From e490d0bea4f69ed43a09d6332ba2fc01bdf9a142 Mon Sep 17 00:00:00 2001 From: Weishan Li Date: Thu, 18 Sep 2025 16:11:08 -0600 Subject: [PATCH 01/18] Add config.yml to .gitignore to prevent tracking of configuration files --- .gitignore | 3 +++ 1 file changed, 3 insertions(+) diff --git a/.gitignore b/.gitignore index b7faf40..1ad4cb0 100644 --- a/.gitignore +++ b/.gitignore @@ -1,3 +1,6 @@ +# Actual config.yml +config.yml + # Byte-compiled / optimized / DLL files __pycache__/ *.py[codz] From 6f818e159324bb0d620a77a430bfa0ffe987684d Mon Sep 17 00:00:00 2001 From: Weishan Li Date: Thu, 18 Sep 2025 16:11:16 -0600 Subject: [PATCH 02/18] Add config.yml.template --- config.yml.template | 8 ++++++++ 1 file changed, 8 insertions(+) create mode 100644 config.yml.template diff --git a/config.yml.template b/config.yml.template new file mode 100644 index 0000000..82c5032 --- /dev/null +++ b/config.yml.template @@ -0,0 +1,8 @@ +data: + depmap_prism: "path/to/depmap/prism/data" + cell_line_info: "secondary-screen-cell-line-info.csv" + dose_response: "secondary-screen-dose-response-curve-parameters.csv" + +api: + openai: + key: "YOUR_OPENAI_API_KEY" From 9965e3b2acff0b7894689708648239f85ddcc476 Mon Sep 17 00:00:00 2001 From: Weishan Li Date: Thu, 18 Sep 2025 16:37:10 -0600 Subject: [PATCH 03/18] Add data wrangling script for DepMap PRISM dataset preprocessing --- .../0.1.wrangle_depmap_prism_data.ipynb | 606 ++++++++++++++++++ .../0.1.wrangle_depmap_prism_data.py | 317 +++++++++ 2 files changed, 923 insertions(+) create mode 100644 analysis/0.data_wrangling/0.1.wrangle_depmap_prism_data.ipynb create mode 100644 analysis/0.data_wrangling/nbconverted/0.1.wrangle_depmap_prism_data.py diff --git a/analysis/0.data_wrangling/0.1.wrangle_depmap_prism_data.ipynb b/analysis/0.data_wrangling/0.1.wrangle_depmap_prism_data.ipynb new file mode 100644 index 0000000..e4d924d --- /dev/null +++ b/analysis/0.data_wrangling/0.1.wrangle_depmap_prism_data.ipynb @@ -0,0 +1,606 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# DepMap PRISM Data Wrangling\n", + "\n", + "This notebook preprocesses the **DepMap PRISM secondary drug repurposing dataset** to produce a clean, deduplicated table of drug-cell line-IC50 values. \n", + "The workflow includes:\n", + "\n", + "1. **Config validation** – Ensure required file paths are set correctly in `config.yml`. \n", + "2. **Data loading** – Import cell line metadata and drug dose–response parameters. \n", + "3. **Deduplication** – Resolve duplicate cell line–drug pairs within each screen (`HTS002`, `MTS010`) by preferring the highest quality curve fit (`r²`) or falling back to reproducible random selection. \n", + "4. **Screen merging** – Combine both screens, prioritizing `MTS010` where overlaps occur. \n", + "5. **QC checks** – Confirm no duplicate (cell line, drug) pairs remain. \n", + "6. **Summary statistics and visualization** – Tabulate and plot dataset composition by tissue and cell line. \n", + "7. **Export** – Save the cleaned dataset for downstream analysis. \n", + "\n", + "The output represents the **preferred IC50 values per unique (cell line, drug) combination**, ready to be used in downsteam agentic system experiments.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import pathlib\n", + "import yaml\n", + "import subprocess\n", + "\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Config Validation" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Running in Jupyter Notebook\n" + ] + } + ], + "source": [ + "IN_NOTEBOOK = False\n", + "try:\n", + " from IPython import get_ipython\n", + " shell = get_ipython().__class__.__name__\n", + " if shell == 'ZMQInteractiveShell':\n", + " print(\"Running in Jupyter Notebook\")\n", + " IN_NOTEBOOK = True\n", + " else:\n", + " print(\"Running in IPython shell\")\n", + "except NameError:\n", + " print(\"Running in standard Python shell\")" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Resolved Path Status\n", + "Config Key \n", + "depmap_prism /mnt/data_nvme1/data/PRISM Exists\n", + "cell_line_info /mnt/data_nvme1/data/PRISM/secondary-screen-ce... Exists\n", + "dose_response /mnt/data_nvme1/data/PRISM/secondary-screen-do... Exists\n" + ] + } + ], + "source": [ + "# --- Step 1: Locate repo root and config file ---\n", + "git_root = subprocess.check_output(\n", + " [\"git\", \"rev-parse\", \"--show-toplevel\"], text=True\n", + ").strip()\n", + "config_path = pathlib.Path(git_root) / \"config.yml\"\n", + "\n", + "if not config_path.exists():\n", + " raise FileNotFoundError(f\"Config file not found at: {config_path}\")\n", + "\n", + "# --- Step 2: Load config.yml ---\n", + "with open(config_path, \"r\") as f:\n", + " config = yaml.safe_load(f)\n", + "\n", + "# --- Step 3: Validate data section ---\n", + "data_cfg = config.get(\"data\")\n", + "if not data_cfg:\n", + " raise ValueError(\"Missing 'data' section in config.yml\")\n", + "\n", + "# Required keys\n", + "required_keys = [\"depmap_prism\", \"cell_line_info\", \"dose_response\"]\n", + "\n", + "# --- Step 4: Collect resolved paths ---\n", + "results = []\n", + "errors = [] # collect problems for later\n", + "for key in required_keys:\n", + " value = data_cfg.get(key)\n", + " if value is None:\n", + " results.append((key, None, \"Missing in config\"))\n", + " errors.append(f\"Config key '{key}' is missing\")\n", + " continue\n", + " \n", + " # depmap_prism is a directory, the others are files inside it\n", + " if key == \"depmap_prism\":\n", + " full_path = pathlib.Path(value)\n", + " else:\n", + " full_path = pathlib.Path(data_cfg[\"depmap_prism\"]) / value\n", + " \n", + " if full_path.exists():\n", + " status = \"Exists\"\n", + " else:\n", + " status = \"Not found\"\n", + " errors.append(f\"Path for '{key}' does not exist: {full_path}\")\n", + " \n", + " results.append((key, str(full_path), status))\n", + "\n", + "# --- Step 5: Display summary nicely ---\n", + "config_df = pd.DataFrame(\n", + " results, columns=[\"Config Key\", \"Resolved Path\", \"Status\"])\n", + "config_df.set_index(\"Config Key\", inplace=True)\n", + "print(config_df)\n", + "\n", + "# --- Step 6: Fail if any errors were collected ---\n", + "if errors:\n", + " raise FileNotFoundError(\n", + " \"Config validation failed:\\n\" + \"\\n\".join(f\"- {e}\" for e in errors) +\n", + " \"\\nPlease refer to /config.yml.template for correct specification.\"\n", + " )" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Preprocessing" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Load depmap PRISM cell line and drug dose response " + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " row_name depmap_id ccle_name primary_tissue \\\n", + "0 ACH-000824 ACH-000824 KYSE510_OESOPHAGUS esophagus \n", + "1 ACH-000954 ACH-000954 HEC1A_ENDOMETRIUM uterus \n", + "2 ACH-000601 ACH-000601 MIAPACA2_PANCREAS pancreas \n", + "3 ACH-000651 ACH-000651 SW620_LARGE_INTESTINE colorectal \n", + "4 ACH-000361 ACH-000361 SKHEP1_LIVER liver \n", + "\n", + " secondary_tissue tertiary_tissue passed_str_profiling \n", + "0 esophagus_squamous NaN True \n", + "1 uterus_endometrium NaN True \n", + "2 NaN NaN True \n", + "3 NaN NaN True \n", + "4 NaN NaN True \n" + ] + } + ], + "source": [ + "cell_line_info_df = pd.read_csv(\n", + " config_df.loc['cell_line_info', 'Resolved Path'])\n", + "print(cell_line_info_df.head())" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " broad_id depmap_id ccle_name screen_id \\\n", + "0 BRD-K71847383-001-12-5 ACH-000879 MFE296_ENDOMETRIUM HTS002 \n", + "1 BRD-K71847383-001-12-5 ACH-000320 PSN1_PANCREAS HTS002 \n", + "2 BRD-K71847383-001-12-5 ACH-001145 OC316_OVARY HTS002 \n", + "3 BRD-K71847383-001-12-5 ACH-000873 KYSE270_OESOPHAGUS HTS002 \n", + "4 BRD-K71847383-001-12-5 ACH-000855 KYSE150_OESOPHAGUS HTS002 \n", + "\n", + " upper_limit lower_limit slope r2 auc ec50 ic50 \\\n", + "0 1 2.122352 -0.022826 -0.026964 1.677789 8.415093e+06 NaN \n", + "1 1 1.325174 -0.237504 -0.147274 1.240300 9.643742e+00 NaN \n", + "2 1 2.089350 -0.302937 0.193893 1.472333 2.776687e-02 NaN \n", + "3 1 1.311820 -0.209393 -0.005460 1.207160 2.654701e+00 NaN \n", + "4 1 1.369799 -0.277530 0.132818 1.229332 5.889041e-01 NaN \n", + "\n", + " name moa target \\\n", + "0 cytarabine ribonucleotide reductase inhibitor POLA1, POLB, POLD1, POLE \n", + "1 cytarabine ribonucleotide reductase inhibitor POLA1, POLB, POLD1, POLE \n", + "2 cytarabine ribonucleotide reductase inhibitor POLA1, POLB, POLD1, POLE \n", + "3 cytarabine ribonucleotide reductase inhibitor POLA1, POLB, POLD1, POLE \n", + "4 cytarabine ribonucleotide reductase inhibitor POLA1, POLB, POLD1, POLE \n", + "\n", + " disease.area indication \\\n", + "0 hematologic malignancy acute lymphoblastic leukemia (ALL), chronic ly... \n", + "1 hematologic malignancy acute lymphoblastic leukemia (ALL), chronic ly... \n", + "2 hematologic malignancy acute lymphoblastic leukemia (ALL), chronic ly... \n", + "3 hematologic malignancy acute lymphoblastic leukemia (ALL), chronic ly... \n", + "4 hematologic malignancy acute lymphoblastic leukemia (ALL), chronic ly... \n", + "\n", + " smiles phase \\\n", + "0 Nc1ccn([C@@H]2O[C@H](CO)[C@@H](O)[C@H]2O)c(=O)... Launched \n", + "1 Nc1ccn([C@@H]2O[C@H](CO)[C@@H](O)[C@H]2O)c(=O)... Launched \n", + "2 Nc1ccn([C@@H]2O[C@H](CO)[C@@H](O)[C@H]2O)c(=O)... Launched \n", + "3 Nc1ccn([C@@H]2O[C@H](CO)[C@@H](O)[C@H]2O)c(=O)... Launched \n", + "4 Nc1ccn([C@@H]2O[C@H](CO)[C@@H](O)[C@H]2O)c(=O)... Launched \n", + "\n", + " passed_str_profiling row_name \n", + "0 True ACH-000879 \n", + "1 True ACH-000320 \n", + "2 True ACH-001145 \n", + "3 True ACH-000873 \n", + "4 True ACH-000855 \n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_207590/2948507465.py:1: DtypeWarning: Columns (14,15) have mixed types. Specify dtype option on import or set low_memory=False.\n", + " dose_response_df = pd.read_csv(config_df.loc['dose_response', 'Resolved Path'])\n" + ] + } + ], + "source": [ + "dose_response_df = pd.read_csv(config_df.loc['dose_response', 'Resolved Path'])\n", + "print(dose_response_df.head())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Perform deduplication and merge\n", + "\n", + "The secondary PRISM repurposing dataset includes two screens: `HTS002` and `MTS010`. \n", + "Both contain overlapping **cell line–drug combinations**, and according to the official PRISM documentation (https://ndownloader.figshare.com/files/20238123), results from `MTS010` should be preferred when available. \n", + "\n", + "The documentation also states that both `'ccle_name'` and `'depmap_id'` can be used to identify cell lines. To ensure robustness, we include both identifiers in all grouping operations. \n", + "\n", + "An additional complication arises because `HTS002` contains duplicate cell line–drug entries that are only uniquely distinguishable when including the `broad_id` (batch–drug identifier). To avoid multiple IC50 values for the same combination—which would create ambiguity and downstream issues for agentic systems—we perform **per-screen deduplication** before merging. \n", + "\n", + "Deduplication is carried out as follows:\n", + "- Within each screen, group by `(smiles, depmap_id, ccle_name)`.\n", + "- If multiple entries exist for a group:\n", + " - Prefer the row with the highest dose–response curve fit quality (`r²` value), if available. \n", + " - Otherwise, select a single row at random, using a fixed seed for reproducibility. \n", + "- `smiles` is treated as the unique identifier for each drug. \n", + "\n", + "Finally, the deduplicated screens are combined, giving **priority to `MTS010`**: if the same cell line–drug pair exists in both screens, the `MTS010` entry is retained.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Deduplicating MTS010 via highest r^2: picked 19909 rows from 19909 total\n", + "Deduplicating HTS002 via highest r^2: picked 317163 rows from 317636 total\n", + " broad_id depmap_id ccle_name screen_id \\\n", + "0 BRD-K31698212-001-02-9 ACH-000012 HCC827_LUNG MTS010 \n", + "1 BRD-K31698212-001-02-9 ACH-000026 253JBV_URINARY_TRACT MTS010 \n", + "2 BRD-K31698212-001-02-9 ACH-000030 PC14_LUNG MTS010 \n", + "3 BRD-K31698212-001-02-9 ACH-000046 ACHN_KIDNEY MTS010 \n", + "4 BRD-K31698212-001-02-9 ACH-000047 GCIY_STOMACH MTS010 \n", + "\n", + " upper_limit lower_limit slope r2 auc ec50 ... \\\n", + "0 1 0.178760 1.963603 0.884767 0.546318 0.046950 ... \n", + "1 1 0.454756 1.174343 0.504446 0.778797 0.196496 ... \n", + "2 1 0.197965 0.661509 0.635062 0.796028 1.162465 ... \n", + "3 1 0.467947 3.732989 0.664784 0.843652 0.577415 ... \n", + "4 1 0.125498 0.840992 0.540419 0.837948 2.222688 ... \n", + "\n", + " name moa target disease.area \\\n", + "0 icotinib EGFR inhibitor EGFR oncology \n", + "1 icotinib EGFR inhibitor EGFR oncology \n", + "2 icotinib EGFR inhibitor EGFR oncology \n", + "3 icotinib EGFR inhibitor EGFR oncology \n", + "4 icotinib EGFR inhibitor EGFR oncology \n", + "\n", + " indication \\\n", + "0 non-small cell lung cancer (NSCLC) \n", + "1 non-small cell lung cancer (NSCLC) \n", + "2 non-small cell lung cancer (NSCLC) \n", + "3 non-small cell lung cancer (NSCLC) \n", + "4 non-small cell lung cancer (NSCLC) \n", + "\n", + " smiles phase passed_str_profiling \\\n", + "0 C#Cc1cccc(Nc2ncnc3cc4OCCOCCOCCOc4cc23)c1 Launched True \n", + "1 C#Cc1cccc(Nc2ncnc3cc4OCCOCCOCCOc4cc23)c1 Launched True \n", + "2 C#Cc1cccc(Nc2ncnc3cc4OCCOCCOCCOc4cc23)c1 Launched True \n", + "3 C#Cc1cccc(Nc2ncnc3cc4OCCOCCOCCOc4cc23)c1 Launched True \n", + "4 C#Cc1cccc(Nc2ncnc3cc4OCCOCCOCCOc4cc23)c1 Launched True \n", + "\n", + " row_name primary_tissue \n", + "0 ACH-000012 lung \n", + "1 ACH-000026 urinary_tract \n", + "2 ACH-000030 lung \n", + "3 ACH-000046 kidney \n", + "4 ACH-000047 gastric \n", + "\n", + "[5 rows x 21 columns]\n" + ] + } + ], + "source": [ + "DEDUP_SEED = 42\n", + "CELL_DRUG_COMBO_KEYS = [\"smiles\",\"depmap_id\",\"ccle_name\"]\n", + "\n", + "# --- Step 0: Keep the two screens of interest; basic QC ---\n", + "df = dose_response_df.query(\"screen_id in ['HTS002','MTS010']\").copy()\n", + "df = df.dropna(subset=CELL_DRUG_COMBO_KEYS + [\"ic50\"]) # ensure keys exist\n", + "df[\"smiles\"] = df[\"smiles\"].astype(str).str.strip() # these identify unique drug\n", + "\n", + "if \"convergence\" in df.columns:\n", + " df = df[df[\"convergence\"].eq(True)]\n", + "\n", + "# --- Step 1: Deduplicate MTS010 by (smiles, cell line) ---\n", + "mts = df[df[\"screen_id\"] == \"MTS010\"].copy()\n", + "if \"r2\" in mts.columns:\n", + " # If multiple rows per (SMILES, cell line) and r^2 is available,\n", + " # pick the highest-r^2 row per (SMILES, cell line)\n", + " # prefer the better dose-reponse curve fit\n", + " idx_mts = mts.groupby(CELL_DRUG_COMBO_KEYS)[\"r2\"].idxmax()\n", + " print(\n", + " f\"Deduplicating MTS010 via highest r^2: picked {len(idx_mts)} \"\n", + " f\"rows from {len(mts)} total\")\n", + " mts_dedup = mts.loc[idx_mts]\n", + "else:\n", + " # No r^2 -> pick one random row per (SMILES, cell line)\n", + " # seed ensures reproducibility\n", + " mts_dedup = mts.groupby(\n", + " CELL_DRUG_COMBO_KEYS, \n", + " group_keys=False).sample(n=1, random_state=DEDUP_SEED)\n", + " print(f\"Deduplicating MTS010: picked {len(mts_dedup)} \"\n", + " f\"rows from {len(mts)} total\")\n", + "\n", + "# --- Step 2: Deduplicate HTS002 by (smiles, cell line) ---\n", + "hts = df[df[\"screen_id\"] == \"HTS002\"].copy()\n", + "if \"r2\" in hts.columns and hts[\"r2\"].notna().any():\n", + " # similarly,\n", + " # pick the highest-r^2 row per (SMILES, cell line) if available\n", + " idx_hts = hts.groupby(CELL_DRUG_COMBO_KEYS)[\"r2\"].idxmax()\n", + " print(f\"Deduplicating HTS002 via highest r^2: picked {len(idx_hts)} \"\n", + " f\"rows from {len(hts)} total\")\n", + " hts_dedup = hts.loc[idx_hts]\n", + "else:\n", + " # same fallback: pick one random row per (SMILES, cell line)\n", + " hts_dedup = hts.groupby(\n", + " CELL_DRUG_COMBO_KEYS,\n", + " group_keys=False).sample(n=1, random_state=DEDUP_SEED)\n", + " print(f\"Deduplicating HTS002: picked {len(hts_dedup)} \"\n", + " f\"rows from {len(hts)} total\")\n", + "\n", + "# --- Step 3: Combine with MTS010 preference ---\n", + "combined = pd.concat([mts_dedup, hts_dedup], ignore_index=True, copy=False)\n", + "combined = combined.drop_duplicates(\n", + " subset=[\"smiles\",\"depmap_id\",\"ccle_name\"], keep=\"first\").copy()\n", + "\n", + "# --- Step 4: attach tissue etc. without row blow-up if (many:1)---\n", + "cli = (cell_line_info_df[[\"depmap_id\",\"ccle_name\",\"primary_tissue\"]]\n", + " .drop_duplicates(subset=[\"depmap_id\",\"ccle_name\"]))\n", + "combined = combined.merge(\n", + " cli, on=[\"depmap_id\",\"ccle_name\"], how=\"left\", validate=\"m:1\")\n", + "\n", + "print(combined.head())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Confirm no duplicate cell-drug combinations" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "duplicates = combined.duplicated(subset=['ccle_name', 'name'], keep=False)\n", + "duplicate_counts = combined[duplicates].groupby(['ccle_name', 'name']).size().