diff --git a/.gitignore b/.gitignore index 4e4a379..bf2ce7c 100644 --- a/.gitignore +++ b/.gitignore @@ -172,4 +172,7 @@ _* results/ # remove background jobs -*nohup.out +nohup.out + +# remove log +*.logs diff --git a/notebooks/0.download-data/1.download-data.ipynb b/notebooks/0.download-data/1.download-data.ipynb index 6a6d223..74c6dfa 100644 --- a/notebooks/0.download-data/1.download-data.ipynb +++ b/notebooks/0.download-data/1.download-data.ipynb @@ -326,6 +326,14 @@ " print(\"shape: \", cfret_df.shape)\n", " cfret_df.head()" ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "da89481a", + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { diff --git a/notebooks/0.download-data/2.preprocessing.ipynb b/notebooks/0.download-data/2.preprocessing.ipynb index 6b40b1d..d1d1d06 100644 --- a/notebooks/0.download-data/2.preprocessing.ipynb +++ b/notebooks/0.download-data/2.preprocessing.ipynb @@ -23,7 +23,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 2, "id": "0387feba", "metadata": {}, "outputs": [], @@ -51,7 +51,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 3, "id": "d0f8b798", "metadata": {}, "outputs": [], @@ -59,19 +59,23 @@ "def load_and_concat_profiles(\n", " profile_dir: str | pathlib.Path,\n", " shared_features: Optional[list[str]] = None,\n", + " shared_contains_meta: bool = False,\n", " specific_plates: Optional[list[pathlib.Path]] = None,\n", ") -> pl.DataFrame:\n", " \"\"\"\n", - " Load all profile files from a directory and concatenate them into a single Polars DataFrame.\n", + " Load all profile files from a directory and concatenate them into a single Polars\n", + " DataFrame.\n", "\n", " Parameters\n", " ----------\n", " profile_dir : str or pathlib.Path\n", " Directory containing the profile files (.parquet).\n", " shared_features : Optional[list[str]], optional\n", - " List of shared feature names to filter the profiles. If None, all features are loaded.\n", + " List of shared feature names to filter the profiles. If None, all features are\n", + " loaded.\n", " specific_plates : Optional[list[pathlib.Path]], optional\n", - " List of specific plate file paths to load. If None, all profiles in the directory are loaded.\n", + " List of specific plate file paths to load. If None, all profiles in the\n", + " directory are loaded.\n", "\n", " Returns\n", " -------\n", @@ -93,13 +97,24 @@ " \"All elements in specific_plates must be pathlib.Path objects\"\n", " )\n", "\n", - " def load_profile(file: pathlib.Path) -> pl.DataFrame:\n", + " def load_profile(profile_path: pathlib.Path) -> pl.DataFrame:\n", " \"\"\"internal function to load a single profile file.\"\"\"\n", - " profile_df = pl.read_parquet(file)\n", - " meta_cols, _ = split_meta_and_features(profile_df)\n", + "\n", + " # load profiles\n", + " profile_df = pl.read_parquet(profile_path)\n", + "\n", + " # print shape\n", + " print(f\"Loaded profile {profile_path.name} with shape {profile_df.shape}\")\n", + "\n", + " # if provided shared feature list does not contain meta, split and select\n", + " # then get it from the profile, if it does, just select the shared features\n", + " # directly\n", " if shared_features is not None:\n", - " # Only select metadata and shared features\n", - " return profile_df.select(meta_cols + shared_features)\n", + " if not shared_contains_meta:\n", + " meta_cols, _ = split_meta_and_features(profile_df)\n", + " return profile_df.select(meta_cols + shared_features)\n", + "\n", + " return profile_df.select(shared_features)\n", " return profile_df\n", "\n", " # Use specific_plates if provided, otherwise gather all .parquet files\n", @@ -179,7 +194,61 @@ " pl.DataFrame\n", " DataFrame with cleaned column names\n", " \"\"\"\n", - " return df.rename(lambda x: x.replace(prefix, \"\") if prefix in x else x)" + " return df.rename(lambda x: x.replace(prefix, \"\") if prefix in x else x)\n", + "\n", + "\n", + "def find_shared_features_across_parquets(\n", + " profile_paths: list[str | pathlib.Path],\n", + ") -> list[str]:\n", + " \"\"\"\n", + " Finds the intersection of column names across multiple parquet files.\n", + "\n", + " This function returns the list of column names that are present in every provided parquet file.\n", + " The order of columns is preserved from the first file. Uses LazyFrame.collect_schema().names()\n", + " to avoid expensive full reads and the PerformanceWarning.\n", + "\n", + " Parameters\n", + " ----------\n", + " profile_paths : list of str or pathlib.Path\n", + " List of paths to parquet files.\n", + "\n", + " Returns\n", + " -------\n", + " list of str\n", + " List of shared column names present in all files, in the order from the first file.\n", + "\n", + " Raises\n", + " ------\n", + " FileNotFoundError\n", + " If no parquet files are provided or any file does not exist.\n", + " \"\"\"\n", + " if not profile_paths:\n", + " raise FileNotFoundError(\"No parquet files provided\")\n", + "\n", + " # check if they are all strings if so, convert to pathlib.Path\n", + " if all(isinstance(p, str) for p in profile_paths):\n", + " profile_paths = [pathlib.Path(p) for p in profile_paths]\n", + "\n", + " for p in profile_paths:\n", + " if not p.exists():\n", + " raise FileNotFoundError(f\"Profile file not found: {p}\")\n", + "\n", + " # set the first file columns as the initial set\n", + " first_cols = pl.scan_parquet(profile_paths[0]).collect_schema().names()\n", + " common = set(first_cols)\n", + "\n", + " # iterate through the rest of the files and find shared columns\n", + " # of the rest of the profiles\n", + " for p in profile_paths[1:]:\n", + " cols = pl.scan_parquet(p).collect_schema().names()\n", + " common &= set(cols)\n", + " if not common:\n", + " # Early exit if no shared columns remain\n", + " return []\n", + "\n", + " # Preserve first file ordering (Meta and features order)\n", + " shared_features = [c for c in first_cols if c in common]\n", + " return shared_features" ] }, { @@ -216,6 +285,9 @@ " cfret_profiles_dir / \"localhost230405150001_sc_feature_selected.parquet\"\n", ").resolve(strict=True)\n", "\n", + "# cfret-screen profiles path\n", + "cfret_screen_profiles_path = profiles_dir / \"cfret-screen\"\n", + "\n", "# Setting feature selection path\n", "shared_features_config_path = (\n", " profiles_dir / \"cpjump1\" / \"feature_selected_sc_qc_features.json\"\n", @@ -227,6 +299,12 @@ " profiles_dir / \"mitocheck\" / \"normalized_data\"\n", ").resolve(strict=True)\n", "\n", + "# seting cfret-screen profiles paths\n", + "cfret_screen_profiles_paths = [\n", + " path.resolve(strict=True)\n", + " for path in cfret_screen_profiles_path.glob(\"*_sc_feature_selected.parquet\")\n", + "]\n", + "\n", "# output directories\n", "cpjump1_output_dir = (profiles_dir / \"cpjump1\").resolve()\n", "cpjump1_output_dir.mkdir(exist_ok=True)\n", @@ -313,10 +391,8 @@ "\n", "# Saving metadata and features of the concat profile into a json file\n", "meta_features_dict = {\n", - " \"concat-profiles\": {\n", - " \"meta-features\": meta_cols,\n", - " \"shared-features\": features_cols,\n", - " }\n", + " \"metadata-features\": meta_cols,\n", + " \"morphology-features\": features_cols,\n", "}\n", "with open(cpjump1_output_dir / \"concat_profiles_meta_features.json\", \"w\") as f:\n", " json.dump(meta_features_dict, f, indent=4)\n", @@ -565,6 +641,96 @@ "# overwrite dataset with cell\n", "cfret_profiles.select(meta_cols + features_cols).write_parquet(cfret_profiles_path)" ] + }, + { + "cell_type": "markdown", + "id": "ea8f7f65", + "metadata": {}, + "source": [ + "## Preprocessing CFReT Screen Dataset\n", + "\n", + "This section preprocesses the CFReT Screen dataset by concatenating all plate profiles into a single unified dataframe. This represents the first batch of plates, which are technical replicates containing identical treatment conditions and dosages across all plates.\n", + "\n", + "**Dataset characteristics:**\n", + "- Each plate contains both positive (n=3) and negative (n=3) controls\n", + "- All treatment plates share the same experimental conditions\n", + "- Technical replicates is at the plate level\n", + "\n", + "**Preprocessing steps:**\n", + "\n", + "1. **Feature alignment**: Identify shared features across all CFReT Screen plates to ensure consistent feature space\n", + "2. **Profile concatenation**: Merge all plate profiles into a single comprehensive dataframe using the shared feature set\n", + "3. **Unique cell identification**: Add `Metadata_cell_id` column with unique hash values to enable precise single-cell tracking " + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "83e0411f", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "total shared features in cfret-screen profiles: 494\n", + "Loaded profile localhost240927060001_sc_feature_selected.parquet with shape (12397, 652)\n", + "Loaded profile localhost240928120001_sc_feature_selected.parquet with shape (12745, 641)\n", + "Loaded profile localhost240926150001_sc_feature_selected.parquet with shape (16566, 657)\n", + "Loaded profile localhost240927120001_sc_feature_selected.parquet with shape (12902, 684)\n", + "'Metadata_cell_id' column already exists in the DataFrame. Set force=True to overwrite the existing column.\n" + ] + } + ], + "source": [ + "# find shared features across cfret-screen profiles and load and concat them\n", + "cfret_screen_shared_features = find_shared_features_across_parquets(\n", + " cfret_screen_profiles_paths\n", + ")\n", + "print(\n", + " \"total shared features in cfret-screen profiles:\", len(cfret_screen_shared_features)\n", + ")\n", + "\n", + "cfret_screen_concat_profiles = load_and_concat_profiles(\n", + " profile_dir=cfret_screen_profiles_path,\n", + " shared_features=cfret_screen_shared_features,\n", + " shared_contains_meta=True,\n", + ")\n", + "\n", + "# add unique cell ID as a string type\n", + "cfret_screen_concat_profiles = cfret_screen_concat_profiles.with_columns(\n", + " cfret_screen_concat_profiles.hash_rows(seed=0)\n", + " .alias(\"Metadata_cell_id\")\n", + " .cast(pl.Utf8)\n", + ")\n", + "\n", + "# split the metadata and features and reorganize features in the concat profile\n", + "cfret_screen_meta_cols, cfret_screen_features_cols = split_meta_and_features(\n", + " cfret_screen_concat_profiles\n", + ")\n", + "cfret_screen_concat_profiles = cfret_screen_concat_profiles.select(\n", + " cfret_screen_meta_cols + cfret_screen_features_cols\n", + ")\n", + "\n", + "# save feature space config to json file\n", + "with open(cfret_profiles_dir / \"cfret_screen_feature_space_configs.json\", \"w\") as f:\n", + " json.dump(\n", + " {\n", + " \"metadata-features\": cfret_screen_meta_cols,\n", + " \"morphology-features\": cfret_screen_features_cols,\n", + " },\n", + " f,\n", + " indent=4,\n", + " )\n", + "\n", + "# add cell id hash\n", + "cfret_screen_concat_profiles = add_cell_id_hash(cfret_screen_concat_profiles)\n", + "\n", + "# save concatenated cfret-screen profiles\n", + "cfret_screen_concat_profiles.write_parquet(\n", + " cfret_screen_profiles_path / \"cfret_screen_concat_profiles.parquet\"\n", + ")" + ] } ], "metadata": { diff --git a/notebooks/0.download-data/nbconverted/2.preprocessing.py b/notebooks/0.download-data/nbconverted/2.preprocessing.py index b643cef..e454162 100644 --- a/notebooks/0.download-data/nbconverted/2.preprocessing.py +++ b/notebooks/0.download-data/nbconverted/2.preprocessing.py @@ -15,7 +15,7 @@ # # These preprocessing steps ensure that all datasets are standardized, well-documented, and ready for comparative and integrative analyses. -# In[1]: +# In[2]: import json @@ -31,25 +31,29 @@ # # Contains helper function that pertains to this notebook. -# In[2]: +# In[3]: def load_and_concat_profiles( profile_dir: str | pathlib.Path, shared_features: list[str] | None = None, + shared_contains_meta: bool = False, specific_plates: list[pathlib.Path] | None = None, ) -> pl.DataFrame: """ - Load all profile files from a directory and concatenate them into a single Polars DataFrame. + Load all profile files from a directory and concatenate them into a single Polars + DataFrame. Parameters ---------- profile_dir : str or pathlib.Path Directory containing the profile files (.parquet). shared_features : Optional[list[str]], optional - List of shared feature names to filter the profiles. If None, all features are loaded. + List of shared feature names to filter the profiles. If None, all features are + loaded. specific_plates : Optional[list[pathlib.Path]], optional - List of specific plate file paths to load. If None, all profiles in the directory are loaded. + List of specific plate file paths to load. If None, all profiles in the + directory are loaded. Returns ------- @@ -71,13 +75,24 @@ def load_and_concat_profiles( "All elements in specific_plates must be pathlib.Path objects" ) - def load_profile(file: pathlib.Path) -> pl.DataFrame: + def load_profile(profile_path: pathlib.Path) -> pl.DataFrame: """internal function to load a single profile file.""" - profile_df = pl.read_parquet(file) - meta_cols, _ = split_meta_and_features(profile_df) + + # load profiles + profile_df = pl.read_parquet(profile_path) + + # print shape + print(f"Loaded profile {profile_path.name} with shape {profile_df.shape}") + + # if provided shared feature list does not contain meta, split and select + # then get it from the profile, if it does, just select the shared features + # directly if shared_features is not None: - # Only select metadata and shared features - return profile_df.select(meta_cols + shared_features) + if not shared_contains_meta: + meta_cols, _ = split_meta_and_features(profile_df) + return profile_df.select(meta_cols + shared_features) + + return profile_df.select(shared_features) return profile_df # Use specific_plates if provided, otherwise gather all .parquet files @@ -160,6 +175,60 @@ def remove_feature_prefixes(df: pl.DataFrame, prefix: str = "CP__") -> pl.DataFr