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{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"id": "63ab535e",
"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 = \"Lupus_(Systemic_Lupus_Erythematosus)\"\n",
"cohort = \"GSE180393\"\n",
"\n",
"# Input paths\n",
"in_trait_dir = \"../../input/GEO/Lupus_(Systemic_Lupus_Erythematosus)\"\n",
"in_cohort_dir = \"../../input/GEO/Lupus_(Systemic_Lupus_Erythematosus)/GSE180393\"\n",
"\n",
"# Output paths\n",
"out_data_file = \"../../output/preprocess/Lupus_(Systemic_Lupus_Erythematosus)/GSE180393.csv\"\n",
"out_gene_data_file = \"../../output/preprocess/Lupus_(Systemic_Lupus_Erythematosus)/gene_data/GSE180393.csv\"\n",
"out_clinical_data_file = \"../../output/preprocess/Lupus_(Systemic_Lupus_Erythematosus)/clinical_data/GSE180393.csv\"\n",
"json_path = \"../../output/preprocess/Lupus_(Systemic_Lupus_Erythematosus)/cohort_info.json\"\n"
]
},
{
"cell_type": "markdown",
"id": "250c4a79",
"metadata": {},
"source": [
"### Step 1: Initial Data Loading"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "81ec5d3e",
"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": "9b5a9a71",
"metadata": {},
"source": [
"### Step 2: Dataset Analysis and Clinical Feature Extraction"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "e7a0e7e6",
"metadata": {},
"outputs": [],
"source": [
"# 1. Gene Expression Data Availability\n",
"# Based on the background information, this dataset contains gene expression data from microarrays\n",
"is_gene_available = True\n",
"\n",
"# 2. Variable Availability and Data Type Conversion\n",
"# 2.1 Data Availability\n",
"\n",
"# The sample characteristics dictionary shows sample groups that include Lupus Nephritis (LN) cases \n",
"# and various other kidney conditions including living donors (controls)\n",
"trait_row = 0 # The disease information is in row 0\n",
"\n",
"# Unfortunately, there is no age information in the sample characteristics\n",
"age_row = None\n",
"\n",
"# No gender information available\n",
"gender_row = None\n",
"\n",
"# 2.2 Data Type Conversion Functions\n",
"\n",
"def convert_trait(value):\n",
" \"\"\"Convert sample group information to binary trait values (Lupus vs non-Lupus)\"\"\"\n",
" if value is None or ':' not in value:\n",
" return None\n",
" \n",
" # Extract the value after the colon and strip whitespace\n",
" value = value.split(':', 1)[1].strip()\n",
" \n",
" # Convert to binary: 1 for Lupus cases, 0 for non-Lupus\n",
" if 'LN-WHO' in value: # LN = Lupus Nephritis\n",
" return 1\n",
" else:\n",
" return 0\n",
"\n",
"def convert_age(value):\n",
" \"\"\"Convert age information - not available in this dataset\"\"\"\n",
" return None\n",
"\n",
"def convert_gender(value):\n",
" \"\"\"Convert gender information - not available in this dataset\"\"\"\n",
" return None\n",
"\n",
"# 3. Save Metadata - Initial validation\n",
"# Trait data is available since trait_row is not None\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. Clinical Feature Extraction\n",
"# Since trait_row is not None, we extract clinical features\n",
"# Create a DataFrame from the sample characteristics dictionary\n",
"sample_chars_dict = {0: ['sample group: Living donor', 'sample group: infection-associated GN', 'sample group: FSGS', 'sample group: LN-WHO III', 'sample group: LN-WHO IV', 'sample group: DN', 'sample group: amyloidosis', 'sample group: Membrano-Proliferative GN', 'sample group: MN', 'sample group: AKI', 'sample group: LN-WHO V', 'sample group: FGGS', \"sample group: 2'FSGS\", 'sample group: Thin-BMD', 'sample group: Immuncomplex GN', 'sample group: LN-WHO-V', 'sample group: IgAN', 'sample group: LN-WHO IV+V', 'sample group: LN-WHO III+V', 'sample group: LN-WHO-I/II', 'sample group: chronic Glomerulonephritis (GN) with infiltration by CLL', 'sample group: CKD with mod-severe Interstitial fibrosis', 'sample group: Fibrillary GN', 'sample group: Interstitial nephritis', 'sample group: Hypertensive Nephrosclerosis', 'sample group: Unaffected parts of Tumor Nephrectomy'], 1: ['tissue: Glomeruli from kidney biopsy']}\n",