\\\n", + " reset_index(name='count')\n", + "duplicate_counts = duplicate_counts[duplicate_counts['count'] > 1]\n", + "\n", + "if not duplicate_counts.empty:\n", + " raise ValueError(\n", + " f\"Found {len(duplicate_counts)} duplicate (cell line, drug) \"\n", + " f\"pairs:\\n{duplicate_counts}\"\n", + " )" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Tabulate/visualize data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### primary tissue - cell line count" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " primary_tissue ccle_name count\n", + "0 bile_duct HUCCT1_BILIARY_TRACT 778\n", + "1 bile_duct HUH28_BILIARY_TRACT 643\n", + "2 bile_duct SNU1079_BILIARY_TRACT 544\n", + "3 bile_duct SNU1196_BILIARY_TRACT 652\n", + "4 bile_duct SNU245_BILIARY_TRACT 413\n", + "5 bile_duct SNU308_BILIARY_TRACT 563\n", + "6 bile_duct SNU869_BILIARY_TRACT 621\n", + "7 bone A673_BONE 629\n", + "8 bone CADOES1_BONE 555\n", + "9 bone CAL78_BONE 557\n", + "10 bone EWS502_BONE 690\n", + "11 bone G292CLONEA141B1_BONE 454\n", + "12 bone HOS_BONE 761\n", + "13 bone MG63_BONE 851\n", + "14 bone MHHES1_BONE 636\n", + "15 bone SJSA1_BONE 517\n", + "16 bone SKES1_BONE 730\n", + "17 bone SW1353_BONE 512\n", + "18 bone U2OS_BONE 611\n", + "19 breast BT474_BREAST 474\n" + ] + } + ], + "source": [ + "grouped_counts = combined.groupby(['primary_tissue', 'ccle_name']).size().\\\n", + " reset_index(name='count')\n", + "print(grouped_counts.head(20))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### number of cell-drug combiantions in dataset, grouped by primary tissue" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "if IN_NOTEBOOK:\n", + " unique_counts = grouped_counts.groupby('primary_tissue')[\n", + " 'ccle_name'].nunique().reset_index(name='unique_ccle_count')\n", + "\n", + " # grouped_counts: columns = ['primary_tissue', 'ccle_name', 'count']\n", + " # unique_counts: columns = ['primary_tissue', 'unique_ccle_count']\n", + "\n", + " # 1) Pick a consistent tissue order\n", + " order = (grouped_counts.groupby('primary_tissue')['count']\n", + " .median().sort_values(ascending=False).index)\n", + "\n", + " # limit to top-N tissues to keep the x-axis readable\n", + " TOP_N = 20\n", + " if TOP_N is not None:\n", + " keep = list(order[:TOP_N])\n", + " grouped_counts = grouped_counts[\n", + " grouped_counts['primary_tissue'].isin(keep)]\n", + " unique_counts = unique_counts[\n", + " unique_counts['primary_tissue'].isin(keep)]\n", + " order = [t for t in order if t in keep]\n", + "\n", + " # Ensure the bottom bar data follows the same order\n", + " unique_counts = unique_counts.set_index('primary_tissue').reindex(order).\\\n", + " reset_index()\n", + "\n", + " # 2) Make vertically stacked subplots with a shared x-axis\n", + " fig, (ax_top, ax_bot) = plt.subplots(\n", + " 2, 1, figsize=(14, 9), sharex=True,\n", + " gridspec_kw={'height_ratios': [2, 1]}\n", + " )\n", + "\n", + " # --- Top: distribution per tissue (box + dots) ---\n", + " sns.boxplot(\n", + " data=grouped_counts, \n", + " x='primary_tissue', \n", + " y='count', \n", + " order=order, \n", + " ax=ax_top)\n", + " sns.stripplot(data=grouped_counts, x='primary_tissue', y='count',\n", + " order=order, ax=ax_top, jitter=True, alpha=0.5)\n", + " ax_top.set_xlabel('')\n", + " ax_top.set_ylabel('# (molecule, cell line) combos')\n", + " ax_top.set_title('Distribution of combos per cell line within each tissue')\n", + "\n", + " # --- Bottom: number of unique cell lines per tissue (bar) ---\n", + " sns.barplot(data=unique_counts, x='primary_tissue', y='unique_ccle_count',\n", + " order=order, ax=ax_bot)\n", + " ax_bot.set_xlabel('Primary tissue')\n", + " ax_bot.set_ylabel('# unique CCLE names')\n", + "\n", + " # Rotate x labels only on the bottom axis\n", + " for label in ax_bot.get_xticklabels():\n", + " label.set_rotation(90)\n", + "\n", + " plt.tight_layout()\n", + " plt.show()\n", + "else:\n", + " print(\"Skipping plotting since not in a notebook environment.\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Export preprocessed data" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "output_path = pathlib.Path(git_root) \\\n", + " / \"data\" / \"processed\" / \"processed_depmap_prism_ic50.csv\"\n", + "output_path.parent.mkdir(parents=True, exist_ok=True)\n", + "combined.to_csv(output_path, index=False)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "dspy-litl-env", + "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": "3.11.13" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/analysis/0.data_wrangling/nbconverted/0.1.wrangle_depmap_prism_data.py b/analysis/0.data_wrangling/nbconverted/0.1.wrangle_depmap_prism_data.py new file mode 100644 index 0000000..245286c --- /dev/null +++ b/analysis/0.data_wrangling/nbconverted/0.1.wrangle_depmap_prism_data.py @@ -0,0 +1,317 @@ +#!/usr/bin/env python +# coding: utf-8 + +# # DepMap PRISM Data Wrangling +# +# This notebook preprocesses the **DepMap PRISM secondary drug repurposing dataset** to produce a clean, deduplicated table of drug-cell line-IC50 values. +# The workflow includes: +# +# 1. **Config validation** – Ensure required file paths are set correctly in `config.yml`. +# 2. **Data loading** – Import cell line metadata and drug dose–response parameters. +# 3. **Deduplication** – Resolve duplicate cell line–drug pairs within each screen (`HTS002`, `MTS010`) by preferring the highest quality curve fit (`r²`) or falling back to reproducible random selection. +# 4. **Screen merging** – Combine both screens, prioritizing `MTS010` where overlaps occur. +# 5. **QC checks** – Confirm no duplicate (cell line, drug) pairs remain. +# 6. **Summary statistics and visualization** – Tabulate and plot dataset composition by tissue and cell line. +# 7. **Export** – Save the cleaned dataset for downstream analysis. +# +# The output represents the **preferred IC50 values per unique (cell line, drug) combination**, ready to be used in downsteam agentic system experiments. +# + +# In[1]: + + +import pathlib +import yaml +import subprocess + +import pandas as pd +import matplotlib.pyplot as plt +import seaborn as sns + + +# ### Config Validation + +# In[2]: + + +IN_NOTEBOOK = False +try: + from IPython import get_ipython + shell = get_ipython().__class__.__name__ + if shell == 'ZMQInteractiveShell': + print("Running in Jupyter Notebook") + IN_NOTEBOOK = True + else: + print("Running in IPython shell") +except NameError: + print("Running in standard Python shell") + + +# In[3]: + + +# --- Step 1: Locate repo root and config file --- +git_root = subprocess.check_output( + ["git", "rev-parse", "--show-toplevel"], text=True +).strip() +config_path = pathlib.Path(git_root) / "config.yml" + +if not config_path.exists(): + raise FileNotFoundError(f"Config file not found at: {config_path}") + +# --- Step 2: Load config.yml --- +with open(config_path, "r") as f: + config = yaml.safe_load(f) + +# --- Step 3: Validate data section --- +data_cfg = config.get("data") +if not data_cfg: + raise ValueError("Missing 'data' section in config.yml") + +# Required keys +required_keys = ["depmap_prism", "cell_line_info", "dose_response"] + +# --- Step 4: Collect resolved paths --- +results = [] +errors = [] # collect problems for later +for key in required_keys: + value = data_cfg.get(key) + if value is None: + results.append((key, None, "Missing in config")) + errors.append(f"Config key '{key}' is missing") + continue + + # depmap_prism is a directory, the others are files inside it + if key == "depmap_prism": + full_path = pathlib.Path(value) + else: + full_path = pathlib.Path(data_cfg["depmap_prism"]) / value + + if full_path.exists(): + status = "Exists" + else: + status = "Not found" + errors.append(f"Path for '{key}' does not exist: {full_path}") + + results.append((key, str(full_path), status)) + +# --- Step 5: Display summary nicely --- +config_df = pd.DataFrame( + results, columns=["Config Key", "Resolved Path", "Status"]) +config_df.set_index("Config Key", inplace=True) +print(config_df) + +# --- Step 6: Fail if any errors were collected --- +if errors: + raise FileNotFoundError( + "Config validation failed:\n" + "\n".join(f"- {e}" for e in errors) + + "\nPlease refer to /config.yml.template for correct specification." + ) + + +# ## Preprocessing + +# ### Load depmap PRISM cell line and drug dose response + +# In[4]: + + +cell_line_info_df = pd.read_csv( + config_df.loc['cell_line_info', 'Resolved Path']) +print(cell_line_info_df.head()) + + +# In[5]: + + +dose_response_df = pd.read_csv(config_df.loc['dose_response', 'Resolved Path']) +print(dose_response_df.head()) + + +# ### Perform deduplication and merge +# +# The secondary PRISM repurposing dataset includes two screens: `HTS002` and `MTS010`. +# Both contain overlapping **cell line–drug combinations**, and according to the official PRISM documentation (https://ndownloader.figshare.com/files/20238123), results from `MTS010` should be preferred when available. +# +# The documentation also states that both `'ccle_name'` and `'depmap_id'` can be used to identify cell lines. To ensure robustness, we include both identifiers in all grouping operations. +# +# An additional complication arises because `HTS002` contains duplicate cell line–drug entries that are only uniquely distinguishable when including the `broad_id` (batch–drug identifier). To avoid multiple IC50 values for the same combination—which would create ambiguity and downstream issues for agentic systems—we perform **per-screen deduplication** before merging. +# +# Deduplication is carried out as follows: +# - Within each screen, group by `(smiles, depmap_id, ccle_name)`. +# - If multiple entries exist for a group: +# - Prefer the row with the highest dose–response curve fit quality (`r²` value), if available. +# - Otherwise, select a single row at random, using a fixed seed for reproducibility. +# - `smiles` is treated as the unique identifier for each drug. +# +# Finally, the deduplicated screens are combined, giving **priority to `MTS010`**: if the same cell line–drug pair exists in both screens, the `MTS010` entry is retained. +# + +# In[6]: + + +DEDUP_SEED = 42 +CELL_DRUG_COMBO_KEYS = ["smiles","depmap_id","ccle_name"] + +# --- Step 0: Keep the two screens of interest; basic QC --- +df = dose_response_df.query("screen_id in ['HTS002','MTS010']").copy() +df = df.dropna(subset=CELL_DRUG_COMBO_KEYS + ["ic50"]) # ensure keys exist +df["smiles"] = df["smiles"].astype(str).str.strip() # these identify unique drug + +if "convergence" in df.columns: + df = df[df["convergence"].eq(True)] + +# --- Step 1: Deduplicate MTS010 by (smiles, cell line) --- +mts = df[df["screen_id"] == "MTS010"].copy() +if "r2" in mts.columns: + # If multiple rows per (SMILES, cell line) and r^2 is available, + # pick the highest-r^2 row per (SMILES, cell line) + # prefer the better dose-reponse curve fit + idx_mts = mts.groupby(CELL_DRUG_COMBO_KEYS)["r2"].idxmax() + print( + f"Deduplicating MTS010 via highest r^2: picked {len(idx_mts)} " + f"rows from {len(mts)} total") + mts_dedup = mts.loc[idx_mts] +else: + # No r^2 -> pick one random row per (SMILES, cell line) + # seed ensures reproducibility + mts_dedup = mts.groupby( + CELL_DRUG_COMBO_KEYS, + group_keys=False).sample(n=1, random_state=DEDUP_SEED) + print(f"Deduplicating MTS010: picked {len(mts_dedup)} " + f"rows from {len(mts)} total") + +# --- Step 2: Deduplicate HTS002 by (smiles, cell line) --- +hts = df[df["screen_id"] == "HTS002"].copy() +if "r2" in hts.columns and hts["r2"].notna().any(): + # similarly, + # pick the highest-r^2 row per (SMILES, cell line) if available + idx_hts = hts.groupby(CELL_DRUG_COMBO_KEYS)["r2"].idxmax() + print(f"Deduplicating HTS002 via highest r^2: picked {len(idx_hts)} " + f"rows from {len(hts)} total") + hts_dedup = hts.loc[idx_hts] +else: + # same fallback: pick one random row per (SMILES, cell line) + hts_dedup = hts.groupby( + CELL_DRUG_COMBO_KEYS, + group_keys=False).sample(n=1, random_state=DEDUP_SEED) + print(f"Deduplicating HTS002: picked {len(hts_dedup)} " + f"rows from {len(hts)} total") + +# --- Step 3: Combine with MTS010 preference --- +combined = pd.concat([mts_dedup, hts_dedup], ignore_index=True, copy=False) +combined = combined.drop_duplicates( + subset=["smiles","depmap_id","ccle_name"], keep="first").copy() + +# --- Step 4: attach tissue etc. without row blow-up if (many:1)--- +cli = (cell_line_info_df[["depmap_id","ccle_name","primary_tissue"]] + .drop_duplicates(subset=["depmap_id","ccle_name"])) +combined = combined.merge( + cli, on=["depmap_id","ccle_name"], how="left", validate="m:1") + +print(combined.head()) + + +# ### Confirm no duplicate cell-drug combinations + +# In[7]: + + +duplicates = combined.duplicated(subset=['ccle_name', 'name'], keep=False) +duplicate_counts = combined[duplicates].groupby(['ccle_name', 'name']).size().\ + reset_index(name='count') +duplicate_counts = duplicate_counts[duplicate_counts['count'] > 1] + +if not duplicate_counts.empty: + raise ValueError( + f"Found {len(duplicate_counts)} duplicate (cell line, drug) " + f"pairs:\n{duplicate_counts}" + ) + + +# ## Tabulate/visualize data + +# ### primary tissue - cell line count + +# In[8]: + + +grouped_counts = combined.groupby(['primary_tissue', 'ccle_name']).size().\ + reset_index(name='count') +print(grouped_counts.head(20)) + + +# ### number of cell-drug combiantions in dataset, grouped by primary tissue + +# In[9]: + + +if IN_NOTEBOOK: + unique_counts = grouped_counts.groupby('primary_tissue')[ + 'ccle_name'].nunique().reset_index(name='unique_ccle_count') + + # grouped_counts: columns = ['primary_tissue', 'ccle_name', 'count'] + # unique_counts: columns = ['primary_tissue', 'unique_ccle_count'] + + # 1) Pick a consistent tissue order + order = (grouped_counts.groupby('primary_tissue')['count'] + .median().sort_values(ascending=False).index) + + # limit to top-N tissues to keep the x-axis readable + TOP_N = 20 + if TOP_N is not None: + keep = list(order[:TOP_N]) + grouped_counts = grouped_counts[ + grouped_counts['primary_tissue'].isin(keep)] + unique_counts = unique_counts[ + unique_counts['primary_tissue'].isin(keep)] + order = [t for t in order if t in keep] + + # Ensure the bottom bar data follows the same order + unique_counts = unique_counts.set_index('primary_tissue').reindex(order).