return df.rename(lambda x: x.replace(prefix, "") if prefix in x else x) +def find_shared_features_across_parquets( + profile_paths: list[str | pathlib.Path], +) -> list[str]: + """ + Finds the intersection of column names across multiple parquet files. + + This function returns the list of column names that are present in every provided parquet file. + The order of columns is preserved from the first file. Uses LazyFrame.collect_schema().names() + to avoid expensive full reads and the PerformanceWarning. + + Parameters + ---------- + profile_paths : list of str or pathlib.Path + List of paths to parquet files. + + Returns + ------- + list of str + List of shared column names present in all files, in the order from the first file. + + Raises + ------ + FileNotFoundError + If no parquet files are provided or any file does not exist. + """ + if not profile_paths: + raise FileNotFoundError("No parquet files provided") + + # check if they are all strings if so, convert to pathlib.Path + if all(isinstance(p, str) for p in profile_paths): + profile_paths = [pathlib.Path(p) for p in profile_paths] + + for p in profile_paths: + if not p.exists(): + raise FileNotFoundError(f"Profile file not found: {p}") + + # set the first file columns as the initial set + first_cols = pl.scan_parquet(profile_paths[0]).collect_schema().names() + common = set(first_cols) + + # iterate through the rest of the files and find shared columns + # of the rest of the profiles + for p in profile_paths[1:]: + cols = pl.scan_parquet(p).collect_schema().names() + common &= set(cols) + if not common: + # Early exit if no shared columns remain + return [] + + # Preserve first file ordering (Meta and features order) + shared_features = [c for c in first_cols if c in common] + return shared_features + + # Defining the input and output directories used throughout the notebook. # # > **Note:** The shared profiles utilized here are sourced from the [JUMP-single-cell](https://github.com/WayScience/JUMP-single-cell) repository. All preprocessing and profile generation steps are performed in that repository, and this notebook focuses on downstream analysis using the generated profiles. @@ -184,6 +253,9 @@ def remove_feature_prefixes(df: pl.DataFrame, prefix: str = "CP__") -> pl.DataFr cfret_profiles_dir / "localhost230405150001_sc_feature_selected.parquet" ).resolve(strict=True) +# cfret-screen profiles path +cfret_screen_profiles_path = profiles_dir / "cfret-screen" + # Setting feature selection path shared_features_config_path = ( profiles_dir / "cpjump1" / "feature_selected_sc_qc_features.json" @@ -195,6 +267,12 @@ def remove_feature_prefixes(df: pl.DataFrame, prefix: str = "CP__") -> pl.DataFr profiles_dir / "mitocheck" / "normalized_data" ).resolve(strict=True) +# seting cfret-screen profiles paths +cfret_screen_profiles_paths = [ + path.resolve(strict=True) + for path in cfret_screen_profiles_path.glob("*_sc_feature_selected.parquet") +] + # output directories cpjump1_output_dir = (profiles_dir / "cpjump1").resolve() cpjump1_output_dir.mkdir(exist_ok=True) @@ -261,10 +339,8 @@ def remove_feature_prefixes(df: pl.DataFrame, prefix: str = "CP__") -> pl.DataFr # Saving metadata and features of the concat profile into a json file meta_features_dict = { - "concat-profiles": { - "meta-features": meta_cols, - "shared-features": features_cols, - } + "metadata-features": meta_cols, + "morphology-features": features_cols, } with open(cpjump1_output_dir / "concat_profiles_meta_features.json", "w") as f: json.dump(meta_features_dict, f, indent=4) @@ -469,3 +545,70 @@ def remove_feature_prefixes(df: pl.DataFrame, prefix: str = "CP__") -> pl.DataFr # overwrite dataset with cell cfret_profiles.select(meta_cols + features_cols).write_parquet(cfret_profiles_path) + + +# ## Preprocessing CFReT Screen Dataset +# +# This section preprocesses the CFReT Screen dataset by concatenating all plate profiles into a single unified dataframe. This represents the first batch of plates, which are technical replicates containing identical treatment conditions and dosages across all plates. +# +# **Dataset characteristics:** +# - Each plate contains both positive (n=3) and negative (n=3) controls +# - All treatment plates share the same experimental conditions +# - Technical replicates is at the plate level +# +# **Preprocessing steps:** +# +# 1. **Feature alignment**: Identify shared features across all CFReT Screen plates to ensure consistent feature space +# 2. **Profile concatenation**: Merge all plate profiles into a single comprehensive dataframe using the shared feature set +# 3. **Unique cell identification**: Add `Metadata_cell_id` column with unique hash values to enable precise single-cell tracking + +# In[10]: + + +# find shared features across cfret-screen profiles and load and concat them +cfret_screen_shared_features = find_shared_features_across_parquets( + cfret_screen_profiles_paths +) +print( + "total shared features in cfret-screen profiles:", len(cfret_screen_shared_features) +) + +cfret_screen_concat_profiles = load_and_concat_profiles( + profile_dir=cfret_screen_profiles_path, + shared_features=cfret_screen_shared_features, + shared_contains_meta=True, +) + +# add unique cell ID as a string type +cfret_screen_concat_profiles = cfret_screen_concat_profiles.with_columns( + cfret_screen_concat_profiles.hash_rows(seed=0) + .alias("Metadata_cell_id") + .cast(pl.Utf8) +) + +# split the metadata and features and reorganize features in the concat profile +cfret_screen_meta_cols, cfret_screen_features_cols = split_meta_and_features( + cfret_screen_concat_profiles +) +cfret_screen_concat_profiles = cfret_screen_concat_profiles.select( + cfret_screen_meta_cols + cfret_screen_features_cols +) + +# save feature space config to json file +with open(cfret_profiles_dir / "cfret_screen_feature_space_configs.json", "w") as f: + json.dump( + { + "metadata-features": cfret_screen_meta_cols, + "morphology-features": cfret_screen_features_cols, + }, + f, + indent=4, + ) + +# add cell id hash +cfret_screen_concat_profiles = add_cell_id_hash(cfret_screen_concat_profiles) + +# save concatenated cfret-screen profiles +cfret_screen_concat_profiles.write_parquet( + cfret_screen_profiles_path / "cfret_screen_concat_profiles.parquet" +) diff --git a/notebooks/2.cfret-analysis/r_buscar_env.yaml b/notebooks/2.cfret-analysis/r_buscar_env.yaml new file mode 100644 index 0000000..4997164 --- /dev/null +++ b/notebooks/2.cfret-analysis/r_buscar_env.yaml @@ -0,0 +1,61 @@ +name: r_buscar +channels: + - conda-forge + - bioconda + - defaults +dependencies: + # R base and essentials + - r-base=4.3.* + - r-essentials + + # Data manipulation + - r-tidyverse=2.0.* + - r-dplyr=1.1.* + - r-tidyr=1.3.* + - r-readr=2.1.* + - r-tibble=3.2.* + - r-stringr=1.5.* + - r-purrr=1.0.* + + # Data I/O + - r-arrow=14.* # For reading parquet files + - r-data.table=1.14.* + - r-jsonlite=1.8.* + + # Plotting essentials + - r-ggplot2=3.4.* + - r-ggrepel=0.9.* # For better label placement + - r-patchwork=1.1.* # For combining plots + - r-cowplot=1.1.* # Publication-ready plots + - r-ggpubr=0.6.* # Publication-ready themes + - r-ggsci=3.0.* # Scientific journal color palettes + - r-scales=1.3.* # Scale functions for ggplot2 + - r-gridextra=2.3 # Arrange multiple plots + + # Advanced plotting + - r-pheatmap=1.0.* # Heatmaps + - r-complexheatmap # Advanced heatmaps (bioconda) + - r-viridis=0.6.* # Color palettes + - r-rcolorbrewer=1.1.* # Color palettes + - r-ggridges=0.5.* # Ridge plots + - r-plotly=4.10.* # Interactive plots + + # Statistical analysis + - r-mass=7.3.* + - r-car=3.1.* + - r-lme4=1.1.* + - r-emmeans=1.8.* + + # Bioinformatics (if needed) + - bioconductor-complexheatmap + + # Utilities + - r-here=1.0.* # Path management + - r-glue=1.6.* # String interpolation + - r-magrittr=2.0.* # Pipe operator + - r-devtools=2.4.* # Development tools + + # Jupyter support (optional, if using R notebooks) + - r-irkernel=1.3.* + - jupyter + - notebook diff --git a/notebooks/3.cfret-screen-analysis/1.cfret_screen_analysis.ipynb b/notebooks/3.cfret-screen-analysis/1.cfret_screen_analysis.ipynb new file mode 100644 index 0000000..7c02e02 --- /dev/null +++ b/notebooks/3.cfret-screen-analysis/1.cfret_screen_analysis.ipynb @@ -0,0 +1,1134 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "9ac3fc80", + "metadata": {}, + "source": [ + "# CFReT-Screen analysis\n", + "\n", + "In this notebook, we will be applying `buscar` to the CFReT initial screen.\n", + "\n", + "The resource for this dataset can be found [here](https://github.com/WayScience/targeted_fibrosis_drug_screen/tree/main/3.preprocessing_features)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "a052f353", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/erikserrano/Software/miniconda3/envs/buscar/lib/python3.12/site-packages/louvain/__init__.py:54: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81.\n", + " from pkg_resources import get_distribution, DistributionNotFound\n" + ] + } + ], + "source": [ + "import sys\n", + "import json\n", + "import pathlib\n", + "\n", + "import numpy as np\n", + "import polars as pl\n", + "import seaborn as sns\n", + "import matplotlib.pyplot as plt\n", + "\n", + "sys.path.append(\"../../\")\n", + "from utils.io_utils import load_profiles\n", + "\n", + "# from utils.metrics import measure_phenotypic_activity\n", + "from utils.preprocess import apply_pca\n", + "from utils.data_utils import split_meta_and_features\n", + "from utils.signatures import get_signatures\n", + "from utils.heterogeneity import optimized_clustering\n", + "from utils.metrics import measure_phenotypic_activity\n", + "from utils.identify_hits import identify_compound_hit" + ] + }, + { + "cell_type": "markdown", + "id": "c44a7ad6", + "metadata": {}, + "source": [ + "## Parameters\n", + "\n", + "Below are the parameters used for this notebook. The CFReT-screen dataset contains two hearts: **Healthy (Heart 7)** and **Failing (Heart 19)**, which has been diagnosed with dilated cardiomyopathy.\n", + "\n", + "DMSO Control Naming Convention\n", + "\n", + "To distinguish between control conditions from different heart sources, the `Metadata_treatment` column values are modified as follows:\n", + "- **Healthy controls** (Heart 7 + DMSO): `\"DMSO_heart_7\"`\n", + "- **Failing controls** (Heart 19 + DMSO): `\"DMSO_heart_19\"`\n", + "\n", + "Parameter Definitions:\n", + "- **`healthy_ref_treatment`**: Reference treatment name for healthy controls\n", + "- **`failing_ref_treatment`**: Reference treatment name for failing heart controls \n", + "- **`treatment_col`**: Column name containing treatment metadata\n", + "- **`cfret_screen_cluster_param_grid`**: Dictionary defining the hyperparameter search space for clustering optimization when assessing heterogeneity across treatments. Includes:\n", + " - `cluster_resolution`: Granularity of clusters (float, 0.1–2.2)\n", + " - `n_neighbors`: Number of neighbors for graph construction (int, 5–100)\n", + " - `cluster_method`: Clustering algorithm (categorical: leiden)\n", + " - `neighbor_distance_metric`: Distance metric for neighbor computation (categorical: euclidean, cosine, manhattan)" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "e66b0c55", + "metadata": {}, + "outputs": [], + "source": [ + "# setting parameters\n", + "healthy_ref_treatment = \"DMSO_heart_7\"\n", + "failing_ref_treatment = \"DMSO_heart_19\"\n", + "treatment_col = \"Metadata_treatment\"\n", + "\n", + "# parameters used for clustering optimization\n", + "cfret_screen_cluster_param_grid = {\n", + " # Clustering resolution: how granular the clusters should be\n", + " \"cluster_resolution\": {\"type\": \"float\", \"low\": 0.1, \"high\": 2.2},\n", + " # Number of neighbors for graph construction\n", + " \"n_neighbors\": {\"type\": \"int\", \"low\": 5, \"high\": 100},\n", + " # Clustering algorithm\n", + " \"cluster_method\": {\"type\": \"categorical\", \"choices\": [\"leiden\"]},\n", + " # Distance metric for neighbor computation\n", + " \"neighbor_distance_metric\": {\n", + " \"type\": \"categorical\",\n", + " \"choices\": [\"euclidean\", \"cosine\", \"manhattan\"],\n", + " },\n", + "}" + ] + }, + { + "cell_type": "markdown", + "id": "6ea34b95", + "metadata": {}, + "source": [ + "setting paths" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "61c684d4", + "metadata": {}, + "outputs": [], + "source": [ + "# load in raw data from\n", + "cfret_data_dir = pathlib.Path(\n", + " \"../0.download-data/data/sc-profiles/cfret-screen\"\n", + ").resolve(strict=True)\n", + "cfret_profiles_path = (cfret_data_dir / \"cfret_screen_concat_profiles.parquet\").resolve(\n", + " strict=True\n", + ")\n", + "\n", + "# make results dir\n", + "results_dir = pathlib.Path(\"./results/cfret-screen\").resolve()\n", + "results_dir.mkdir(parents=True, exist_ok=True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d46f7bb0", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + "shape: (5, 495)\n", + "┌───────────┬───────────┬───────────┬───────────┬───┬───────────┬───────────┬───────────┬──────────┐\n", + "│ Metadata_ ┆ Metadata_ ┆ Metadata_ ┆ Metadata_ ┆ … ┆ Nuclei_Te ┆ Nuclei_Te ┆ Nuclei_Te ┆ Nuclei_T │\n", + "│ WellRow ┆ WellCol ┆ heart_num ┆ cell_type ┆ ┆ xture_Inv ┆ xture_Inv ┆ xture_Inv ┆ exture_S │\n", + "│ --- ┆ --- ┆ ber ┆ --- ┆ ┆ erseDiffe ┆ erseDiffe ┆ erseDiffe ┆ umEntrop │\n", + "│ str ┆ i64 ┆ --- ┆ str ┆ ┆ ren… ┆ ren… ┆ ren… ┆ y_PM_3… │\n", + "│ ┆ ┆ i64 ┆ ┆ ┆ --- ┆ --- ┆ --- ┆ --- │\n", + "│ ┆ ┆ ┆ ┆ ┆ f64 ┆ f64 ┆ f64 ┆ f64 │\n", + "╞═══════════╪═══════════╪═══════════╪═══════════╪═══╪═══════════╪═══════════╪═══════════╪══════════╡\n", + "│ B ┆ 2 ┆ 7 ┆ healthy ┆ … ┆ 0.025132 ┆ 0.531559 ┆ 0.161083 ┆ -0.08431 │\n", + "│ ┆ ┆ ┆ ┆ ┆ ┆ ┆ ┆ 1 │\n", + "│ B ┆ 2 ┆ 7 ┆ healthy ┆ … ┆ -0.054664 ┆ -0.974624 ┆ -1.157279 ┆ 1.004183 │\n", + "│ B ┆ 2 ┆ 7 ┆ healthy ┆ … ┆ 0.415166 ┆ 