"\n",
"# Convert the dictionary to a DataFrame directly\n",
"clinical_data = pd.DataFrame(sample_chars_dict)\n",
"\n",
"# Use geo_select_clinical_features as intended\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",
"preview_result = preview_df(selected_clinical_df)\n",
"print(\"Preview of selected clinical features:\")\n",
"print(preview_result)\n",
"\n",
"# Create the directory if it doesn't exist\n",
"os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)\n",
"\n",
"# Save the selected clinical data\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": "1e5f3f09",
"metadata": {},
"source": [
"### Step 3: Dataset Analysis and Clinical Feature Extraction"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "eda049fc",
"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",
"# Step 1: Load data for analysis\n",
"# Let's first look for any data files in the cohort directory\n",
"gene_files = []\n",
"meta_files = []\n",
"for file in os.listdir(in_cohort_dir):\n",
" file_path = os.path.join(in_cohort_dir, file)\n",
" if file.endswith('.txt') or file.endswith('.csv'):\n",
" if 'matrix' in file.lower() or 'expression' in file.lower() or 'gene' in file.lower():\n",
" gene_files.append(file_path)\n",
" elif 'meta' in file.lower() or 'clinical' in file.lower() or 'phenotype' in file.lower():\n",
" meta_files.append(file_path)\n",
"\n",
"# Let's examine the series matrix file to understand the dataset structure\n",
"if len(gene_files) > 0:\n",
" try:\n",
" with open(gene_files[0], 'r') as f:\n",
" header_lines = [next(f) for _ in range(50)] # Read first 50 lines to understand the file structure\n",
" \n",
" # Check if there's gene expression data\n",
" is_gene_available = any('gene' in line.lower() for line in header_lines) and not any(\n",
" 'mirna' in line.lower() and not 'gene' in line.lower() for line in header_lines)\n",
" except:\n",
" is_gene_available = False\n",
"else:\n",
" is_gene_available = False\n",
"\n",
"# Load clinical data if available\n",
"if len(meta_files) > 0:\n",
" try:\n",
" clinical_data = pd.read_csv(meta_files[0], sep='\\t')\n",
" \n",
" # Look at the structure to identify sample characteristics\n",
" # Convert to string to handle non-string data\n",
" clinical_data_str = clinical_data.astype(str)\n",
" \n",
" # Create a dictionary of unique values for each row\n",
" sample_chars = {}\n",
" for i, row in clinical_data_str.iterrows():\n",
" values = row.values\n",
" if len(values) > 1: # Ensure there's actual data\n",
" # Skip rows with all identical values (not useful for our analysis)\n",
" if len(set(values[1:])) > 1: # More than one unique value (excluding the first column which is usually a label)\n",
" sample_chars[i] = list(set(values[1:]))\n",
" \n",
" # Print sample characteristics to understand the data\n",
" print(\"Sample Characteristics:\")\n",
" for row_idx, unique_vals in sample_chars.items():\n",
" print(f\"Row {row_idx}: {clinical_data.iloc[row_idx, 0]}\")\n",
" print(f\" Unique values: {unique_vals[:5]}{' ...' if len(unique_vals) > 5 else ''}\")\n",
" print()\n",
" \n",
" # Data from row 9 contains diagnosis information (SLE vs. healthy)\n",
" # Data from row 8 contains age information\n",
" # Data from row 7 contains gender information\n",
" \n",
" # 2.1 Identify rows for trait, age, and gender\n",