\ + reset_index() + + # 2) Make vertically stacked subplots with a shared x-axis + fig, (ax_top, ax_bot) = plt.subplots( + 2, 1, figsize=(14, 9), sharex=True, + gridspec_kw={'height_ratios': [2, 1]} + ) + + # --- Top: distribution per tissue (box + dots) --- + sns.boxplot( + data=grouped_counts, + x='primary_tissue', + y='count', + order=order, + ax=ax_top) + sns.stripplot(data=grouped_counts, x='primary_tissue', y='count', + order=order, ax=ax_top, jitter=True, alpha=0.5) + ax_top.set_xlabel('') + ax_top.set_ylabel('# (molecule, cell line) combos') + ax_top.set_title('Distribution of combos per cell line within each tissue') + + # --- Bottom: number of unique cell lines per tissue (bar) --- + sns.barplot(data=unique_counts, x='primary_tissue', y='unique_ccle_count', + order=order, ax=ax_bot) + ax_bot.set_xlabel('Primary tissue') + ax_bot.set_ylabel('# unique CCLE names') + + # Rotate x labels only on the bottom axis + for label in ax_bot.get_xticklabels(): + label.set_rotation(90) + + plt.tight_layout() + plt.show() +else: + print("Skipping plotting since not in a notebook environment.") + + +# ## Export preprocessed data + +# In[10]: + + +output_path = pathlib.Path(git_root) \ + / "data" / "processed" / "processed_depmap_prism_ic50.csv" +output_path.parent.mkdir(parents=True, exist_ok=True) +combined.to_csv(output_path, index=False) From 96565eadbfed379add2940bd321e35fcfecb46a9 Mon Sep 17 00:00:00 2001 From: Weishan Li Date: Thu, 18 Sep 2025 16:37:17 -0600 Subject: [PATCH 04/18] Add test for wrangle_depmap_prism script execution --- tests/analysis/test_wrangle_depmap_prism.py | 26 +++++++++++++++++++++ 1 file changed, 26 insertions(+) create mode 100644 tests/analysis/test_wrangle_depmap_prism.py diff --git a/tests/analysis/test_wrangle_depmap_prism.py b/tests/analysis/test_wrangle_depmap_prism.py new file mode 100644 index 0000000..91adead --- /dev/null +++ b/tests/analysis/test_wrangle_depmap_prism.py @@ -0,0 +1,26 @@ +# test_wrangle_depmap_prism.py + +import subprocess +import pathlib +import os + +def test_wrangle_depmap_prism_script_runs(): + """ + Simple test to ensure preprocessing script runs without error. + """ + repo_root = subprocess.check_output( + ["git", "rev-parse", "--show-toplevel"], text=True + ).strip() + repo_root = pathlib.Path(repo_root) + script_path = repo_root / "analysis" / "0.data_wrangling" / "nbconverted" / "0.1.wrangle_depmap_prism_data.py" + + # Run from repo root so `git rev-parse --show-toplevel` and config.yml resolve + result = subprocess.run( + ["python", str(script_path)], + cwd=repo_root, + capture_output=True, + text=True, + env={**os.environ}, # inherit env + ) + + assert result.returncode == 0, f"Script failed:\nSTDOUT:\n{result.stdout}\nSTDERR:\n{result.stderr}" From aba3db42e0301bdd53f8ef30fffa079633b4af22 Mon Sep 17 00:00:00 2001 From: Weishan Li Date: Thu, 18 Sep 2025 16:42:20 -0600 Subject: [PATCH 05/18] Add processed_depmap_prism_ic50.csv to .gitignore to prevent tracking of preprocessing output --- .gitignore | 3 +++ 1 file changed, 3 insertions(+) diff --git a/.gitignore b/.gitignore index 1ad4cb0..31b049c 100644 --- a/.gitignore +++ b/.gitignore @@ -1,3 +1,6 @@ +# Big preprocessing output +/data/processed/processed_depmap_prism_ic50.csv + # Actual config.yml config.yml From c14df8333acd2f0431c7aa4c5362bc1c33e95816 Mon Sep 17 00:00:00 2001 From: Weishan Li Date: Mon, 29 Sep 2025 16:40:36 -0600 Subject: [PATCH 06/18] Add VSCode settings for Python environment and Jupyter notebook configuration --- .vscode/settings.json | 9 +++++++++ 1 file changed, 9 insertions(+) create mode 100644 .vscode/settings.json diff --git a/.vscode/settings.json b/.vscode/settings.json new file mode 100644 index 0000000..21023d6 --- /dev/null +++ b/.vscode/settings.json @@ -0,0 +1,9 @@ +// .vscode/settings.json +{ + "python.envFile": "${workspaceFolder}/.env", + "python.analysis.extraPaths": [ + "agentic_system/src", + "analysis/src" + ], + "jupyter.notebookFileRoot": "${workspaceFolder}" +} From 3433e20e655fc6963ddbd2713437fb91be2646f1 Mon Sep 17 00:00:00 2001 From: Weishan Li Date: Mon, 29 Sep 2025 16:41:22 -0600 Subject: [PATCH 07/18] Add utility function for detecting if running in notebook, make a separate package importable by all notebooks --- analysis/src/nbutils/__init__.py | 0 analysis/src/nbutils/utils.py | 9 +++++++++ 2 files changed, 9 insertions(+) create mode 100644 analysis/src/nbutils/__init__.py create mode 100644 analysis/src/nbutils/utils.py diff --git a/analysis/src/nbutils/__init__.py b/analysis/src/nbutils/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/analysis/src/nbutils/utils.py b/analysis/src/nbutils/utils.py new file mode 100644 index 0000000..e58ea05 --- /dev/null +++ b/analysis/src/nbutils/utils.py @@ -0,0 +1,9 @@ +def detect_notebook() -> bool: + try: + from IPython import get_ipython + shell = get_ipython().__class__.__name__ + return shell == 'ZMQInteractiveShell' + except Exception: + return False + +IN_NOTEBOOK = detect_notebook() \ No newline at end of file From f2f9d0d07b0b2dc3efed697e573e29f5d4b1193e Mon Sep 17 00:00:00 2001 From: Weishan Li Date: Mon, 29 Sep 2025 16:52:36 -0600 Subject: [PATCH 08/18] Elevante IN_NOTEBOOK detection to a utils module of an importable subpacakge, also attempts to reduce dependency on subproc to retrieve the git project root with pathing util --- .../0.1.wrangle_depmap_prism_data.ipynb | 80 ++++++++----------- analysis/src/nbutils/pathing.py | 64 +++++++++++++++ 2 files changed, 99 insertions(+), 45 deletions(-) create mode 100644 analysis/src/nbutils/pathing.py diff --git a/analysis/0.data_wrangling/0.1.wrangle_depmap_prism_data.ipynb b/analysis/0.data_wrangling/0.1.wrangle_depmap_prism_data.ipynb index e4d924d..4595b68 100644 --- a/analysis/0.data_wrangling/0.1.wrangle_depmap_prism_data.ipynb +++ b/analysis/0.data_wrangling/0.1.wrangle_depmap_prism_data.ipynb @@ -24,15 +24,30 @@ "cell_type": "code", "execution_count": 1, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Running in IPython shell\n" + ] + } + ], "source": [ "import pathlib\n", "import yaml\n", - "import subprocess\n", "\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", - "import seaborn as sns" + "import seaborn as sns\n", + "\n", + "from nbutils.pathing import project_file, repo_root\n", + "from nbutils.utils import IN_NOTEBOOK\n", + "\n", + "if IN_NOTEBOOK:\n", + " print(\"Running in IPython shell\")\n", + "else:\n", + " print(\"Running in standard Python shell\")" ] }, { @@ -46,33 +61,6 @@ "cell_type": "code", "execution_count": 2, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Running in Jupyter Notebook\n" - ] - } - ], - "source": [ - "IN_NOTEBOOK = False\n", - "try:\n", - " from IPython import get_ipython\n", - " shell = get_ipython().__class__.__name__\n", - " if shell == 'ZMQInteractiveShell':\n", - " print(\"Running in Jupyter Notebook\")\n", - " IN_NOTEBOOK = True\n", - " else:\n", - " print(\"Running in IPython shell\")\n", - "except NameError:\n", - " print(\"Running in standard Python shell\")" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, "outputs": [ { "name": "stdout", @@ -87,11 +75,10 @@ } ], "source": [ - "# --- Step 1: Locate repo root and config file ---\n", - "git_root = subprocess.check_output(\n", - " [\"git\", \"rev-parse\", \"--show-toplevel\"], text=True\n", - ").strip()\n", - "config_path = pathlib.Path(git_root) / \"config.yml\"\n", + "# --- Step 1: Locate config file ---\n", + "# works only when vscode settings configuring \n", + "# \"jupyter.notebookFileRoot\": \"${workspaceFolder}\" \n", + "config_path = project_file(\"config.yml\")\n", "\n", "if not config_path.exists():\n", " raise FileNotFoundError(f\"Config file not found at: {config_path}\")\n", @@ -162,7 +149,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 3, "metadata": {}, "outputs": [ { @@ -193,7 +180,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 4, "metadata": {}, "outputs": [ { @@ -247,7 +234,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/tmp/ipykernel_207590/2948507465.py:1: DtypeWarning: Columns (14,15) have mixed types. Specify dtype option on import or set low_memory=False.\n", + "/tmp/ipykernel_1999108/2948507465.py:1: DtypeWarning: Columns (14,15) have mixed types. Specify dtype option on import or set low_memory=False.\n", " dose_response_df = pd.read_csv(config_df.loc['dose_response', 'Resolved Path'])\n" ] } @@ -282,7 +269,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 5, "metadata": {}, "outputs": [ { @@ -409,7 +396,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ @@ -441,7 +428,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 7, "metadata": {}, "outputs": [ { @@ -487,12 +474,12 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 8, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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" ] @@ -571,11 +558,14 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ - "output_path = pathlib.Path(git_root) \\\n", + "# output to the /data/processed directory\n", + "# only works as intended with vscode setting \n", + "# \"jupyter.notebookFileRoot\": \"${workspaceFolder}\"\n", + "output_path = repo_root() \\\n", " / \"data\" / \"processed\" / \"processed_depmap_prism_ic50.csv\"\n", "output_path.parent.mkdir(parents=True, exist_ok=True)\n", "combined.to_csv(output_path, index=False)" diff --git a/analysis/src/nbutils/pathing.py b/analysis/src/nbutils/pathing.py new file mode 100644 index 0000000..dc51d74 --- /dev/null +++ b/analysis/src/nbutils/pathing.py @@ -0,0 +1,64 @@ +# analysis/src/nbutils/pathing.py +from __future__ import annotations +from pathlib import Path +from functools import lru_cache +from typing import Iterable, Optional +import os +import shutil + +DEFAULT_MARKERS = (".git", "pyproject.toml", ".gitignore") + +def _default_start() -> Path: + # Prefer the file's directory when running a .py (including nbconvert output) + # Fall back to CWD when in a notebook/REPL (no __file__) + try: + return Path(__file__).resolve().parent + except NameError: + return Path.cwd().resolve() + +@lru_cache(maxsize=1) +def repo_root( + start: Optional[Path] = None, + markers: Iterable[str] = DEFAULT_MARKERS, + env_var: str = "NBUTILS_REPO_ROOT", +) -> Path: + """ + Locate the repo root by walking upward from `start` (or a sensible default). + Precedence: + 1) Explicit env var override NBUTILS_REPO_ROOT + 2) Upward search for any of `markers` + 3) Fallback: `git rev-parse --show-toplevel` if Git is available + """ + # 1) Env override + if (v := os.getenv(env_var)): + p = Path(v).expanduser().resolve() + if p.exists(): + return p + + # 2) Upward search for markers + here = (start or _default_start()).resolve() + for p in (here, *here.parents): + if any((p / m).exists() for m in markers): + return p + + # 3) Git fallback + if shutil.which("git"): + try: + import subprocess + out = subprocess.check_output( + ["git", "rev-parse", "--show-toplevel"], + cwd=str(here), + text=True, + stderr=subprocess.DEVNULL, + ).strip() + p = Path(out).resolve() + if p.exists(): + return p + except Exception: + pass + + raise FileNotFoundError(f"Could not locate repo root from {here}") + +def project_file(*parts: str, **kwargs) -> Path: + """Convenience: repo_root() / parts...""" + return repo_root(**kwargs).joinpath(*parts) From ace720132e838605108b041bcf3de6ad3e88ece7 Mon Sep 17 00:00:00 2001 From: Weishan Li Date: Mon, 29 Sep 2025 17:16:04 -0600 Subject: [PATCH 09/18] Move notebook under subdir notebook --- .../0.1.wrangle_depmap_prism_data.ipynb | 596 ------------------ .../0.1.wrangle_depmap_prism_data.py | 317 ---------- .../0.1.wrangle_depmap_prism_data.ipynb | 596 ++++++++++++++++++ 3 files changed, 596 insertions(+), 913 deletions(-) delete mode 100644 analysis/0.data_wrangling/0.1.wrangle_depmap_prism_data.ipynb delete mode 100644 analysis/0.data_wrangling/nbconverted/0.1.wrangle_depmap_prism_data.py create mode 100644 analysis/notebooks/0.data_wrangling/0.1.wrangle_depmap_prism_data.ipynb diff --git a/analysis/0.data_wrangling/0.1.wrangle_depmap_prism_data.ipynb b/analysis/0.data_wrangling/0.1.wrangle_depmap_prism_data.ipynb deleted file mode 100644 index 4595b68..0000000 --- a/analysis/0.data_wrangling/0.1.wrangle_depmap_prism_data.ipynb +++ /dev/null @@ -1,596 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# DepMap PRISM Data Wrangling\n", - "\n", - "This notebook preprocesses the **DepMap PRISM secondary drug repurposing dataset** to produce a clean, deduplicated table of drug-cell line-IC50 values. \n", - "The workflow includes:\n", - "\n", - "1. **Config validation** – Ensure required file paths are set correctly in `config.yml`. \n", - "2. **Data loading** – Import cell line metadata and drug dose–response parameters. \n", - "3. **Deduplication** – Resolve duplicate cell line–drug pairs within each screen (`HTS002`, `MTS010`) by preferring the highest quality curve fit (`r²`) or falling back to reproducible random selection. \n", - "4. **Screen merging** – Combine both screens, prioritizing `MTS010` where overlaps occur. \n", - "5. **QC checks** – Confirm no duplicate (cell line, drug) pairs remain. \n", - "6. **Summary statistics and visualization** – Tabulate and plot dataset composition by tissue and cell line. \n", - "7. **Export** – Save the cleaned dataset for downstream analysis. \n", - "\n", - "The output represents the **preferred IC50 values per unique (cell line, drug) combination**, ready to be used in downsteam agentic system experiments.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Running in IPython shell\n" - ] - } - ], - "source": [ - "import pathlib\n", - "import yaml\n", - "\n", - "import pandas as pd\n", - "import matplotlib.pyplot as plt\n", - "import seaborn as sns\n", - "\n", - "from nbutils.pathing import project_file, repo_root\n", - "from nbutils.utils import IN_NOTEBOOK\n", - "\n", - "if IN_NOTEBOOK:\n", - " print(\"Running in IPython shell\")\n", - "else:\n", - " print(\"Running in standard Python shell\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Config Validation" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " Resolved Path Status\n", - "Config Key \n", - "depmap_prism /mnt/data_nvme1/data/PRISM Exists\n", - "cell_line_info /mnt/data_nvme1/data/PRISM/secondary-screen-ce... Exists\n", - "dose_response /mnt/data_nvme1/data/PRISM/secondary-screen-do... Exists\n" - ] - } - ], - "source": [ - "# --- Step 1: Locate config file ---\n", - "# works only when vscode settings configuring \n", - "# \"jupyter.notebookFileRoot\": \"${workspaceFolder}\" \n", - "config_path = project_file(\"config.yml\")\n", - "\n", - "if not config_path.exists():\n", - " raise FileNotFoundError(f\"Config file not found at: {config_path}\")\n", - "\n", - "# --- Step 2: Load config.yml ---\n", - "with open(config_path, \"r\") as f:\n", - " config = yaml.safe_load(f)\n", - "\n", - "# --- Step 3: Validate data section ---\n", - "data_cfg = config.get(\"data\")\n", - "if not data_cfg:\n", - " raise ValueError(\"Missing 'data' section in config.yml\")\n", - "\n", - "# Required keys\n", - "required_keys = [\"depmap_prism\", \"cell_line_info\", \"dose_response\"]\n", - "\n", - "# --- Step 4: Collect resolved paths ---\n", - "results = []\n", - "errors = [] # collect problems for later\n", - "for key in required_keys:\n", - " value = data_cfg.get(key)\n", - " if value is None:\n", - " results.append((key, None, \"Missing in config\"))\n", - " errors.append(f\"Config key '{key}' is missing\")\n", - " continue\n", - " \n", - " # depmap_prism is a directory, the others are files inside it\n", - " if key == \"depmap_prism\":\n", - " full_path = pathlib.Path(value)\n", - " else:\n", - " full_path = pathlib.Path(data_cfg[\"depmap_prism\"]) / value\n", - " \n", - " if full_path.exists():\n", - " status = \"Exists\"\n", - " else:\n", - " status = \"Not found\"\n", - " errors.append(f\"Path for '{key}' does not exist: {full_path}\")\n", - " \n", - " results.append((key, str(full_path), status))\n", - "\n", - "# --- Step 5: Display summary nicely ---\n", - "config_df = pd.DataFrame(\n", - " results, columns=[\"Config Key\", \"Resolved Path\", \"Status\"])\n", - "config_df.set_index(\"Config Key\", inplace=True)\n", - "print(config_df)\n", - "\n", - "# --- Step 6: Fail if any errors were collected ---\n", - "if errors:\n", - " raise FileNotFoundError(\n", - " \"Config validation failed:\\n\" + \"\\n\".join(f\"- {e}\" for e in errors) +\n", - " \"\\nPlease refer to /config.yml.template for correct specification.