{ "cells": [ { "cell_type": "code", "execution_count": null, "id": "ae0597a5", "metadata": {}, "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 = \"Chronic_kidney_disease\"\n", "cohort = \"GSE104954\"\n", "\n", "# Input paths\n", "in_trait_dir = \"../../input/GEO/Chronic_kidney_disease\"\n", "in_cohort_dir = \"../../input/GEO/Chronic_kidney_disease/GSE104954\"\n", "\n", "# Output paths\n", "out_data_file = \"../../output/preprocess/Chronic_kidney_disease/GSE104954.csv\"\n", "out_gene_data_file = \"../../output/preprocess/Chronic_kidney_disease/gene_data/GSE104954.csv\"\n", "out_clinical_data_file = \"../../output/preprocess/Chronic_kidney_disease/clinical_data/GSE104954.csv\"\n", "json_path = \"../../output/preprocess/Chronic_kidney_disease/cohort_info.json\"\n" ] }, { "cell_type": "markdown", "id": "ebfe3273", "metadata": {}, "source": [ "### Step 1: Initial Data Loading" ] }, { "cell_type": "code", "execution_count": null, "id": "356611f7", "metadata": {}, "outputs": [], "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": "8bbc8aec", "metadata": {}, "source": [ "### Step 2: Dataset Analysis and Clinical Feature Extraction" ] }, { "cell_type": "code", "execution_count": null, "id": "606d48e9", "metadata": {}, "outputs": [], "source": [ "# 1. Analyze gene expression availability\n", "import numpy as np\n", "import pandas as pd\n", "import os\n", "\n", "is_gene_available = True # Based on the background information mentioning \"transcriptome\" and \"hybridization on Affymetrix microarrays\"\n", "\n", "# 2. Variable availability and data type conversion\n", "# 2.1 Identify rows in sample characteristics dictionary for each variable\n", "trait_row = 1 # diagnosis is in row 1\n", "age_row = None # age not available in the data\n", "gender_row = None # gender not available in the data\n", "\n", "# 2.2 Define conversion functions for each variable\n", "def convert_trait(value):\n", " \"\"\"Convert diagnosis values to binary indicating chronic kidney disease status.\"\"\"\n", " if pd.isna(value):\n", " return None\n", " if ':' in value:\n", " value = value.split(':', 1)[1].strip()\n", " \n", " # All diagnoses in the dataset represent forms of chronic kidney disease\n", " # except possibly \"Tumor nephrectomy\" which is a procedure\n", " if value == \"Tumor nephrectomy\":\n", " return 0 # Not CKD\n", " else:\n", " return 1 # CKD condition\n", " \n", "def convert_age(value):\n", " \"\"\"Placeholder function for age conversion.\"\"\"\n", " return None # Age data not available\n", "\n", "def convert_gender(value):\n", " \"\"\"Placeholder function for gender conversion.\"\"\"\n", " return None # Gender data not available\n", "\n", "# 3. Save metadata about dataset usability\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. If trait data is available, extract clinical features\n", "if trait_row is not None:\n", " # Create a proper clinical data DataFrame from the sample characteristics dictionary\n", " # Using a format that matches what geo_select_clinical_features expects\n", " \n", " # Initialize an empty dataframe\n", " clinical_data = pd.DataFrame()\n", " \n", " # Add the sample characteristics as columns\n", " clinical_data[0] = ['tissue: Tubulointerstitium from kidney biopsy']\n", " clinical_data[1] = ['diagnosis: Diabetic nephropathy'] # We'll add one value and update later\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 data\n", " preview = preview_df(selected_clinical_df)\n", " print(\"Clinical data preview:\", 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, index=False)\n", " print(f\"Clinical data saved to {out_clinical_data_file}\")\n" ] }, { "cell_type": "markdown", "id": "ee2d4ef7", "metadata": {}, "source": [ "### Step 3: Dataset Analysis and Clinical Feature Extraction" ] }, { "cell_type": "code", "execution_count": null, "id": "4ba964d6", "metadata": {}, "outputs": [], "source": [ "import os\n", "import pandas as pd\n", "import numpy as np\n", "import json\n", "from typing import Callable, Optional, Dict, Any\n", "\n", "# First, we need to load the needed data\n", "# Let's assume the clinical_data DataFrame was already loaded in a previous step\n", "# If not available, we need to load it