0.695386 ┆ 0.509317 ┆ -0.66912 │\n", + "│ ┆ ┆ ┆ ┆ ┆ ┆ ┆ ┆ 2 │\n", + "│ B ┆ 2 ┆ 7 ┆ healthy ┆ … ┆ -0.524341 ┆ -0.36156 ┆ 0.09598 ┆ -0.09907 │\n", + "│ ┆ ┆ ┆ ┆ ┆ ┆ ┆ ┆ 9 │\n", + "│ B ┆ 2 ┆ 7 ┆ healthy ┆ … ┆ -0.929627 ┆ -2.14462 ┆ -2.443222 ┆ 1.224159 │\n", + "└───────────┴───────────┴───────────┴───────────┴───┴───────────┴───────────┴───────────┴──────────┘" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# loading profiles\n", + "cfret_screen_df = load_profiles(cfret_profiles_path)\n", + "cfret_screen_meta, cfret_screen_feats = split_meta_and_features(cfret_screen_df)\n", + "\n", + "# updating the treatment name to reflect the heart source for DMSO in healthy cells\n", + "# this is our reference for healthy cells when measuring phenotypic activity\n", + "cfret_screen_df = cfret_screen_df.with_columns(\n", + " pl.when(\n", + " (pl.col(\"Metadata_treatment\") == \"DMSO\")\n", + " & (pl.col(\"Metadata_cell_type\") == \"healthy\")\n", + " )\n", + " .then(pl.lit(\"DMSO_heart_7\"))\n", + " .otherwise(pl.col(\"Metadata_treatment\"))\n", + " .alias(\"Metadata_treatment\")\n", + ")\n", + "cfret_screen_df = cfret_screen_df.with_columns(\n", + " pl.when(\n", + " (pl.col(\"Metadata_treatment\") == \"DMSO\")\n", + " & (pl.col(\"Metadata_cell_type\") == \"failing\")\n", + " )\n", + " .then(pl.lit(\"DMSO_heart_19\"))\n", + " .otherwise(pl.col(\"Metadata_treatment\"))\n", + " .alias(\"Metadata_treatment\")\n", + ")\n", + "\n", + "# Generate feature value shuffled dataframe for control\n", + "shuffled_cfret_screen_df = cfret_screen_feats.select(\n", + " [pl.col(col).shuffle() for col in cfret_screen_feats.columns]\n", + ")\n", + "\n", + "# Display data\n", + "cfret_screen_df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "3f0a4a9c", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "number of healthy cells 1720\n", + "number of failing cells 4915\n" + ] + } + ], + "source": [ + "print(\n", + " f\"number of healthy cells {cfret_screen_df.filter(pl.col('Metadata_treatment') == 'DMSO_heart_7').height}\"\n", + ")\n", + "print(\n", + " f\"number of failing cells {cfret_screen_df.filter(pl.col('Metadata_treatment') == 'DMSO_heart_19').height}\"\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "d33e33a9", + "metadata": {}, + "source": [ + "## Preprocessing" + ] + }, + { + "cell_type": "markdown", + "id": "7b3fc684", + "metadata": {}, + "source": [ + "Filtering Treatments with Low Cell Counts:\n", + "\n", + "Treatments with low cell counts were removed from the analysis. This reduction in cell numbers is typically caused by cellular toxicity, which leads to cell death and consequently results in insufficient cell representation for downstream analysis.\n", + "\n", + "Low cell count treatments also pose challenges when assessing heterogeneity, as there are not enough data points to form meaningful clusters. To address this, highly toxic compounds with very few surviving cells were excluded from the BUSCAR analysis.\n", + "\n", + "A threshold of 10% was applied based on Scanpy documentation, which recommends having at least 15–100 data points to compute a reliable neighborhood graph. To validate this threshold, we generated a histogram of cell counts and marked the 10th percentile with a red line. Treatments falling below this threshold were removed and excluded from the BUSCAR pipeline." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "a3535dba", + "metadata": {}, + "outputs": [], + "source": [ + "# count number of cells per Metadata_treatment and ensure 'count' is Int64\n", + "counts = cfret_screen_df[\"Metadata_treatment\"].value_counts()\n", + "counts = counts.with_columns(pl.col(\"count\").cast(pl.Int64))\n", + "counts = counts.sort(\"count\", descending=True)\n", + "counts = counts.to_pandas()" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "146c965b", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "10th percentile of cell counts: 23.3 cells\n" + ] + } + ], + "source": [ + "# using numpy to calculate 10th percentile\n", + "tenth_percentile = np.round(np.percentile(counts[\"count\"], 10), 3)\n", + "print(f\"10th percentile of cell counts: {tenth_percentile} cells\")" + ] + }, + { + "cell_type": "markdown", + "id": "50867f87", + "metadata": {}, + "source": [ + "Plotting cell count distribution" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "3d6fd55b", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# setting seaborn style and figure size\n", + "sns.set(style=\"whitegrid\")\n", + "plt.figure(figsize=(12, 6), dpi=200)\n", + "\n", + "# plot histogram with seaborn\n", + "ax = sns.histplot(data=counts, x=\"count\", bins=100, color=\"skyblue\", kde=True)\n", + "\n", + "# add 10th percentile vertical line and annotation (tenth_percentile already defined)\n", + "ax.axvline(\n", + " x=tenth_percentile,\n", + " color=\"red\",\n", + " linestyle=\"--\",\n", + " linewidth=2,\n", + " label=f\"10th percentile ({int(tenth_percentile)} cells)\",\n", + ")\n", + "ymin, ymax = ax.get_ylim()\n", + "ax.text(\n", + " tenth_percentile,\n", + " ymax * 0.9,\n", + " f\"10th pct = {tenth_percentile:.0f}\",\n", + " color=\"red\",\n", + " rotation=90,\n", + " va=\"top\",\n", + " ha=\"right\",\n", + " backgroundcolor=\"white\",\n", + ")\n", + "\n", + "# labeling the plot\n", + "ax.set_xlabel(\"Number of Cells\")\n", + "ax.set_ylabel(\"Metadata_treatment\")\n", + "ax.set_title(\"Cell Count per treeatment in CFRET screen\")\n", + "\n", + "# adding legend\n", + "ax.legend()\n", + "\n", + "# adjust layout\n", + "plt.tight_layout()\n", + "\n", + "# save the plot\n", + "plt.savefig(results_dir / \"cell_count_per_treatment_cfret_screen.png\", dpi=500)\n", + "\n", + "# display plot\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "66ed5921", + "metadata": {}, + "source": [ + "Removing cells under those specific treatments" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "d8d45e76", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Removed treatments due to low cell counts (below 10th percentile): ['UCD-0159290', 'UCD-0001783', 'UCD-0159264', 'UCD-0018091', 'UCD-0001792', 'UCD-0000721']\n" + ] + }, + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + "shape: (5, 495)\n", + "┌───────────┬───────────┬───────────┬───────────┬───┬───────────┬───────────┬───────────┬──────────┐\n", + "│ Metadata_ ┆ Metadata_ ┆ Metadata_ ┆ Metadata_ ┆ … ┆ Nuclei_Te ┆ Nuclei_Te ┆ Nuclei_Te ┆ Nuclei_T │\n", + "│ WellRow ┆ WellCol ┆ heart_num ┆ cell_type ┆ ┆ xture_Inv ┆ xture_Inv ┆ xture_Inv ┆ exture_S │\n", + "│ --- ┆ --- ┆ ber ┆ --- ┆ ┆ erseDiffe ┆ erseDiffe ┆ erseDiffe ┆ umEntrop │\n", + "│ str ┆ i64 ┆ --- ┆ str ┆ ┆ ren… ┆ ren… ┆ ren… ┆ y_PM_3… │\n", + "│ ┆ ┆ i64 ┆ ┆ ┆ --- ┆ --- ┆ --- ┆ --- │\n", + "│ ┆ ┆ ┆ ┆ ┆ f64 ┆ f64 ┆ f64 ┆ f64 │\n", + "╞═══════════╪═══════════╪═══════════╪═══════════╪═══╪═══════════╪═══════════╪═══════════╪══════════╡\n", + "│ B ┆ 2 ┆ 7 ┆ healthy ┆ … ┆ 0.025132 ┆ 0.531559 ┆ 0.161083 ┆ -0.08431 │\n", + "│ ┆ ┆ ┆ ┆ ┆ ┆ ┆ ┆ 1 │\n", + "│ B ┆ 2 ┆ 7 ┆ healthy ┆ … ┆ -0.054664 ┆ -0.974624 ┆ -1.157279 ┆ 1.004183 │\n", + "│ B ┆ 2 ┆ 7 ┆ healthy ┆ … ┆ 0.415166 ┆ 0.695386 ┆ 0.509317 ┆ -0.66912 │\n", + "│ ┆ ┆ ┆ ┆ ┆ ┆ ┆ ┆ 2 │\n", + "│ B ┆ 2 ┆ 7 ┆ healthy ┆ … ┆ -0.524341 ┆ -0.36156 ┆ 0.09598 ┆ -0.09907 │\n", + "│ ┆ ┆ ┆ ┆ ┆ ┆ ┆ ┆ 9 │\n", + "│ B ┆ 2 ┆ 7 ┆ healthy ┆ … ┆ -0.929627 ┆ -2.14462 ┆ -2.443222 ┆ 1.224159 │\n", + "└───────────┴───────────┴───────────┴───────────┴───┴───────────┴───────────┴───────────┴──────────┘" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# remove treatments with cell counts below the 10th percentile\n", + "kept_treatments = counts[counts[\"count\"] >= tenth_percentile][\n", + " \"Metadata_treatment\"\n", + "].tolist()\n", + "cfret_screen_df = cfret_screen_df.filter(\n", + " pl.col(\"Metadata_treatment\").is_in(kept_treatments)\n", + ")\n", + "\n", + "# print the treatments that were removed\n", + "removed_treatments = counts[counts[\"count\"] < tenth_percentile][\n", + " \"Metadata_treatment\"\n", + "].tolist()\n", + "print(\n", + " \"Removed treatments due to low cell counts (below 10th percentile):\",\n", + " removed_treatments,\n", + ")\n", + "\n", + "cfret_screen_df.head()" + ] + }, + { + "cell_type": "markdown", + "id": "0a1a597a", + "metadata": {}, + "source": [ + "## Buscar pipeline" + ] + }, + { + "cell_type": "markdown", + "id": "045a19d1", + "metadata": {}, + "source": [ + "Get on and off signatures" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "437ca668", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "length of on and off signatures: 405 69\n" + ] + } + ], + "source": [ + "# once the data is loaded, separate the controls\n", + "# here we want the healthy DMSO cells to be the target since the screen consists\n", + "# of failing cells treated with compounds\n", + "healthy_ref_df = cfret_screen_df.filter(pl.col(\"Metadata_treatment\") == \"DMSO_heart_7\")\n", + "failing_ref_df = cfret_screen_df.filter(pl.col(\"Metadata_treatment\") == \"DMSO_heart_19\")\n", + "\n", + "# creating signatures\n", + "on_sigs, off_sigs, _ = get_signatures(\n", + " ref_profiles=healthy_ref_df,\n", + " exp_profiles=failing_ref_df,\n", + " morph_feats=cfret_screen_feats,\n", + " test_method=\"mann_whitney_u\",\n", + ")\n", + "\n", + "print(\"length of on and off signatures:\", len(on_sigs), len(off_sigs))\n", + "\n", + "# save signatures\n", + "signatures_dir = results_dir / \"CFRet-screen-signatures.json\"\n", + "with open(signatures_dir, \"w\") as sig_file:\n", + " json.dump(\n", + " {\"on_signatures\": on_sigs, \"off_signatures\": off_sigs}, sig_file, indent=4\n", + " )" + ] + }, + { + "cell_type": "markdown", + "id": "22e3bfd0", + "metadata": {}, + "source": [ + "Assess heterogeneity" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "ff087290", + "metadata": {}, + "outputs": [], + "source": [ + "# Convert raw feature space to PCA space that explains 95% of variance\n", + "cfret_screen_pca_df = apply_pca(\n", + " profiles=cfret_screen_df,\n", + " meta_features=cfret_screen_meta,\n", + " morph_features=cfret_screen_feats,\n", + " var_explained=0.95,\n", + ")\n", + "\n", + "# split meta and features again after PCA\n", + "cfret_screen_pca_feats = cfret_screen_pca_df.drop(cfret_screen_meta).columns" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7bc3980e", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Optimizing clustering for 46 treatment(s) with 30 job(s)...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/erikserrano/Software/miniconda3/envs/buscar/lib/python3.12/site-packages/louvain/__init__.py:54: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81.\n", + " from pkg_resources import get_distribution, DistributionNotFound\n", + "/home/erikserrano/Software/miniconda3/envs/buscar/lib/python3.12/site-packages/louvain/__init__.py:54: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81.\n", + " from pkg_resources import get_distribution, DistributionNotFound\n", + "/home/erikserrano/Software/miniconda3/envs/buscar/lib/python3.12/site-packages/louvain/__init__.py:54: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81.\n", + " from pkg_resources import get_distribution, DistributionNotFound\n", + "/home/erikserrano/Software/miniconda3/envs/buscar/lib/python3.12/site-packages/louvain/__init__.py:54: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81.\n", + " from pkg_resources import get_distribution, DistributionNotFound\n", + "/home/erikserrano/Software/miniconda3/envs/buscar/lib/python3.12/site-packages/louvain/__init__.py:54: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81.\n", + " from pkg_resources import get_distribution, DistributionNotFound\n", + "/home/erikserrano/Software/miniconda3/envs/buscar/lib/python3.12/site-packages/louvain/__init__.py:54: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81.\n", + " from pkg_resources import get_distribution, DistributionNotFound\n", + "[I 2025-11-21 21:41:36,936] A new study created in memory with name: CFReT_screen_clustering_UCD-0159258\n", + "/home/erikserrano/Software/miniconda3/envs/buscar/lib/python3.12/site-packages/louvain/__init__.py:54: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81.\n", + " from pkg_resources import get_distribution, DistributionNotFound\n", + "[I 2025-11-21 21:41:36,957] A new study created in memory with name: CFReT_screen_clustering_UCD-0159256\n", + "/home/erikserrano/Software/miniconda3/envs/buscar/lib/python3.12/site-packages/louvain/__init__.py:54: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81.\n", + " from pkg_resources import get_distribution, DistributionNotFound\n", + "/home/erikserrano/Software/miniconda3/envs/buscar/lib/python3.12/site-packages/louvain/__init__.py:54: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81.\n", + " from pkg_resources import get_distribution, DistributionNotFound\n", + "/home/erikserrano/Software/miniconda3/envs/buscar/lib/python3.12/site-packages/louvain/__init__.py:54: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81.\n", + " from pkg_resources import get_distribution, DistributionNotFound\n", + "/home/erikserrano/Software/miniconda3/envs/buscar/lib/python3.12/site-packages/louvain/__init__.py:54: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81.\n", + " from pkg_resources import get_distribution, DistributionNotFound\n", + "/home/erikserrano/Software/miniconda3/envs/buscar/lib/python3.12/site-packages/louvain/__init__.py:54: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81.\n", + " from pkg_resources import get_distribution, DistributionNotFound\n", + "/home/erikserrano/Software/miniconda3/envs/buscar/lib/python3.12/site-packages/louvain/__init__.py:54: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81.\n", + " from pkg_resources import get_distribution, DistributionNotFound\n", + "/home/erikserrano/Software/miniconda3/envs/buscar/lib/python3.12/site-packages/louvain/__init__.py:54: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81.