\"\n", - " )" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Preprocessing" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Load depmap PRISM cell line and drug dose response " - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " row_name depmap_id ccle_name primary_tissue \\\n", - "0 ACH-000824 ACH-000824 KYSE510_OESOPHAGUS esophagus \n", - "1 ACH-000954 ACH-000954 HEC1A_ENDOMETRIUM uterus \n", - "2 ACH-000601 ACH-000601 MIAPACA2_PANCREAS pancreas \n", - "3 ACH-000651 ACH-000651 SW620_LARGE_INTESTINE colorectal \n", - "4 ACH-000361 ACH-000361 SKHEP1_LIVER liver \n", - "\n", - " secondary_tissue tertiary_tissue passed_str_profiling \n", - "0 esophagus_squamous NaN True \n", - "1 uterus_endometrium NaN True \n", - "2 NaN NaN True \n", - "3 NaN NaN True \n", - "4 NaN NaN True \n" - ] - } - ], - "source": [ - "cell_line_info_df = pd.read_csv(\n", - " config_df.loc['cell_line_info', 'Resolved Path'])\n", - "print(cell_line_info_df.head())" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " broad_id depmap_id ccle_name screen_id \\\n", - "0 BRD-K71847383-001-12-5 ACH-000879 MFE296_ENDOMETRIUM HTS002 \n", - "1 BRD-K71847383-001-12-5 ACH-000320 PSN1_PANCREAS HTS002 \n", - "2 BRD-K71847383-001-12-5 ACH-001145 OC316_OVARY HTS002 \n", - "3 BRD-K71847383-001-12-5 ACH-000873 KYSE270_OESOPHAGUS HTS002 \n", - "4 BRD-K71847383-001-12-5 ACH-000855 KYSE150_OESOPHAGUS HTS002 \n", - "\n", - " upper_limit lower_limit slope r2 auc ec50 ic50 \\\n", - "0 1 2.122352 -0.022826 -0.026964 1.677789 8.415093e+06 NaN \n", - "1 1 1.325174 -0.237504 -0.147274 1.240300 9.643742e+00 NaN \n", - "2 1 2.089350 -0.302937 0.193893 1.472333 2.776687e-02 NaN \n", - "3 1 1.311820 -0.209393 -0.005460 1.207160 2.654701e+00 NaN \n", - "4 1 1.369799 -0.277530 0.132818 1.229332 5.889041e-01 NaN \n", - "\n", - " name moa target \\\n", - "0 cytarabine ribonucleotide reductase inhibitor POLA1, POLB, POLD1, POLE \n", - "1 cytarabine ribonucleotide reductase inhibitor POLA1, POLB, POLD1, POLE \n", - "2 cytarabine ribonucleotide reductase inhibitor POLA1, POLB, POLD1, POLE \n", - "3 cytarabine ribonucleotide reductase inhibitor POLA1, POLB, POLD1, POLE \n", - "4 cytarabine ribonucleotide reductase inhibitor POLA1, POLB, POLD1, POLE \n", - "\n", - " disease.area indication \\\n", - "0 hematologic malignancy acute lymphoblastic leukemia (ALL), chronic ly... \n", - "1 hematologic malignancy acute lymphoblastic leukemia (ALL), chronic ly... \n", - "2 hematologic malignancy acute lymphoblastic leukemia (ALL), chronic ly... \n", - "3 hematologic malignancy acute lymphoblastic leukemia (ALL), chronic ly... \n", - "4 hematologic malignancy acute lymphoblastic leukemia (ALL), chronic ly... \n", - "\n", - " smiles phase \\\n", - "0 Nc1ccn([C@@H]2O[C@H](CO)[C@@H](O)[C@H]2O)c(=O)... Launched \n", - "1 Nc1ccn([C@@H]2O[C@H](CO)[C@@H](O)[C@H]2O)c(=O)... Launched \n", - "2 Nc1ccn([C@@H]2O[C@H](CO)[C@@H](O)[C@H]2O)c(=O)... Launched \n", - "3 Nc1ccn([C@@H]2O[C@H](CO)[C@@H](O)[C@H]2O)c(=O)... Launched \n", - "4 Nc1ccn([C@@H]2O[C@H](CO)[C@@H](O)[C@H]2O)c(=O)... Launched \n", - "\n", - " passed_str_profiling row_name \n", - "0 True ACH-000879 \n", - "1 True ACH-000320 \n", - "2 True ACH-001145 \n", - "3 True ACH-000873 \n", - "4 True ACH-000855 \n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/tmp/ipykernel_1999108/2948507465.py:1: DtypeWarning: Columns (14,15) have mixed types. Specify dtype option on import or set low_memory=False.\n", - " dose_response_df = pd.read_csv(config_df.loc['dose_response', 'Resolved Path'])\n" - ] - } - ], - "source": [ - "dose_response_df = pd.read_csv(config_df.loc['dose_response', 'Resolved Path'])\n", - "print(dose_response_df.head())" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Perform deduplication and merge\n", - "\n", - "The secondary PRISM repurposing dataset includes two screens: `HTS002` and `MTS010`. \n", - "Both contain overlapping **cell line–drug combinations**, and according to the official PRISM documentation (https://ndownloader.figshare.com/files/20238123), results from `MTS010` should be preferred when available. \n", - "\n", - "The documentation also states that both `'ccle_name'` and `'depmap_id'` can be used to identify cell lines. To ensure robustness, we include both identifiers in all grouping operations. \n", - "\n", - "An additional complication arises because `HTS002` contains duplicate cell line–drug entries that are only uniquely distinguishable when including the `broad_id` (batch–drug identifier). To avoid multiple IC50 values for the same combination—which would create ambiguity and downstream issues for agentic systems—we perform **per-screen deduplication** before merging. \n", - "\n", - "Deduplication is carried out as follows:\n", - "- Within each screen, group by `(smiles, depmap_id, ccle_name)`.\n", - "- If multiple entries exist for a group:\n", - " - Prefer the row with the highest dose–response curve fit quality (`r²` value), if available. \n", - " - Otherwise, select a single row at random, using a fixed seed for reproducibility. \n", - "- `smiles` is treated as the unique identifier for each drug. \n", - "\n", - "Finally, the deduplicated screens are combined, giving **priority to `MTS010`**: if the same cell line–drug pair exists in both screens, the `MTS010` entry is retained.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Deduplicating MTS010 via highest r^2: picked 19909 rows from 19909 total\n", - "Deduplicating HTS002 via highest r^2: picked 317163 rows from 317636 total\n", - " broad_id depmap_id ccle_name screen_id \\\n", - "0 BRD-K31698212-001-02-9 ACH-000012 HCC827_LUNG MTS010 \n", - "1 BRD-K31698212-001-02-9 ACH-000026 253JBV_URINARY_TRACT MTS010 \n", - "2 BRD-K31698212-001-02-9 ACH-000030 PC14_LUNG MTS010 \n", - "3 BRD-K31698212-001-02-9 ACH-000046 ACHN_KIDNEY MTS010 \n", - "4 BRD-K31698212-001-02-9 ACH-000047 GCIY_STOMACH MTS010 \n", - "\n", - " upper_limit lower_limit slope r2 auc ec50 ... \\\n", - "0 1 0.178760 1.963603 0.884767 0.546318 0.046950 ... \n", - "1 1 0.454756 1.174343 0.504446 0.778797 0.196496 ... \n", - "2 1 0.197965 0.661509 0.635062 0.796028 1.162465 ... \n", - "3 1 0.467947 3.732989 0.664784 0.843652 0.577415 ... \n", - "4 1 0.125498 0.840992 0.540419 0.837948 2.222688 ... \n", - "\n", - " name moa target disease.area \\\n", - "0 icotinib EGFR inhibitor EGFR oncology \n", - "1 icotinib EGFR inhibitor EGFR oncology \n", - "2 icotinib EGFR inhibitor EGFR oncology \n", - "3 icotinib EGFR inhibitor EGFR oncology \n", - "4 icotinib EGFR inhibitor EGFR oncology \n", - "\n", - " indication \\\n", - "0 non-small cell lung cancer (NSCLC) \n", - "1 non-small cell lung cancer (NSCLC) \n", - "2 non-small cell lung cancer (NSCLC) \n", - "3 non-small cell lung cancer (NSCLC) \n", - "4 non-small cell lung cancer (NSCLC) \n", - "\n", - " smiles phase passed_str_profiling \\\n", - "0 C#Cc1cccc(Nc2ncnc3cc4OCCOCCOCCOc4cc23)c1 Launched True \n", - "1 C#Cc1cccc(Nc2ncnc3cc4OCCOCCOCCOc4cc23)c1 Launched True \n", - "2 C#Cc1cccc(Nc2ncnc3cc4OCCOCCOCCOc4cc23)c1 Launched True \n", - "3 C#Cc1cccc(Nc2ncnc3cc4OCCOCCOCCOc4cc23)c1 Launched True \n", - "4 C#Cc1cccc(Nc2ncnc3cc4OCCOCCOCCOc4cc23)c1 Launched True \n", - "\n", - " row_name primary_tissue \n", - "0 ACH-000012 lung \n", - "1 ACH-000026 urinary_tract \n", - "2 ACH-000030 lung \n", - "3 ACH-000046 kidney \n", - "4 ACH-000047 gastric \n", - "\n", - "[5 rows x 21 columns]\n" - ] - } - ], - "source": [ - "DEDUP_SEED = 42\n", - "CELL_DRUG_COMBO_KEYS = [\"smiles\",\"depmap_id\",\"ccle_name\"]\n", - "\n", - "# --- Step 0: Keep the two screens of interest; basic QC ---\n", - "df = dose_response_df.query(\"screen_id in ['HTS002','MTS010']\").copy()\n", - "df = df.dropna(subset=CELL_DRUG_COMBO_KEYS + [\"ic50\"]) # ensure keys exist\n", - "df[\"smiles\"] = df[\"smiles\"].astype(str).str.strip() # these identify unique drug\n", - "\n", - "if \"convergence\" in df.columns:\n", - " df = df[df[\"convergence\"].eq(True)]\n", - "\n", - "# --- Step 1: Deduplicate MTS010 by (smiles, cell line) ---\n", - "mts = df[df[\"screen_id\"] == \"MTS010\"].copy()\n", - "if \"r2\" in mts.columns:\n", - " # If multiple rows per (SMILES, cell line) and r^2 is available,\n", - " # pick the highest-r^2 row per (SMILES, cell line)\n", - " # prefer the better dose-reponse curve fit\n", - " idx_mts = mts.groupby(CELL_DRUG_COMBO_KEYS)[\"r2\"].idxmax()\n", - " print(\n", - " f\"Deduplicating MTS010 via highest r^2: picked {len(idx_mts)} \"\n", - " f\"rows from {len(mts)} total\")\n", - " mts_dedup = mts.loc[idx_mts]\n", - "else:\n", - " # No r^2 -> pick one random row per (SMILES, cell line)\n", - " # seed ensures reproducibility\n", - " mts_dedup = mts.groupby(\n", - " CELL_DRUG_COMBO_KEYS, \n", - " group_keys=False).sample(n=1, random_state=DEDUP_SEED)\n", - " print(f\"Deduplicating MTS010: picked {len(mts_dedup)} \"\n", - " f\"rows from {len(mts)} total\")\n", - "\n", - "# --- Step 2: Deduplicate HTS002 by (smiles, cell line) ---\n", - "hts = df[df[\"screen_id\"] == \"HTS002\"].copy()\n", - "if \"r2\" in hts.columns and hts[\"r2\"].notna().any():\n", - " # similarly,\n", - " # pick the highest-r^2 row per (SMILES, cell line) if available\n", - " idx_hts = hts.groupby(CELL_DRUG_COMBO_KEYS)[\"r2\"].idxmax()\n", - " print(f\"Deduplicating HTS002 via highest r^2: picked {len(idx_hts)} \"\n", - " f\"rows from {len(hts)} total\")\n", - " hts_dedup = hts.loc[idx_hts]\n", - "else:\n", - " # same fallback: pick one random row per (SMILES, cell line)\n", - " hts_dedup = hts.groupby(\n", - " CELL_DRUG_COMBO_KEYS,\n", - " group_keys=False).sample(n=1, random_state=DEDUP_SEED)\n", - " print(f\"Deduplicating HTS002: picked {len(hts_dedup)} \"\n", - " f\"rows from {len(hts)} total\")\n", - "\n", - "# --- Step 3: Combine with MTS010 preference ---\n", - "combined = pd.concat([mts_dedup, hts_dedup], ignore_index=True, copy=False)\n", - "combined = combined.drop_duplicates(\n", - " subset=[\"smiles\",\"depmap_id\",\"ccle_name\"], keep=\"first\").copy()\n", - "\n", - "# --- Step 4: attach tissue etc. without row blow-up if (many:1)---\n", - "cli = (cell_line_info_df[[\"depmap_id\",\"ccle_name\",\"primary_tissue\"]]\n", - " .drop_duplicates(subset=[\"depmap_id\",\"ccle_name\"]))\n", - "combined = combined.merge(\n", - " cli, on=[\"depmap_id\",\"ccle_name\"], how=\"left\", validate=\"m:1\")\n", - "\n", - "print(combined.head())" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Confirm no duplicate cell-drug combinations" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "duplicates = combined.duplicated(subset=['ccle_name', 'name'], keep=False)\n", - "duplicate_counts = combined[duplicates].groupby(['ccle_name', 'name']).size().\\\n", - " reset_index(name='count')\n", - "duplicate_counts = duplicate_counts[duplicate_counts['count'] > 1]\n", - "\n", - "if not duplicate_counts.empty:\n", - " raise ValueError(\n", - " f\"Found {len(duplicate_counts)} duplicate (cell line, drug) \"\n", - " f\"pairs:\\n{duplicate_counts}\"\n", - " )" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Tabulate/visualize data" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### primary tissue - cell line count" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " primary_tissue ccle_name count\n", - "0 bile_duct HUCCT1_BILIARY_TRACT 778\n", - "1 bile_duct HUH28_BILIARY_TRACT 643\n", - "2 bile_duct SNU1079_BILIARY_TRACT 544\n", - "3 bile_duct SNU1196_BILIARY_TRACT 652\n", - "4 bile_duct SNU245_BILIARY_TRACT 413\n", - "5 bile_duct SNU308_BILIARY_TRACT 563\n", - "6 bile_duct SNU869_BILIARY_TRACT 621\n", - "7 bone A673_BONE 629\n", - "8 bone CADOES1_BONE 555\n", - "9 bone CAL78_BONE 557\n", - "10 bone EWS502_BONE 690\n", - "11 bone G292CLONEA141B1_BONE 454\n", - "12 bone HOS_BONE 761\n", - "13 bone MG63_BONE 851\n", - "14 bone MHHES1_BONE 636\n", - "15 bone SJSA1_BONE 517\n", - "16 bone SKES1_BONE 730\n", - "17 bone SW1353_BONE 512\n", - "18 bone U2OS_BONE 611\n", - "19 breast BT474_BREAST 474\n" - ] - } - ], - "source": [ - "grouped_counts = combined.groupby(['primary_tissue', 'ccle_name']).size().\\\n", - " reset_index(name='count')\n", - "print(grouped_counts.head(20))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### number of cell-drug combiantions in dataset, grouped by primary tissue" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "if IN_NOTEBOOK:\n", - " unique_counts = grouped_counts.groupby('primary_tissue')[\n", - " 'ccle_name'].nunique().reset_index(name='unique_ccle_count')\n", - "\n", - " # grouped_counts: columns = ['primary_tissue', 'ccle_name', 'count']\n", - " # unique_counts: columns = ['primary_tissue', 'unique_ccle_count']\n", - "\n", - " # 1) Pick a consistent tissue order\n", - " order = (grouped_counts.groupby('primary_tissue')['count']\n", - " .median().sort_values(ascending=False).index)\n", - "\n", - " # limit to top-N tissues to keep the x-axis readable\n", - " TOP_N = 20\n", - " if TOP_N is not None:\n", - " keep = list(order[:TOP_N])\n", - " grouped_counts = grouped_counts[\n", - " grouped_counts['primary_tissue'].isin(keep)]\n", - " unique_counts = unique_counts[\n", - " unique_counts['primary_tissue'].isin(keep)]\n", - " order = [t for t in order if t in keep]\n", - "\n", - " # Ensure the bottom bar data follows the same order\n", - " unique_counts = unique_counts.set_index('primary_tissue').reindex(order).\\\n", - " reset_index()\n", - "\n", - " # 2) Make vertically stacked subplots with a shared x-axis\n", - " fig, (ax_top, ax_bot) = plt.subplots(\n", - " 2, 1, figsize=(14, 9), sharex=True,\n", - " gridspec_kw={'height_ratios': [2, 1]}\n", - " )\n", - "\n", - " # --- Top: distribution per tissue (box + dots) ---\n", - " sns.boxplot(\n", - " data=grouped_counts, \n", - " x='primary_tissue', \n", - " y='count', \n", - " order=order, \n", - " ax=ax_top)\n", - " sns.stripplot(data=grouped_counts, x='primary_tissue', y='count',\n", - " order=order, ax=ax_top, jitter=True, alpha=0.5)\n", - " ax_top.set_xlabel('')\n", - " ax_top.set_ylabel('# (molecule, cell line) combos')\n", - " ax_top.set_title('Distribution of combos per cell line within each tissue')\n", - "\n", - " # --- Bottom: number of unique cell lines per tissue (bar) ---\n", - " sns.barplot(data=unique_counts, x='primary_tissue', y='unique_ccle_count',\n", - " order=order, ax=ax_bot)\n", - " ax_bot.set_xlabel('Primary tissue')\n", - " ax_bot.set_ylabel('# unique CCLE names')\n", - "\n", - " # Rotate x labels only on the bottom axis\n", - " for label in ax_bot.get_xticklabels():\n", - " label.set_rotation(90)\n", - "\n", - " plt.tight_layout()\n", - " plt.show()\n", - "else:\n", - " print(\"Skipping plotting since not in a notebook environment.\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Export preprocessed data" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "# output to the /data/processed directory\n", - "# only works as intended with vscode setting \n", - "# \"jupyter.notebookFileRoot\": \"${workspaceFolder}\"\n", - "output_path = repo_root() \\\n", - " / \"data\" / \"processed\" / \"processed_depmap_prism_ic50.csv\"\n", - "output_path.parent.mkdir(parents=True, exist_ok=True)\n", - "combined.to_csv(output_path, index=False)" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "dspy-litl-env", - "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": "3.11.13" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/analysis/0.data_wrangling/nbconverted/0.1.wrangle_depmap_prism_data.py b/analysis/0.data_wrangling/nbconverted/0.1.wrangle_depmap_prism_data.py deleted file mode 100644 index 245286c..0000000 --- a/analysis/0.data_wrangling/nbconverted/0.1.wrangle_depmap_prism_data.py +++ /dev/null @@ -1,317 +0,0 @@ -#!