{ "cells": [ { "cell_type": "code", "execution_count": 1, "id": "62cd86d9", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T06:56:50.668591Z", "iopub.status.busy": "2025-03-25T06:56:50.668184Z", "iopub.status.idle": "2025-03-25T06:56:50.831556Z", "shell.execute_reply": "2025-03-25T06:56:50.831223Z" } }, "outputs": [], "source": [ "import sys\n", "import os\n", "sys.path.append(os.path.abspath(os.path.join(os.getcwd(), '../..')))\n", "\n", "# Path Configuration\n", "from tools.preprocess import *\n", "\n", "# Processing context\n", "trait = \"Bladder_Cancer\"\n", "cohort = \"GSE185264\"\n", "\n", "# Input paths\n", "in_trait_dir = \"../../input/GEO/Bladder_Cancer\"\n", "in_cohort_dir = \"../../input/GEO/Bladder_Cancer/GSE185264\"\n", "\n", "# Output paths\n", "out_data_file = \"../../output/preprocess/Bladder_Cancer/GSE185264.csv\"\n", "out_gene_data_file = \"../../output/preprocess/Bladder_Cancer/gene_data/GSE185264.csv\"\n", "out_clinical_data_file = \"../../output/preprocess/Bladder_Cancer/clinical_data/GSE185264.csv\"\n", "json_path = \"../../output/preprocess/Bladder_Cancer/cohort_info.json\"\n" ] }, { "cell_type": "markdown", "id": "117d9a6b", "metadata": {}, "source": [ "### Step 1: Initial Data Loading" ] }, { "cell_type": "code", "execution_count": 2, "id": "02a8ea67", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T06:56:50.832933Z", "iopub.status.busy": "2025-03-25T06:56:50.832801Z", "iopub.status.idle": "2025-03-25T06:56:50.859069Z", "shell.execute_reply": "2025-03-25T06:56:50.858783Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Background Information:\n", "!Series_title\t\"Identification of a Novel Inflamed Tumor Microenvironment Signature as a Predictive Biomarker of Bacillus Calmette-Guérin Immunotherapy in Non–Muscle-Invasive Bladder Cancer\"\n", "!Series_summary\t\"Improved risk stratification and predictive biomarkers of treatment response are needed for non–muscle-invasive bladder cancer (NMIBC). Here we assessed the clinical utility of targeted RNA and DNA molecular profiling in NMIBC. We performed RNA-based profiling by NanoString nCounter on non–muscle-invasive bladder cancer (NMIBC) clinical specimens and found that a novel expression signature of an inflamed tumor microenvironment (TME), but not molecular subtyping, was associated with improved recurrence-free survival after bacillus Calmette-Guérin (BCG) immunotherapy. We further demonstrated that immune checkpoint gene expression was not associated with higher recurrence rates after BCG.\"\n", "!Series_overall_design\t\"Gene expression in NMIBC samples was profiled by NanoString nCounter, an RNA quantification platform, from two independent cohorts (n = 28, n = 50).\"\n", "Sample Characteristics Dictionary:\n", "{0: ['studyid.sampleid: NA', 'studyid.sampleid: P-0005606-T01-IM5', 'studyid.sampleid: P-0006902-T01-IM5', 'studyid.sampleid: P-0009371-T01-IM5', 'studyid.sampleid: P-0004941-T01-IM5', 'studyid.sampleid: P-0005087-T01-IM5', 'studyid.sampleid: P-0003261-T01-IM5', 'studyid.sampleid: P-0003878-T01-IM5', 'studyid.sampleid: P-0004757-T01-IM5', 'studyid.sampleid: P-0003438-T01-IM5', 'studyid.sampleid: P-0003823-T01-IM5', 'studyid.sampleid: P-0003352-T01-IM5', 'studyid.sampleid: P-0003690-T01-IM5', 'studyid.sampleid: P-0003433-T01-IM5', 'studyid.sampleid: P-0008240-T01-IM5', 'studyid.sampleid: P-0004424-T01-IM5', 'studyid.sampleid: P-0003408-T01-IM5', 'studyid.sampleid: P-0003238-T01-IM5', 'studyid.sampleid: P-0008867-T01-IM5', 'studyid.sampleid: P-0003257-T01-IM5', 'studyid.sampleid: P-0006645-T01-IM5', 'studyid.sampleid: P-0003817-T01-IM5', 'studyid.sampleid: P-0006142-T01-IM5', 'studyid.sampleid: P-0006291-T01-IM5', 'studyid.sampleid: P-0007966-T01-IM5', 'studyid.sampleid: P-0006194-T01-IM5', 'studyid.sampleid: P-0003403-T01-IM5', 'studyid.sampleid: P-0007285-T01-IM5', 'studyid.sampleid: P-0004224-T01-IM5', 'studyid.sampleid: P-0008834-T01-IM5'], 1: ['study: UNC', 'study: MSK'], 2: ['tissue: Bladder Cancer'], 3: ['hede: Early Basal-like (H3)', 'hede: Luminal CIS-like (H2)', 'hede: Luminal (H1)'], 4: ['mda: Basal', 'mda: TP53', 'mda: NA', 'mda: Luminal'], 5: ['lund: GenomicUnstable', 'lund: SCC-Like', 'lund: NA', 'lund: Infiltrated', 'lund: UrobasalA', 'lund: UrobasalB'], 6: ['immune: high', 'immune: low', 'immune: medium'], 7: ['Sex: F', 'Sex: M'], 8: ['Stage: Ta', 'Stage: T1', 'Stage: Ta/T1'], 9: ['grade: Low', 'grade: High', 'grade: .'], 10: ['cis: No', 'cis: Yes'], 11: ['tumor_no: NA', 'tumor_no: 1', 'tumor_no: 2'], 12: ['recurrence: NA', 'recurrence: No.Recurrence', 'recurrence: Recurrence'], 13: ['bcg.y.n: NA', 'bcg.y.n: Treated.BCG'], 14: ['bcg: NA', 'bcg: BCG', 'bcg: Observation', 'bcg: Cystectomy', 'bcg: MMC']}\n" ] } ], "source": [ "from tools.preprocess import *\n", "# 1. Identify the paths to the SOFT file and the matrix file\n", "soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)\n", "\n", "# 2. Read the matrix file to obtain background information and sample characteristics data\n", "background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']\n", "clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']\n", "background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)\n", "\n", "# 3. Obtain the sample characteristics dictionary from the clinical dataframe\n", "sample_characteristics_dict = get_unique_values_by_row(clinical_data)\n", "\n", "# 4. Explicitly print out all the background information and the sample characteristics dictionary\n", "print(\"Background Information:\")\n", "print(background_info)\n", "print(\"Sample Characteristics Dictionary:\")\n", "print(sample_characteristics_dict)\n" ] }, { "cell_type": "markdown", "id": "b75167d7", "metadata": {}, "source": [ "### Step 2: Dataset Analysis and Clinical Feature Extraction" ] }, { "cell_type": "code", "execution_count": 3, "id": "d2a5a4e0", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T06:56:50.860076Z", "iopub.status.busy": "2025-03-25T06:56:50.859968Z", "iopub.status.idle": "2025-03-25T06:56:50.870375Z", "shell.execute_reply": "2025-03-25T06:56:50.870098Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Clinical features preview:\n", "{0: [nan, 0.0], 1: [1.0, 1.0], 2: [nan, nan], 3: [nan, nan], 4: [nan, nan], 5: [nan, nan], 6: [nan, nan], 7: [nan, nan], 8: [nan, nan], 9: [nan, nan], 10: [nan, nan], 11: [nan, nan], 12: [nan, nan], 13: [nan, nan], 14: [nan, nan], 15: [nan, nan], 16: [nan, nan], 17: [nan, nan], 18: [nan, nan], 19: [nan, nan], 20: [nan, nan], 21: [nan, nan], 22: [nan, nan], 23: [nan, nan], 24: [nan, nan], 25: [nan, nan], 26: [nan, nan], 27: [nan, nan], 28: [nan, nan], 29: [nan, nan]}\n", "Clinical data saved to: ../../output/preprocess/Bladder_Cancer/clinical_data/GSE185264.csv\n" ] } ], "source": [ "# 1. Determine gene expression data availability\n", "is_gene_available = True # Based on the Series_summary and overall_design, this contains RNA-based profiling\n", "\n", "# 2.1 Determine data availability for trait, age, and gender\n", "# For trait (Bladder Cancer), we'll use bcg response as the trait since this is the focus of the study\n", "trait_row = 13 # bcg.y.n field\n", "# Age is not available in the data\n", "age_row = None\n", "# Gender is available\n", "gender_row = 7 # Sex field\n", "\n", "# 2.2 Define conversion functions for each variable\n", "def convert_trait(value):\n", " \"\"\"Convert BCG treatment status to binary format.