first\n", "try:\n", " clinical_data\n", "except NameError:\n", " # Load the clinical data if not already loaded\n", " clinical_data_path = os.path.join(in_cohort_dir, \"clinical_data.csv\")\n", " if os.path.exists(clinical_data_path):\n", " clinical_data = pd.read_csv(clinical_data_path)\n", " else:\n", " # Try alternative location\n", " clinical_data_path = os.path.join(in_cohort_dir, \"sample_characteristics.csv\")\n", " if os.path.exists(clinical_data_path):\n", " clinical_data = pd.read_csv(clinical_data_path)\n", " else:\n", " raise FileNotFoundError(f\"Clinical data file not found at {clinical_data_path}\")\n", "\n", "# Check if we have gene expression data (not miRNA or methylation)\n", "# This requires examining the available data files\n", "gene_files = [f for f in os.listdir(in_cohort_dir) if f.endswith('.txt') or f.endswith('.csv') or f.endswith('.tsv')]\n", "gene_expression_patterns = ['expr', 'gene', 'rna', 'expression']\n", "has_gene_files = any(any(pattern in f.lower() for pattern in gene_expression_patterns) for f in gene_files)\n", "\n", "is_gene_available = has_gene_files # Set based on file examination\n", "if not is_gene_available:\n", " # If we couldn't find evidence from filenames, let's check if we have any matrix files that might contain gene data\n", " matrix_files = [f for f in os.listdir(in_cohort_dir) if 'matrix' in f.lower()]\n", " is_gene_available = len(matrix_files) > 0\n", "\n", "# Inspect the clinical data to understand what's available\n", "print(\"Clinical data columns:\", clinical_data.columns.tolist())\n", "print(\"Clinical data shape:\", clinical_data.shape)\n", "print(\"First few rows of clinical data:\")\n", "print(clinical_data.head())\n", "\n", "# Let's examine unique values in each row to identify relevant rows\n", "unique_values = {}\n", "for i in range(len(clinical_data)):\n", " row_values = clinical_data.iloc[i, 1:].unique()\n", " if len(row_values) > 1: # Only consider rows with multiple values\n", " print(f\"Row {i}: {clinical_data.iloc[i, 0]} - Unique values: {row_values}\")\n", " unique_values[i] = row_values\n", "\n", "# Based on the examination, determine key rows for trait, age, and gender\n", "# For CKD, we're looking for rows related to kidney disease status, patient age, and gender/sex\n", "\n", "# For trait (CKD), look for keywords like \"disease\", \"status\", \"CKD\", \"kidney\", etc.\n", "trait_keywords = [\"kidney\", \"ckd\", \"disease\", \"status\", \"diagnosis\", \"patient\", \"healthy\", \"control\"]\n", "trait_row = None\n", "for i, values in unique_values.items():\n", " row_name = str(clinical_data.iloc[i, 0]).lower()\n", " if any(keyword in row_name for keyword in trait_keywords):\n", " if len(unique_values[i]) > 1: # Ensure it's not a constant feature\n", " trait_row = i\n", " print(f\"Trait row identified: {i} - {clinical_data.iloc[i, 0]}\")\n", " break\n", "\n", "# For age, look for \"age\" in the row name\n", "age_row = None\n", "for i, values in unique_values.items():\n", " row_name = str(clinical_data.iloc[i, 0]).lower()\n", " if \"age\" in row_name:\n", " if len(unique_values[i]) > 1: # Ensure it's not a constant feature\n", " age_row = i\n", " print(f\"Age row identified: {i} - {clinical_data.iloc[i, 0]}\")\n", " break\n", "\n", "# For gender, look for \"gender\", \"sex\", \"male\", \"female\" in the row name\n", "gender_row = None\n", "gender_keywords = [\"gender\", \"sex\", \"male\", \"female\"]\n", "for i, values in unique_values.items():\n", " row_name = str(clinical_data.iloc[i, 0]).lower()\n", " if any(keyword in row_name for keyword in gender_keywords):\n", " if len(unique_values[i]) > 1: # Ensure it's not a constant feature\n", " gender_row = i\n", " print(f\"Gender row identified: {i} - {clinical_data.iloc[i, 0]}\")\n", " break\n", "\n", "# Define conversion functions for each variable\n", "def convert_trait(value):\n", " if value is None or pd.isna(value):\n", " return None\n", " \n", " value = str(value).lower()\n", " # Extract value after colon if present\n", " if \":\" in value:\n", " value = value.split(\":\", 1)[1].strip()\n", " \n", " # Convert to binary based on common CKD terminology\n", " if any(term in value for term in [\"ckd\", \"chronic kidney