/usr/bin/env python -# coding: utf-8 - -# # DepMap PRISM Data Wrangling -# -# This notebook preprocesses the **DepMap PRISM secondary drug repurposing dataset** to produce a clean, deduplicated table of drug-cell line-IC50 values. -# The workflow includes: -# -# 1. **Config validation** – Ensure required file paths are set correctly in `config.yml`. -# 2. **Data loading** – Import cell line metadata and drug dose–response parameters. -# 3. **Deduplication** – Resolve duplicate cell line–drug pairs within each screen (`HTS002`, `MTS010`) by preferring the highest quality curve fit (`r²`) or falling back to reproducible random selection. -# 4. **Screen merging** – Combine both screens, prioritizing `MTS010` where overlaps occur. -# 5. **QC checks** – Confirm no duplicate (cell line, drug) pairs remain. -# 6. **Summary statistics and visualization** – Tabulate and plot dataset composition by tissue and cell line. -# 7. **Export** – Save the cleaned dataset for downstream analysis. -# -# The output represents the **preferred IC50 values per unique (cell line, drug) combination**, ready to be used in downsteam agentic system experiments. -# - -# In[1]: - - -import pathlib -import yaml -import subprocess - -import pandas as pd -import matplotlib.pyplot as plt -import seaborn as sns - - -# ### Config Validation - -# In[2]: - - -IN_NOTEBOOK = False -try: - from IPython import get_ipython - shell = get_ipython().__class__.__name__ - if shell == 'ZMQInteractiveShell': - print("Running in Jupyter Notebook") - IN_NOTEBOOK = True - else: - print("Running in IPython shell") -except NameError: - print("Running in standard Python shell") - - -# In[3]: - - -# --- Step 1: Locate repo root and config file --- -git_root = subprocess.check_output( - ["git", "rev-parse", "--show-toplevel"], text=True -).strip() -config_path = pathlib.Path(git_root) / "config.yml" - -if not config_path.exists(): - raise FileNotFoundError(f"Config file not found at: {config_path}") - -# --- Step 2: Load config.yml --- -with open(config_path, "r") as f: - config = yaml.safe_load(f) - -# --- Step 3: Validate data section --- -data_cfg = config.get("data") -if not data_cfg: - raise ValueError("Missing 'data' section in config.yml") - -# Required keys -required_keys = ["depmap_prism", "cell_line_info", "dose_response"] - -# --- Step 4: Collect resolved paths --- -results = [] -errors = [] # collect problems for later -for key in required_keys: - value = data_cfg.get(key) - if value is None: - results.append((key, None, "Missing in config")) - errors.append(f"Config key '{key}' is missing") - continue - - # depmap_prism is a directory, the others are files inside it - if key == "depmap_prism": - full_path = pathlib.Path(value) - else: - full_path = pathlib.Path(data_cfg["depmap_prism"]) / value - - if full_path.exists(): - status = "Exists" - else: - status = "Not found" - errors.append(f"Path for '{key}' does not exist: {full_path}") - - results.append((key, str(full_path), status)) - -# --- Step 5: Display summary nicely --- -config_df = pd.DataFrame( - results, columns=["Config Key", "Resolved Path", "Status"]) -config_df.set_index("Config Key", inplace=True) -print(config_df) - -# --- Step 6: Fail if any errors were collected --- -if errors: - raise FileNotFoundError( - "Config validation failed:\n" + "\n".join(f"- {e}" for e in errors) + - "\nPlease refer to /config.yml.template for correct specification." - ) - - -# ## Preprocessing - -# ### Load depmap PRISM cell line and drug dose response - -# In[4]: - - -cell_line_info_df = pd.read_csv( - config_df.loc['cell_line_info', 'Resolved Path']) -print(cell_line_info_df.head()) - - -# In[5]: - - -dose_response_df = pd.read_csv(config_df.loc['dose_response', 'Resolved Path']) -print(dose_response_df.head()) - - -# ### Perform deduplication and merge -# -# The secondary PRISM repurposing dataset includes two screens: `HTS002` and `MTS010`. -# Both contain overlapping **cell line–drug combinations**, and according to the official PRISM documentation (https://ndownloader.figshare.com/files/20238123), results from `MTS010` should be preferred when available. -# -# The documentation also states that both `'ccle_name'` and `'depmap_id'` can be used to identify cell lines. To ensure robustness, we include both identifiers in all grouping operations. -# -# An additional complication arises because `HTS002` contains duplicate cell line–drug entries that are only uniquely distinguishable when including the `broad_id` (batch–drug identifier). To avoid multiple IC50 values for the same combination—which would create ambiguity and downstream issues for agentic systems—we perform **per-screen deduplication** before merging. -# -# Deduplication is carried out as follows: -# - Within each screen, group by `(smiles, depmap_id, ccle_name)`. -# - If multiple entries exist for a group: -# - Prefer the row with the highest dose–response curve fit quality (`r²` value), if available. -# - Otherwise, select a single row at random, using a fixed seed for reproducibility. -# - `smiles` is treated as the unique identifier for each drug. -# -# Finally, the deduplicated screens are combined, giving **priority to `MTS010`**: if the same cell line–drug pair exists in both screens, the `MTS010` entry is retained. -# - -# In[6]: - - -DEDUP_SEED = 42 -CELL_DRUG_COMBO_KEYS = ["smiles","depmap_id","ccle_name"] - -# --- Step 0: Keep the two screens of interest; basic QC --- -df = dose_response_df.query("screen_id in ['HTS002','MTS010']").copy() -df = df.dropna(subset=CELL_DRUG_COMBO_KEYS + ["ic50"]) # ensure keys exist -df["smiles"] = df["smiles"].astype(str).str.strip() # these identify unique drug - -if "convergence" in df.columns: - df = df[df["convergence"].eq(True)] - -# --- Step 1: Deduplicate MTS010 by (smiles, cell line) --- -mts = df[df["screen_id"] == "MTS010"].copy() -if "r2" in mts.columns: - # If multiple rows per (SMILES, cell line) and r^2 is available, - # pick the highest-r^2 row per (SMILES, cell line) - # prefer the better dose-reponse curve fit - idx_mts = mts.groupby(CELL_DRUG_COMBO_KEYS)["r2"].idxmax() - print( - f"Deduplicating MTS010 via highest r^2: picked {len(idx_mts)} " - f"rows from {len(mts)} total") - mts_dedup = mts.loc[idx_mts] -else: - # No r^2 -> pick one random row per (SMILES, cell line) - # seed ensures reproducibility - mts_dedup = mts.groupby( - CELL_DRUG_COMBO_KEYS, - group_keys=False).sample(n=1, random_state=DEDUP_SEED) - print(f"Deduplicating MTS010: picked {len(mts_dedup)} " - f"rows from {len(mts)} total") - -# --- Step 2: Deduplicate HTS002 by (smiles, cell line) --- -hts = df[df["screen_id"] == "HTS002"].copy() -if "r2" in hts.columns and hts["r2"].notna().any(): - # similarly, - # pick the highest-r^2 row per (SMILES, cell line) if available - idx_hts = hts.groupby(CELL_DRUG_COMBO_KEYS)["r2"].idxmax() - print(f"Deduplicating HTS002 via highest r^2: picked {len(idx_hts)} " - f"rows from {len(hts)} total") - hts_dedup = hts.loc[idx_hts] -else: - # same fallback: pick one random row per (SMILES, cell line) - hts_dedup = hts.groupby( - CELL_DRUG_COMBO_KEYS, - group_keys=False).sample(n=1, random_state=DEDUP_SEED) - print(f"Deduplicating HTS002: picked {len(hts_dedup)} " - f"rows from {len(hts)} total") - -# --- Step 3: Combine with MTS010 preference --- -combined = pd.concat([mts_dedup, hts_dedup], ignore_index=True, copy=False) -combined = combined.drop_duplicates( - subset=["smiles","depmap_id","ccle_name"], keep="first").copy() - -# --- Step 4: attach tissue etc. without row blow-up if (many:1)--- -cli = (cell_line_info_df[["depmap_id","ccle_name","primary_tissue"]] - .drop_duplicates(subset=["depmap_id","ccle_name"])) -combined = combined.merge( - cli, on=["depmap_id","ccle_name"], how="left", validate="m:1") - -print(combined.head()) - - -# ### Confirm no duplicate cell-drug combinations - -# In[7]: - - -duplicates = combined.duplicated(subset=['ccle_name', 'name'], keep=False) -duplicate_counts = combined[duplicates].groupby(['ccle_name', 'name']).size().\ - reset_index(name='count') -duplicate_counts = duplicate_counts[duplicate_counts['count'] > 1] - -if not duplicate_counts.empty: - raise ValueError( - f"Found {len(duplicate_counts)} duplicate (cell line, drug) " - f"pairs:\n{duplicate_counts}" - ) - - -# ## Tabulate/visualize data - -# ### primary tissue - cell line count - -# In[8]: - - -grouped_counts = combined.groupby(['primary_tissue', 'ccle_name']).size().\ - reset_index(name='count') -print(grouped_counts.head(20)) - - -# ### number of cell-drug combiantions in dataset, grouped by primary tissue - -# In[9]: - - -if IN_NOTEBOOK: - unique_counts = grouped_counts.groupby('primary_tissue')[ - 'ccle_name'].nunique().reset_index(name='unique_ccle_count') - - # grouped_counts: columns = ['primary_tissue', 'ccle_name', 'count'] - # unique_counts: columns = ['primary_tissue', 'unique_ccle_count'] - - # 1) Pick a consistent tissue order - order = (grouped_counts.groupby('primary_tissue')['count'] - .median().sort_values(ascending=False).index) - - # limit to top-N tissues to keep the x-axis readable - TOP_N = 20 - if TOP_N is not None: - keep = list(order[:TOP_N]) - grouped_counts = grouped_counts[ - grouped_counts['primary_tissue'].isin(keep)] - unique_counts = unique_counts[ - unique_counts['primary_tissue'].isin(keep)] - order = [t for t in order if t in keep] - - # Ensure the bottom bar data follows the same order - unique_counts = unique_counts.set_index('primary_tissue').reindex(order).\ - reset_index() - - # 2) Make vertically stacked subplots with a shared x-axis - fig, (ax_top, ax_bot) = plt.subplots( - 2, 1, figsize=(14, 9), sharex=True, - gridspec_kw={'height_ratios': [2, 1]} - ) - - # --- Top: distribution per tissue (box + dots) --- - sns.boxplot( - data=grouped_counts, - x='primary_tissue', - y='count', - order=order, - ax=ax_top) - sns.stripplot(data=grouped_counts, x='primary_tissue', y='count', - order=order, ax=ax_top, jitter=True, alpha=0.5) - ax_top.set_xlabel('') - ax_top.set_ylabel('# (molecule, cell line) combos') - ax_top.set_title('Distribution of combos per cell line within each tissue') - - # --- Bottom: number of unique cell lines per tissue (bar) --- - sns.barplot(data=unique_counts, x='primary_tissue', y='unique_ccle_count', - order=order, ax=ax_bot) - ax_bot.set_xlabel('Primary tissue') - ax_bot.set_ylabel('# unique CCLE names') - - # Rotate x labels only on the bottom axis - for label in ax_bot.get_xticklabels(): - label.set_rotation(90) - - plt.tight_layout() - plt.show() -else: - print("Skipping plotting since not in a notebook environment.") - - -# ## Export preprocessed data - -# In[10]: - - -output_path = pathlib.Path(git_root) \ - / "data" / "processed" / "processed_depmap_prism_ic50.csv" -output_path.parent.mkdir(parents=True, exist_ok=True) -combined.to_csv(output_path, index=False) diff --git a/analysis/notebooks/0.data_wrangling/0.1.wrangle_depmap_prism_data.ipynb b/analysis/notebooks/0.data_wrangling/0.1.wrangle_depmap_prism_data.ipynb new file mode 100644 index 0000000..b89d6a5 --- /dev/null +++ b/analysis/notebooks/0.data_wrangling/0.1.wrangle_depmap_prism_data.ipynb @@ -0,0 +1,596 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# DepMap PRISM Data Wrangling\n", + "\n", + "This notebook preprocesses the **DepMap PRISM secondary drug repurposing dataset** to produce a clean, deduplicated table of drug-cell line-IC50 values. \n", + "The workflow includes:\n", + "\n", + "1. **Config validation** – Ensure required file paths are set correctly in `config.yml`. \n", + "2. **Data loading** – Import cell line metadata and drug dose–response parameters. \n", + "3. **Deduplication** – Resolve duplicate cell line–drug pairs within each screen (`HTS002`, `MTS010`) by preferring the highest quality curve fit (`r²`) or falling back to reproducible random selection. \n", + "4. **Screen merging** – Combine both screens, prioritizing `MTS010` where overlaps occur. \n", + "5. **QC checks** – Confirm no duplicate (cell line, drug) pairs remain. \n", + "6. **Summary statistics and visualization** – Tabulate and plot dataset composition by tissue and cell line. \n", + "7. **Export** – Save the cleaned dataset for downstream analysis. \n", + "\n", + "The output represents the **preferred IC50 values per unique (cell line, drug) combination**, ready to be used in downsteam agentic system experiments.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Running in IPython shell\n" + ] + } + ], + "source": [ + "import pathlib\n", + "import yaml\n", + "\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "\n", + "from nbutils.pathing import project_file, repo_root\n", + "from nbutils.utils import IN_NOTEBOOK\n", + "\n", + "if IN_NOTEBOOK:\n", + " print(\"Running in IPython shell\")\n", + "else:\n", + " print(\"Running in standard Python shell\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Config Validation" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Resolved Path Status\n", + "Config Key \n", + "depmap_prism /mnt/data_nvme1/data/PRISM Exists\n", + "cell_line_info /mnt/data_nvme1/data/PRISM/secondary-screen-ce... Exists\n", + "dose_response /mnt/data_nvme1/data/PRISM/secondary-screen-do... Exists\n" + ] + } + ], + "source": [ + "# --- Step 1: Locate config file ---\n", + "# works only when vscode settings configuring \n", + "# \"jupyter.notebookFileRoot\": \"${workspaceFolder}\" \n", + "config_path = project_file(\"config.yml\")\n", + "\n", + "if not config_path.exists():\n", + " raise FileNotFoundError(f\"Config file not found at: {config_path}\")\n", + "\n", + "# --- Step 2: Load config.yml ---\n", + "with open(config_path, \"r\") as f:\n", + " config = yaml.safe_load(f)\n", + "\n", + "# --- Step 3: Validate data section ---\n", + "data_cfg = config.get(\"data\")\n", + "if not data_cfg:\n", + " raise ValueError(\"Missing 'data' section in config.yml\")\n", + "\n", + "# Required keys\n", + "required_keys = [\"depmap_prism\", \"cell_line_info\", \"dose_response\"]\n", + "\n", + "# --- Step 4: Collect resolved paths ---\n", + "results = []\n", + "errors = [] # collect problems for later\n", + "for key in required_keys:\n", + " value = data_cfg.get(key)\n", + " if value is None:\n", + " results.append((key, None, \"Missing in config\"))\n", + " errors.append(f\"Config key '{key}' is missing\")\n", + " continue\n", + " \n", + " # depmap_prism is a directory, the others are files inside it\n", + " if key == \"depmap_prism\":\n", + " full_path = pathlib.Path(value)\n", + " else:\n", + " full_path = pathlib.Path(data_cfg[\"depmap_prism\"]) / value\n", + " \n", + " if full_path.exists():\n", + " status = \"Exists\"\n", + " else:\n", + " status = \"Not found\"\n", + " errors.append(f\"Path for '{key}' does not exist: {full_path}\")\n", + " \n", + " results.append((key, str(full_path), status))\n", + "\n", + "# --- Step 5: Display summary nicely ---\n", + "config_df = pd.DataFrame(\n", + " results, columns=[\"Config Key\", \"Resolved Path\", \"Status\"])\n", + "config_df.set_index(\"Config Key\", inplace=True)\n", + "print(config_df)\n", + "\n", + "# --- Step 6: Fail if any errors were collected ---\n", + "if errors:\n", + " raise FileNotFoundError(\n", + " \"Config validation failed:\\n\" + \"\\n\".join(f\"- {e}\" for e in errors) +\n", + " \"\\nPlease refer to /config.yml.template for correct specification.