\n", + " from pkg_resources import get_distribution, DistributionNotFound\n", + "/home/erikserrano/Software/miniconda3/envs/buscar/lib/python3.12/site-packages/louvain/__init__.py:54: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81.\n", + " from pkg_resources import get_distribution, DistributionNotFound\n", + "[I 2025-11-21 21:41:37,054] A new study created in memory with name: CFReT_screen_clustering_UCD-0001014\n", + "/home/erikserrano/Software/miniconda3/envs/buscar/lib/python3.12/site-packages/louvain/__init__.py:54: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81.\n", + " from pkg_resources import get_distribution, DistributionNotFound\n", + "/home/erikserrano/Software/miniconda3/envs/buscar/lib/python3.12/site-packages/louvain/__init__.py:54: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81.\n", + " from pkg_resources import get_distribution, DistributionNotFound\n", + "[I 2025-11-21 21:41:37,139] A new study created in memory with name: CFReT_screen_clustering_UCD-0159274\n", + "[I 2025-11-21 21:41:37,157] A new study created in memory with name: CFReT_screen_clustering_UCD-0159283\n", + "[I 2025-11-21 21:41:37,198] A new study created in memory with name: CFReT_screen_clustering_UCD-0001810\n", + "/home/erikserrano/Software/miniconda3/envs/buscar/lib/python3.12/site-packages/louvain/__init__.py:54: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81.\n", + " from pkg_resources import get_distribution, DistributionNotFound\n", + "[I 2025-11-21 21:41:37,349] A new study created in memory with name: CFReT_screen_clustering_UCD-0017999\n", + "/home/erikserrano/Software/miniconda3/envs/buscar/lib/python3.12/site-packages/louvain/__init__.py:54: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81.\n", + " from pkg_resources import get_distribution, DistributionNotFound\n", + "[I 2025-11-21 21:41:37,369] A new study created in memory with name: CFReT_screen_clustering_UCD-0159279\n", + "[I 2025-11-21 21:41:37,392] A new study created in memory with name: CFReT_screen_clustering_UCD-0159262\n", + "[I 2025-11-21 21:41:37,399] A new study created in memory with name: CFReT_screen_clustering_UCD-0018179\n", + "[I 2025-11-21 21:41:37,406] A new study created in memory with name: CFReT_screen_clustering_UCD-0018207\n", + "[I 2025-11-21 21:41:37,418] A new study created in memory with name: CFReT_screen_clustering_UCD-0001766\n", + "[I 2025-11-21 21:41:37,431] A new study created in memory with name: CFReT_screen_clustering_DMSO_heart_7\n", + "[I 2025-11-21 21:41:37,438] A new study created in memory with name: CFReT_screen_clustering_UCD-0159263\n", + "[I 2025-11-21 21:41:37,501] A new study created in memory with name: CFReT_screen_clustering_UCD-0001915\n", + "[I 2025-11-21 21:41:37,513] A new study created in memory with name: CFReT_screen_clustering_UCD-0159273\n", + "/home/erikserrano/Software/miniconda3/envs/buscar/lib/python3.12/site-packages/louvain/__init__.py:54: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81.\n", + " from pkg_resources import get_distribution, DistributionNotFound\n", + "[I 2025-11-21 21:41:37,542] A new study created in memory with name: CFReT_screen_clustering_UCD-0159284\n", + "/home/erikserrano/Software/miniconda3/envs/buscar/lib/python3.12/site-packages/louvain/__init__.py:54: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81.\n", + " from pkg_resources import get_distribution, DistributionNotFound\n", + "[I 2025-11-21 21:41:37,691] A new study created in memory with name: CFReT_screen_clustering_UCD-0159270\n", + "/home/erikserrano/Software/miniconda3/envs/buscar/lib/python3.12/site-packages/louvain/__init__.py:54: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81.\n", + " from pkg_resources import get_distribution, DistributionNotFound\n", + "/home/erikserrano/Software/miniconda3/envs/buscar/lib/python3.12/site-packages/louvain/__init__.py:54: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81.\n", + " from pkg_resources import get_distribution, DistributionNotFound\n", + "/home/erikserrano/Software/miniconda3/envs/buscar/lib/python3.12/site-packages/louvain/__init__.py:54: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81.\n", + " from pkg_resources import get_distribution, DistributionNotFound\n", + "/home/erikserrano/Software/miniconda3/envs/buscar/lib/python3.12/site-packages/louvain/__init__.py:54: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81.\n", + " from pkg_resources import get_distribution, DistributionNotFound\n", + "/home/erikserrano/Software/miniconda3/envs/buscar/lib/python3.12/site-packages/louvain/__init__.py:54: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81.\n", + " from pkg_resources import get_distribution, DistributionNotFound\n", + "/home/erikserrano/Software/miniconda3/envs/buscar/lib/python3.12/site-packages/louvain/__init__.py:54: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81.\n", + " from pkg_resources import get_distribution, DistributionNotFound\n", + "[I 2025-11-21 21:41:38,042] A new study created in memory with name: CFReT_screen_clustering_UCD-0159289\n", + "/home/erikserrano/Software/miniconda3/envs/buscar/lib/python3.12/site-packages/louvain/__init__.py:54: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81.\n", + " from pkg_resources import get_distribution, DistributionNotFound\n", + "/home/erikserrano/Software/miniconda3/envs/buscar/lib/python3.12/site-packages/louvain/__init__.py:54: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81.\n", + " from pkg_resources import get_distribution, DistributionNotFound\n", + "[I 2025-11-21 21:41:38,107] A new study created in memory with name: CFReT_screen_clustering_UCD-0159257\n", + "[I 2025-11-21 21:41:38,141] A new study created in memory with name: CFReT_screen_clustering_UCD-0159285\n", + "[I 2025-11-21 21:41:38,240] A new study created in memory with name: CFReT_screen_clustering_DMSO_heart_19\n", + "/home/erikserrano/Software/miniconda3/envs/buscar/lib/python3.12/site-packages/louvain/__init__.py:54: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81.\n", + " from pkg_resources import get_distribution, DistributionNotFound\n", + "[I 2025-11-21 21:41:38,314] A new study created in memory with name: CFReT_screen_clustering_UCD-0001775\n", + "[I 2025-11-21 21:41:38,320] A new study created in memory with name: CFReT_screen_clustering_UCD-0159261\n", + "[I 2025-11-21 21:41:38,383] A new study created in memory with name: CFReT_screen_clustering_UCD-0001804\n", + "[I 2025-11-21 21:41:38,388] A new study created in memory with name: CFReT_screen_clustering_UCD-0001844\n", + "[I 2025-11-21 21:41:38,442] A new study created in memory with name: CFReT_screen_clustering_UCD-0159269\n", + "[I 2025-11-21 21:41:38,495] A new study created in memory with name: CFReT_screen_clustering_UCD-0000450\n", + "[I 2025-11-21 21:41:38,496] A new study created in memory with name: CFReT_screen_clustering_UCD-0001040\n", + "[I 2025-11-21 21:41:38,745] A new study created in memory with name: CFReT_screen_clustering_UCD-0001016\n", + "/home/erikserrano/Projects/buscar/notebooks/3.cfret-screen-analysis/../../utils/heterogeneity.py:236: 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(\n", + "[I 2025-11-21 21:41:46,809] Trial 0 finished with value: 0.051552717333278805 and parameters: {'cluster_resolution': 0.9579619090415782, 'n_neighbors': 13, 'cluster_method': 'leiden', 'neighbor_distance_metric': 'manhattan'}. Best is trial 0 with value: 0.051552717333278805.\n", + "/home/erikserrano/Projects/buscar/notebooks/3.cfret-screen-analysis/../../utils/heterogeneity.py:236: 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(\n", + "/home/erikserrano/Projects/buscar/notebooks/3.cfret-screen-analysis/../../utils/heterogeneity.py:236: 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(\n", + "/home/erikserrano/Projects/buscar/notebooks/3.cfret-screen-analysis/../../utils/heterogeneity.py:236: 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(\n", + "[I 2025-11-21 21:41:47,574] Trial 1 finished with value: 0.02433416640157381 and parameters: {'cluster_resolution': 1.6281805664227689, 'n_neighbors': 98, 'cluster_method': 'leiden', 'neighbor_distance_metric': 'cosine'}. Best is trial 0 with value: 0.051552717333278805.\n", + "/home/erikserrano/Projects/buscar/notebooks/3.cfret-screen-analysis/../../utils/heterogeneity.py:236: 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(\n", + "/home/erikserrano/Projects/buscar/notebooks/3.cfret-screen-analysis/../../utils/heterogeneity.py:236: 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(\n", + "[I 2025-11-21 21:41:47,827] Trial 0 finished with value: -1.0 and parameters: {'cluster_resolution': 0.15245619897612683, 'n_neighbors': 76, 'cluster_method': 'leiden', 'neighbor_distance_metric': 'euclidean'}. Best is trial 0 with value: -1.0.\n", + "/home/erikserrano/Projects/buscar/notebooks/3.cfret-screen-analysis/../../utils/heterogeneity.py:236: 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(\n", + "/home/erikserrano/Projects/buscar/notebooks/3.cfret-screen-analysis/../../utils/heterogeneity.py:236: 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(\n", + "/home/erikserrano/Projects/buscar/notebooks/3.cfret-screen-analysis/../../utils/heterogeneity.py:236: 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(\n", + "[I 2025-11-21 21:41:48,174] Trial 0 finished with value: -0.0068136195193078805 and parameters: {'cluster_resolution': 1.916077878414102, 'n_neighbors': 29, 'cluster_method': 'leiden', 'neighbor_distance_metric': 'cosine'}. Best is trial 0 with value: -0.0068136195193078805.\n", + "[I 2025-11-21 21:41:48,220] A new study created in memory with name: CFReT_screen_clustering_UCD-0159275\n", + "/home/erikserrano/Projects/buscar/notebooks/3.cfret-screen-analysis/../../utils/heterogeneity.py:236: 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(\n", + "/home/erikserrano/Projects/buscar/notebooks/3.cfret-screen-analysis/../../utils/heterogeneity.py:236: 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(\n", + "/home/erikserrano/Projects/buscar/notebooks/3.cfret-screen-analysis/../../utils/heterogeneity.py:236: 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(\n", + "/home/erikserrano/Projects/buscar/notebooks/3.cfret-screen-analysis/../../utils/heterogeneity.py:236: 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(\n", + "/home/erikserrano/Projects/buscar/notebooks/3.cfret-screen-analysis/../../utils/heterogeneity.py:236: 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(\n", + "[I 2025-11-21 21:41:48,539] Trial 0 finished with value: 0.0424053927645916 and parameters: {'cluster_resolution': 1.0415335746283736, 'n_neighbors': 51, 'cluster_method': 'leiden', 'neighbor_distance_metric': 'cosine'}. Best is trial 0 with value: 0.0424053927645916.\n", + "/home/erikserrano/Projects/buscar/notebooks/3.cfret-screen-analysis/../../utils/heterogeneity.py:236: 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(\n", + "/home/erikserrano/Projects/buscar/notebooks/3.cfret-screen-analysis/../../utils/heterogeneity.py:236: 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(\n", + "/home/erikserrano/Projects/buscar/notebooks/3.cfret-screen-analysis/../../utils/heterogeneity.py:236: 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(\n", + "[I 2025-11-21 21:41:48,617] Trial 1 finished with value: 0.044143487542837055 and parameters: {'cluster_resolution': 0.46639823003811576, 'n_neighbors': 70, 'cluster_method': 'leiden', 'neighbor_distance_metric': 'cosine'}. Best is trial 1 with value: 0.044143487542837055.\n", + "/home/erikserrano/Projects/buscar/notebooks/3.cfret-screen-analysis/../../utils/heterogeneity.py:236: 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(\n", + "[I 2025-11-21 21:41:48,887] Trial 1 finished with value: 0.06456273505672518 and parameters: {'cluster_resolution': 0.8230715142786573, 'n_neighbors': 49, 'cluster_method': 'leiden', 'neighbor_distance_metric': 'euclidean'}. Best is trial 1 with value: 0.06456273505672518.\n", + "/home/erikserrano/Projects/buscar/notebooks/3.cfret-screen-analysis/../../utils/heterogeneity.py:236: 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(\n", + "[I 2025-11-21 21:41:48,995] Trial 0 finished with value: 0.011301827836661994 and parameters: {'cluster_resolution': 1.1770064242354266, 'n_neighbors': 20, 'cluster_method': 'leiden', 'neighbor_distance_metric': 'cosine'}. Best is trial 0 with value: 0.011301827836661994.\n", + "/home/erikserrano/Projects/buscar/notebooks/3.cfret-screen-analysis/../../utils/heterogeneity.py:236: 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(\n", + "/home/erikserrano/Projects/buscar/notebooks/3.cfret-screen-analysis/../../utils/heterogeneity.py:236: 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(\n", + "[I 2025-11-21 21:41:49,136] Trial 0 finished with value: 0.011014276991034423 and parameters: {'cluster_resolution': 0.7930943264793587, 'n_neighbors': 6, 'cluster_method': 'leiden', 'neighbor_distance_metric': 'manhattan'}. Best is trial 0 with value: 0.011014276991034423.\n", + "/home/erikserrano/Projects/buscar/notebooks/3.cfret-screen-analysis/../../utils/heterogeneity.py:236: 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(\n", + "[I 2025-11-21 21:41:49,279] Trial 0 finished with value: 0.01680104795622003 and parameters: {'cluster_resolution': 0.6981913968968398, 'n_neighbors': 62, 'cluster_method': 'leiden', 'neighbor_distance_metric': 'manhattan'}. Best is trial 0 with value: 0.01680104795622003.\n", + "/home/erikserrano/Projects/buscar/notebooks/3.cfret-screen-analysis/../../utils/heterogeneity.py:236: 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(\n", + "/home/erikserrano/Projects/buscar/notebooks/3.cfret-screen-analysis/../../utils/heterogeneity.py:236: 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(\n", + "[I 2025-11-21 21:41:49,294] Trial 0 finished with value: 0.05119241758899662 and parameters: {'cluster_resolution': 0.5782847549480996, 'n_neighbors': 25, 'cluster_method': 'leiden', 'neighbor_distance_metric': 'manhattan'}. Best is trial 0 with value: 0.05119241758899662.