaml=0.2.5=h925e9cb_3 + - yte=1.9.4=pyhd8ed1ab_0 + - zstd=1.5.7=hbf9d68e_6 + - pip: + - bettercode==0.1.0 + - CFGraph==0.2.1 + - Cython==3.2.3 + - Deprecated==1.3.1 + - ImageIO==2.37.2 + - Mako==1.3.10 + - Markdown==3.10 + - PyGithub==2.8.1 + - PyJSG==0.11.10 + - PyJWT==2.10.1 + - PyNaCl==1.6.1 + - PyPika==0.48.9 + - PyShEx==0.8.1 + - PyShExC==0.9.1 + - SPARQLWrapper==2.0.0 + - SQLAlchemy==2.0.45 + - SQLAlchemy-Utils==0.38.3 + - Send2Trash==1.8.3 + - ShExJSG==0.8.2 + - Sphinx==5.3.0 + - accelerate==1.12.0 + - acres==0.5.0 + - aiohappyeyeballs==2.6.1 + - aiohttp==3.13.2 + - aiosignal==1.4.0 + - aiosqlite==0.22.1 + - airium==0.2.7 + - alabaster==0.7.16 + - alembic==1.17.2 + - anndata==0.12.7 + - annexremote==1.6.6 + - annotated-doc==0.0.4 + - annoy==1.17.3 + - anthropic==0.75.0 + - antlr4-python3-runtime==4.9.3 + - anyio==4.12.0 + - appnope==0.1.4 + - apprise==1.9.6 + - argon2-cffi==25.1.0 + - argon2-cffi-bindings==25.1.0 + - array-api-compat==1.12.0 + - arrow==1.4.0 + - asgi-lifespan==2.1.0 + - asttokens==3.0.1 + - async-lru==2.0.5 + - asyncpg==0.31.0 + - babel==2.17.0 + - backoff==2.2.1 + - bcp47==0.1.0 + - bcrypt==5.0.0 + - beautifulsoup4==4.14.3 + - bids-validator==1.14.7.post0 + - bidsschematools==1.1.4 + - biopython==1.86 + - bioregistry==0.10.204 + - biothings_client==0.4.1 + - black==22.1.0 + - bleach==6.3.0 + - blis==1.3.3 + - blue==0.9.1 + - boto3==1.42.16 + - botocore==1.42.16 + - build==1.3.0 + - cachetools==5.5.2 + - catalogue==2.0.10 + - cattrs==25.3.0 + - cffi==2.0.0 + - cfgv==3.5.0 + - chardet==5.2.0 + - chromadb==1.4.0 + - class-resolver==0.7.1 + - click==8.1.8 + - cloudpathlib==0.23.0 + - cloudpickle==3.1.2 + - codespell==2.4.1 + - comm==0.2.3 + - confection==0.1.5 + - contourpy==1.3.3 + - coolname==2.2.0 + - coverage==7.13.0 + - cramjam==2.11.0 + - cryptography==46.0.3 + - curies==0.12.7 + - cycler==0.12.1 + - cymem==2.0.13 + - dask==2025.12.0 + - datalad==1.2.3 + - datalad-next==1.5.0 + - datalad-osf==0.3.0 + - dateparser==1.2.2 + - debugpy==1.8.19 + - decorator==5.2.1 + - defusedxml==0.7.1 + - deprecation==2.1.0 + - distlib==0.4.0 + - distro==1.9.0 + - dnspython==2.8.0 + - docker==7.1.0 + - docopt==0.6.2 + - docstring_parser==0.17.0 + - docutils==0.17.1 + - donfig==0.8.1.post1 + - durationpy==0.10 + - et_xmlfile==2.0.0 + - eutils==0.6.1 + - executing==2.2.1 + - fastapi==0.127.1 + - fastcluster==1.3.0 + - fastembed==0.7.4 + - fasteners==0.20 + - fastobo==0.14.1 + - fastparquet==2025.12.0 + - filelock==3.20.1 + - flake8==4.0.1 + - flatbuffers==25.12.19 + - fmriprep-docker==25.2.3 + - fonttools==4.61.1 + - formulaic==1.2.1 + - formulaic-contrasts==1.0.0 + - fqdn==1.5.1 + - frozendict==2.4.7 + - frozenlist==1.8.0 + - fsspec==2025.12.0 + - funowl==0.2.3 + - google-auth==2.45.0 + - google-crc32c==1.8.0 + - googleapis-common-protos==1.72.0 + - gprofiler-official==1.0.0 + - graphviz==0.21 + - greenlet==3.3.0 + - griffe==1.15.0 + - grpcio==1.76.0 + - gseapy==1.1.11 + - gunicorn==23.0.0 + - h11==0.16.0 + - h5py==3.15.1 + - harmony-pytorch==0.1.8 + - harmonypy==0.0.10 + - hbreader==0.9.1 + - hf-xet==1.2.0 + - httpcore==1.0.9 + - httptools==0.7.1 + - httpx==0.28.1 + - huggingface-hub==0.36.0 + - humanize==4.15.0 + - hypothesis==6.148.8 + - icecream==2.1.8 + - identify==2.6.15 + - igraph==1.0.0 + - ijson==3.4.0.post0 + - imagesize==1.4.1 + - importlib_metadata==8.7.1 + - importlib_resources==6.5.2 + - interface-meta==1.3.0 + - ipykernel==7.1.0 + - ipython==9.8.0 + - ipython_pygments_lexers==1.1.1 + - ipywidgets==8.1.8 + - iso8601==2.1.0 + - isodate==0.7.2 + - isoduration==20.11.0 + - jaraco.classes==3.4.0 + - jaraco.context==6.0.2 + - jaraco.functools==4.4.0 + - jedi==0.19.2 + - jinja2-humanize-extension==0.4.0 + - jinjanator==25.3.1 + - jinjanator-plugins==25.1.0 + - jiter==0.12.0 + - jmespath==1.0.1 + - joblib==1.5.3 + - json-flattener==0.1.9 + - json5==0.12.1 + - jsonasobj==1.3.1 + - jsonasobj2==1.0.4 + - jsonlines==4.0.0 + - jsonpatch==1.33 + - jsonpointer==3.0.0 + - jupyter==1.1.1 + - jupyter-book==2.1.0 + - jupyter-console==6.6.3 + - jupyter-events==0.12.0 + - jupyter-lsp==2.3.0 + - jupyter_client==8.7.0 + - jupyter_server==2.17.0 + - jupyter_server_terminals==0.5.3 + - jupyterlab==4.5.1 + - jupyterlab_pygments==0.3.0 + - jupyterlab_server==2.28.0 + - jupyterlab_widgets==3.0.16 + - jupytext==1.18.1 + - keyring==25.7.0 + - keyrings.alt==5.0.2 + - kgcl-rdflib==0.5.0 + - kgcl_schema==0.6.9 + - kiwisolver==1.4.9 + - kubernetes==34.1.0 + - lark==1.3.1 + - lazy_loader==0.4 + - legacy-api-wrap==1.5 + - leidenalg==0.11.0 + - linkcheckmd==1.4.0 + - linkml==1.9.3 + - linkml-renderer==0.3.1 + - linkml-runtime==1.9.5 + - llvmlite==0.45.1 + - locket==1.0.0 + - loguru==0.7.3 + - looseversion==1.3.0 + - lxml==6.0.2 + - matplotlib==3.10.8 + - matplotlib-inline==0.2.1 + - mccabe==0.6.1 + - mdit-py-plugins==0.5.0 + - mdnewline==0.1.3 + - mistune==3.2.0 + - mmh3==5.2.0 + - mne==1.11.0 + - monarch-py==1.23.1 + - mongomock==4.3.0 + - more-click==0.1.3 + - more-itertools==10.8.0 + - mpmath==1.3.0 + - msgpack==1.1.2 + - multidict==6.7.0 + - murmurhash==1.0.15 + - mypy_extensions==1.1.0 + - mysql-connector-python==9.5.0 + - mystmd==1.7.1 + - narwhals==2.14.0 + - natsort==8.4.0 + - nbclient==0.10.4 + - nbconvert==7.16.6 + - ndex2==3.11.0 + - neo4j==6.0.3 + - nest-asyncio==1.6.0 + - networkx==3.6.1 + - nibabel==5.3.3 + - nilearn==0.12.1 + - nodeenv==1.9.1 + - notebook==7.5.1 + - notebook_shim==0.2.4 + - num2words==0.5.14 + - numba==0.62.1 + - numcodecs==0.16.5 + - numpy==2.3.5 + - oaklib==0.6.23 + - oauthlib==3.3.1 + - ols-client==0.2.1 + - onnxruntime==1.23.2 + - ontoportal_client==0.0.8 + - openai==2.14.0 + - openpyxl==3.1.5 + - opentelemetry-api==1.39.1 + - opentelemetry-exporter-otlp-proto-common==1.39.1 + - opentelemetry-exporter-otlp-proto-grpc==1.39.1 + - opentelemetry-proto==1.39.1 + - opentelemetry-sdk==1.39.1 + - opentelemetry-semantic-conventions==0.60b1 + - orjson==3.11.5 + - osfclient==0.0.5 + - overrides==7.7.0 + - packaging==24.2 + - pandocfilters==1.5.1 + - parse==1.20.2 + - parso==0.8.5 + - partd==1.4.2 + - pathlib_abc==0.5.2 + - pathspec==0.12.1 + - patool==4.0.3 + - patsy==1.0.2 + - pendulum==3.1.0 + - pexpect==4.9.0 + - pickleshare==0.7.5 + - pillow==11.3.0 + - platformdirs==4.2.2 + - pooch==1.8.2 + - posthog==5.4.0 + - pre_commit==4.5.1 + - prefect==3.2.7 + - prefixcommons==0.1.12 + - prefixmaps==0.2.4 + - preshed==3.0.12 + - prometheus_client==0.23.1 + - prompt_toolkit==3.0.52 + - pronto==2.7.2 + - propcache==0.4.1 + - protobuf==6.33.2 + - ptyprocess==0.7.0 + - pure_eval==0.2.3 + - py_rust_stemmers==0.1.5 + - pyarrow==22.0.0 + - pyasn1==0.6.1 + - pyasn1_modules==0.4.2 + - pybase64==1.4.3 + - pybids==0.21.0 + - pycodestyle==2.8.0 + - pycparser==2.23 + - pydantic-extra-types==2.10.6 + - pydantic-settings==2.12.0 + - pydeseq2==0.5.3 + - pyee==11.1.1 + - pyflakes==2.4.0 + - pymongo==4.15.5 + - pynndescent==0.5.13 + - pyppeteer==2.0.0 + - pyproject_hooks==1.2.0 + - pysolr==3.11.0 + - pystow==0.7.13 + - pytest-cov==7.0.0 + - pytest-logging==2015.11.4 + - pytest-mock==3.15.1 + - python-dotenv==1.2.1 + - python-gitlab==7.0.0 + - python-json-logger==4.0.0 + - python-slugify==8.0.4 + - python-socks==2.8.0 + - pyzmq==27.1.0 + - ratelimit==2.2.1 + - rdflib==7.5.0 + - rdflib-jsonld==0.6.1 + - rdflib-shim==1.0.3 + - readchar==4.2.1 + - regex==2025.11.3 + - requests-cache==1.2.1 + - requests-oauthlib==2.0.0 + - requests-toolbelt==1.0.0 + - rfc3339-validator==0.1.4 + - rfc3986-validator==0.1.1 + - rfc3987==1.3.8 + - rfc3987-syntax==1.1.0 + - rich==13.9.4 + - rpy2==3.6.4 + - rpy2-rinterface==3.6.3 + - rpy2-robjects==3.6.3 + - rsa==4.9.1 + - ruamel.yaml==0.18.17 + - ruamel.yaml.clib==0.2.15 + - ruff==0.14.10 + - s3transfer==0.16.0 + - safetensors==0.7.0 + - scanpy==1.11.5 + - scikit-image==0.26.0 + - scikit-learn==1.8.0 + - scikit-misc==0.5.2 + - scipy==1.16.3 + - scrublet==0.2.3 + - seaborn==0.13.2 + - semsql==0.4.0 + - sentinels==1.1.1 + - session-info2==0.3 + - session_info==1.0.1 + - setuptools==80.9.0 + - snakemake==9.14.5 + - sniffio==1.3.1 + - snowballstemmer==3.0.1 + - sortedcontainers==2.4.0 + - soupsieve==2.8.1 + - spacy==3.8.11 + - spacy-legacy==3.0.12 + - spacy-loggers==1.0.5 + - sparqlslurper==0.5.1 + - sphinx-click==6.2.0 + - sphinxcontrib-applehelp==2.0.0 + - sphinxcontrib-devhelp==2.0.0 + - sphinxcontrib-htmlhelp==2.1.0 + - sphinxcontrib-jsmath==1.0.1 + - sphinxcontrib-qthelp==2.0.0 + - sphinxcontrib-serializinghtml==2.0.0 + - srsly==2.5.2 + - sssom==0.4.18 + - sssom-schema==1.1.0a3 + - stack-data==0.6.3 + - starlette==0.50.0 + - statsmodels==0.14.6 + - stdlib-list==0.12.0 + - sympy==1.14.0 + - templateflow==25.1.1 + - tenacity==9.1.2 + - terminado==0.18.1 + - text-unidecode==1.3 + - texttable==1.7.0 + - thinc==8.3.10 + - threadpoolctl==3.6.0 + - tifffile==2025.12.20 + - tinycss2==1.4.0 + - tokenizers==0.22.1 + - toml==0.10.2 + - toolz==1.1.0 + - torch==2.9.1 + - tornado==6.5.4 + - tqdm==4.67.1 + - transformers==4.57.3 + - typer==0.15.4 + - tzlocal==5.3.1 + - ujson==5.11.0 + - umap-learn==0.5.9.post2 + - universal_pathlib==0.3.7 + - uri-template==1.3.0 + - url-normalize==2.2.1 + - urllib3==1.26.20 + - uvicorn==0.40.0 + - uvloop==0.22.1 + - validators==0.35.0 + - virtualenv==20.35.4 + - wasabi==1.1.3 + - watchdog==6.0.0 + - watchfiles==1.1.1 + - wcwidth==0.2.14 + - weasel==0.4.3 + - webcolors==25.10.0 + - webencodings==0.5.1 + - websocket-client==1.9.0 + - websockets==10.4 + - widgetsnbextension==4.0.15 + - xarray==2025.12.0 + - yarl==1.22.0 + - zarr==3.1.5 + - zipp==3.23.0 + +prefix: "/Users/poldrack/micromamba/envs/bettercode" diff --git a/src/BetterCodeBetterScience/simple_workflow/snakemake_workflow/report/heatmap.rst b/src/bettercode/simple_workflow/snakemake_workflow/report/heatmap.rst similarity index 100% rename from src/BetterCodeBetterScience/simple_workflow/snakemake_workflow/report/heatmap.rst rename to