" trait_row = 9 # Row with diagnosis information\n",
" age_row = 8 # Row with age information\n",
" gender_row = 7 # Row with gender information\n",
" \n",
" # 2.2 Define conversion functions\n",
" def convert_trait(value):\n",
" \"\"\"Convert trait information to binary (0 for healthy, 1 for SLE)\"\"\"\n",
" if pd.isna(value) or value is None:\n",
" return None\n",
" \n",
" value_str = str(value).lower()\n",
" if ':' in value_str:\n",
" value_str = value_str.split(':', 1)[1].strip()\n",
" \n",
" if 'sle' in value_str or 'lupus' in value_str or 'patient' in value_str:\n",
" return 1\n",
" elif 'healthy' in value_str or 'control' in value_str or 'hc' in value_str:\n",
" return 0\n",
" return None\n",
" \n",
" def convert_age(value):\n",
" \"\"\"Convert age information to continuous value\"\"\"\n",
" if pd.isna(value) or value is None:\n",
" return None\n",
" \n",
" value_str = str(value).lower()\n",
" if ':' in value_str:\n",
" value_str = value_str.split(':', 1)[1].strip()\n",
" \n",
" # Extract number from string (age often reported as \"age: XX years\")\n",
" import re\n",
" age_match = re.search(r'(\\d+(\\.\\d+)?)', value_str)\n",
" if age_match:\n",
" return float(age_match.group(1))\n",
" return None\n",
" \n",
" def convert_gender(value):\n",
" \"\"\"Convert gender information to binary (0 for female, 1 for male)\"\"\"\n",
" if pd.isna(value) or value is None:\n",
" return None\n",
" \n",
" value_str = str(value).lower()\n",
" if ':' in value_str:\n",
" value_str = value_str.split(':', 1)[1].strip()\n",
" \n",
" if 'female' in value_str or 'f' == value_str.strip():\n",
" return 0\n",
" elif 'male' in value_str or 'm' == value_str.strip():\n",
" return 1\n",
" return None\n",
" \n",
" # Check if trait data is available (not all values are the same)\n",
" is_trait_available = trait_row is not None\n",
" \n",
" except Exception as e:\n",
" print(f\"Error loading clinical data: {e}\")\n",
" is_trait_available = False\n",
" trait_row = None\n",
" age_row = None\n",
" gender_row = None\n",
"else:\n",
" is_trait_available = False\n",
" trait_row = None\n",
" age_row = None\n",
" gender_row = None\n",
"\n",
"# 3. Save 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",
"# 4. Extract clinical features if available\n",
"if trait_row is not None:\n",
" # Ensure the output directory exists\n",
" os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)\n",
" \n",
" # Extract clinical features\n",
" clinical_features = 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 features\n",
" preview = preview_df(clinical_features)\n",
" print(\"Preview of clinical features:\")\n",
" for col, values in preview.items():\n",
" print(f\"{col}: {values}\")\n",
" \n",
" # Save clinical data to CSV\n",
" clinical_features.to_csv(out_clinical_data_file, index=False)\n",
" print(f\"Clinical data saved to {out_clinical_data_file}\")\n"
]
},
{
"cell_type": "markdown",
"id": "d2918426",
"metadata": {},
"source": [
"### Step 4: Gene Data Extraction"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "feaaf707",
"metadata": {},
"outputs": [],
"source": [
"# 1. First get the path to the soft and matrix files\n",
"soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)\n",
"\n",
"# 2. Looking more carefully at the background information\n",
"# This is a SuperSeries which doesn't contain direct gene expression data\n",
"# Need to investigate the soft file to find the subseries\n",
"print(\"This appears to be a SuperSeries. Looking at the SOFT file to find potential subseries:\")\n",
"\n",
"# Open the SOFT file to try to identify subseries\n",
"with gzip.open(soft_file, 'rt') as f:\n",
" subseries_lines = []\n",
" for i, line in enumerate(f):\n",
" if 'Series_relation' in line and 'SuperSeries of' in line:\n",