\"\n", + " )" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Preprocessing" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Load depmap PRISM cell line and drug dose response " + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " row_name depmap_id ccle_name primary_tissue \\\n", + "0 ACH-000824 ACH-000824 KYSE510_OESOPHAGUS esophagus \n", + "1 ACH-000954 ACH-000954 HEC1A_ENDOMETRIUM uterus \n", + "2 ACH-000601 ACH-000601 MIAPACA2_PANCREAS pancreas \n", + "3 ACH-000651 ACH-000651 SW620_LARGE_INTESTINE colorectal \n", + "4 ACH-000361 ACH-000361 SKHEP1_LIVER liver \n", + "\n", + " secondary_tissue tertiary_tissue passed_str_profiling \n", + "0 esophagus_squamous NaN True \n", + "1 uterus_endometrium NaN True \n", + "2 NaN NaN True \n", + "3 NaN NaN True \n", + "4 NaN NaN True \n" + ] + } + ], + "source": [ + "cell_line_info_df = pd.read_csv(\n", + " config_df.loc['cell_line_info', 'Resolved Path'])\n", + "print(cell_line_info_df.head())" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " broad_id depmap_id ccle_name screen_id \\\n", + "0 BRD-K71847383-001-12-5 ACH-000879 MFE296_ENDOMETRIUM HTS002 \n", + "1 BRD-K71847383-001-12-5 ACH-000320 PSN1_PANCREAS HTS002 \n", + "2 BRD-K71847383-001-12-5 ACH-001145 OC316_OVARY HTS002 \n", + "3 BRD-K71847383-001-12-5 ACH-000873 KYSE270_OESOPHAGUS HTS002 \n", + "4 BRD-K71847383-001-12-5 ACH-000855 KYSE150_OESOPHAGUS HTS002 \n", + "\n", + " upper_limit lower_limit slope r2 auc ec50 ic50 \\\n", + "0 1 2.122352 -0.022826 -0.026964 1.677789 8.415093e+06 NaN \n", + "1 1 1.325174 -0.237504 -0.147274 1.240300 9.643742e+00 NaN \n", + "2 1 2.089350 -0.302937 0.193893 1.472333 2.776687e-02 NaN \n", + "3 1 1.311820 -0.209393 -0.005460 1.207160 2.654701e+00 NaN \n", + "4 1 1.369799 -0.277530 0.132818 1.229332 5.889041e-01 NaN \n", + "\n", + " name moa target \\\n", + "0 cytarabine ribonucleotide reductase inhibitor POLA1, POLB, POLD1, POLE \n", + "1 cytarabine ribonucleotide reductase inhibitor POLA1, POLB, POLD1, POLE \n", + "2 cytarabine ribonucleotide reductase inhibitor POLA1, POLB, POLD1, POLE \n", + "3 cytarabine ribonucleotide reductase inhibitor POLA1, POLB, POLD1, POLE \n", + "4 cytarabine ribonucleotide reductase inhibitor POLA1, POLB, POLD1, POLE \n", + "\n", + " disease.area indication \\\n", + "0 hematologic malignancy acute lymphoblastic leukemia (ALL), chronic ly... \n", + "1 hematologic malignancy acute lymphoblastic leukemia (ALL), chronic ly... \n", + "2 hematologic malignancy acute lymphoblastic leukemia (ALL), chronic ly... \n", + "3 hematologic malignancy acute lymphoblastic leukemia (ALL), chronic ly... \n", + "4 hematologic malignancy acute lymphoblastic leukemia (ALL), chronic ly... \n", + "\n", + " smiles phase \\\n", + "0 Nc1ccn([C@@H]2O[C@H](CO)[C@@H](O)[C@H]2O)c(=O)... Launched \n", + "1 Nc1ccn([C@@H]2O[C@H](CO)[C@@H](O)[C@H]2O)c(=O)... Launched \n", + "2 Nc1ccn([C@@H]2O[C@H](CO)[C@@H](O)[C@H]2O)c(=O)... Launched \n", + "3 Nc1ccn([C@@H]2O[C@H](CO)[C@@H](O)[C@H]2O)c(=O)... Launched \n", + "4 Nc1ccn([C@@H]2O[C@H](CO)[C@@H](O)[C@H]2O)c(=O)... Launched \n", + "\n", + " passed_str_profiling row_name \n", + "0 True ACH-000879 \n", + "1 True ACH-000320 \n", + "2 True ACH-001145 \n", + "3 True ACH-000873 \n", + "4 True ACH-000855 \n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_2003083/2948507465.py:1: DtypeWarning: Columns (14,15) have mixed types. Specify dtype option on import or set low_memory=False.\n", + " dose_response_df = pd.read_csv(config_df.loc['dose_response', 'Resolved Path'])\n" + ] + } + ], + "source": [ + "dose_response_df = pd.read_csv(config_df.loc['dose_response', 'Resolved Path'])\n", + "print(dose_response_df.head())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Perform deduplication and merge\n", + "\n", + "The secondary PRISM repurposing dataset includes two screens: `HTS002` and `MTS010`. \n", + "Both contain overlapping **cell line–drug combinations**, and according to the official PRISM documentation (https://ndownloader.figshare.com/files/20238123), results from `MTS010` should be preferred when available. \n", + "\n", + "The documentation also states that both `'ccle_name'` and `'depmap_id'` can be used to identify cell lines. To ensure robustness, we include both identifiers in all grouping operations. \n", + "\n", + "An additional complication arises because `HTS002` contains duplicate cell line–drug entries that are only uniquely distinguishable when including the `broad_id` (batch–drug identifier). To avoid multiple IC50 values for the same combination—which would create ambiguity and downstream issues for agentic systems—we perform **per-screen deduplication** before merging. \n", + "\n", + "Deduplication is carried out as follows:\n", + "- Within each screen, group by `(smiles, depmap_id, ccle_name)`.\n", + "- If multiple entries exist for a group:\n", + " - Prefer the row with the highest dose–response curve fit quality (`r²` value), if available. \n", + " - Otherwise, select a single row at random, using a fixed seed for reproducibility. \n", + "- `smiles` is treated as the unique identifier for each drug. \n", + "\n", + "Finally, the deduplicated screens are combined, giving **priority to `MTS010`**: if the same cell line–drug pair exists in both screens, the `MTS010` entry is retained.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Deduplicating MTS010 via highest r^2: picked 19909 rows from 19909 total\n", + "Deduplicating HTS002 via highest r^2: picked 317163 rows from 317636 total\n", + " broad_id depmap_id ccle_name screen_id \\\n", + "0 BRD-K31698212-001-02-9 ACH-000012 HCC827_LUNG MTS010 \n", + "1 BRD-K31698212-001-02-9 ACH-000026 253JBV_URINARY_TRACT MTS010 \n", + "2 BRD-K31698212-001-02-9 ACH-000030 PC14_LUNG MTS010 \n", + "3 BRD-K31698212-001-02-9 ACH-000046 ACHN_KIDNEY MTS010 \n", + "4 BRD-K31698212-001-02-9 ACH-000047 GCIY_STOMACH MTS010 \n", + "\n", + " upper_limit lower_limit slope r2 auc ec50 ... \\\n", + "0 1 0.178760 1.963603 0.884767 0.546318 0.046950 ... \n", + "1 1 0.454756 1.174343 0.504446 0.778797 0.196496 ... \n", + "2 1 0.197965 0.661509 0.635062 0.796028 1.162465 ... \n", + "3 1 0.467947 3.732989 0.664784 0.843652 0.577415 ... \n", + "4 1 0.125498 0.840992 0.540419 0.837948 2.222688 ... \n", + "\n", + " name moa target disease.area \\\n", + "0 icotinib EGFR inhibitor EGFR oncology \n", + "1 icotinib EGFR inhibitor EGFR oncology \n", + "2 icotinib EGFR inhibitor EGFR oncology \n", + "3 icotinib EGFR inhibitor EGFR oncology \n", + "4 icotinib EGFR inhibitor EGFR oncology \n", + "\n", + " indication \\\n", + "0 non-small cell lung cancer (NSCLC) \n", + "1 non-small cell lung cancer (NSCLC) \n", + "2 non-small cell lung cancer (NSCLC) \n", + "3 non-small cell lung cancer (NSCLC) \n", + "4 non-small cell lung cancer (NSCLC) \n", + "\n", + " smiles phase passed_str_profiling \\\n", + "0 C#Cc1cccc(Nc2ncnc3cc4OCCOCCOCCOc4cc23)c1 Launched True \n", + "1 C#Cc1cccc(Nc2ncnc3cc4OCCOCCOCCOc4cc23)c1 Launched True \n", + "2 C#Cc1cccc(Nc2ncnc3cc4OCCOCCOCCOc4cc23)c1 Launched True \n", + "3 C#Cc1cccc(Nc2ncnc3cc4OCCOCCOCCOc4cc23)c1 Launched True \n", + "4 C#Cc1cccc(Nc2ncnc3cc4OCCOCCOCCOc4cc23)c1 Launched True \n", + "\n", + " row_name primary_tissue \n", + "0 ACH-000012 lung \n", + "1 ACH-000026 urinary_tract \n", + "2 ACH-000030 lung \n", + "3 ACH-000046 kidney \n", + "4 ACH-000047 gastric \n", + "\n", + "[5 rows x 21 columns]\n" + ] + } + ], + "source": [ + "DEDUP_SEED = 42\n", + "CELL_DRUG_COMBO_KEYS = [\"smiles\",\"depmap_id\",\"ccle_name\"]\n", + "\n", + "# --- Step 0: Keep the two screens of interest; basic QC ---\n", + "df = dose_response_df.query(\"screen_id in ['HTS002','MTS010']\").copy()\n", + "df = df.dropna(subset=CELL_DRUG_COMBO_KEYS + [\"ic50\"]) # ensure keys exist\n", + "df[\"smiles\"] = df[\"smiles\"].astype(str).str.strip() # these identify unique drug\n", + "\n", + "if \"convergence\" in df.columns:\n", + " df = df[df[\"convergence\"].eq(True)]\n", + "\n", + "# --- Step 1: Deduplicate MTS010 by (smiles, cell line) ---\n", + "mts = df[df[\"screen_id\"] == \"MTS010\"].copy()\n", + "if \"r2\" in mts.columns:\n", + " # If multiple rows per (SMILES, cell line) and r^2 is available,\n", + " # pick the highest-r^2 row per (SMILES, cell line)\n", + " # prefer the better dose-reponse curve fit\n", + " idx_mts = mts.groupby(CELL_DRUG_COMBO_KEYS)[\"r2\"].idxmax()\n", + " print(\n", + " f\"Deduplicating MTS010 via highest r^2: picked {len(idx_mts)} \"\n", + " f\"rows from {len(mts)} total\")\n", + " mts_dedup = mts.loc[idx_mts]\n", + "else:\n", + " # No r^2 -> pick one random row per (SMILES, cell line)\n", + " # seed ensures reproducibility\n", + " mts_dedup = mts.groupby(\n", + " CELL_DRUG_COMBO_KEYS, \n", + " group_keys=False).sample(n=1, random_state=DEDUP_SEED)\n", + " print(f\"Deduplicating MTS010: picked {len(mts_dedup)} \"\n", + " f\"rows from {len(mts)} total\")\n", + "\n", + "# --- Step 2: Deduplicate HTS002 by (smiles, cell line) ---\n", + "hts = df[df[\"screen_id\"] == \"HTS002\"].copy()\n", + "if \"r2\" in hts.columns and hts[\"r2\"].notna().any():\n", + " # similarly,\n", + " # pick the highest-r^2 row per (SMILES, cell line) if available\n", + " idx_hts = hts.groupby(CELL_DRUG_COMBO_KEYS)[\"r2\"].idxmax()\n", + " print(f\"Deduplicating HTS002 via highest r^2: picked {len(idx_hts)} \"\n", + " f\"rows from {len(hts)} total\")\n", + " hts_dedup = hts.loc[idx_hts]\n", + "else:\n", + " # same fallback: pick one random row per (SMILES, cell line)\n", + " hts_dedup = hts.groupby(\n", + " CELL_DRUG_COMBO_KEYS,\n", + " group_keys=False).sample(n=1, random_state=DEDUP_SEED)\n", + " print(f\"Deduplicating HTS002: picked {len(hts_dedup)} \"\n", + " f\"rows from {len(hts)} total\")\n", + "\n", + "# --- Step 3: Combine with MTS010 preference ---\n", + "combined = pd.concat([mts_dedup, hts_dedup], ignore_index=True, copy=False)\n", + "combined = combined.drop_duplicates(\n", + " subset=[\"smiles\",\"depmap_id\",\"ccle_name\"], keep=\"first\").copy()\n", + "\n", + "# --- Step 4: attach tissue etc. without row blow-up if (many:1)---\n", + "cli = (cell_line_info_df[[\"depmap_id\",\"ccle_name\",\"primary_tissue\"]]\n", + " .drop_duplicates(subset=[\"depmap_id\",\"ccle_name\"]))\n", + "combined = combined.merge(\n", + " cli, on=[\"depmap_id\",\"ccle_name\"], how=\"left\", validate=\"m:1\")\n", + "\n", + "print(combined.head())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Confirm no duplicate cell-drug combinations" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "duplicates = combined.duplicated(subset=['ccle_name', 'name'], keep=False)\n", + "duplicate_counts = combined[duplicates].groupby(['ccle_name', 'name']).size().\\\n", + " reset_index(name='count')\n", + "duplicate_counts = duplicate_counts[duplicate_counts['count'] > 1]\n", + "\n", + "if not duplicate_counts.empty:\n", + " raise ValueError(\n", + " f\"Found {len(duplicate_counts)} duplicate (cell line, drug) \"\n", + " f\"pairs:\\n{duplicate_counts}\"\n", + " )" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Tabulate/visualize data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### primary tissue - cell line count" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " primary_tissue ccle_name count\n", + "0 bile_duct HUCCT1_BILIARY_TRACT 778\n", + "1 bile_duct HUH28_BILIARY_TRACT 643\n", + "2 bile_duct SNU1079_BILIARY_TRACT 544\n", + "3 bile_duct SNU1196_BILIARY_TRACT 652\n", + "4 bile_duct SNU245_BILIARY_TRACT 413\n", + "5 bile_duct SNU308_BILIARY_TRACT 563\n", + "6 bile_duct SNU869_BILIARY_TRACT 621\n", + "7 bone A673_BONE 629\n", + "8 bone CADOES1_BONE 555\n", + "9 bone CAL78_BONE 557\n", + "10 bone EWS502_BONE 690\n", + "11 bone G292CLONEA141B1_BONE 454\n", + "12 bone HOS_BONE 761\n", + "13 bone MG63_BONE 851\n", + "14 bone MHHES1_BONE 636\n", + "15 bone SJSA1_BONE 517\n", + "16 bone SKES1_BONE 730\n", + "17 bone SW1353_BONE 512\n", + "18 bone U2OS_BONE 611\n", + "19 breast BT474_BREAST 474\n" + ] + } + ], + "source": [ + "grouped_counts = combined.groupby(['primary_tissue', 'ccle_name']).size().\\\n", + " reset_index(name='count')\n", + "print(grouped_counts.head(20))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### number of cell-drug combiantions in dataset, grouped by primary tissue" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "if IN_NOTEBOOK:\n", + " unique_counts = grouped_counts.groupby('primary_tissue')[\n", + " 'ccle_name'].nunique().reset_index(name='unique_ccle_count')\n", + "\n", + " # grouped_counts: columns = ['primary_tissue', 'ccle_name', 'count']\n", + " # unique_counts: columns = ['primary_tissue', 'unique_ccle_count']\n", + "\n", + " # 1) Pick a consistent tissue order\n", + " order = (grouped_counts.groupby('primary_tissue')['count']\n", + " .median().sort_values(ascending=False).index)\n", + "\n", + " # limit to top-N tissues to keep the x-axis readable\n", + " TOP_N = 20\n", + " if TOP_N is not None:\n", + " keep = list(order[:TOP_N])\n", + " grouped_counts = grouped_counts[\n", + " grouped_counts['primary_tissue'].isin(keep)]\n", + " unique_counts = unique_counts[\n", + " unique_counts['primary_tissue'].isin(keep)]\n", + " order = [t for t in order if t in keep]\n", + "\n", + " # Ensure the bottom bar data follows the same order\n", + " unique_counts = unique_counts.set_index('primary_tissue').reindex(order).\\\n", + " reset_index()\n", + "\n", + " # 2) Make vertically stacked subplots with a shared x-axis\n", + " fig, (ax_top, ax_bot) = plt.subplots(\n", + " 2, 1, figsize=(14, 9), sharex=True,\n", + " gridspec_kw={'height_ratios': [2, 1]}\n", + " )\n", + "\n", + " # --- Top: distribution per tissue (box + dots) ---\n", + " sns.boxplot(\n", + " data=grouped_counts, \n", + " x='primary_tissue', \n", + " y='count', \n", + " order=order, \n", + " ax=ax_top)\n", + " sns.stripplot(data=grouped_counts, x='primary_tissue', y='count',\n", + " order=order, ax=ax_top, jitter=True, alpha=0.5)\n", + " ax_top.set_xlabel('')\n", + " ax_top.set_ylabel('# (molecule, cell line) combos')\n", + " ax_top.set_title('Distribution of combos per cell line within each tissue')\n", + "\n", + " # --- Bottom: number of unique cell lines per tissue (bar) ---\n", + " sns.barplot(data=unique_counts, x='primary_tissue', y='unique_ccle_count',\n", + " order=order, ax=ax_bot)\n", + " ax_bot.set_xlabel('Primary tissue')\n", + " ax_bot.set_ylabel('# unique CCLE names')\n", + "\n", + " # Rotate x labels only on the bottom axis\n", + " for label in ax_bot.get_xticklabels():\n", + " label.set_rotation(90)\n", + "\n", + " plt.tight_layout()\n", + " plt.show()\n", + "else:\n", + " print(\"Skipping plotting since not in a notebook environment.\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Export preprocessed data" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "# output to the /data/processed directory\n", + "# only works as intended with vscode setting \n", + "# \"jupyter.notebookFileRoot\": \"${workspaceFolder}\"\n", + "output_path = repo_root() \\\n", + " / \"data\" / \"processed\" / \"processed_depmap_prism_ic50.csv\"\n", + "output_path.parent.mkdir(parents=True, exist_ok=True)\n", + "combined.to_csv(output_path, index=False)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "dspy-litl-env", + "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": "3.11.13" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} From cfe2bdd2216fbc5d2265a23f50618b6231fc8eea Mon Sep 17 00:00:00 2001 From: Weishan Li Date: Mon, 29 Sep 2025 17:16:21 -0600 Subject: [PATCH 10/18] Update default markers in pathing utility to include .env and LICENSE --- analysis/src/nbutils/pathing.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/analysis/src/nbutils/pathing.py b/analysis/src/nbutils/pathing.py index dc51d74..93d8955 100644 --- a/analysis/src/nbutils/pathing.py +++ b/analysis/src/nbutils/pathing.py @@ -6,7 +6,7 @@ import os import shutil -DEFAULT_MARKERS = (".git", "pyproject.toml", ".gitignore") +DEFAULT_MARKERS = (".git", ".env", "LICENSE") def _default_start() -> Path: # Prefer the file's directory when running a .py (including nbconvert output) From 8764ad929128d30d738a690675821a255f3d6ba8 Mon Sep 17 00:00:00 2001 From: Weishan Li Date: Mon, 29 Sep 2025 17:16:34 -0600 Subject: [PATCH 11/18] Add pyproject.toml for nbutils package configuration --- analysis/pyproject.toml | 13 +++++++++++++ 1 file changed, 13 insertions(+) create mode 100644 analysis/pyproject.toml diff --git a/analysis/pyproject.toml b/analysis/pyproject.toml new file mode 100644 index 0000000..370f9a4 --- /dev/null +++ b/analysis/pyproject.toml @@ -0,0 +1,13 @@ +[project] +name = "nbutils" +version = "0.1.0" +description = "Notebook-only utilities for this repo" +requires-python = ">=3.9" +authors = [{ name = "Weishan Li" }] + +[build-system] +requires = ["setuptools>=68", "wheel"] +build-backend = "setuptools.build_meta" + +[tool.setuptools.packages.find] +where = ["src"] From 21278244f2db70d0735e229e436f72bb949caef1 Mon Sep 17 00:00:00 2001 From: Weishan Li Date: Mon, 29 Sep 2025 17:16:53 -0600 Subject: [PATCH 12/18] Add script to convert Jupyter notebooks to Python scripts --- analysis/nbconvert_all.sh | 51 +++ .../0.1.wrangle_depmap_prism_data.py | 311 ++++++++++++++++++ 2 files changed, 362 insertions(+) create mode 100755 analysis/nbconvert_all.sh create mode 100644 analysis/scripts/0.data_wrangling/0.1.wrangle_depmap_prism_data.py diff --git a/analysis/nbconvert_all.sh b/analysis/nbconvert_all.sh new file mode 100755 index 0000000..409db3f --- /dev/null +++ b/analysis/nbconvert_all.sh @@ -0,0 +1,51 @@ +#!