\"\"\"\n", " if value is None or pd.isna(value) or 'NA' in value:\n", " return None\n", " \n", " # Extract value after colon\n", " if ':' in value:\n", " value = value.split(':', 1)[1].strip()\n", " \n", " if 'Treated.BCG' in value:\n", " return 1 # BCG treated\n", " else:\n", " return 0 # Not treated with BCG\n", "\n", "def convert_age(value):\n", " \"\"\"Convert age to numeric value.\"\"\"\n", " # Age is not available\n", " return None\n", "\n", "def convert_gender(value):\n", " \"\"\"Convert gender to binary format: female=0, male=1.\"\"\"\n", " if value is None or pd.isna(value):\n", " return None\n", " \n", " # Extract value after colon\n", " if ':' in value:\n", " value = value.split(':', 1)[1].strip()\n", " \n", " if value == 'F':\n", " return 0 # Female\n", " elif value == 'M':\n", " return 1 # Male\n", " else:\n", " return None # Unknown or other\n", "\n", "# 3. Save metadata\n", "is_trait_available = trait_row is not None\n", "validate_and_save_cohort_info(\n", " is_final=False,\n", " cohort=cohort,\n", " info_path=json_path,\n", " is_gene_available=is_gene_available,\n", " is_trait_available=is_trait_available\n", ")\n", "\n", "# 4. Extract clinical features if trait data is available\n", "if trait_row is not None:\n", " # Create the clinical data DataFrame correctly\n", " sample_chars = {\n", " 0: ['studyid.sampleid: NA', 'studyid.sampleid: P-0005606-T01-IM5', 'studyid.sampleid: P-0006902-T01-IM5', 'studyid.sampleid: P-0009371-T01-IM5', 'studyid.sampleid: P-0004941-T01-IM5', 'studyid.sampleid: P-0005087-T01-IM5', 'studyid.sampleid: P-0003261-T01-IM5', 'studyid.sampleid: P-0003878-T01-IM5', 'studyid.sampleid: P-0004757-T01-IM5', 'studyid.sampleid: P-0003438-T01-IM5', 'studyid.sampleid: P-0003823-T01-IM5', 'studyid.sampleid: P-0003352-T01-IM5', 'studyid.sampleid: P-0003690-T01-IM5', 'studyid.sampleid: P-0003433-T01-IM5', 'studyid.sampleid: P-0008240-T01-IM5', 'studyid.sampleid: P-0004424-T01-IM5', 'studyid.sampleid: P-0003408-T01-IM5', 'studyid.sampleid: P-0003238-T01-IM5', 'studyid.sampleid: P-0008867-T01-IM5', 'studyid.sampleid: P-0003257-T01-IM5', 'studyid.sampleid: P-0006645-T01-IM5', 'studyid.sampleid: P-0003817-T01-IM5', 'studyid.sampleid: P-0006142-T01-IM5', 'studyid.sampleid: P-0006291-T01-IM5', 'studyid.sampleid: P-0007966-T01-IM5', 'studyid.sampleid: P-0006194-T01-IM5', 'studyid.sampleid: P-0003403-T01-IM5', 'studyid.sampleid: P-0007285-T01-IM5', 'studyid.sampleid: P-0004224-T01-IM5', 'studyid.sampleid: P-0008834-T01-IM5'],\n", " 1: ['study: UNC', 'study: MSK'], \n", " 2: ['tissue: Bladder Cancer'], \n", " 3: ['hede: Early Basal-like (H3)', 'hede: Luminal CIS-like (H2)', 'hede: Luminal (H1)'], \n", " 4: ['mda: Basal', 'mda: TP53', 'mda: NA', 'mda: Luminal'], \n", " 5: ['lund: GenomicUnstable', 'lund: SCC-Like', 'lund: NA', 'lund: Infiltrated', 'lund: UrobasalA', 'lund: UrobasalB'], \n", " 6: ['immune: high', 'immune: low', 'immune: medium'], \n", " 7: ['Sex: F', 'Sex: M'], \n", " 8: ['Stage: Ta', 'Stage: T1', 'Stage: Ta/T1'], \n", " 9: ['grade: Low', 'grade: High', 'grade: .'], \n", " 10: ['cis: No', 'cis: Yes'], \n", " 11: ['tumor_no: NA', 'tumor_no: 1', 'tumor_no: 2'], \n", " 12: ['recurrence: NA', 'recurrence: No.Recurrence', 'recurrence: Recurrence'], \n", " 13: ['bcg.y.n: NA', 'bcg.y.n: Treated.BCG'], \n", " 14: ['bcg: NA', 'bcg: BCG', 'bcg: Observation', 'bcg: Cystectomy', 'bcg: MMC']\n", " }\n", " \n", " # Create a proper DataFrame from the sample characteristics\n", " clinical_data = pd.DataFrame.from_dict(sample_chars, orient='index')\n", " \n", " # Extract clinical features\n", " selected_clinical_df = geo_select_clinical_features(\n", " clinical_df=clinical_data,\n", " trait=trait,\n", " trait_row=trait_row,\n", " convert_trait=convert_trait,\n", " age_row=age_row,\n", " convert_age=convert_age,\n", " gender_row=gender_row,\n", " convert_gender=convert_gender\n", " )\n", " \n", " # Preview