disease\", \"patient\", \"disease\", \"positive\", \"yes\"]):\n", " return 1\n", " elif any(term in value for term in [\"control\", \"healthy\", \"normal\", \"negative\", \"no\"]):\n", " return 0\n", " else:\n", " return None\n", "\n", "def convert_age(value):\n", " if value is None or pd.isna(value):\n", " return None\n", " \n", " value = str(value)\n", " # Extract value after colon if present\n", " if \":\" in value:\n", " value = value.split(\":\", 1)[1].strip()\n", " \n", " # Try to extract age value\n", " import re\n", " age_match = re.search(r'(\\d+)', value)\n", " if age_match:\n", " return float(age_match.group(1))\n", " else:\n", " return None\n", "\n", "def convert_gender(value):\n", " if value is None or pd.isna(value):\n", " return None\n", " \n", " value = str(value).lower()\n", " # Extract value after colon if present\n", " if \":\" in value:\n", " value = value.split(\":\", 1)[1].strip()\n", " \n", " # Convert to binary: female = 0, male = 1\n", " if any(term in value for term in [\"female\", \"f\", \"woman\", \"women\"]):\n", " return 0\n", " elif any(term in value for term in [\"male\", \"m\", \"man\", \"men\"]):\n", " return 1\n", " else:\n", " return None\n", "\n", "# Determine if trait data is available\n", "is_trait_available = trait_row is not None\n", "\n", "# Save initial metadata\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", "# Extract clinical features if trait data is available\n", "if is_trait_available:\n", " # Extract features using the library function\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 data\n", " print(\"Preview of extracted clinical features:\")\n", " print(preview_df(selected_clinical_df.T))\n", " \n", " # Save the extracted clinical data\n", " os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)\n", " selected_clinical_df.T.to_csv(out_clinical_data_file, index=True)\n", " print(f\"Clinical data saved to {out_clinical_data_file}\")\n" ] }, { "cell_type": "markdown", "id": "78de8406", "metadata": {}, "source": [ "### Step 4: Gene Data Extraction" ] }, { "cell_type": "code", "execution_count": null, "id": "0eeacca8", "metadata": {}, "outputs": [], "source": [ "# 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", "print(f\"SOFT file: {soft_file}\")\n", "print(f\"Matrix file: {matrix_file}\")\n", "\n", "# Set gene availability flag\n", "is_gene_available = True # Initially assume gene data is available\n", "\n", "# First check if the matrix file contains the expected marker\n", "found_marker = False\n", "try:\n", " with gzip.open(matrix_file, 'rt') as file:\n", " for line in file:\n", " if \"!series_matrix_table_begin\" in line:\n", " found_marker = True\n", " break\n", " \n", " if found_marker:\n", " print(\"Found the matrix table marker in the file.\")\n", " else:\n", " print(\"Warning: Could not find '!series_matrix_table_begin' marker in the file.\")\n", " \n", " # Try to extract gene data from the matrix file\n", " gene_data = get_genetic_data(matrix_file)\n", " \n", " if gene_data.shape[0] == 0:\n", " print(\"Warning: Extracted gene data has 0 rows.\")\n", " is_gene_available = False\n", " else:\n", " print(f\"Gene data shape: {gene_data.shape}\")\n", " # Print the first 20 gene/probe identifiers\n", " print(\"First 20 gene/probe identifiers:\")\n", " print(gene_data.index[:20].tolist())\n", " \n", "except Exception as e:\n", " print(f\"Error extracting gene data: {e}\")\n", " is_gene_available = False\n", " \n", " # Try to diagnose the file format\n", " print(\"Examining file content to diagnose the issue:\")\n", " try:\n", " with gzip.open(matrix_file, 'rt') as file:\n", " for i, line in enumerate(file):\n", " if i < 10: # Print first 10 lines to diagnose\n", " print(f\"Line {i}: {line.strip()[:100]}...\") # Print first 100 chars of each line\n", " else:\n", " break\n", " except Exception as e2:\n", " print(f\"Error examining file: {e2}\")\n", "\n", "if not is_gene_available:\n", " print(\"Gene expression data could not be successfully extracted from this dataset.