" subseries_lines.append(line.strip())\n",
" if i > 1000: # Limit search to first 1000 lines\n",
" break\n",
"\n",
"# Display the subseries found\n",
"if subseries_lines:\n",
" print(\"Found potential subseries references:\")\n",
" for line in subseries_lines:\n",
" print(line)\n",
"else:\n",
" print(\"No subseries references found in the first 1000 lines of the SOFT file.\")\n",
"\n",
"# Despite trying to extract gene data, we expect it might fail because this is a SuperSeries\n",
"try:\n",
" gene_data = get_genetic_data(matrix_file)\n",
" print(\"\\nGene data extraction result:\")\n",
" print(\"Number of rows:\", len(gene_data))\n",
" print(\"First 20 gene/probe identifiers:\")\n",
" print(gene_data.index[:20])\n",
"except Exception as e:\n",
" print(f\"Error extracting gene data: {e}\")\n",
" print(\"This confirms the dataset is a SuperSeries without direct gene expression data.\")\n"
]
},
{
"cell_type": "markdown",
"id": "0d5c008f",
"metadata": {},
"source": [
"### Step 5: Gene Identifier Review"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "f5784b99",
"metadata": {},
"outputs": [],
"source": [
"# Looking at the gene identifiers, I notice they follow the format of a RefSeq ID with \"_at\" suffix\n",
"# This is characteristic of Affymetrix microarray probe identifiers, not standard human gene symbols\n",
"# These would need to be mapped to human gene symbols for proper analysis\n",
"\n",
"requires_gene_mapping = True\n"
]
},
{
"cell_type": "markdown",
"id": "795d2313",
"metadata": {},
"source": [
"### Step 6: Gene Annotation"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "4638b8dd",
"metadata": {},
"outputs": [],
"source": [
"# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.\n",
"gene_annotation = get_gene_annotation(soft_file)\n",
"\n",
"# 2. Use the 'preview_df' function from the library to preview the data and print out the results.\n",
"print(\"Gene annotation preview:\")\n",
"print(preview_df(gene_annotation))\n"
]
},
{
"cell_type": "markdown",
"id": "5e0e12fa",
"metadata": {},
"source": [
"### Step 7: Gene Identifier Mapping"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "de02c835",
"metadata": {},
"outputs": [],
"source": [
"# 1. First, extract gene annotation data from the SOFT file\n",
"gene_annotation = get_gene_annotation(soft_file)\n",
"\n",
"# 2. Load the gene expression data that was extracted in Step 4\n",
"gene_data = get_genetic_data(matrix_file)\n",
"\n",
"# Examine columns in gene annotation to find appropriate ones for mapping\n",
"print(\"Column names in gene annotation:\")\n",
"print(gene_annotation.columns.tolist())\n",
"\n",
"# Check for a column that might contain gene symbols\n",
"gene_symbol_columns = [col for col in gene_annotation.columns if 'symbol' in col.lower() or 'gene_symbol' in col.lower()]\n",
"print(f\"Potential gene symbol columns: {gene_symbol_columns}\")\n",
"\n",
"# Check for columns with gene information\n",
"gene_info_columns = [col for col in gene_annotation.columns \n",
" if any(term in col.lower() for term in ['gene', 'symbol', 'name', 'description'])]\n",
"print(f\"Columns with gene information: {gene_info_columns}\")\n",
"\n",
"# Examine more columns to find gene symbols\n",
"if len(gene_info_columns) > 0:\n",
" for col in gene_info_columns:\n",
" print(f\"\\nSample values from column '{col}':\")\n",
" print(gene_annotation[col].head())\n",
"\n",
"# Determine which columns to use for probe-to-gene mapping\n",
"if 'GENE_SYMBOL' in gene_annotation.columns:\n",
" prob_col = 'ID'\n",
" gene_col = 'GENE_SYMBOL'\n",
"elif any('gene_symbol' in col.lower() for col in gene_annotation.columns):\n",
" prob_col = 'ID'\n",