\n", + "/home/erikserrano/Projects/buscar/notebooks/3.cfret-screen-analysis/../../utils/heterogeneity.py:236: 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(\n", + "/home/erikserrano/Projects/buscar/notebooks/3.cfret-screen-analysis/../../utils/heterogeneity.py:236: 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(\n", + "[I 2025-11-21 21:41:49,450] A new study created in memory with name: CFReT_screen_clustering_UCD-0001613\n", + "/home/erikserrano/Projects/buscar/notebooks/3.cfret-screen-analysis/../../utils/heterogeneity.py:236: 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(\n", + "[I 2025-11-21 21:41:49,674] A new study created in memory with name: CFReT_screen_clustering_UCD-0001921\n", + "[I 2025-11-21 21:41:49,705] Trial 0 finished with value: -0.061643378192473286 and parameters: {'cluster_resolution': 2.0559027631953612, 'n_neighbors': 84, 'cluster_method': 'leiden', 'neighbor_distance_metric': 'euclidean'}. Best is trial 0 with value: -0.061643378192473286.\n", + "[I 2025-11-21 21:41:49,710] Trial 0 finished with value: -0.0008330515466489801 and parameters: {'cluster_resolution': 1.360059781754819, 'n_neighbors': 88, 'cluster_method': 'leiden', 'neighbor_distance_metric': 'euclidean'}. Best is trial 0 with value: -0.0008330515466489801.\n", + "[I 2025-11-21 21:41:49,770] Trial 0 finished with value: 0.1017864061382544 and parameters: {'cluster_resolution': 0.31910048844094696, 'n_neighbors': 28, 'cluster_method': 'leiden', 'neighbor_distance_metric': 'cosine'}. Best is trial 0 with value: 0.1017864061382544.\n", + "[I 2025-11-21 21:41:49,824] Trial 0 finished with value: -1.0 and parameters: {'cluster_resolution': 0.330124467439918, 'n_neighbors': 43, 'cluster_method': 'leiden', 'neighbor_distance_metric': 'manhattan'}. Best is trial 0 with value: -1.0.\n", + "/home/erikserrano/Projects/buscar/notebooks/3.cfret-screen-analysis/../../utils/heterogeneity.py:236: 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(\n", + "[I 2025-11-21 21:41:49,905] Trial 0 finished with value: -0.0029549051286162655 and parameters: {'cluster_resolution': 2.1195482127218557, 'n_neighbors': 5, 'cluster_method': 'leiden', 'neighbor_distance_metric': 'manhattan'}. Best is trial 0 with value: -0.0029549051286162655.\n", + "[I 2025-11-21 21:41:49,942] Trial 0 finished with value: -0.002905089081159221 and parameters: {'cluster_resolution': 1.2459789444244442, 'n_neighbors': 61, 'cluster_method': 'leiden', 'neighbor_distance_metric': 'manhattan'}. Best is trial 0 with value: -0.002905089081159221.\n", + "[I 2025-11-21 21:41:50,000] Trial 0 finished with value: 0.035485784017005965 and parameters: {'cluster_resolution': 0.6711562818919401, 'n_neighbors': 100, 'cluster_method': 'leiden', 'neighbor_distance_metric': 'manhattan'}. Best is trial 0 with value: 0.035485784017005965.\n", + "[I 2025-11-21 21:41:50,120] Trial 0 finished with value: 0.007516636909903132 and parameters: {'cluster_resolution': 1.0544036964208816, 'n_neighbors': 21, 'cluster_method': 'leiden', 'neighbor_distance_metric': 'euclidean'}. Best is trial 0 with value: 0.007516636909903132.\n", + "[I 2025-11-21 21:41:50,187] Trial 1 finished with value: 0.02260290731959052 and parameters: {'cluster_resolution': 0.7872371778541689, 'n_neighbors': 45, 'cluster_method': 'leiden', 'neighbor_distance_metric': 'manhattan'}. Best is trial 1 with value: 0.02260290731959052.\n", + "[I 2025-11-21 21:41:50,199] Trial 0 finished with value: 0.03655473303188121 and parameters: {'cluster_resolution': 0.8378582755884492, 'n_neighbors': 46, 'cluster_method': 'leiden', 'neighbor_distance_metric': 'euclidean'}. Best is trial 0 with value: 0.03655473303188121.\n", + "[I 2025-11-21 21:41:50,214] Trial 0 finished with value: 0.10615484219386685 and parameters: {'cluster_resolution': 0.4709986051914493, 'n_neighbors': 95, 'cluster_method': 'leiden', 'neighbor_distance_metric': 'manhattan'}. Best is trial 0 with value: 0.10615484219386685.\n", + "[I 2025-11-21 21:41:50,287] Trial 0 finished with value: -1.0 and parameters: {'cluster_resolution': 0.10054261568399628, 'n_neighbors': 65, 'cluster_method': 'leiden', 'neighbor_distance_metric': 'euclidean'}. Best is trial 0 with value: -1.0.\n", + "[I 2025-11-21 21:41:50,302] Trial 0 finished with value: 0.015359952985368533 and parameters: {'cluster_resolution': 0.6403589869468924, 'n_neighbors': 74, 'cluster_method': 'leiden', 'neighbor_distance_metric': 'cosine'}. Best is trial 0 with value: 0.015359952985368533.\n", + "[I 2025-11-21 21:41:50,354] Trial 0 finished with value: -1.0 and parameters: {'cluster_resolution': 0.2873493437045338, 'n_neighbors': 42, 'cluster_method': 'leiden', 'neighbor_distance_metric': 'manhattan'}. Best is trial 0 with value: -1.0.\n", + "[I 2025-11-21 21:41:50,439] Trial 1 finished with value: -0.0019642839317216888 and parameters: {'cluster_resolution': 1.8398734934902004, 'n_neighbors': 20, 'cluster_method': 'leiden', 'neighbor_distance_metric': 'cosine'}. Best is trial 0 with value: 0.05119241758899662.\n", + "[I 2025-11-21 21:41:50,582] Trial 1 finished with value: -0.0033563108382063033 and parameters: {'cluster_resolution': 1.7353083701371663, 'n_neighbors': 9, 'cluster_method': 'leiden', 'neighbor_distance_metric': 'euclidean'}. Best is trial 1 with value: -0.0033563108382063033.\n", + "[I 2025-11-21 21:41:50,711] Trial 0 finished with value: -0.000690516044774023 and parameters: {'cluster_resolution': 1.6557217947130078, 'n_neighbors': 43, 'cluster_method': 'leiden', 'neighbor_distance_metric': 'manhattan'}. Best is trial 0 with value: -0.000690516044774023.\n", + "[I 2025-11-21 21:41:50,792] Trial 1 finished with value: 0.08618327000175442 and parameters: {'cluster_resolution': 1.0847704793865285, 'n_neighbors': 23, 'cluster_method': 'leiden', 'neighbor_distance_metric': 'manhattan'}. Best is trial 0 with value: 0.10615484219386685.\n", + "/home/erikserrano/Projects/buscar/notebooks/3.cfret-screen-analysis/../../utils/heterogeneity.py:236: 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(\n", + "[I 2025-11-21 21:41:50,868] Trial 0 finished with value: 0.04925355769287974 and parameters: {'cluster_resolution': 0.5799218772616079, 'n_neighbors': 16, 'cluster_method': 'leiden', 'neighbor_distance_metric': 'cosine'}. Best is trial 0 with value: 0.04925355769287974.\n", + "[I 2025-11-21 21:41:50,910] Trial 0 finished with value: 0.006628169774525352 and parameters: {'cluster_resolution': 1.1014261379768582, 'n_neighbors': 30, 'cluster_method': 'leiden', 'neighbor_distance_metric': 'manhattan'}. Best is trial 0 with value: 0.006628169774525352.\n", + "[I 2025-11-21 21:41:50,921] Trial 0 finished with value: -0.01572747371987522 and parameters: {'cluster_resolution': 2.16444881203875, 'n_neighbors': 15, 'cluster_method': 'leiden', 'neighbor_distance_metric': 'cosine'}. Best is trial 0 with value: -0.01572747371987522.\n", + "[I 2025-11-21 21:41:50,926] Trial 1 finished with value: -0.003681036108855817 and parameters: {'cluster_resolution': 1.9969331650729323, 'n_neighbors': 9, 'cluster_method': 'leiden', 'neighbor_distance_metric': 'cosine'}. Best is trial 0 with value: -0.0029549051286162655.\n", + "/home/erikserrano/Projects/buscar/notebooks/3.cfret-screen-analysis/../../utils/heterogeneity.py:236: 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(\n", + "[I 2025-11-21 21:41:51,182] Trial 1 finished with value: 0.11588041484442861 and parameters: {'cluster_resolution': 0.4424669352652143, 'n_neighbors': 88, 'cluster_method': 'leiden', 'neighbor_distance_metric': 'manhattan'}. Best is trial 1 with value: 0.11588041484442861.\n", + "[I 2025-11-21 21:41:51,277] Trial 1 finished with value: 0.0017951493949188272 and parameters: {'cluster_resolution': 1.0671073050057354, 'n_neighbors': 15, 'cluster_method': 'leiden', 'neighbor_distance_metric': 'cosine'}. Best is trial 0 with value: 0.011014276991034423.\n", + "[I 2025-11-21 21:41:51,296] Trial 1 finished with value: 0.0211740700063741 and parameters: {'cluster_resolution': 0.5296463939706878, 'n_neighbors': 9, 'cluster_method': 'leiden', 'neighbor_distance_metric': 'cosine'}. Best is trial 0 with value: 0.03655473303188121.\n", + "[I 2025-11-21 21:41:51,439] Trial 0 finished with value: 0.003811427914741992 and parameters: {'cluster_resolution': 1.229216309764472, 'n_neighbors': 39, 'cluster_method': 'leiden', 'neighbor_distance_metric': 'cosine'}. Best is trial 0 with value: 0.003811427914741992.\n", + "[I 2025-11-21 21:41:51,589] A new study created in memory with name: CFReT_screen_clustering_UCD-0159293\n", + "[I 2025-11-21 21:41:51,627] Trial 1 finished with value: -1.0 and parameters: {'cluster_resolution': 0.24163149591387315, 'n_neighbors': 49, 'cluster_method': 'leiden', 'neighbor_distance_metric': 'manhattan'}. Best is trial 0 with value: 0.035485784017005965.\n", + "[I 2025-11-21 21:41:51,661] Trial 1 finished with value: -0.010080853369672009 and parameters: {'cluster_resolution': 2.1136030398529506, 'n_neighbors': 15, 'cluster_method': 'leiden', 'neighbor_distance_metric': 'cosine'}. Best is trial 0 with value: 0.015359952985368533.\n", + "[I 2025-11-21 21:41:51,727] A new study created in memory with name: CFReT_screen_clustering_UCD-0001808\n", + "[I 2025-11-21 21:41:51,778] A new study created in memory with name: CFReT_screen_clustering_UCD-0001801\n", + "[I 2025-11-21 21:41:51,778] A new study created in memory with name: CFReT_screen_clustering_UCD-0159265\n", + "[I 2025-11-21 21:41:51,789] Trial 1 finished with value: -1.0 and parameters: {'cluster_resolution': 0.3260022960958597, 'n_neighbors': 88, 'cluster_method': 'leiden', 'neighbor_distance_metric': 'euclidean'}. Best is trial 0 with value: -0.0008330515466489801.\n", + "[I 2025-11-21 21:41:52,018] Trial 1 finished with value: -1.0 and parameters: {'cluster_resolution': 0.23373343995177134, 'n_neighbors': 93, 'cluster_method': 'leiden', 'neighbor_distance_metric': 'euclidean'}. Best is trial 0 with value: 0.007516636909903132.\n", + "[I 2025-11-21 21:41:52,023] Trial 0 finished with value: 0.017080350538708333 and parameters: {'cluster_resolution': 0.47629250412097834, 'n_neighbors': 54, 'cluster_method': 'leiden', 'neighbor_distance_metric': 'cosine'}. Best is trial 0 with value: 0.017080350538708333.\n", + "[I 2025-11-21 21:41:52,030] A new study created in memory with name: CFReT_screen_clustering_UCD-0159271\n", + "[I 2025-11-21 21:41:52,330] Trial 1 finished with value: -1.0 and parameters: {'cluster_resolution': 0.40911644917108114, 'n_neighbors': 100, 'cluster_method': 'leiden', 'neighbor_distance_metric': 'manhattan'}. Best is trial 0 with value: -0.002905089081159221.\n", + "[I 2025-11-21 21:41:52,363] Trial 0 finished with value: -0.00375753263241175 and parameters: {'cluster_resolution': 1.986967168222464, 'n_neighbors': 82, 'cluster_method': 'leiden', 'neighbor_distance_metric': 'cosine'}. Best is trial 0 with value: -0.00375753263241175.\n", + "[I 2025-11-21 21:41:52,454] Trial 0 finished with value: 0.003934508985258803 and parameters: {'cluster_resolution': 0.8073606700941288, 'n_neighbors': 77, 'cluster_method': 'leiden', 'neighbor_distance_metric': 'manhattan'}. Best is trial 0 with value: 0.003934508985258803.\n", + "[I 2025-11-21 21:41:52,500] Trial 0 finished with value: -0.022990974409185724 and parameters: {'cluster_resolution': 1.6145283140264624, 'n_neighbors': 66, 'cluster_method': 'leiden', 'neighbor_distance_metric': 'cosine'}. Best is trial 0 with value: -0.022990974409185724.\n", + "[I 2025-11-21 21:41:52,616] Trial 1 finished with value: -1.0 and parameters: {'cluster_resolution': 0.19730621058364323, 'n_neighbors': 87, 'cluster_method': 'leiden', 'neighbor_distance_metric': 'euclidean'}. Best is trial 0 with value: -0.01572747371987522.\n", + "[I 2025-11-21 21:41:52,634] Trial 0 finished with value: -1.0 and parameters: {'cluster_resolution': 0.24998877347676585, 'n_neighbors': 54, 'cluster_method': 'leiden', 'neighbor_distance_metric': 'manhattan'}. Best is trial 0 with value: -1.0.\n", + "[I 2025-11-21 21:41:52,679] Trial 0 finished with value: -0.05748399484616586 and parameters: {'cluster_resolution': 2.112797550694061, 'n_neighbors': 99, 'cluster_method': 'leiden', 'neighbor_distance_metric': 'euclidean'}. Best is trial 0 with value: -0.05748399484616586.\n", + "[I 2025-11-21 21:41:52,695] Trial 1 finished with value: 0.12160807065692089 and parameters: {'cluster_resolution': 0.34549620010570903, 'n_neighbors': 69, 'cluster_method': 'leiden', 'neighbor_distance_metric': 'cosine'}. Best is trial 1 with value: 0.12160807065692089.\n", + "[I 2025-11-21 21:41:52,725] Trial 0 finished with value: -0.012861342685891073 and parameters: {'cluster_resolution': 1.3974489395194865, 'n_neighbors': 24, 'cluster_method': 'leiden', 'neighbor_distance_metric': 'cosine'}. Best is trial 0 with value: -0.012861342685891073.\n", + "[I 2025-11-21 21:41:53,155] Trial 1 finished with value: 0.05918061698447849 and parameters: {'cluster_resolution': 0.5940590085940342, 'n_neighbors': 72, 'cluster_method': 'leiden', 'neighbor_distance_metric': 'euclidean'}. Best is trial 1 with value: 0.05918061698447849.\n", + "[I 2025-11-21 21:41:53,169] Trial 1 finished with value: 0.0057895614445007005 and parameters: {'cluster_resolution': 1.4132501494538443, 'n_neighbors': 55, 'cluster_method': 'leiden', 'neighbor_distance_metric': 'manhattan'}. Best is trial 1 with value: 0.0057895614445007005.