the extracted clinical features\n", " preview = preview_df(selected_clinical_df)\n", " print(\"Clinical features preview:\")\n", " print(preview)\n", " \n", " # Save clinical data to CSV\n", " os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)\n", " selected_clinical_df.to_csv(out_clinical_data_file)\n", " print(f\"Clinical data saved to: {out_clinical_data_file}\")\n" ] }, { "cell_type": "markdown", "id": "c0b3edaf", "metadata": {}, "source": [ "### Step 3: Gene Data Extraction" ] }, { "cell_type": "code", "execution_count": 4, "id": "25e105be", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T06:56:50.871347Z", "iopub.status.busy": "2025-03-25T06:56:50.871247Z", "iopub.status.idle": "2025-03-25T06:56:50.884346Z", "shell.execute_reply": "2025-03-25T06:56:50.884069Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Index(['53BP1', 'ABCD3', 'ACTB', 'ADIRF', 'ADPRHL2', 'AFTPH', 'AHNAK2', 'AKT',\n", " 'ALDH1L1', 'ALOX5', 'ALOX5AP', 'ANLN', 'APEX1', 'APH1B', 'APOBEC3A',\n", " 'APOBEC3B', 'APOBEC3C', 'APOBEC3D', 'APOBEC3F', 'APOBEC3G'],\n", " dtype='object', name='ID')\n" ] } ], "source": [ "# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.\n", "gene_data = get_genetic_data(matrix_file)\n", "\n", "# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.\n", "print(gene_data.index[:20])\n" ] }, { "cell_type": "markdown", "id": "79d77abc", "metadata": {}, "source": [ "### Step 4: Gene Identifier Review" ] }, { "cell_type": "code", "execution_count": 5, "id": "abf8c1bd", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T06:56:50.885295Z", "iopub.status.busy": "2025-03-25T06:56:50.885195Z", "iopub.status.idle": "2025-03-25T06:56:50.886915Z", "shell.execute_reply": "2025-03-25T06:56:50.886652Z" } }, "outputs": [], "source": [ "# Based on the provided gene identifiers, I can analyze whether they are standard human gene symbols or other identifiers\n", "\n", "# Looking at the sample gene identifiers:\n", "# - 53BP1, ACTB, AKT: These are standard human gene symbols\n", "# - APOBEC3A, APOBEC3B, etc.: These are proper human gene symbols for the APOBEC3 family\n", "# - ALDH1L1, ALOX5, etc.: These are standard human gene nomenclature\n", "\n", "# All of these appear to be standard HGNC (HUGO Gene Nomenclature Committee) gene symbols\n", "# They follow the conventional naming patterns for human genes\n", "# No mapping appears to be needed as these are already human gene symbols\n", "\n", "requires_gene_mapping = False\n" ] }, { "cell_type": "markdown", "id": "7f251872", "metadata": {}, "source": [ "### Step 5: Data Normalization and Linking" ] }, { "cell_type": "code", "execution_count": 6, "id": "7b991f1e", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T06:56:50.887861Z", "iopub.status.busy": "2025-03-25T06:56:50.887765Z", "iopub.status.idle": "2025-03-25T06:56:51.065788Z", "shell.execute_reply": "2025-03-25T06:56:51.065473Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Original gene count: 446\n", "Sample of gene symbols before normalization:\n", "Index(['53BP1', 'ABCD3', 'ACTB', 'ADIRF', 'ADPRHL2', 'AFTPH', 'AHNAK2', 'AKT',\n", " 'ALDH1L1', 'ALOX5', 'ALOX5AP', 'ANLN', 'APEX1', 'APH1B', 'APOBEC3A',\n", " 'APOBEC3B', 'APOBEC3C', 'APOBEC3D', 'APOBEC3F', 'APOBEC3G'],\n", " dtype='object', name='ID')\n", "Normalized gene count: 432\n", "Gene data saved to ../../output/preprocess/Bladder_Cancer/gene_data/GSE185264.csv\n", "Found 15 GSM IDs in clinical data\n", "First 5 GSM IDs: ['!Sample_characteristics_ch1', '!Sample_characteristics_ch1', '!Sample_characteristics_ch1', '!Sample_characteristics_ch1', '!Sample_characteristics_ch1']\n", "Number of common samples between clinical and gene data: 78\n", "Clinical data saved to ../../output/preprocess/Bladder_Cancer/clinical_data/GSE185264.csv\n", "Linked data shape after proper ID matching: (78, 434)\n", "Percentage of missing values in trait: 53.85%\n", "Linked data shape after handling missing values: (36, 434)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Quartiles for 'Bladder_Cancer':\n", " 25%: 1.0\n", " 50% (Median): 1.0\n", " 75%: 1.0\n", "Min: 1.0\n", "Max: 1.0\n", "The distribution of the feature 'Bladder_Cancer' in this dataset is severely biased.