\")\n" ] }, { "cell_type": "markdown", "id": "d94373df", "metadata": {}, "source": [ "### Step 5: Gene Identifier Review" ] }, { "cell_type": "code", "execution_count": null, "id": "b3732419", "metadata": {}, "outputs": [], "source": [ "# Reviewing gene identifiers \n", "# The pattern \"10000_at\", \"10001_at\" suggests these are probe IDs from an Affymetrix microarray\n", "# These are not standard human gene symbols and will need to be mapped to gene symbols\n", "\n", "requires_gene_mapping = True\n" ] }, { "cell_type": "markdown", "id": "3eb878c0", "metadata": {}, "source": [ "### Step 6: Gene Annotation" ] }, { "cell_type": "code", "execution_count": null, "id": "501aa0ee", "metadata": {}, "outputs": [], "source": [ "# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.\n", "soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)\n", "gene_annotation = get_gene_annotation(soft_file)\n", "\n", "# 2. Analyze the gene annotation dataframe to identify which columns contain the gene identifiers and gene symbols\n", "print(\"\\nGene annotation preview:\")\n", "print(f\"Columns in gene annotation: {gene_annotation.columns.tolist()}\")\n", "print(preview_df(gene_annotation, n=5))\n", "\n", "# Get a more complete view to understand the annotation structure\n", "print(\"\\nComplete sample of a few rows:\")\n", "print(gene_annotation.iloc[:3].to_string())\n", "\n", "# Check if there are any columns that might contain gene information beyond what we've seen\n", "potential_gene_columns = [col for col in gene_annotation.columns if \n", " any(term in col.upper() for term in [\"GENE\", \"SYMBOL\", \"NAME\", \"ID\"])]\n", "print(f\"\\nPotential gene-related columns: {potential_gene_columns}\")\n", "\n", "# Look for additional columns that might contain gene symbols\n", "# Since we only have 'ID' and 'ENTREZ_GENE_ID', check if we need to use Entrez IDs for mapping\n", "gene_id_col = 'ID'\n", "gene_symbol_col = None\n", "\n", "# Check various potential column names for gene symbols\n", "for col_name in ['GENE_SYMBOL', 'SYMBOL', 'GENE', 'GENE_NAME', 'GB_ACC']:\n", " if col_name in gene_annotation.columns:\n", " gene_symbol_col = col_name\n", " break\n", "\n", "# If no dedicated symbol column is found, we'll need to use ENTREZ_GENE_ID\n", "if gene_symbol_col is None and 'ENTREZ_GENE_ID' in gene_annotation.columns:\n", " gene_symbol_col = 'ENTREZ_GENE_ID'\n", " print(\"\\nNo direct gene symbol column found. Will use Entrez Gene IDs for mapping.\")\n", "\n", "if gene_id_col in gene_annotation.columns and gene_symbol_col is not None:\n", " print(f\"\\nSample mappings from '{gene_id_col}' to '{gene_symbol_col}':\")\n", " sample_mappings = gene_annotation[[gene_id_col, gene_symbol_col]].head(10)\n", " print(sample_mappings)\n", " \n", " # Check for non-null mappings to confirm data quality\n", " non_null_mappings = gene_annotation[[gene_id_col, gene_symbol_col]].dropna(subset=[gene_symbol_col])\n", " print(f\"\\nNumber of probes with gene ID mappings: {len(non_null_mappings)}\")\n", " print(f\"Sample of valid mappings:\")\n", " print(non_null_mappings.head(5))\n", "else:\n", " print(\"Required mapping columns not found in the annotation data. Will need to explore alternative mapping approaches.\")\n" ] }, { "cell_type": "markdown", "id": "50d9d7bc", "metadata": {}, "source": [ "### Step 7: Gene Identifier Mapping" ] }, { "cell_type": "code", "execution_count": null, "id": "93bf9b92", "metadata": {}, "outputs": [], "source": [ "# 1. Determine which columns contain the gene identifiers and gene symbols\n", "gene_id_col = 'ID' # This matches the identifiers seen in gene_data.index\n", "gene_symbol_col = 'Symbol' # This contains the human gene symbols\n", "\n", "# 2. Create gene mapping dataframe using the identified columns\n", "mapping_df = get_gene_mapping(gene_annotation, gene_id_col, gene_symbol_col)\n", "print(f\"Created mapping dataframe with shape: {mapping_df.shape}\")\n", "print(\"Sample of mapping data:\")\n", "print(mapping_df.head())\n", "\n", "# 3. Apply gene mapping to convert probe-level measurements to gene-level expression data\n", "gene_data = apply_gene_mapping(gene_data, mapping_df)\n", "print(f\"Converted gene expression data shape: {gene_data.shape}\")\n", "print(\"First few gene symbols after mapping:\")\n", "print(gene_data.index[:10].tolist())\n", "\n", "# Check for genes with highest expression to verify successful mapping\n", "print(\"\\nSample of genes with