\n", + "[I 2025-11-21 21:41:53,170] A new study created in memory with name: CFReT_screen_clustering_UCD-0001835\n", + "[I 2025-11-21 21:41:53,207] Trial 1 finished with value: -1.0 and parameters: {'cluster_resolution': 0.1087240474319129, 'n_neighbors': 76, 'cluster_method': 'leiden', 'neighbor_distance_metric': 'manhattan'}. Best is trial 0 with value: -1.0.\n", + "[I 2025-11-21 21:41:53,405] Trial 1 finished with value: -0.044365112941149386 and parameters: {'cluster_resolution': 1.235826623349134, 'n_neighbors': 48, 'cluster_method': 'leiden', 'neighbor_distance_metric': 'manhattan'}. Best is trial 0 with value: 0.1017864061382544.\n", + "[I 2025-11-21 21:41:53,407] Trial 0 finished with value: 0.03072872864654188 and parameters: {'cluster_resolution': 1.5820364155303759, 'n_neighbors': 40, 'cluster_method': 'leiden', 'neighbor_distance_metric': 'manhattan'}. Best is trial 0 with value: 0.03072872864654188.\n", + "[I 2025-11-21 21:41:53,570] Trial 1 finished with value: 0.023593708730650472 and parameters: {'cluster_resolution': 0.7213025557760123, 'n_neighbors': 45, 'cluster_method': 'leiden', 'neighbor_distance_metric': 'euclidean'}. Best is trial 1 with value: 0.023593708730650472.\n", + "[I 2025-11-21 21:41:53,616] A new study created in memory with name: CFReT_screen_clustering_UCD-0159280\n", + "[I 2025-11-21 21:41:53,668] A new study created in memory with name: CFReT_screen_clustering_UCD-0159286\n", + "[I 2025-11-21 21:41:53,679] Trial 0 finished with value: 0.004596105984601713 and parameters: {'cluster_resolution': 1.114716613455917, 'n_neighbors': 79, 'cluster_method': 'leiden', 'neighbor_distance_metric': 'cosine'}. Best is trial 0 with value: 0.004596105984601713.\n", + "[I 2025-11-21 21:41:53,821] Trial 1 finished with value: -0.11581795067299187 and parameters: {'cluster_resolution': 1.6857323682594212, 'n_neighbors': 52, 'cluster_method': 'leiden', 'neighbor_distance_metric': 'manhattan'}. Best is trial 0 with value: -0.012861342685891073.\n", + "[I 2025-11-21 21:41:53,970] Trial 1 finished with value: 0.08035998230769666 and parameters: {'cluster_resolution': 0.4869016642528722, 'n_neighbors': 87, 'cluster_method': 'leiden', 'neighbor_distance_metric': 'euclidean'}. Best is trial 1 with value: 0.08035998230769666.\n", + "[I 2025-11-21 21:41:53,984] A new study created in memory with name: CFReT_screen_clustering_UCD-0018131\n", + "[I 2025-11-21 21:41:54,024] Trial 0 finished with value: -0.019792891987631643 and parameters: {'cluster_resolution': 1.3861580583451385, 'n_neighbors': 15, 'cluster_method': 'leiden', 'neighbor_distance_metric': 'euclidean'}. Best is trial 0 with value: -0.019792891987631643.\n", + "[I 2025-11-21 21:41:54,084] A new study created in memory with name: CFReT_screen_clustering_UCD-0001024\n", + "[I 2025-11-21 21:41:54,097] Trial 0 finished with value: 0.00807219269274278 and parameters: {'cluster_resolution': 1.9980252655413688, 'n_neighbors': 24, 'cluster_method': 'leiden', 'neighbor_distance_metric': 'manhattan'}. Best is trial 0 with value: 0.00807219269274278.\n", + "[I 2025-11-21 21:41:54,118] Trial 1 finished with value: -1.0 and parameters: {'cluster_resolution': 0.23530883078709713, 'n_neighbors': 79, 'cluster_method': 'leiden', 'neighbor_distance_metric': 'manhattan'}. Best is trial 0 with value: 0.003934508985258803.\n", + "[I 2025-11-21 21:41:54,161] Trial 1 finished with value: -1.0 and parameters: {'cluster_resolution': 0.3238211097049247, 'n_neighbors': 86, 'cluster_method': 'leiden', 'neighbor_distance_metric': 'euclidean'}. Best is trial 0 with value: 0.017080350538708333.\n", + "[I 2025-11-21 21:41:54,179] Trial 1 finished with value: 0.012467995250121336 and parameters: {'cluster_resolution': 0.5994971696232013, 'n_neighbors': 75, 'cluster_method': 'leiden', 'neighbor_distance_metric': 'manhattan'}. Best is trial 1 with value: 0.012467995250121336.\n", + "[I 2025-11-21 21:41:54,459] Trial 1 finished with value: -0.06019758904401077 and parameters: {'cluster_resolution': 1.4383869178599122, 'n_neighbors': 31, 'cluster_method': 'leiden', 'neighbor_distance_metric': 'manhattan'}. Best is trial 0 with value: -0.019792891987631643.\n", + "[I 2025-11-21 21:41:54,536] Trial 1 finished with value: -0.008950002124262559 and parameters: {'cluster_resolution': 1.2542607886261945, 'n_neighbors': 37, 'cluster_method': 'leiden', 'neighbor_distance_metric': 'euclidean'}. Best is trial 0 with value: -0.00375753263241175.\n", + "[I 2025-11-21 21:41:54,566] A new study created in memory with name: CFReT_screen_clustering_UCD-0001829\n", + "[I 2025-11-21 21:41:54,596] A new study created in memory with name: CFReT_screen_clustering_UCD-0159259\n", + "[I 2025-11-21 21:41:54,599] A new study created in memory with name: CFReT_screen_clustering_UCD-0001842\n", + "[I 2025-11-21 21:41:54,732] Trial 0 finished with value: 0.00313574078398014 and parameters: {'cluster_resolution': 1.3235193961935592, 'n_neighbors': 32, 'cluster_method': 'leiden', 'neighbor_distance_metric': 'euclidean'}. Best is trial 0 with value: 0.00313574078398014.\n", + "[I 2025-11-21 21:41:54,876] Trial 0 finished with value: 0.004811809551581636 and parameters: {'cluster_resolution': 1.14073578208598, 'n_neighbors': 45, 'cluster_method': 'leiden', 'neighbor_distance_metric': 'cosine'}. Best is trial 0 with value: 0.004811809551581636.\n", + "[I 2025-11-21 21:41:54,895] Trial 1 finished with value: -0.008398986581395084 and parameters: {'cluster_resolution': 1.7396290635155816, 'n_neighbors': 22, 'cluster_method': 'leiden', 'neighbor_distance_metric': 'euclidean'}. Best is trial 1 with value: -0.008398986581395084.\n", + "[I 2025-11-21 21:41:55,227] Trial 1 finished with value: 0.012305164660017609 and parameters: {'cluster_resolution': 1.8989092776881225, 'n_neighbors': 13, 'cluster_method': 'leiden', 'neighbor_distance_metric': 'euclidean'}. Best is trial 1 with value: 0.012305164660017609.\n", + "[I 2025-11-21 21:41:55,435] Trial 0 finished with value: 0.10591701919731593 and parameters: {'cluster_resolution': 0.34903783900782626, 'n_neighbors': 15, 'cluster_method': 'leiden', 'neighbor_distance_metric': 'euclidean'}. Best is trial 0 with value: 0.10591701919731593.\n", + "[I 2025-11-21 21:41:55,462] Trial 1 finished with value: 0.008209480849029276 and parameters: {'cluster_resolution': 1.3137726166334238, 'n_neighbors': 11, 'cluster_method': 'leiden', 'neighbor_distance_metric': 'manhattan'}. Best is trial 1 with value: 0.008209480849029276.\n", + "[I 2025-11-21 21:41:55,579] Trial 1 finished with value: 0.0212350102370221 and parameters: {'cluster_resolution': 0.671019441206927, 'n_neighbors': 99, 'cluster_method': 'leiden', 'neighbor_distance_metric': 'manhattan'}. Best is trial 1 with value: 0.0212350102370221.\n", + "[I 2025-11-21 21:41:55,762] Trial 1 finished with value: 0.02248056708139957 and parameters: {'cluster_resolution': 2.0863894044466362, 'n_neighbors': 73, 'cluster_method': 'leiden', 'neighbor_distance_metric': 'euclidean'}. Best is trial 0 with value: 0.03072872864654188.\n", + "[I 2025-11-21 21:41:55,966] Trial 1 finished with value: 0.0004037679958901738 and parameters: {'cluster_resolution': 0.9897732067437842, 'n_neighbors': 53, 'cluster_method': 'leiden', 'neighbor_distance_metric': 'cosine'}. Best is trial 1 with value: 0.0004037679958901738.\n", + "[I 2025-11-21 21:41:56,175] Trial 0 finished with value: 0.005358804162630786 and parameters: {'cluster_resolution': 0.8434392689188753, 'n_neighbors': 69, 'cluster_method': 'leiden', 'neighbor_distance_metric': 'euclidean'}. Best is trial 0 with value: 0.005358804162630786.\n", + "[I 2025-11-21 21:41:56,218] Trial 0 finished with value: 0.2467483962182524 and parameters: {'cluster_resolution': 0.262336958957558, 'n_neighbors': 55, 'cluster_method': 'leiden', 'neighbor_distance_metric': 'cosine'}. Best is trial 0 with value: 0.2467483962182524.\n", + "[I 2025-11-21 21:41:56,306] Trial 0 finished with value: -0.00048012388156834604 and parameters: {'cluster_resolution': 1.9043148916877957, 'n_neighbors': 45, 'cluster_method': 'leiden', 'neighbor_distance_metric': 'euclidean'}. Best is trial 0 with value: -0.00048012388156834604.\n", + "[I 2025-11-21 21:41:56,344] Trial 1 finished with value: 0.05783331110842148 and parameters: {'cluster_resolution': 0.3743924356498194, 'n_neighbors': 55, 'cluster_method': 'leiden', 'neighbor_distance_metric': 'cosine'}. Best is trial 1 with value: 0.05783331110842148.\n", + "[I 2025-11-21 21:41:56,834] Trial 0 finished with value: -0.007631951601751959 and parameters: {'cluster_resolution': 1.1935192080900463, 'n_neighbors': 28, 'cluster_method': 'leiden', 'neighbor_distance_metric': 'manhattan'}. Best is trial 0 with value: -0.007631951601751959.\n", + "[I 2025-11-21 21:41:57,092] Trial 1 finished with value: 0.01959367543624807 and parameters: {'cluster_resolution': 1.702092351809396, 'n_neighbors': 23, 'cluster_method': 'leiden', 'neighbor_distance_metric': 'manhattan'}. Best is trial 0 with value: 0.10591701919731593.\n", + "[I 2025-11-21 21:41:57,141] Trial 1 finished with value: 0.010510995457211438 and parameters: {'cluster_resolution': 0.43869230996926756, 'n_neighbors': 16, 'cluster_method': 'leiden', 'neighbor_distance_metric': 'cosine'}. Best is trial 1 with value: 0.010510995457211438.\n", + "[I 2025-11-21 21:41:57,153] Trial 1 finished with value: 0.0003562552337337257 and parameters: {'cluster_resolution': 1.7521661341533552, 'n_neighbors': 33, 'cluster_method': 'leiden', 'neighbor_distance_metric': 'manhattan'}. Best is trial 0 with value: 0.004811809551581636.\n", + "[I 2025-11-21 21:41:57,483] Trial 0 finished with value: -0.017626559509459766 and parameters: {'cluster_resolution': 1.6874654662174542, 'n_neighbors': 74, 'cluster_method': 'leiden', 'neighbor_distance_metric': 'manhattan'}. Best is trial 0 with value: -0.017626559509459766.\n", + "[I 2025-11-21 21:41:57,782] Trial 1 finished with value: 0.008073198574233453 and parameters: {'cluster_resolution': 1.512351275005161, 'n_neighbors': 52, 'cluster_method': 'leiden', 'neighbor_distance_metric': 'euclidean'}. Best is trial 1 with value: 0.008073198574233453.\n", + "[I 2025-11-21 21:41:57,852] Trial 1 finished with value: -1.0 and parameters: {'cluster_resolution': 0.151786175794872, 'n_neighbors': 55, 'cluster_method': 'leiden', 'neighbor_distance_metric': 'manhattan'}. Best is trial 0 with value: -0.00048012388156834604.\n", + "[I 2025-11-21 21:41:59,055] Trial 1 finished with value: 0.0005829005618925827 and parameters: {'cluster_resolution': 1.4262526091689776, 'n_neighbors': 24, 'cluster_method': 'leiden', 'neighbor_distance_metric': 'manhattan'}. Best is trial 1 with value: 0.0005829005618925827.\n", + "[I 2025-11-21 21:41:59,156] Trial 1 finished with value: -0.017433582816213804 and parameters: {'cluster_resolution': 1.6349594176674707, 'n_neighbors': 74, 'cluster_method': 'leiden', 'neighbor_distance_metric': 'cosine'}. Best is trial 0 with value: 0.2467483962182524.\n", + "[I 2025-11-21 21:41:59,447] Trial 1 finished with value: -0.028812649420638204 and parameters: {'cluster_resolution': 1.5782011544324503, 'n_neighbors': 90, 'cluster_method': 'leiden', 'neighbor_distance_metric': 'manhattan'}. Best is trial 0 with value: -0.007631951601751959.\n", + "[I 2025-11-21 21:42:01,616] Trial 0 finished with value: -0.01156512851642597 and parameters: {'cluster_resolution': 2.1415770674556747, 'n_neighbors': 100, 'cluster_method': 'leiden', 'neighbor_distance_metric': 'euclidean'}. Best is trial 0 with value: -0.01156512851642597.\n", + "[I 2025-11-21 21:42:39,809] Trial 1 finished with value: -0.007201972912521659 and parameters: {'cluster_resolution': 1.6050924636725854, 'n_neighbors': 89, 'cluster_method': 'leiden', 'neighbor_distance_metric': 'cosine'}. Best is trial 1 with value: -0.007201972912521659.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " DMSO_heart_7: silhouette=0.000, params={'cluster_resolution': 0.9897732067437842, 'n_neighbors': 53, 'cluster_method': 'leiden', 'neighbor_distance_metric': 'cosine'}\n", + " UCD-0159256: silhouette=-0.003, params={'cluster_resolution': 1.7353083701371663, 'n_neighbors': 9, 'cluster_method': 'leiden', 'neighbor_distance_metric': 'euclidean'}\n", + " UCD-0001766: silhouette=0.023, params={'cluster_resolution': 0.7872371778541689, 'n_neighbors': 45, 'cluster_method': 'leiden', 'neighbor_distance_metric': 'manhattan'}\n", + " UCD-0159262: silhouette=0.106, params={'cluster_resolution': 0.4709986051914493, 'n_neighbors': 95, 'cluster_method': 'leiden', 'neighbor_distance_metric': 'manhattan'}\n", + " UCD-0001915: silhouette=0.051, params={'cluster_resolution': 0.5782847549480996, 'n_neighbors': 25, 'cluster_method': 'leiden', 'neighbor_distance_metric': 'manhattan'}\n", + " UCD-0159279: silhouette=0.044, params={'cluster_resolution': 0.46639823003811576, 'n_neighbors': 70, 'cluster_method': 'leiden', 'neighbor_distance_metric': 'cosine'}\n", + " UCD-0159283: silhouette=0.006, params={'cluster_resolution': 1.4132501494538443, 'n_neighbors': 55, 'cluster_method': 'leiden', 'neighbor_distance_metric': 'manhattan'}\n", + " UCD-0001040: silhouette=0.008, params={'cluster_resolution': 1.0544036964208816, 'n_neighbors': 21, 'cluster_method': 'leiden', 'neighbor_distance_metric': 'euclidean'}\n", + " UCD-0000450: silhouette=0.015, params={'cluster_resolution': 0.6403589869468924, 'n_neighbors': 74, 'cluster_method': 'leiden', 'neighbor_distance_metric': 'cosine'}\n", + " UCD-0159257: silhouette=0.008, params={'cluster_resolution': 1.3137726166334238, 'n_neighbors': 11, 'cluster_method': 'leiden', 'neighbor_distance_metric': 'manhattan'}\n", + " UCD-0001810: silhouette=-0.003, params={'cluster_resolution': 2.1195482127218557, 'n_neighbors': 5, 'cluster_method': 'leiden', 'neighbor_distance_metric': 'manhattan'}\n", + " UCD-0159263: silhouette=-0.001, params={'cluster_resolution': 1.360059781754819, 'n_neighbors': 88, 'cluster_method': 'leiden', 'neighbor_distance_metric': 'euclidean'}\n", + " UCD-0159269: silhouette=0.116, params={'cluster_resolution': 0.4424669352652143, 'n_neighbors': 88, 'cluster_method': 'leiden', 'neighbor_distance_metric': 'manhattan'}\n", + " UCD-0159273: silhouette=-0.004, params={'cluster_resolution': 1.986967168222464, 'n_neighbors': 82, 'cluster_method': 'leiden', 'neighbor_distance_metric': 'cosine'}\n", + " UCD-0159284: silhouette=-1.000, params={'cluster_resolution': 0.2873493437045338, 'n_neighbors': 42, 'cluster_method': 'leiden', 'neighbor_distance_metric': 'manhattan'}\n", + " UCD-0017999: silhouette=0.102, params={'cluster_resolution': 0.31910048844094696, 'n_neighbors': 28, 'cluster_method': 'leiden', 'neighbor_distance_metric': 'cosine'}\n", + " UCD-0001014: silhouette=0.059, params={'cluster_resolution': 0.5940590085940342, 'n_neighbors': 72, 'cluster_method': 'leiden', 'neighbor_distance_metric': 'euclidean'}\n", + " UCD-0159258: silhouette=-0.003, params={'cluster_resolution': 1.2459789444244442, 'n_neighbors': 61, 'cluster_method': 'leiden', 'neighbor_distance_metric': 'manhattan'}\n", + " UCD-0159261: silhouette=0.037, params={'cluster_resolution': 0.8378582755884492, 'n_neighbors': 46, 'cluster_method': 'leiden', 'neighbor_distance_metric': 'euclidean'}\n", + " UCD-0001775: silhouette=0.017, params={'cluster_resolution': 0.47629250412097834, 'n_neighbors': 54, 'cluster_method': 'leiden', 'neighbor_distance_metric': 'cosine'}\n", + " UCD-0159274: silhouette=0.011, params={'cluster_resolution': 0.7930943264793587, 'n_neighbors': 6, 'cluster_method': 'leiden', 'neighbor_distance_metric': 'manhattan'}\n", + " DMSO_heart_19: silhouette=-0.007, params={'cluster_resolution': 1.6050924636725854, 'n_neighbors': 89, 'cluster_method': 'leiden', 'neighbor_distance_metric': 'cosine'}\n", + " UCD-0159285: silhouette=0.008, params={'cluster_resolution': 1.512351275005161, 'n_neighbors': 52, 'cluster_method': 'leiden', 'neighbor_distance_metric': 'euclidean'}\n", + " UCD-0159289: silhouette=0.080, params={'cluster_resolution': 0.4869016642528722, 'n_neighbors': 87, 'cluster_method': 'leiden', 'neighbor_distance_metric': 'euclidean'}\n", + " UCD-0018179: silhouette=0.052, params={'cluster_resolution': 0.9579619090415782, 'n_neighbors': 13, 'cluster_method': 'leiden', 'neighbor_distance_metric': 'manhattan'}\n", + " UCD-0001016: silhouette=-0.008, params={'cluster_resolution': 1.7396290635155816, 'n_neighbors': 22, 'cluster_method': 'leiden', 'neighbor_distance_metric': 'euclidean'}\n", + " UCD-0001804: silhouette=0.004, params={'cluster_resolution': 0.8073606700941288, 'n_neighbors': 77, 'cluster_method': 'leiden', 'neighbor_distance_metric': 'manhattan'}\n", + " UCD-0018207: silhouette=0.035, params={'cluster_resolution': 0.6711562818919401, 'n_neighbors': 100, 'cluster_method': 'leiden', 'neighbor_distance_metric': 'manhattan'}\n", + " UCD-0001844: silhouette=0.065, params={'cluster_resolution': 0.8230715142786573, 'n_neighbors': 49, 'cluster_method': 'leiden', 'neighbor_distance_metric': 'euclidean'}\n", + " UCD-0159270: silhouette=0.021, params={'cluster_resolution': 0.671019441206927, 'n_neighbors': 99, 'cluster_method': 'leiden', 'neighbor_distance_metric': 'manhattan'}\n", + " UCD-0159275: silhouette=0.012, params={'cluster_resolution': 0.5994971696232013, 'n_neighbors': 75, 'cluster_method': 'leiden', 'neighbor_distance_metric': 'manhattan'}\n", + " UCD-0001613: silhouette=-0.016, params={'cluster_resolution': 2.16444881203875, 'n_neighbors': 15, 'cluster_method': 'leiden', 'neighbor_distance_metric': 'cosine'}\n", + " UCD-0001921: silhouette=0.122, params={'cluster_resolution': 0.34549620010570903, 'n_neighbors': 69, 'cluster_method': 'leiden', 'neighbor_distance_metric': 'cosine'}\n", + " UCD-0159293: silhouette=0.031, params={'cluster_resolution': 1.5820364155303759, 'n_neighbors': 40, 'cluster_method': 'leiden', 'neighbor_distance_metric': 'manhattan'}\n", + " UCD-0001808: silhouette=0.024, params={'cluster_resolution': 0.7213025557760123, 'n_neighbors': 45, 'cluster_method': 'leiden', 'neighbor_distance_metric': 'euclidean'}\n", + " UCD-0001801: silhouette=-0.013, params={'cluster_resolution': 1.3974489395194865, 'n_neighbors': 24, 'cluster_method': 'leiden', 'neighbor_distance_metric': 'cosine'}\n", + " UCD-0159265: silhouette=0.005, params={'cluster_resolution': 1.14073578208598, 'n_neighbors': 45, 'cluster_method': 'leiden', 'neighbor_distance_metric': 'cosine'}\n", + " UCD-0159271: silhouette=0.012, params={'cluster_resolution': 1.8989092776881225, 'n_neighbors': 13, 'cluster_method': 'leiden', 'neighbor_distance_metric': 'euclidean'}\n", + " UCD-0001835: silhouette=0.058, params={'cluster_resolution': 0.3743924356498194, 'n_neighbors': 55, 'cluster_method': 'leiden', 'neighbor_distance_metric': 'cosine'}\n", + " UCD-0159280: silhouette=0.011, params={'cluster_resolution': 0.43869230996926756, 'n_neighbors': 16, 'cluster_method': 'leiden', 'neighbor_distance_metric': 'cosine'}\n", + " UCD-0159286: silhouette=-0.020, params={'cluster_resolution': 1.3861580583451385, 'n_neighbors': 15, 'cluster_method': 'leiden', 'neighbor_distance_metric': 'euclidean'}\n", + " UCD-0018131: silhouette=0.247, params={'cluster_resolution': 0.262336958957558, 'n_neighbors': 55, 'cluster_method': 'leiden', 'neighbor_distance_metric': 'cosine'}\n", + " UCD-0001024: silhouette=0.001, params={'cluster_resolution': 1.4262526091689776, 'n_neighbors': 24, 'cluster_method': 'leiden', 'neighbor_distance_metric': 'manhattan'}\n", + " UCD-0001829: silhouette=-0.008, params={'cluster_resolution': 1.1935192080900463, 'n_neighbors': 28, 'cluster_method': 'leiden', 'neighbor_distance_metric': 'manhattan'}\n", + " UCD-0159259: silhouette=0.106, params={'cluster_resolution': 0.34903783900782626, 'n_neighbors': 15, 'cluster_method': 'leiden', 'neighbor_distance_metric': 'euclidean'}\n", + " UCD-0001842: silhouette=-0.000, params={'cluster_resolution': 1.9043148916877957, 'n_neighbors': 45, 'cluster_method': 'leiden', 'neighbor_distance_metric': 'euclidean'}\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "sys:1: CategoricalRemappingWarning: Local categoricals have different encodings, expensive re-encoding is done to perform this merge operation. Consider using a StringCache or an Enum type if the categories are known in advance\n" + ] + } + ], + "source": [ + "# setting best params outputs\n", + "cfret_screen_treatment_best_params_outpath = (\n", + " results_dir / \"cfret_screen_treatment_clustering_params.json\"\n", + ").resolve()\n", + "cfret_screen_treatment_cluster_df_outpath = (\n", + " results_dir / \"cfret_screen_treatment_clustered.parquet\"\n", + ").resolve()\n", + "\n", + "# here we are clustering each treatment-heart combination\n", + "# this will allow us to see how each heart responds to each treatment\n", + "cfret_screen_treatment_clustered_df, cfret_screen_treatment_clustered_best_params = (\n", + " optimized_clustering(\n", + " profiles=cfret_screen_df,\n", + " meta_features=cfret_screen_meta,\n", + " morph_features=cfret_screen_feats,\n", + " treatment_col=\"Metadata_treatment\",\n", + " param_grid=cfret_screen_cluster_param_grid,\n", + " n_trials=500,\n", + " n_jobs=-22,\n", + " study_name=\"CFReT_screen_clustering\",\n", + " )\n", + ")\n", + "\n", + "# save best params as json and dataframe as parquet\n", + "cfret_screen_treatment_clustered_df.write_parquet(\n", + " cfret_screen_treatment_cluster_df_outpath\n", + ")\n", + "with open(cfret_screen_treatment_best_params_outpath, \"w\") as f:\n", + " json.dump(\n", + " cfret_screen_treatment_clustered_best_params,\n", + " f,\n", + " indent=4,\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "8a4a9979", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "shape: (5, 499)
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"B"27"healthy"null"DMSO_heart_7"null1031.77331687.4488341023.15870596.84995293"localhost240927060001""B02"2244"f08""13601323271362343116"-1.714346-2.535695-0.2005322.762689-0.6139780.1246890.33025-0.0384171.281422-0.987717-1.1240531.35118-0.382761-0.324415-2.406365-2.811065…1.2908731.6473380.5072651.0489530.574748-0.159257-0.5702050.79213-0.870146-2.6261830.0315591.241171-0.044313-0.2576330.132283-0.0047991.9277040.1031522.30752.455422-0.7011680.677342-1.218404-2.1899190.371659-0.508734-1.278283-1.529378-2.088097-0.929627-2.14462-2.4432221.224159"DMSO_heart_7_leiden_2"30517200.177326
" + ], + "text/plain": [ + "shape: (5, 499)\n", + "┌───────────┬───────────┬───────────┬───────────┬───┬───────────┬───────────┬───────────┬──────────┐\n", + "│ Metadata_ ┆ Metadata_ ┆ Metadata_ ┆ Metadata_ ┆ … ┆ Metadata_ ┆ Metadata_ ┆ Metadata_ ┆ Metadata │\n", + "│ WellRow ┆ WellCol ┆ heart_num ┆ cell_type ┆ ┆ cluster_i ┆ cluster_n ┆ treatment ┆ _cluster │\n", + "│ --- ┆ --- ┆ ber ┆ --- ┆ ┆ d ┆ _cells ┆ _n_cells ┆ _ratio │\n", + "│ str ┆ i64 ┆ --- ┆ str ┆ ┆ --- ┆ --- ┆ --- ┆ --- │\n", + "│ ┆ ┆ i64 ┆ ┆ ┆ cat ┆ u32 ┆ u32 ┆ f64 │\n", + "╞═══════════╪═══════════╪═══════════╪═══════════╪═══╪═══════════╪═══════════╪═══════════╪══════════╡\n", + "│ B ┆ 2 ┆ 7 ┆ healthy ┆ … ┆ DMSO_hear ┆ 365 ┆ 1720 ┆ 0.212209 │\n", + "│ ┆ ┆ ┆ ┆ ┆ t_7_leide ┆ ┆ ┆ │\n", + "│ ┆ ┆ ┆ ┆ ┆ n_0 ┆ ┆ ┆ │\n", + "│ B ┆ 2 ┆ 7 ┆ healthy ┆ … ┆ DMSO_hear ┆ 288 ┆ 1720 ┆ 0.167442 │\n", + "│ ┆ ┆ ┆ ┆ ┆ t_7_leide ┆ ┆ ┆ │\n", + "│ ┆ ┆ ┆ ┆ ┆ n_3 ┆ ┆ ┆ │\n", + "│ B ┆ 2 ┆ 7 ┆ healthy ┆ … ┆ DMSO_hear ┆ 284 ┆ 1720 ┆ 0.165116 │\n", + "│ ┆ ┆ ┆ ┆ ┆ t_7_leide ┆ ┆ ┆ │\n", + "│ ┆ ┆ ┆ ┆ ┆ n_4 ┆ ┆ ┆ │\n", + "│ B ┆ 2 ┆ 7 ┆ healthy ┆ … ┆ DMSO_hear ┆ 365 ┆ 1720 ┆ 0.212209 │\n", + "│ ┆ ┆ ┆ ┆ ┆ t_7_leide ┆ ┆ ┆ │\n", + "│ ┆ ┆ ┆ ┆ ┆ n_0 ┆ ┆ ┆ │\n", + "│ B ┆ 2 ┆ 7 ┆ healthy ┆ … ┆ DMSO_hear ┆ 305 ┆ 1720 ┆ 0.177326 │\n", + "│ ┆ ┆ ┆ ┆ ┆ t_7_leide ┆ ┆ ┆ │\n", + "│ ┆ ┆ ┆ ┆ ┆ n_2 ┆ ┆ ┆ │\n", + "└───────────┴───────────┴───────────┴───────────┴───┴───────────┴───────────┴───────────┴──────────┘" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# now merge the cluster labels back to the main dataframe\n", + "cfret_screen_df = cfret_screen_df.join(\n", + " cfret_screen_treatment_clustered_df, on=\"Metadata_cell_id\", how=\"left\"\n", + ")\n", + "cfret_screen_df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "dc6be2d0", + "metadata": {}, + "outputs": [], + "source": [ + "treatment_phenotypic_dist_scores = measure_phenotypic_activity(\n", + " profiles=cfret_screen_df,\n", + " on_signature=on_sigs,\n", + " off_signature=off_sigs,\n", + " ref_treatment=\"DMSO_heart_7\",\n", + " cluster_col=\"Metadata_cluster_id\",\n", + " treatment_col=treatment_col,\n", + ")\n", + "\n", + "# save those as csv files\n", + "treatment_phenotypic_dist_scores.write_csv(\n", + " results_dir / \"cfret_screen_treatment_phenotypic_dist_scores.csv\"\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "83bfbb82", + "metadata": {}, + "outputs": [], + "source": [ + "treatment_rankings = identify_compound_hit(\n", + " distance_df=treatment_phenotypic_dist_scores, method=\"weighted_sum\"\n", + ")\n", + "\n", + "# save as csv files\n", + "treatment_rankings.write_csv(results_dir / \"cfret_screen_treatment_rankings.csv\")" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "d3395186", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "shape: (2_296, 6)
ref_clustertreatmenttrt_clusteron_distoff_distexp_cluster_ratio
strstrstrf64f64f64
"DMSO_heart_7_leiden_3""UCD-0159273""UCD-0159273_leiden_13"20.1075128.3828440.039298
"DMSO_heart_7_leiden_0""UCD-0159273""UCD-0159273_leiden_13"21.6270549.3716360.039298
"DMSO_heart_7_leiden_6""UCD-0159273""UCD-0159273_leiden_13"24.22867510.3007840.039298
"DMSO_heart_7_leiden_2""UCD-0159273""UCD-0159273_leiden_13"28.83254510.0666610.039298
"DMSO_heart_7_leiden_1""UCD-0159273""UCD-0159273_leiden_13"20.8831438.1143540.039298
………………
"DMSO_heart_7_leiden_6""UCD-0159263""UCD-0159263_leiden_3"27.97270512.3679470.124474
"DMSO_heart_7_leiden_2""UCD-0159263""UCD-0159263_leiden_3"28.05091610.5259810.124474
"DMSO_heart_7_leiden_1""UCD-0159263""UCD-0159263_leiden_3"24.94403510.5839160.124474
"DMSO_heart_7_leiden_4""UCD-0159263""UCD-0159263_leiden_3"26.07129210.9822880.124474
"DMSO_heart_7_leiden_5""UCD-0159263""UCD-0159263_leiden_3"24.95134710.9928880.124474