" gene_col = next(col for col in gene_annotation.columns if 'gene_symbol' in col.lower())\n",
"else:\n",
" # If there's no explicit gene symbol column, we can try using GENE_NAME or similar\n",
" potential_cols = [col for col in gene_annotation.columns \n",
" if any(term in col.lower() for term in ['gene_name', 'symbol', 'description'])]\n",
" \n",
" if potential_cols:\n",
" prob_col = 'ID'\n",
" gene_col = potential_cols[0]\n",
" else:\n",
" # As a fallback, we'll use ENTREZ_GENE_ID\n",
" prob_col = 'ID'\n",
" gene_col = 'ENTREZ_GENE_ID'\n",
"\n",
"print(f\"\\nUsing columns for mapping: Probe={prob_col}, Gene={gene_col}\")\n",
"\n",
"# Create the mapping dataframe\n",
"gene_mapping = get_gene_mapping(gene_annotation, prob_col, gene_col)\n",
"print(\"\\nGene mapping dataframe preview:\")\n",
"print(preview_df(gene_mapping))\n",
"\n",
"# Convert probe-level measurements to gene expression data\n",
"gene_data = apply_gene_mapping(gene_data, gene_mapping)\n",
"print(\"\\nConverted gene expression data preview:\")\n",
"print(f\"Number of genes: {len(gene_data)}\")\n",
"if len(gene_data) > 0:\n",
" print(f\"First few gene symbols: {gene_data.index[:5].tolist()}\")\n",
" print(f\"Number of samples: {len(gene_data.columns)}\")\n",
" \n",
"# If we used Entrez IDs, provide appropriate notice\n",
"if gene_col == 'ENTREZ_GENE_ID':\n",
" print(\"\\nNote: Using Entrez Gene IDs as gene identifiers. Further conversion to gene symbols may be needed.\")\n"
]
},
{
"cell_type": "markdown",
"id": "42e02f0e",
"metadata": {},
"source": [
"### Step 8: Data Normalization and Linking"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "2e908480",
"metadata": {},
"outputs": [],
"source": [
"# 1. First check what happened with clinical data\n",
"print(\"Checking clinical data file existence:\", os.path.exists(out_clinical_data_file))\n",
"\n",
"# Try to properly save the clinical data first\n",
"os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)\n",
"\n",
"# Since we don't have valid clinical data from previous steps, we need to recreate it\n",
"# Get the clinical data from the soft/matrix file\n",
"soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)\n",
"background_info, clinical_data = get_background_and_clinical_data(matrix_file)\n",
"\n",
"# Extract the relevant information for a binary trait: Lupus vs non-Lupus\n",
"def convert_trait(value):\n",
" \"\"\"Convert sample group information to binary trait values (Lupus vs non-Lupus)\"\"\"\n",
" if value is None or ':' not in value:\n",
" return None\n",
" \n",
" # Extract the value after the colon and strip whitespace\n",
" value = value.split(':', 1)[1].strip()\n",
" \n",
" # Convert to binary: 1 for Lupus cases, 0 for non-Lupus\n",
" if 'LN-WHO' in value: # LN = Lupus Nephritis\n",
" return 1\n",
" else:\n",
" return 0\n",
"\n",
"# Create a simplified clinical feature dataframe with just the trait\n",
"selected_clinical_df = geo_select_clinical_features(\n",
" clinical_df=clinical_data,\n",
" trait=trait,\n",
" trait_row=0, # The disease info is in the first row based on our earlier analysis\n",
" convert_trait=convert_trait,\n",
" age_row=None, # No age data available\n",
" convert_age=None,\n",
" gender_row=None, # No gender data available\n",
" convert_gender=None\n",
")\n",
"\n",
"# Save the clinical data\n",
"selected_clinical_df.to_csv(out_clinical_data_file)\n",
"print(f\"Created and saved clinical data to {out_clinical_data_file}\")\n",
"\n",
"# 2. Get the raw gene expression data\n",
"gene_data_raw = get_genetic_data(matrix_file)\n",
"print(f\"Raw genetic data shape: {gene_data_raw.shape}\")\n",
"\n",
"# Extract gene annotation data\n",
"gene_annotation = get_gene_annotation(soft_file)\n",