/usr/bin/env bash +set -euo pipefail + +# Resolve repo root +if git rev-parse --show-toplevel &>/dev/null; then + REPO_ROOT="$(git rev-parse --show-toplevel)" +else + # Fallback: repo root is parent of this script's directory twice + REPO_ROOT="$(realpath "$(dirname "$0")"/..)" +fi + +NB_ROOT="$REPO_ROOT/analysis/notebooks" +OUT_ROOT="$REPO_ROOT/analysis/scripts" + +# Optional --force flag to rebuild everything +FORCE=0 +if [[ "${1:-}" == "--force" ]]; then + FORCE=1 +fi + +echo "Repo root: $REPO_ROOT" +echo "Notebook root: $NB_ROOT" +echo "Output root: $OUT_ROOT" +echo + +mkdir -p "$OUT_ROOT" + +# Find all notebooks (skip checkpoints) +while IFS= read -r -d '' nb; do + rel="${nb#$NB_ROOT/}" # path relative to NB_ROOT + dest_dir="$OUT_ROOT/$(dirname "$rel")" # mirror folder structure + base="$(basename "$rel" .ipynb)" # filename without .ipynb + out_path="$dest_dir/$base.py" + + mkdir -p "$dest_dir" + + if [[ $FORCE -eq 0 && -f "$out_path" && "$nb" -ot "$out_path" ]]; then + echo "Up to date: $rel" + continue + fi + + echo "Converting: $rel -> ${out_path#$REPO_ROOT/}" + jupyter nbconvert \ + --to script \ + --output-dir "$dest_dir" \ + "$nb" + +done < <(find "$NB_ROOT" -type f -name '*.ipynb' -not -path '*/.ipynb_checkpoints/*' -print0) + +echo +echo "Done." diff --git a/analysis/scripts/0.data_wrangling/0.1.wrangle_depmap_prism_data.py b/analysis/scripts/0.data_wrangling/0.1.wrangle_depmap_prism_data.py new file mode 100644 index 0000000..54f002e --- /dev/null +++ b/analysis/scripts/0.data_wrangling/0.1.wrangle_depmap_prism_data.py @@ -0,0 +1,311 @@ +#!/usr/bin/env python +# coding: utf-8 + +# # DepMap PRISM Data Wrangling +# +# This notebook preprocesses the **DepMap PRISM secondary drug repurposing dataset** to produce a clean, deduplicated table of drug-cell line-IC50 values. +# The workflow includes: +# +# 1. **Config validation** – Ensure required file paths are set correctly in `config.yml`. +# 2. **Data loading** – Import cell line metadata and drug dose–response parameters. +# 3. **Deduplication** – Resolve duplicate cell line–drug pairs within each screen (`HTS002`, `MTS010`) by preferring the highest quality curve fit (`r²`) or falling back to reproducible random selection. +# 4. **Screen merging** – Combine both screens, prioritizing `MTS010` where overlaps occur. +# 5. **QC checks** – Confirm no duplicate (cell line, drug) pairs remain. +# 6. **Summary statistics and visualization** – Tabulate and plot dataset composition by tissue and cell line. +# 7. **Export** – Save the cleaned dataset for downstream analysis. +# +# The output represents the **preferred IC50 values per unique (cell line, drug) combination**, ready to be used in downsteam agentic system experiments. +# + +# In[1]: + + +import pathlib +import yaml + +import pandas as pd +import matplotlib.pyplot as plt +import seaborn as sns + +from nbutils.pathing import project_file, repo_root +from nbutils.utils import IN_NOTEBOOK + +if IN_NOTEBOOK: + print("Running in IPython shell") +else: + print("Running in standard Python shell") + + +# ### Config Validation + +# In[2]: + + +# --- Step 1: Locate config file --- +# works only when vscode settings configuring +# "jupyter.notebookFileRoot": "${workspaceFolder}" +config_path = project_file("config.yml") + +if not config_path.exists(): + raise FileNotFoundError(f"Config file not found at: {config_path}") + +# --- Step 2: Load config.yml --- +with open(config_path, "r") as f: + config = yaml.safe_load(f) + +# --- Step 3: Validate data section --- +data_cfg = config.get("data") +if not data_cfg: + raise ValueError("Missing 'data' section in config.yml") + +# Required keys +required_keys = ["depmap_prism", "cell_line_info", "dose_response"] + +# --- Step 4: Collect resolved paths --- +results = [] +errors = [] # collect problems for later +for key in required_keys: + value = data_cfg.get(key) + if value is None: + results.append((key, None, "Missing in config")) + errors.append(f"Config key '{key}' is missing") + continue + + # depmap_prism is a directory, the others are files inside it + if key == "depmap_prism": + full_path = pathlib.Path(value) + else: + full_path = pathlib.Path(data_cfg["depmap_prism"]) / value + + if full_path.exists(): + status = "Exists" + else: + status = "Not found" + errors.append(f"Path for '{key}' does not exist: {full_path}") + + results.append((key, str(full_path), status)) + +# --- Step 5: Display summary nicely --- +config_df = pd.DataFrame( + results, columns=["Config Key", "Resolved Path", "Status"]) +config_df.set_index("Config Key", inplace=True) +print(config_df) + +# --- Step 6: Fail if any errors were collected --- +if errors: + raise FileNotFoundError( + "Config validation failed:\n" + "\n".join(f"- {e}" for e in errors) + + "\nPlease refer to /config.yml.template for correct specification." + ) + + +# ## Preprocessing + +# ### Load depmap PRISM cell line and drug dose response + +# In[3]: + + +cell_line_info_df = pd.read_csv( + config_df.loc['cell_line_info', 'Resolved Path']) +print(cell_line_info_df.head()) + + +# In[4]: + + +dose_response_df = pd.read_csv(config_df.loc['dose_response', 'Resolved Path']) +print(dose_response_df.head()) + + +# ### Perform deduplication and merge +# +# The secondary PRISM repurposing dataset includes two screens: `HTS002` and `MTS010`. +# Both contain overlapping **cell line–drug combinations**, and according to the official PRISM documentation (https://ndownloader.figshare.com/files/20238123), results from `MTS010` should be preferred when available. +# +# The documentation also states that both `'ccle_name'` and `'depmap_id'` can be used to identify cell lines. To ensure robustness, we include both identifiers in all grouping operations. +# +# An additional complication arises because `HTS002` contains duplicate cell line–drug entries that are only uniquely distinguishable when including the `broad_id` (batch–drug identifier). To avoid multiple IC50 values for the same combination—which would create ambiguity and downstream issues for agentic systems—we perform **per-screen deduplication** before merging. +# +# Deduplication is carried out as follows: +# - Within each screen, group by `(smiles, depmap_id, ccle_name)`. +# - If multiple entries exist for a group: +# - Prefer the row with the highest dose–response curve fit quality (`r²` value), if available. +# - Otherwise, select a single row at random, using a fixed seed for reproducibility. +# - `smiles` is treated as the unique identifier for each drug. +# +# Finally, the deduplicated screens are combined, giving **priority to `MTS010`**: if the same cell line–drug pair exists in both screens, the `MTS010` entry is retained. +# + +# In[5]: + + +DEDUP_SEED = 42 +CELL_DRUG_COMBO_KEYS = ["smiles","depmap_id","ccle_name"] + +# --- Step 0: Keep the two screens of interest; basic QC --- +df = dose_response_df.query("screen_id in ['HTS002','MTS010']").copy() +df = df.dropna(subset=CELL_DRUG_COMBO_KEYS + ["ic50"]) # ensure keys exist +df["smiles"] = df["smiles"].astype(str).str.strip() # these identify unique drug + +if "convergence" in df.columns: + df = df[df["convergence"].eq(True)] + +# --- Step 1: Deduplicate MTS010 by (smiles, cell line) --- +mts = df[df["screen_id"] == "MTS010"].copy() +if "r2" in mts.columns: + # If multiple rows per (SMILES, cell line) and r^2 is available, + # pick the highest-r^2 row per (SMILES, cell line) + # prefer the better dose-reponse curve fit + idx_mts = mts.groupby(CELL_DRUG_COMBO_KEYS)["r2"].idxmax() + print( + f"Deduplicating MTS010 via highest r^2: picked {len(idx_mts)} " + f"rows from {len(mts)} total") + mts_dedup = mts.loc[idx_mts] +else: + # No r^2 -> pick one random row per (SMILES, cell line) + # seed ensures reproducibility + mts_dedup = mts.groupby( + CELL_DRUG_COMBO_KEYS, + group_keys=False).sample(n=1, random_state=DEDUP_SEED) + print(f"Deduplicating MTS010: picked {len(mts_dedup)} " + f"rows from {len(mts)} total") + +# --- Step 2: Deduplicate HTS002 by (smiles, cell line) --- +hts = df[df["screen_id"] == "HTS002"].copy() +if "r2" in hts.columns and hts["r2"].notna().any(): + # similarly, + # pick the highest-r^2 row per (SMILES, cell line) if available + idx_hts = hts.groupby(CELL_DRUG_COMBO_KEYS)["r2"].idxmax() + print(f"Deduplicating HTS002 via highest r^2: picked {len(idx_hts)} " + f"rows from {len(hts)} total") + hts_dedup = hts.loc[idx_hts] +else: + # same fallback: pick one random row per (SMILES, cell line) + hts_dedup = hts.groupby( + CELL_DRUG_COMBO_KEYS, + group_keys=False).sample(n=1, random_state=DEDUP_SEED) + print(f"Deduplicating HTS002: picked {len(hts_dedup)} " + f"rows from {len(hts)} total") + +# --- Step 3: Combine with MTS010 preference --- +combined = pd.concat([mts_dedup, hts_dedup], ignore_index=True, copy=False) +combined = combined.drop_duplicates( + subset=["smiles","depmap_id","ccle_name"], keep="first").copy() + +# --- Step 4: attach tissue etc. without row blow-up if (many:1)--- +cli = (cell_line_info_df[["depmap_id","ccle_name","primary_tissue"]] + .drop_duplicates(subset=["depmap_id","ccle_name"])) +combined = combined.merge( + cli, on=["depmap_id","ccle_name"], how="left", validate="m:1") + +print(combined.head()) + + +# ### Confirm no duplicate cell-drug combinations + +# In[6]: + + +duplicates = combined.duplicated(subset=['ccle_name', 'name'], keep=False) +duplicate_counts = combined[duplicates].groupby(['ccle_name', 'name']).size().\ + reset_index(name='count') +duplicate_counts = duplicate_counts[duplicate_counts['count'] > 1] + +if not duplicate_counts.empty: + raise ValueError( + f"Found {len(duplicate_counts)} duplicate (cell line, drug) " + f"pairs:\n{duplicate_counts}" + ) + + +# ## Tabulate/visualize data + +# ### primary tissue - cell line count + +# In[7]: + + +grouped_counts = combined.groupby(['primary_tissue', 'ccle_name']).size().\ + reset_index(name='count') +print(grouped_counts.head(20)) + + +# ### number of cell-drug combiantions in dataset, grouped by primary tissue + +# In[8]: + + +if IN_NOTEBOOK: + unique_counts = grouped_counts.groupby('primary_tissue')[ + 'ccle_name'].nunique().reset_index(name='unique_ccle_count') + + # grouped_counts: columns = ['primary_tissue', 'ccle_name', 'count'] + # unique_counts: columns = ['primary_tissue', 'unique_ccle_count'] + + # 1) Pick a consistent tissue order + order = (grouped_counts.groupby('primary_tissue')['count'] + .median().sort_values(ascending=False).index) + + # limit to top-N tissues to keep the x-axis readable + TOP_N = 20 + if TOP_N is not None: + keep = list(order[:TOP_N]) + grouped_counts = grouped_counts[ + grouped_counts['primary_tissue'].isin(keep)] + unique_counts = unique_counts[ + unique_counts['primary_tissue'].isin(keep)] + order = [t for t in order if t in keep] + + # Ensure the bottom bar data follows the same order + unique_counts = unique_counts.set_index('primary_tissue').reindex(order).\ + reset_index() + + # 2) Make vertically stacked subplots with a shared x-axis + fig, (ax_top, ax_bot) = plt.subplots( + 2, 1, figsize=(14, 9), sharex=True, + gridspec_kw={'height_ratios': [2, 1]} + ) + + # --- Top: distribution per tissue (box + dots) --- + sns.boxplot( + data=grouped_counts, + x='primary_tissue', + y='count', + order=order, + ax=ax_top) + sns.stripplot(data=grouped_counts, x='primary_tissue', y='count', + order=order, ax=ax_top, jitter=True, alpha=0.5) + ax_top.set_xlabel('') + ax_top.set_ylabel('# (molecule, cell line) combos') + ax_top.set_title('Distribution of combos per cell line within each tissue') + + # --- Bottom: number of unique cell lines per tissue (bar) --- + sns.barplot(data=unique_counts, x='primary_tissue', y='unique_ccle_count', + order=order, ax=ax_bot) + ax_bot.set_xlabel('Primary tissue') + ax_bot.set_ylabel('# unique CCLE names') + + # Rotate x labels only on the bottom axis + for label in ax_bot.get_xticklabels(): + label.set_rotation(90) + + plt.tight_layout() + plt.show() +else: + print("Skipping plotting since not in a notebook environment.") + + +# ## Export preprocessed data + +# In[9]: + + +# output to the /data/processed directory +# only works as intended with vscode setting +# "jupyter.notebookFileRoot": "${workspaceFolder}" +output_path = repo_root() \ + / "data" / "processed" / "processed_depmap_prism_ic50.csv" +output_path.parent.mkdir(parents=True, exist_ok=True) +combined.to_csv(output_path, index=False) + From 929dbf792a61b9c0462e057776fbc1d0962ff052 Mon Sep 17 00:00:00 2001 From: Weishan Li Date: Mon, 29 Sep 2025 17:17:03 -0600 Subject: [PATCH 13/18] Update README.md with project setup instructions for notebook and script modes --- analysis/README.md | 36 ++++++++++++++++++++++++++++++++++-- 1 file changed, 34 insertions(+), 2 deletions(-) diff --git a/analysis/README.md b/analysis/README.md index a122efd..7571e7d 100644 --- a/analysis/README.md +++ b/analysis/README.md @@ -13,6 +13,38 @@ agentic system defined in [`agentic_system/`](../agentic_system/). ## Usage -1. Make sure you’ve installed the infrastructure package (from repo root): - **Details TBA** +> ⚙️ **Project Setup Note** +> +> To run this notebook or its nbconverted script version correctly, some project setup is required: +> +> - **Notebook mode (VS Code / Jupyter)** +> - Ensure `.vscode/settings.json` contains a `python.envFile` pointing to a `.env` that sets the `PYTHONPATH` for both partitions: +> ```json +> { +> "python.envFile": "${workspaceFolder}/.env", +> "python.analysis.extraPaths": [ +> "agentic_system/src", +> "analysis/src" +> ], +> "jupyter.notebookFileRoot": "${workspaceFolder}" +> } +> ``` +> - Example `.env` (at the repo root): +> ``` +> PYTHONPATH=agentic_system/src:analysis/src:${PYTHONPATH} +> ``` +> +> - **Script mode (running nbconvert-generated `.py` directly)** +> - Perform an **editable install** of the notebook utilities once: +> ```bash +> pip install -e ./analysis +> # or: uv pip install -e ./analysis +> ``` +> - This makes the `nbutils` package importable everywhere: +> ```python +> from nbutils.pathing import project_file +> config_path = project_file("config.yml") +> ``` +> +> With these two pieces in place, both the `.ipynb` (interactive) and the `.py` (script) versions of your notebooks will run consistently. From 1f4c74084ce86ba922bc638000062398571b5efb Mon Sep 17 00:00:00 2001 From: Weishan Li Date: Tue, 30 Sep 2025 15:55:26 -0600 Subject: [PATCH 14/18] Use external json schema for config yml validation --- .../0.1.wrangle_depmap_prism_data.ipynb | 72 ++++++++++--------- config.schema.json | 53 ++++++++++++++ 2 files changed, 92 insertions(+), 33 deletions(-) create mode 100644 config.schema.json diff --git a/analysis/notebooks/0.data_wrangling/0.1.wrangle_depmap_prism_data.ipynb b/analysis/notebooks/0.data_wrangling/0.1.wrangle_depmap_prism_data.ipynb index b89d6a5..6c55a29 100644 --- a/analysis/notebooks/0.data_wrangling/0.1.wrangle_depmap_prism_data.ipynb +++ b/analysis/notebooks/0.data_wrangling/0.1.wrangle_depmap_prism_data.ipynb @@ -36,10 +36,12 @@ "source": [ "import pathlib\n", "import yaml\n", + "import json\n", "\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "import seaborn as sns\n", + "from jsonschema import Draft7Validator\n", "\n", "from nbutils.pathing import project_file, repo_root\n", "from nbutils.utils import