" + ], + "text/plain": [ + "shape: (2_296, 6)\n", + "┌────────────────────┬─────────────┬───────────────────┬───────────┬───────────┬───────────────────┐\n", + "│ ref_cluster ┆ treatment ┆ trt_cluster ┆ on_dist ┆ off_dist ┆ exp_cluster_ratio │\n", + "│ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- │\n", + "│ str ┆ str ┆ str ┆ f64 ┆ f64 ┆ f64 │\n", + "╞════════════════════╪═════════════╪═══════════════════╪═══════════╪═══════════╪═══════════════════╡\n", + "│ DMSO_heart_7_leide ┆ UCD-0159273 ┆ UCD-0159273_leide ┆ 20.107512 ┆ 8.382844 ┆ 0.039298 │\n", + "│ n_3 ┆ ┆ n_13 ┆ ┆ ┆ │\n", + "│ DMSO_heart_7_leide ┆ UCD-0159273 ┆ UCD-0159273_leide ┆ 21.627054 ┆ 9.371636 ┆ 0.039298 │\n", + "│ n_0 ┆ ┆ n_13 ┆ ┆ ┆ │\n", + "│ DMSO_heart_7_leide ┆ UCD-0159273 ┆ UCD-0159273_leide ┆ 24.228675 ┆ 10.300784 ┆ 0.039298 │\n", + "│ n_6 ┆ ┆ n_13 ┆ ┆ ┆ │\n", + "│ DMSO_heart_7_leide ┆ UCD-0159273 ┆ UCD-0159273_leide ┆ 28.832545 ┆ 10.066661 ┆ 0.039298 │\n", + "│ n_2 ┆ ┆ n_13 ┆ ┆ ┆ │\n", + "│ DMSO_heart_7_leide ┆ UCD-0159273 ┆ UCD-0159273_leide ┆ 20.883143 ┆ 8.114354 ┆ 0.039298 │\n", + "│ n_1 ┆ ┆ n_13 ┆ ┆ ┆ │\n", + "│ … ┆ … ┆ … ┆ … ┆ … ┆ … │\n", + "│ DMSO_heart_7_leide ┆ UCD-0159263 ┆ UCD-0159263_leide ┆ 27.972705 ┆ 12.367947 ┆ 0.124474 │\n", + "│ n_6 ┆ ┆ n_3 ┆ ┆ ┆ │\n", + "│ DMSO_heart_7_leide ┆ UCD-0159263 ┆ UCD-0159263_leide ┆ 28.050916 ┆ 10.525981 ┆ 0.124474 │\n", + "│ n_2 ┆ ┆ n_3 ┆ ┆ ┆ │\n", + "│ DMSO_heart_7_leide ┆ UCD-0159263 ┆ UCD-0159263_leide ┆ 24.944035 ┆ 10.583916 ┆ 0.124474 │\n", + "│ n_1 ┆ ┆ n_3 ┆ ┆ ┆ │\n", + "│ DMSO_heart_7_leide ┆ UCD-0159263 ┆ UCD-0159263_leide ┆ 26.071292 ┆ 10.982288 ┆ 0.124474 │\n", + "│ n_4 ┆ ┆ n_3 ┆ ┆ ┆ │\n", + "│ DMSO_heart_7_leide ┆ UCD-0159263 ┆ UCD-0159263_leide ┆ 24.951347 ┆ 10.992888 ┆ 0.124474 │\n", + "│ n_5 ┆ ┆ n_3 ┆ ┆ ┆ │\n", + "└────────────────────┴─────────────┴───────────────────┴───────────┴───────────┴───────────────────┘" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "treatment_phenotypic_dist_scores" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "buscar", + "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": "3.12.11" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks/3.cfret-screen-analysis/nbconverted/1.cfret_screen_analysis.py b/notebooks/3.cfret-screen-analysis/nbconverted/1.cfret_screen_analysis.py new file mode 100644 index 0000000..dc70291 --- /dev/null +++ b/notebooks/3.cfret-screen-analysis/nbconverted/1.cfret_screen_analysis.py @@ -0,0 +1,369 @@ +#!/usr/bin/env python + +# # CFReT-Screen analysis +# +# In this notebook, we will be applying `buscar` to the CFReT initial screen. +# +# The resource for this dataset can be found [here](https://github.com/WayScience/targeted_fibrosis_drug_screen/tree/main/3.preprocessing_features) +# + +# In[1]: + + +import json +import pathlib +import sys + +import matplotlib.pyplot as plt +import numpy as np +import polars as pl +import seaborn as sns + +sys.path.append("../../") +from utils.data_utils import split_meta_and_features +from utils.heterogeneity import optimized_clustering +from utils.identify_hits import identify_compound_hit +from utils.io_utils import load_profiles +from utils.metrics import measure_phenotypic_activity + +# from utils.metrics import measure_phenotypic_activity +from utils.preprocess import apply_pca +from utils.signatures import get_signatures + +# ## Parameters +# +# Below are the parameters used for this notebook. The CFReT-screen dataset contains two hearts: **Healthy (Heart 7)** and **Failing (Heart 19)**, which has been diagnosed with dilated cardiomyopathy. +# +# DMSO Control Naming Convention +# +# To distinguish between control conditions from different heart sources, the `Metadata_treatment` column values are modified as follows: +# - **Healthy controls** (Heart 7 + DMSO): `"DMSO_heart_7"` +# - **Failing controls** (Heart 19 + DMSO): `"DMSO_heart_19"` +# +# Parameter Definitions: +# - **`healthy_ref_treatment`**: Reference treatment name for healthy controls +# - **`failing_ref_treatment`**: Reference treatment name for failing heart controls +# - **`treatment_col`**: Column name containing treatment metadata +# - **`cfret_screen_cluster_param_grid`**: Dictionary defining the hyperparameter search space for clustering optimization when assessing heterogeneity across treatments. Includes: +# - `cluster_resolution`: Granularity of clusters (float, 0.1–2.2) +# - `n_neighbors`: Number of neighbors for graph construction (int, 5–100) +# - `cluster_method`: Clustering algorithm (categorical: leiden) +# - `neighbor_distance_metric`: Distance metric for neighbor computation (categorical: euclidean, cosine, manhattan) + +# In[2]: + + +# setting parameters +healthy_ref_treatment = "DMSO_heart_7" +failing_ref_treatment = "DMSO_heart_19" +treatment_col = "Metadata_treatment" + +# parameters used for clustering optimization +cfret_screen_cluster_param_grid = { + # Clustering resolution: how granular the clusters should be + "cluster_resolution": {"type": "float", "low": 0.1, "high": 2.2}, + # Number of neighbors for graph construction + "n_neighbors": {"type": "int", "low": 5, "high": 100}, + # Clustering algorithm + "cluster_method": {"type": "categorical", "choices": ["leiden"]}, + # Distance metric for neighbor computation + "neighbor_distance_metric": { + "type": "categorical", + "choices": ["euclidean", "cosine", "manhattan"], + }, +} + + +# setting paths + +# In[3]: + + +# load in raw data from +cfret_data_dir = pathlib.Path( + "../0.download-data/data/sc-profiles/cfret-screen" +).resolve(strict=True) +cfret_profiles_path = (cfret_data_dir / "cfret_screen_concat_profiles.parquet").resolve( + strict=True +) + +# make results dir +results_dir = pathlib.Path("./results/cfret-screen").resolve() +results_dir.mkdir(parents=True, exist_ok=True) + + +# In[4]: + + +# loading profiles +cfret_screen_df = load_profiles(cfret_profiles_path) +cfret_screen_meta, cfret_screen_feats = split_meta_and_features(cfret_screen_df) + +# updating the treatment name to reflect the heart source for DMSO in healthy cells +# this is our reference for healthy cells when measuring phenotypic activity +cfret_screen_df = cfret_screen_df.with_columns( + pl.when( + (pl.col("Metadata_treatment") == "DMSO") + & (pl.col("Metadata_cell_type") == "healthy") + ) + .then(pl.lit("DMSO_heart_7")) + .otherwise(pl.col("Metadata_treatment")) + .alias("Metadata_treatment") +) +cfret_screen_df = cfret_screen_df.with_columns( + pl.when( + (pl.col("Metadata_treatment") == "DMSO") + & (pl.col("Metadata_cell_type") == "failing") + ) + .then(pl.lit("DMSO_heart_19")) + .otherwise(pl.col("Metadata_treatment")) + .alias("Metadata_treatment") +) + +# Display data +cfret_screen_df.head() + + +# In[5]: + + +print( + f"number of healthy cells {cfret_screen_df.filter(pl.col('Metadata_treatment') == 'DMSO_heart_7').height}" +) +print( + f"number of failing cells {cfret_screen_df.filter(pl.col('Metadata_treatment') == 'DMSO_heart_19').height}" +) + + +# ## Preprocessing + +# Filtering Treatments with Low Cell Counts: +# +# Treatments with low cell counts were removed from the analysis. This reduction in cell numbers is typically caused by cellular toxicity, which leads to cell death and consequently results in insufficient cell representation for downstream analysis. +# +# Low cell count treatments also pose challenges when assessing heterogeneity, as there are not enough data points to form meaningful clusters. To address this, highly toxic compounds with very few surviving cells were excluded from the BUSCAR analysis. +# +# A threshold of 10% was applied based on Scanpy documentation, which recommends having at least 15–100 data points to compute a reliable neighborhood graph. To validate this threshold, we generated a histogram of cell counts and marked the 10th percentile with a red line. Treatments falling below this threshold were removed and excluded from the BUSCAR pipeline. + +# In[6]: + + +# count number of cells per Metadata_treatment and ensure 'count' is Int64 +counts = cfret_screen_df["Metadata_treatment"].value_counts() +counts = counts.with_columns(pl.col("count").cast(pl.Int64)) +counts = counts.sort("count", descending=True) +counts = counts.to_pandas() + + +# In[7]: + + +# using numpy to calculate 10th percentile +tenth_percentile = np.round(np.percentile(counts["count"], 10), 3) +print(f"10th percentile of cell counts: {tenth_percentile} cells") + + +# Plotting cell count distribution + +# In[8]: + + +# setting seaborn style and figure size +sns.set(style="whitegrid") +plt.figure(figsize=(12, 6), dpi=200) + +# plot histogram with seaborn +ax = sns.histplot(data=counts, x="count", bins=100, color="skyblue", kde=True) + +# add 10th percentile vertical line and annotation (tenth_percentile already defined) +ax.axvline( + x=tenth_percentile, + color="red", + linestyle="--", + linewidth=2, + label=f"10th percentile ({int(tenth_percentile)} cells)", +) +ymin, ymax = ax.get_ylim() +ax.text( + tenth_percentile, + ymax * 0.9, + f"10th pct = {tenth_percentile:.0f}", + color="red", + rotation=90, + va="top", + ha="right", + backgroundcolor="white", +) + +# labeling the plot +ax.set_xlabel("Number of Cells") +ax.set_ylabel("Metadata_treatment") +ax.set_title("Cell Count per treeatment in CFRET screen") + +# adding legend +ax.legend() + +# adjust layout +plt.tight_layout() + +# save the plot +plt.savefig(results_dir / "cell_count_per_treatment_cfret_screen.png", dpi=500) + +# display plot +plt.show() + + +# Removing cells under those specific treatments + +# In[9]: + + +# remove treatments with cell counts below the 10th percentile +kept_treatments = counts[counts["count"] >= tenth_percentile][ + "Metadata_treatment" +].tolist() +cfret_screen_df = cfret_screen_df.filter( + pl.col("Metadata_treatment").is_in(kept_treatments) +) + +# print the treatments that were removed +removed_treatments = counts[counts["count"] < tenth_percentile][ + "Metadata_treatment" +].tolist() +print( + "Removed treatments due to low cell counts (below 10th percentile):", + removed_treatments, +) + +cfret_screen_df.head() + + +# ## Buscar pipeline + +# Get on and off signatures + +# In[10]: + + +# once the data is loaded, separate the controls +# here we want the healthy DMSO cells to be the target since the screen consists +# of failing cells treated with compounds +healthy_ref_df = cfret_screen_df.filter(pl.col("Metadata_treatment") == "DMSO_heart_7") +failing_ref_df = cfret_screen_df.filter(pl.col("Metadata_treatment") == "DMSO_heart_19") + +# creating signatures +on_sigs, off_sigs, _ = get_signatures( + ref_profiles=healthy_ref_df, + exp_profiles=failing_ref_df, + morph_feats=cfret_screen_feats, + test_method="mann_whitney_u", +) + +print("length of on and off signatures:", len(on_sigs), len(off_sigs)) + +# save signatures +signatures_dir = results_dir / "CFRet-screen-signatures.json" +with open(signatures_dir, "w") as sig_file: + json.dump( + {"on_signatures": on_sigs, "off_signatures": off_sigs}, sig_file, indent=4 + ) + + +# Assess heterogeneity + +# In[11]: + + +# Convert raw feature space to PCA space that explains 95% of variance +cfret_screen_pca_df = apply_pca( + profiles=cfret_screen_df, + meta_features=cfret_screen_meta, + morph_features=cfret_screen_feats, + var_explained=0.95, +) + +# split meta and features again after PCA +cfret_screen_pca_feats = cfret_screen_pca_df.drop(cfret_screen_meta).columns + + +# In[ ]: + + +# setting best params outputs +cfret_screen_treatment_best_params_outpath = ( + results_dir / "cfret_screen_treatment_clustering_params.json" +).resolve() +cfret_screen_treatment_cluster_df_outpath = ( + results_dir / "cfret_screen_treatment_clustered.parquet" +).resolve() + +# here we are clustering each treatment-heart combination +# this will allow us to see how each heart responds to each treatment +cfret_screen_treatment_clustered_df, cfret_screen_treatment_clustered_best_params = ( + optimized_clustering( + profiles=cfret_screen_df, + meta_features=cfret_screen_meta, + morph_features=cfret_screen_feats, + treatment_col="Metadata_treatment", + param_grid=cfret_screen_cluster_param_grid, + n_trials=500, + n_jobs=-22, + study_name="CFReT_screen_clustering", + ) +) + +# save best params as json and dataframe as parquet +cfret_screen_treatment_clustered_df.write_parquet( + cfret_screen_treatment_cluster_df_outpath +) +with open(cfret_screen_treatment_best_params_outpath, "w") as f: + json.dump( + cfret_screen_treatment_clustered_best_params, + f, + indent=4, + ) + + +# In[13]: + + +# now merge the cluster labels back to the main dataframe +cfret_screen_df = cfret_screen_df.join( + cfret_screen_treatment_clustered_df, on="Metadata_cell_id", how="left" +) +cfret_screen_df.head() + + +# In[14]: + + +treatment_phenotypic_dist_scores = measure_phenotypic_activity( + profiles=cfret_screen_df, + on_signature=on_sigs, + off_signature=off_sigs, + ref_treatment="DMSO_heart_7", + cluster_col="Metadata_cluster_id", + treatment_col=treatment_col, +) + +# save those as csv files +treatment_phenotypic_dist_scores.write_csv( + results_dir / "cfret_screen_treatment_phenotypic_dist_scores.csv" +) + + +# In[15]: + + +treatment_rankings = identify_compound_hit( + distance_df=treatment_phenotypic_dist_scores, method="weighted_sum" +) + +# save as csv files +treatment_rankings.write_csv(results_dir / "cfret_screen_treatment_rankings.csv") + + +# In[16]: + + +treatment_phenotypic_dist_scores diff --git a/poetry.lock b/poetry.lock index 4863766..261e9d8 100644 --- a/poetry.lock +++ b/poetry.lock @@ -3,18 +3,18 @@ [metadata] lock-version = "2.1" python-versions = ">=3.10,<3.14" -content-hash = "b864f6c26ead9c23fb27c4bc21744c1337d2ebba342b64c45738e2adc0837164" +content-hash = "654eaf9cf52f7f9d97f18d036264e4254b1875420c9bd56b5dee4307e97b3d1e" [[package]] name = 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compute_earth_movers_distance( metric=distance_metric, ) - # compute on and off emd scores - on_emd = ot.emd2(weights_ref, weights_exp, on_M) - off_emd = ot.emd2(weights_ref, weights_exp, off_M) + # compute on and off emd scores with increased numItermax to avoid warnings + on_emd = ot.emd2(weights_ref, weights_exp, on_M, numItermax=100000) + off_emd = ot.emd2(weights_ref, weights_exp, off_M, numItermax=100000) return on_emd, off_emd @@ -207,6 +207,14 @@ def measure_phenotypic_activity( ref_profiles = profiles.filter(pl.col(treatment_col) == ref_treatment) exp_profiles = profiles.filter(pl.col(treatment_col) != ref_treatment) + # check that there are profiles to compare + if ref_profiles.height == 0: + raise ValueError( + f"No reference profiles found for treatment '{ref_treatment}'." + ) + if exp_profiles.height == 0: + raise ValueError("No experimental profiles found to compare against.") + # get all unique combinations by using group by ref_clusters = ( ref_profiles.group_by(cluster_col) @@ -239,16 +247,27 @@ def measure_phenotypic_activity( pl.col(cluster_col) == ref_cluster ) - exp_cluster_population_df = exp_profiles.filter( - (pl.col(treatment_col) == treatment) - & (pl.col(cluster_col) == exp_cluster) - ) + if exp_cluster is None: + exp_cluster_population_df = exp_profiles.filter( + (pl.col(treatment_col) == treatment) + & (pl.col(cluster_col).is_null()) + ) + else: + exp_cluster_population_df = exp_profiles.filter( + (pl.col(treatment_col) == treatment) + & (pl.col(cluster_col) == exp_cluster) + ) # Skip if either population is empty if ( ref_cluster_population_df.height == 0 or exp_cluster_population_df.height == 0 ): + print( + f"Skipping comparison: ref_cluster={ref_cluster}, " + f"treatment={treatment}, exp_cluster={exp_cluster} " + "- one of the populations is empty." + ) continue # Calculate EMD distances