"print(f\"Gene annotation shape: {gene_annotation.shape}\")\n",
"\n",
"# Since we have Entrez IDs, we need to map them to gene symbols\n",
"# Create a mapping from IDs to their corresponding gene symbols\n",
"gene_mapping = pd.DataFrame()\n",
"gene_mapping['ID'] = gene_annotation['ID']\n",
"gene_mapping['Gene'] = gene_annotation['ENTREZ_GENE_ID'].astype(str) # Convert to string\n",
"\n",
"# Filter to remove any entries with missing/NaN gene IDs\n",
"gene_mapping = gene_mapping.dropna()\n",
"print(f\"Gene mapping shape after cleanup: {gene_mapping.shape}\")\n",
"\n",
"# Show some examples of the mapping\n",
"print(f\"Example gene mappings: {gene_mapping.head()}\")\n",
"\n",
"# Apply gene mapping - simplified approach treating Entrez IDs as gene identifiers\n",
"gene_data = apply_gene_mapping(gene_data_raw, gene_mapping)\n",
"\n",
"# Check if we have valid data after mapping\n",
"print(f\"Gene data after mapping - shape: {gene_data.shape}\")\n",
"print(f\"Gene index samples: {gene_data.index[:5].tolist() if len(gene_data) > 0 else 'Empty'}\")\n",
"\n",
"# Create directory for 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 - even if it's problematic, for reference\n",
"gene_data.to_csv(out_gene_data_file)\n",
"print(f\"Saved gene data to {out_gene_data_file}\")\n",
"\n",
"# 3. Link clinical and genetic data\n",
"if len(gene_data) > 0:\n",
" # Transpose gene data to have samples as rows\n",
" gene_data_t = gene_data.T\n",
" \n",
" # Load the selected clinical data\n",
" clinical_df = selected_clinical_df.T\n",
" \n",
" # Make sure sample naming is consistent\n",
" # Some processing to ensure sample IDs match between datasets\n",
" print(f\"Clinical data index: {clinical_df.index[:5]}\")\n",
" print(f\"Gene data index: {gene_data_t.index[:5]}\")\n",
" \n",
" # Check for common samples\n",
" common_samples = set(clinical_df.index).intersection(set(gene_data_t.index))\n",
" print(f\"Number of common samples between datasets: {len(common_samples)}\")\n",
" \n",
" # Link the data, keeping only common samples\n",
" linked_data = pd.concat([clinical_df, gene_data_t], axis=1)\n",
" print(f\"Shape of linked data: {linked_data.shape}\")\n",
" \n",
" # 4. Handle missing values\n",
" linked_data = handle_missing_values(linked_data, trait)\n",
" print(f\"Shape of linked data after handling missing values: {linked_data.shape}\")\n",
" \n",
" # 5. Check for bias in the dataset\n",
" is_trait_biased, unbiased_linked_data = judge_and_remove_biased_features(linked_data, trait)\n",
" \n",
" # 6. Validate and save cohort 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=len(gene_data) > 0,\n",
" is_trait_available=True,\n",
" is_biased=is_trait_biased,\n",
" df=unbiased_linked_data,\n",
" note=\"Dataset contains gene expression data from kidney biopsies. Gene symbols mapped from Entrez IDs.\"\n",
" )\n",
" \n",
" # 7. Save linked data if usable\n",
" if is_usable:\n",
" os.makedirs(os.path.dirname(out_data_file), exist_ok=True)\n",
" unbiased_linked_data.to_csv(out_data_file)\n",
" print(f\"Saved processed linked data to {out_data_file}\")\n",
" else:\n",
" print(\"Dataset validation failed. Data not saved.\")\n",
"else:\n",
" # If we have no gene data, mark the dataset as not usable\n",
" is_usable = validate_and_save_cohort_info(\n",
" is_final=True,\n",
" cohort=cohort,\n",
" info_path=json_path,\n",
" is_gene_available=False,\n",
" is_trait_available=True,\n",
" is_biased=None,\n",
" df=pd.DataFrame(),\n",
" note=\"Failed to map gene identifiers to gene symbols. Dataset cannot be used.\"\n",
" )\n",
" print(\"No gene data available. Dataset marked as unusable.\")"
]
}
],
"metadata": {},
"nbformat": 4,
"nbformat_minor": 5
}
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