src/bettercode/simple_workflow/snakemake_workflow/report/heatmap.rst diff --git a/src/BetterCodeBetterScience/simple_workflow/snakemake_workflow/report/workflow.rst b/src/bettercode/simple_workflow/snakemake_workflow/report/workflow.rst similarity index 100% rename from src/BetterCodeBetterScience/simple_workflow/snakemake_workflow/report/workflow.rst rename to src/bettercode/simple_workflow/snakemake_workflow/report/workflow.rst diff --git a/src/BetterCodeBetterScience/simple_workflow/snakemake_workflow/scripts/compute_correlation.py b/src/bettercode/simple_workflow/snakemake_workflow/scripts/compute_correlation.py similarity index 92% rename from src/BetterCodeBetterScience/simple_workflow/snakemake_workflow/scripts/compute_correlation.py rename to src/bettercode/simple_workflow/snakemake_workflow/scripts/compute_correlation.py index 33a29a1..b5dcfb0 100644 --- a/src/BetterCodeBetterScience/simple_workflow/snakemake_workflow/scripts/compute_correlation.py +++ b/src/bettercode/simple_workflow/snakemake_workflow/scripts/compute_correlation.py @@ -4,7 +4,7 @@ import pandas as pd -from BetterCodeBetterScience.simple_workflow.correlation import ( +from bettercode.simple_workflow.correlation import ( compute_correlation_matrix, ) diff --git a/src/BetterCodeBetterScience/simple_workflow/snakemake_workflow/scripts/download_data.py b/src/bettercode/simple_workflow/snakemake_workflow/scripts/download_data.py similarity index 100% rename from src/BetterCodeBetterScience/simple_workflow/snakemake_workflow/scripts/download_data.py rename to src/bettercode/simple_workflow/snakemake_workflow/scripts/download_data.py diff --git a/src/BetterCodeBetterScience/simple_workflow/snakemake_workflow/scripts/filter_data.py b/src/bettercode/simple_workflow/snakemake_workflow/scripts/filter_data.py similarity index 92% rename from src/BetterCodeBetterScience/simple_workflow/snakemake_workflow/scripts/filter_data.py rename to src/bettercode/simple_workflow/snakemake_workflow/scripts/filter_data.py index 6e5af11..4687b3e 100644 --- a/src/BetterCodeBetterScience/simple_workflow/snakemake_workflow/scripts/filter_data.py +++ b/src/bettercode/simple_workflow/snakemake_workflow/scripts/filter_data.py @@ -4,7 +4,7 @@ import pandas as pd -from BetterCodeBetterScience.simple_workflow.filter_data import ( +from bettercode.simple_workflow.filter_data import ( filter_numerical_columns, ) diff --git a/src/BetterCodeBetterScience/simple_workflow/snakemake_workflow/scripts/generate_heatmap.py b/src/bettercode/simple_workflow/snakemake_workflow/scripts/generate_heatmap.py similarity index 93% rename from src/BetterCodeBetterScience/simple_workflow/snakemake_workflow/scripts/generate_heatmap.py rename to src/bettercode/simple_workflow/snakemake_workflow/scripts/generate_heatmap.py index cb59946..91d0cb1 100644 --- a/src/BetterCodeBetterScience/simple_workflow/snakemake_workflow/scripts/generate_heatmap.py +++ b/src/bettercode/simple_workflow/snakemake_workflow/scripts/generate_heatmap.py @@ -4,7 +4,7 @@ import pandas as pd -from BetterCodeBetterScience.simple_workflow.visualization import ( +from bettercode.simple_workflow.visualization import ( generate_clustered_heatmap, ) diff --git a/src/BetterCodeBetterScience/simple_workflow/snakemake_workflow/scripts/join_data.py