\n", "\n", "For the feature 'Gender', the least common label is '0.0' with 8 occurrences. This represents 22.22% of the dataset.\n", "The distribution of the feature 'Gender' in this dataset is fine.\n", "\n", "The dataset was determined to be not usable for analysis. Bias in trait: True\n" ] } ], "source": [ "# 1. Normalize gene symbols in the gene expression data\n", "print(f\"Original gene count: {len(gene_data)}\")\n", "print(f\"Sample of gene symbols before normalization:\")\n", "print(gene_data.index[:20]) # Display first 20 gene symbols\n", "\n", "# Normalize the gene data (skip mapping since we already have gene symbols)\n", "normalized_gene_data = normalize_gene_symbols_in_index(gene_data)\n", "print(f\"Normalized gene count: {len(normalized_gene_data)}\")\n", "\n", "# Since this dataset has a small number of genes, we'll use the original data if normalization removes too many\n", "if len(normalized_gene_data) < len(gene_data) * 0.9: # If we lost more than 10% of genes\n", " print(\"Warning: Gene symbol normalization removed too many genes. Using original gene data without normalization.\")\n", " normalized_gene_data = gene_data # Use the original data without normalization\n", "\n", "# Create directory for the gene data file if it doesn't exist\n", "os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)\n", "\n", "# Save the gene data to a CSV file\n", "normalized_gene_data.to_csv(out_gene_data_file)\n", "print(f\"Gene data saved to {out_gene_data_file}\")\n", "\n", "# 2. Fix the clinical data extraction to properly use sample accessions\n", "# Reread the clinical data from the matrix file\n", "background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']\n", "clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']\n", "_, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)\n", "\n", "# Check if we have the Sample_geo_accession column to identify GSM IDs\n", "if '!Sample_geo_accession' in clinical_data.columns:\n", " # Extract the GSM IDs from the clinical data\n", " gsm_ids = clinical_data['!Sample_geo_accession'].tolist()\n", " print(f\"Found {len(gsm_ids)} GSM IDs in clinical data\")\n", " print(f\"First 5 GSM IDs: {gsm_ids[:5]}\")\n", " \n", " # Extract clinical features with proper GSM IDs\n", " selected_clinical_df = geo_select_clinical_features(\n", " clinical_df=clinical_data,\n", " trait=trait,\n", " trait_row=trait_row,\n", " convert_trait=convert_trait,\n", " age_row=age_row,\n", " convert_age=convert_age,\n", " gender_row=gender_row,\n", " convert_gender=convert_gender\n", " )\n", " \n", " # Transpose the clinical dataframe to have samples as rows and features as columns\n", " selected_clinical_df = selected_clinical_df.T\n", " \n", " # Check if the sample IDs match between clinical and gene data\n", " common_samples = set(selected_clinical_df.index).intersection(set(normalized_gene_data.columns))\n", " print(f\"Number of common samples between clinical and gene data: {len(common_samples)}\")\n", " \n", " # Save the properly formatted clinical data\n", " os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)\n", " selected_clinical_df.to_csv(out_clinical_data_file)\n", " print(f\"Clinical data saved to {out_clinical_data_file}\")\n", " \n", " # Create a proper linked dataset using the common samples\n", " if len(common_samples) > 0:\n", " # Filter both datasets to only include common samples\n", " clinical_filtered = selected_clinical_df.loc[list(common_samples)]\n", " gene_filtered = normalized_gene_data[list(common_samples)]\n", " \n", " # Combine the datasets\n", " linked_data = pd.concat([clinical_filtered, gene_filtered.T], axis=1)\n", " print(f\"Linked data shape after proper ID matching: {linked_data.shape}\")\n", " \n", " # 3. Handle missing values in the linked data with more relaxed criteria\n", " # First, check missing value percentages\n", " trait_missing = linked_data[trait].isna().mean() * 100\n", " print(f\"Percentage of missing values in trait: {trait_missing:.2f}%\")\n", " \n", " # Apply the missing value handling\n", " linked_data_cleaned = handle_missing_values(linked_data, trait)\n", " print(f\"Linked data shape after handling missing values: {linked_data_cleaned.shape}\")\n", " \n", " # If we still have adequate data after cleaning\n", " if linked_data_cleaned.shape[0] >= 5 and linked_data_cleaned.shape[1] >= 10: # Lower threshold\n", " # 4. Determine whether the trait and demographic features are severely biased\n", " is_trait_biased, linked_data_cleaned = judge_and_remove_biased_features(linked_data_cleaned, trait)\n", " \n", " # 5. Conduct quality check and save the cohort information\n", " note = \"Dataset contains gene expression data from bladder cancer samples with BCG treatment information.\"\n", " is_usable = validate_and_save_cohort_info(\n", " is_final=True, \n", " cohort=cohort, \n", " info_path=json_path, \n", " is_gene_available=True, \n", " is_trait_available=True, \n", " is_biased=is_trait_biased, \n", " df=linked_data_cleaned, \n", " note=note\n", " )\n", " \n", " # 6. If the linked data is usable, save it as a CSV file\n", " if is_usable:\n", " os.makedirs(os.path.dirname(out_data_file), exist_ok=True)\n", " linked_data_cleaned.to_csv(out_data_file)\n", " print(f\"Linked data saved to {out_data_file}\")\n", " else:\n", " print(f\"The dataset was determined to be not usable for analysis. Bias in trait: {is_trait_biased}\")\n", " else:\n", " print(\"Warning: After handling missing values, insufficient data remains for analysis\")\n", " is_usable = validate_and_save_cohort_info(\n", " is_final=True, \n", " cohort=cohort, \n", " info_path=json_path, \n", " is_gene_available=True, \n", " is_trait_available=True, \n", " is_biased=True,\n", " df=linked_data_cleaned, \n", " note=\"After cleaning, insufficient data remains for analysis.\"\n", " )\n", " print(\"The dataset was determined to be not usable for analysis.\")\n", " else:\n", " print(\"Warning: No common samples found between clinical and gene data\")\n", " is_usable = validate_and_save_cohort_info(\n", " is_final=True, \n", " cohort=cohort, \n", " info_path=json_path, \n", " is_gene_available=True, \n", " is_trait_available=True, \n", " is_biased=True,\n", " df=pd.DataFrame(), \n", " note=\"No common samples found between clinical and gene data.\"\n", " )\n", " print(\"The dataset was determined to be not usable for analysis.\")\n", "else:\n", " print(\"Warning: No GSM IDs found in clinical data\")\n", " is_usable = validate_and_save_cohort_info(\n", " is_final=True, \n", " cohort=cohort, \n", " info_path=json_path, \n", " is_gene_available=True, \n", " is_trait_available=True, \n", " is_biased=True,\n", " df=pd.DataFrame(), \n", " note=\"No GSM IDs found in clinical data.\"\n", " )\n", " print(\"The dataset was determined to be not usable for analysis.\")" ] } ], "metadata": { "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.10.16" } }, "nbformat": 4, "nbformat_minor": 5 }