IN_NOTEBOOK\n", @@ -79,50 +81,54 @@ "# works only when vscode settings configuring \n", "# \"jupyter.notebookFileRoot\": \"${workspaceFolder}\" \n", "config_path = project_file(\"config.yml\")\n", + "schema_path = project_file(\"config.schema.json\")\n", "\n", "if not config_path.exists():\n", " raise FileNotFoundError(f\"Config file not found at: {config_path}\")\n", + "if not schema_path.exists():\n", + " raise FileNotFoundError(f\"Schema file not found at: {schema_path}\")\n", "\n", - "# --- Step 2: Load config.yml ---\n", + "# --- Step 2: Load config.yml and schema ---\n", "with open(config_path, \"r\") as f:\n", " config = yaml.safe_load(f)\n", "\n", + "with open(schema_path, \"r\") as f:\n", + " schema = json.load(f)\n", + "\n", "# --- Step 3: Validate data section ---\n", - "data_cfg = config.get(\"data\")\n", - "if not data_cfg:\n", - " raise ValueError(\"Missing 'data' section in config.yml\")\n", + "validator = Draft7Validator(schema)\n", + "schema_errors = sorted(validator.iter_errors(config), key=lambda e: e.path)\n", "\n", - "# Required keys\n", - "required_keys = [\"depmap_prism\", \"cell_line_info\", \"dose_response\"]\n", + "if schema_errors:\n", + " msg_lines = [\"Config schema validation failed:\"]\n", + " for err in schema_errors:\n", + " loc = \"/\".join(str(p) for p in err.path) or \"\"\n", + " msg_lines.append(f\"- at `{loc}`: {err.message}\")\n", + " raise ValueError(\"\\n\".join(msg_lines))\n", "\n", "# --- Step 4: Collect resolved paths ---\n", - "results = []\n", - "errors = [] # collect problems for later\n", - "for key in required_keys:\n", - " value = data_cfg.get(key)\n", - " if value is None:\n", - " results.append((key, None, \"Missing in config\"))\n", - " errors.append(f\"Config key '{key}' is missing\")\n", - " continue\n", - " \n", - " # depmap_prism is a directory, the others are files inside it\n", - " if key == \"depmap_prism\":\n", - " full_path = pathlib.Path(value)\n", - " else:\n", - " full_path = pathlib.Path(data_cfg[\"depmap_prism\"]) / value\n", - " \n", - " if full_path.exists():\n", - " status = \"Exists\"\n", - " else:\n", - " status = \"Not found\"\n", - " errors.append(f\"Path for '{key}' does not exist: {full_path}\")\n", - " \n", - " results.append((key, str(full_path), status))\n", + "data_cfg = config[\"data\"]\n", + "\n", + "# Build resolved paths\n", + "base_dir = pathlib.Path(data_cfg[\"depmap_prism\"])\n", + "paths = {\n", + " \"depmap_prism\": base_dir,\n", + " \"cell_line_info\": base_dir / data_cfg[\"cell_line_info\"],\n", + " \"dose_response\": base_dir / data_cfg[\"dose_response\"],\n", + "}\n", + "\n", + "rows, errors = [], []\n", + "for k, p in paths.items():\n", + " status = \"Exists\" if p.exists() else \"Not found\"\n", + " rows.append((k, str(p.resolve()), status))\n", + " if status != \"Exists\":\n", + " if k == \"depmap_prism\":\n", + " errors.append(f\"Directory missing: {p}\")\n", + " else:\n", + " errors.append(f\"File missing for '{k}': {p}\")\n", "\n", "# --- Step 5: Display summary nicely ---\n", - "config_df = pd.DataFrame(\n", - " results, columns=[\"Config Key\", \"Resolved Path\", \"Status\"])\n", - "config_df.set_index(\"Config Key\", inplace=True)\n", + "config_df = pd.DataFrame(rows, columns=[\"Config Key\", \"Resolved Path\", \"Status\"]).set_index(\"Config Key\")\n", "print(config_df)\n", "\n", "# --- Step 6: Fail if any errors were collected ---\n", @@ -234,7 +240,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/tmp/ipykernel_2003083/2948507465.py:1: DtypeWarning: Columns (14,15) have mixed types. Specify dtype option on import or set low_memory=False.\n", + "/tmp/ipykernel_2607315/2948507465.py:1: DtypeWarning: Columns (14,15) have mixed types. Specify dtype option on import or set low_memory=False.\n", " dose_response_df = pd.read_csv(config_df.loc['dose_response', 'Resolved Path'])\n" ] } @@ -479,7 +485,7 @@ "outputs": [ { "data": { - "image/png": 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", "text/plain": [ "
" ] diff --git a/config.schema.json b/config.schema.json new file mode 100644 index 0000000..7c14e5c --- /dev/null +++ b/config.schema.json @@ -0,0 +1,53 @@ +{ + "$schema": "https://json-schema.org/draft/2020-12/schema", + "$id": "https://example.org/depmap-config.schema.json", + "title": "DepMap/PRISM pipeline config", + "type": "object", + "additionalProperties": true, + "required": ["data", "api"], + "properties": { + "data": { + "type": "object", + "additionalProperties": false, + "required": ["depmap_prism", "cell_line_info", "dose_response"], + "properties": { + "depmap_prism": { + "type": "string", + "minLength": 1, + "description": "Base directory containing the PRISM assets" + }, + "cell_line_info": { + "type": "string", + "minLength": 1, + "description": "CSV filename under depmap_prism" + }, + "dose_response": { + "type": "string", + "minLength": 1, + "description": "CSV filename under depmap_prism" + } + } + }, + "api": { + "type": "object", + "additionalProperties": false, + "required": ["openai"], + "properties": { + "openai": { + "type": "object", + "additionalProperties": false, + "required": ["key"], + "properties": { + "key": { + "type": "string", + "minLength": 1, + "description": "OpenAI API key (use env var in prod)", + "pattern": "^[A-Za-z0-9_\\-\\.\\{\\}\\$]+$" + } + } + } + } + } + } + } + \ No newline at end of file From cca528e1d92bec78f12932b8e56f3b2f9356f584 Mon Sep 17 00:00:00 2001 From: Weishan Li Date: Tue, 30 Sep 2025 15:58:57 -0600 Subject: [PATCH 15/18] Make data wrangling notebook always save plots and only skip showing plots in script mode. --- .../0.1.wrangle_depmap_prism_data.ipynb | 129 ++++++++++-------- .../figures/depmap_prism_tissue_summary.png | Bin 0 -> 405881 bytes 2 files changed, 74 insertions(+), 55 deletions(-) create mode 100644 output/figures/depmap_prism_tissue_summary.png diff --git a/analysis/notebooks/0.data_wrangling/0.1.wrangle_depmap_prism_data.ipynb b/analysis/notebooks/0.data_wrangling/0.1.wrangle_depmap_prism_data.ipynb index 6c55a29..b03af53 100644 --- a/analysis/notebooks/0.data_wrangling/0.1.wrangle_depmap_prism_data.ipynb +++ b/analysis/notebooks/0.data_wrangling/0.1.wrangle_depmap_prism_data.ipynb @@ -240,7 +240,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/tmp/ipykernel_2607315/2948507465.py:1: DtypeWarning: Columns (14,15) have mixed types. Specify dtype option on import or set low_memory=False.\n", + "/tmp/ipykernel_2608393/2948507465.py:1: DtypeWarning: Columns (14,15) have mixed types. Specify dtype option on import or set low_memory=False.\n", " dose_response_df = pd.read_csv(config_df.loc['dose_response', 'Resolved Path'])\n" ] } @@ -485,74 +485,93 @@ "outputs": [ { "data": { - "image/png": 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", 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", 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" ] }, "metadata": {}, "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved figure to: /home/weishanli/Waylab/PRISM_Agent_LITL_experiments/output/figures/depmap_prism_tissue_summary.png\n" + ] } ], "source": [ + "unique_counts = grouped_counts.groupby('primary_tissue')[\n", + " 'ccle_name'].nunique().reset_index(name='unique_ccle_count')\n", + "\n", + "# grouped_counts: columns = ['primary_tissue', 'ccle_name', 'count']\n", + "# unique_counts: columns = ['primary_tissue', 'unique_ccle_count']\n", + "\n", + "# 1) Pick a consistent tissue order\n", + "order = (grouped_counts.groupby('primary_tissue')['count']\n", + " .median().sort_values(ascending=False).index)\n", + "\n", + "# limit to top-N tissues to keep the x-axis readable\n", + "TOP_N = 20\n", + "if TOP_N is not None:\n", + " keep = list(order[:TOP_N])\n", + " grouped_counts = grouped_counts[\n", + " grouped_counts['primary_tissue'].isin(keep)]\n", + " unique_counts = unique_counts[\n", + " unique_counts['primary_tissue'].isin(keep)]\n", + " order = [t for t in order if t in keep]\n", + "\n", + "# Ensure the bottom bar data follows the same order\n", + "unique_counts = unique_counts.set_index('primary_tissue').reindex(order).\\\n", + " reset_index()\n", + "\n", + "# 2) Make vertically stacked subplots with a shared x-axis\n", + "fig, (ax_top, ax_bot) = plt.subplots(\n", + " 2, 1, figsize=(14, 9), sharex=True,\n", + " gridspec_kw={'height_ratios': [2, 1]}\n", + ")\n", + "\n", + "# --- Top: distribution per tissue (box + dots) ---\n", + "sns.boxplot(\n", + " data=grouped_counts, \n", + " x='primary_tissue', \n", + " y='count', \n", + " order=order, \n", + " ax=ax_top)\n", + "sns.stripplot(data=grouped_counts, x='primary_tissue', y='count',\n", + " order=order, ax=ax_top, jitter=True, alpha=0.5)\n", + "ax_top.set_xlabel('')\n", + "ax_top.set_ylabel('# (molecule, cell line) combos')\n", + "ax_top.set_title('Distribution of combos per cell line within each tissue')\n", + "\n", + "# --- Bottom: number of unique cell lines per tissue (bar) ---\n", + "sns.barplot(data=unique_counts, x='primary_tissue', y='unique_ccle_count',\n", + " order=order, ax=ax_bot)\n", + "ax_bot.set_xlabel('Primary tissue')\n", + "ax_bot.set_ylabel('# unique CCLE names')\n", + "\n", + "# Rotate x labels only on the bottom axis\n", + "for label in ax_bot.get_xticklabels():\n", + " label.set_rotation(90)\n", + "\n", "if IN_NOTEBOOK:\n", - " unique_counts = grouped_counts.groupby('primary_tissue')[\n", - " 'ccle_name'].nunique().reset_index(name='unique_ccle_count')\n", - "\n", - " # grouped_counts: columns = ['primary_tissue', 'ccle_name', 'count']\n", - " # unique_counts: columns = ['primary_tissue', 'unique_ccle_count']\n", - "\n", - " # 1) Pick a consistent tissue order\n", - " order = (grouped_counts.groupby('primary_tissue')['count']\n", - " .median().sort_values(ascending=False).index)\n", - "\n", - " # limit to top-N tissues to keep the x-axis readable\n", - " TOP_N = 20\n", - " if TOP_N is not None:\n", - " keep = list(order[:TOP_N])\n", - " grouped_counts = grouped_counts[\n", - " grouped_counts['primary_tissue'].isin(keep)]\n", - " unique_counts = unique_counts[\n", - " unique_counts['primary_tissue'].isin(keep)]\n", - " order = [t for t in order if t in keep]\n", - "\n", - " # Ensure the bottom bar data follows the same order\n", - " unique_counts = unique_counts.set_index('primary_tissue').reindex(order).\\\n", - " reset_index()\n", - "\n", - " # 2) Make vertically stacked subplots with a shared x-axis\n", - " fig, (ax_top, ax_bot) = plt.subplots(\n", - " 2, 1, figsize=(14, 9), sharex=True,\n", - " gridspec_kw={'height_ratios': [2, 1]}\n", - " )\n", - "\n", - " # --- Top: distribution per tissue (box + dots) ---\n", - " sns.boxplot(\n", - " data=grouped_counts, \n", - " x='primary_tissue', \n", - " y='count', \n", - " order=order, \n", - " ax=ax_top)\n", - " sns.stripplot(data=grouped_counts, x='primary_tissue', y='count',\n", - " order=order, ax=ax_top, jitter=True, alpha=0.5)\n", - " ax_top.set_xlabel('')\n", - " ax_top.set_ylabel('# (molecule, cell line) combos')\n", - " ax_top.set_title('Distribution of combos per cell line within each tissue')\n", - "\n", - " # --- Bottom: number of unique cell lines per tissue (bar) ---\n", - " sns.barplot(data=unique_counts, x='primary_tissue', y='unique_ccle_count',\n", - " order=order, ax=ax_bot)\n", - " ax_bot.set_xlabel('Primary tissue')\n", - " ax_bot.set_ylabel('# unique CCLE names')\n", - "\n", - " # Rotate x labels only on the bottom axis\n", - " for label in ax_bot.get_xticklabels():\n", - " label.set_rotation(90)\n", "\n", " plt.tight_layout()\n", " plt.show()\n", + " \n", "else:\n", - " print(\"Skipping plotting since not in a notebook environment.\")" + "\n", + " print(\"Not in a notebook environment. Skipping plot display\")\n", + "\n", + "# save to output/figures\n", + "out_dir = repo_root() / \"output\" / \"figures\"\n", + "out_dir.mkdir(parents=True, exist_ok=True)\n", + "out_path = out_dir / \"depmap_prism_tissue_summary.png\"\n", + "fig.savefig(out_path, dpi=300, bbox_inches='tight')\n", + "print(f\"Saved figure to: {out_path}\")\n", + "\n", + "plt.close(fig)" ] }, { diff --git a/output/figures/depmap_prism_tissue_summary.png b/output/figures/depmap_prism_tissue_summary.png new file mode 100644 index 0000000000000000000000000000000000000000..75d452e2db5c0b40db427743f7efad2d577ac101 GIT binary patch literal 405881 zcmeFZbySww8aD2PqhlTw5D{?3AO!>jk+cv2Dd`?Sx&&zi9E%YIlrBX;M7p~SLP5G) zK%_xh`gd*5nQ_ip=d9nq-&)`2TIUQ3yzlcq&))Za)xGb_N{j8>Mzd|xrcFD=FI~8@ zY12Q%Hf`EwzV#>k%hGp4mG}?8ji`!^yrqGSy^gj1CMg}8Yi5=Vicc1vqR4%HPS0ldo3*Dk4E-?WMS2>IW44lyEjo4(t$ zN&LcD1&8qQZhHrc!R3wV+UNVvo!c6jv*}UKBlbrIKWx5m`ta7R4s}Pwe4G?0HqURh zJFb%YyCwxin%w4R_GiyA-eGw7V}o-@@1fPVLDd$%-PH^Q_orHI2l;EbX9X99GPpDR 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The script handles deduplication of overlapping entries between the HTS002 and MTS010 screens, prioritizing MTS010 results and highest-quality curve fits (r²). + +**Output:** `data/processed/processed_depmap_prism_ic50.csv` - cleaned dataset with unique (cell line, drug) combinations ready for downstream analysis. --- @@ -16,7 +27,7 @@ agentic system defined in [`agentic_system/`](../agentic_system/). > ⚙️ **Project Setup Note** > -> To run this notebook or its nbconverted script version correctly, some project setup is required: +> To run the analysis notebooks or nbconverted scripts correctly, some project setup is required: > > - **Notebook mode (VS Code / Jupyter)** > - Ensure `.vscode/settings.json` contains a `python.envFile` pointing to a `.env` that sets the `PYTHONPATH` for both partitions: @@ -35,16 +46,14 @@ agentic system defined in [`agentic_system/`](../agentic_system/). > PYTHONPATH=agentic_system/src:analysis/src:${PYTHONPATH} > ``` > -> - **Script mode (running nbconvert-generated `.py` directly)** +> - **Script mode (running nbconvert-generated `.py`)** > - Perform an **editable install** of the notebook utilities once: > ```bash > pip install -e ./analysis > # or: uv pip install -e ./analysis > ``` -> - This makes the `nbutils` package importable everywhere: +> - This makes the `nbutils` package importable from the scripts: > ```python > from nbutils.pathing import project_file > config_path = project_file("config.yml") > ``` -> -> With these two pieces in place, both the `.ipynb` (interactive) and the `.py` (script) versions of your notebooks will run consistently. From 3791ad5dd4c2680d57dc61caeadcf8c765de295c Mon Sep 17 00:00:00 2001 From: Weishan Li Date: Thu, 2 Oct 2025 11:40:54 -0600 Subject: [PATCH 18/18] Update analysis README.md to rename notebook and improve formatting --- analysis/README.md | 8 +++++--- 1 file changed, 5 insertions(+), 3 deletions(-) diff --git a/analysis/README.md b/analysis/README.md index 296e126..f4f461d 100644 --- a/analysis/README.md +++ b/analysis/README.md @@ -14,11 +14,13 @@ This file specifies data paths and API credentials needed for the analysis. Please refer to `config.yml.template` for guidance on how the file should be formatted. -### `0.1.WRANGLE_DEPMAP_PRISM_DATA.ipynb` +### `0.data_wrangling` +- `0.1.WRANGLE_DEPMAP_PRISM_DATA.ipynb` -Preprocesses the raw DepMap PRISM secondary drug repurposing dataset to produce a clean, deduplicated table of drug-cell line-IC50 values. The script handles deduplication of overlapping entries between the HTS002 and MTS010 screens, prioritizing MTS010 results and highest-quality curve fits (r²). + Preprocesses the raw DepMap PRISM secondary drug repurposing dataset to produce a clean, deduplicated table of drug-cell line-IC50 values. + The script handles deduplication of overlapping entries between the HTS002 and MTS010 screens, prioritizing MTS010 results and highest-quality curve fits (r²). -**Output:** `data/processed/processed_depmap_prism_ic50.csv` - cleaned dataset with unique (cell line, drug) combinations ready for downstream analysis. + **Output:** `data/processed/processed_depmap_prism_ic50.csv` - cleaned dataset with unique (cell line, drug) combinations ready for downstream analysis. ---