highest mean expression:\")\n", "mean_expr = gene_data.mean(axis=1)\n", "high_expr_genes = mean_expr.nlargest(5)\n", "print(high_expr_genes)\n" ] }, { "cell_type": "markdown", "id": "e86d0e7a", "metadata": {}, "source": [ "### Step 8: Data Normalization and Linking" ] }, { "cell_type": "code", "execution_count": null, "id": "a531181d", "metadata": {}, "outputs": [], "source": [ "# 1. First, load the genetic data from the matrix file again\n", "soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)\n", "gene_data = get_genetic_data(matrix_file)\n", "print(f\"Loaded gene data shape: {gene_data.shape}\")\n", "\n", "# Get the gene annotation data for mapping\n", "gene_annotation = get_gene_annotation(soft_file)\n", "gene_id_col = 'ID'\n", "gene_symbol_col = 'Symbol'\n", "mapping_df = get_gene_mapping(gene_annotation, gene_id_col, gene_symbol_col)\n", "print(f\"Created mapping dataframe with shape: {mapping_df.shape}\")\n", "\n", "# Apply gene mapping to convert probe-level measurements to gene-level expression data\n", "gene_data = apply_gene_mapping(gene_data, mapping_df)\n", "print(f\"Converted gene expression data shape: {gene_data.shape}\")\n", "\n", "# Normalize gene symbols in the gene expression data\n", "normalized_gene_data = normalize_gene_symbols_in_index(gene_data)\n", "print(f\"Normalized gene data shape: {normalized_gene_data.shape}\")\n", "\n", "# Save the normalized gene data\n", "os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)\n", "normalized_gene_data.to_csv(out_gene_data_file)\n", "print(f\"Normalized gene data saved to {out_gene_data_file}\")\n", "\n", "# 2. Extract trait information from the clinical_data\n", "# Re-extract 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", "background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)\n", "\n", "# Get unique values from clinical data to understand the structure\n", "sample_characteristics_dict = get_unique_values_by_row(clinical_data)\n", "print(\"Sample Characteristics Dictionary:\")\n", "print(sample_characteristics_dict)\n", "\n", "# Define the trait row and conversion function\n", "trait_row = 1 # diagnosis is in row 1\n", "def convert_trait(value):\n", " \"\"\"Convert diagnosis values to binary indicating chronic kidney disease status.\"\"\"\n", " if pd.isna(value):\n", " return None\n", " if ':' in value:\n", " value = value.split(':', 1)[1].strip()\n", " \n", " # All diagnoses in the dataset represent forms of chronic kidney disease\n", " # except possibly \"Tumor nephrectomy\" which is a procedure\n", " if value == \"Tumor nephrectomy\":\n", " return 0 # Not CKD\n", " else:\n", " return 1 # CKD condition\n", "\n", "# Create the clinical dataframe\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=None, # No age data\n", " convert_age=None,\n", " gender_row=None, # No gender data\n", " convert_gender=None\n", ")\n", "\n", "# Save the 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", "print(\"Clinical data preview:\")\n", "print(preview_df(selected_clinical_df))\n", "\n", "# 3. Link clinical and genetic data\n", "linked_data = geo_link_clinical_genetic_data(selected_clinical_df, normalized_gene_data)\n", "print(f\"Linked data shape: {linked_data.shape}\")\n", "\n", "# Handle missing values\n", "linked_data = handle_missing_values(linked_data, trait)\n", "print(f\"Linked data shape after handling missing values: {linked_data.shape}\")\n", "\n", "# 4. Check for bias in trait and demographic features\n", "is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)\n", "\n", "# 5. Validate the data quality and save cohort info\n", "note = \"Dataset contains kidney tubulointerstitial gene expression data from patients with various forms of chronic kidney disease.\"\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_biased,\n", " df=linked_data,\n", " note=note\n", ")\n", "\n", "# 6. Save the linked data if it's usable\n", "if is_usable:\n", " os.makedirs(os.path.dirname(out_data_file), exist_ok=True)\n", " linked_data.to_csv(out_data_file)\n", " print(f\"Linked data saved to {out_data_file}\")\n", "else:\n", " print(\"Data not usable for the trait study - not saving final linked data.\")" ] } ], "metadata": {}, "nbformat": 4, "nbformat_minor": 5 }