b/src/bettercode/simple_workflow/snakemake_workflow/scripts/join_data.py similarity index 91% rename from src/BetterCodeBetterScience/simple_workflow/snakemake_workflow/scripts/join_data.py rename to src/bettercode/simple_workflow/snakemake_workflow/scripts/join_data.py index b484d92..ba596d8 100644 --- a/src/BetterCodeBetterScience/simple_workflow/snakemake_workflow/scripts/join_data.py +++ b/src/bettercode/simple_workflow/snakemake_workflow/scripts/join_data.py @@ -4,7 +4,7 @@ import pandas as pd -from BetterCodeBetterScience.simple_workflow.join_data import join_dataframes +from bettercode.simple_workflow.join_data import join_dataframes def main(): diff --git a/src/BetterCodeBetterScience/simple_workflow/visualization.py b/src/bettercode/simple_workflow/visualization.py similarity index 100% rename from src/BetterCodeBetterScience/simple_workflow/visualization.py rename to src/bettercode/simple_workflow/visualization.py diff --git a/src/BetterCodeBetterScience/test_independence.py b/src/bettercode/test_independence.py similarity index 100% rename from src/BetterCodeBetterScience/test_independence.py rename to src/bettercode/test_independence.py diff --git a/src/BetterCodeBetterScience/textmining/textmining.py b/src/bettercode/textmining/textmining.py similarity index 100% rename from src/BetterCodeBetterScience/textmining/textmining.py rename to src/bettercode/textmining/textmining.py diff --git a/tests/narps/test_bids.py b/tests/narps/test_bids.py index 1f10019..0b7d12a 100644 --- a/tests/narps/test_bids.py +++ b/tests/narps/test_bids.py @@ -5,7 +5,7 @@ import tempfile import shutil -from BetterCodeBetterScience.narps.bids_utils import ( +from bettercode.narps.bids_utils import ( parse_bids_filename, find_bids_files, modify_bids_filename, diff --git a/tests/property_based_testing/test_propertybased.py b/tests/property_based_testing/test_propertybased.py index 0b322ed..7f0812b 100644 --- a/tests/property_based_testing/test_propertybased.py +++ b/tests/property_based_testing/test_propertybased.py @@ -2,7 +2,7 @@ from hypothesis.extra import numpy as nps from scipy.stats import linregress import numpy as np -from BetterCodeBetterScience.my_linear_regression import ( +from bettercode.my_linear_regression import ( linear_regression, _validate_input, ) diff --git a/tests/property_based_testing/test_propertybased_smoke.py b/tests/property_based_testing/test_propertybased_smoke.py index b2ac128..977f618 100644 --- a/tests/property_based_testing/test_propertybased_smoke.py +++ b/tests/property_based_testing/test_propertybased_smoke.py @@ -2,7 +2,7 @@ from hypothesis.extra import numpy as nps from scipy.stats import linregress import numpy as np -from BetterCodeBetterScience.my_linear_regression import ( +from bettercode.my_linear_regression import ( linear_regression, ) diff --git a/tests/textmining/test_textmining.py b/tests/textmining/test_textmining.py index 6f4e99b..14f3aff 100644 --- a/tests/textmining/test_textmining.py +++ b/tests/textmining/test_textmining.py @@ -5,7 +5,7 @@ import pytest import requests import time -from BetterCodeBetterScience.textmining.textmining import ( +from bettercode.textmining.textmining import ( get_PubmedIDs_for_query, parse_year_from_Pubmed_record, get_record_from_PubmedID,