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{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"id": "6adcebbc",
"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 = \"Breast_Cancer\"\n",
"cohort = \"GSE208101\"\n",
"\n",
"# Input paths\n",
"in_trait_dir = \"../../input/GEO/Breast_Cancer\"\n",
"in_cohort_dir = \"../../input/GEO/Breast_Cancer/GSE208101\"\n",
"\n",
"# Output paths\n",
"out_data_file = \"../../output/preprocess/Breast_Cancer/GSE208101.csv\"\n",
"out_gene_data_file = \"../../output/preprocess/Breast_Cancer/gene_data/GSE208101.csv\"\n",
"out_clinical_data_file = \"../../output/preprocess/Breast_Cancer/clinical_data/GSE208101.csv\"\n",
"json_path = \"../../output/preprocess/Breast_Cancer/cohort_info.json\"\n"
]
},
{
"cell_type": "markdown",
"id": "b5dc8926",
"metadata": {},
"source": [
"### Step 1: Initial Data Loading"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "fe978721",
"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": "9877fcf7",
"metadata": {},
"source": [
"### Step 2: Dataset Analysis and Clinical Feature Extraction"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "2528582e",
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"import os\n",
"import json\n",
"from typing import Optional, Callable, Dict, Any\n",
"\n",
"# 1. Gene Expression Data Availability\n",
"# Based on the Series title and description, this dataset contains gene expression data using Clariom D platform\n",
"is_gene_available = True\n",
"\n",
"# 2. Variable Availability and Data Type Conversion\n",
"# 2.1 Data Availability\n",
"# Trait (Breast Cancer): Looking at the sample characteristics, we can see the disease state and loco-regional recurrence timing\n",
"trait_row = 2 # loco-regional recurrence is our trait of interest (early, intermediate, late)\n",
"age_row = None # Age information is not available in the sample characteristics\n",
"gender_row = 0 # Gender information is available\n",
"\n",
"# 2.2 Data Type Conversion\n",
"def convert_trait(value):\n",
" \"\"\"Convert loco-regional recurrence timing to binary values.\"\"\"\n",
" if value is None:\n",
" return None\n",
" \n",
" # Extract value after colon if present\n",
" if \":\" in value:\n",
" value = value.split(\":\", 1)[1].strip()\n",
" \n",
" # Convert to binary: early (< 2 yrs) = 1, others = 0\n",
" if \"EARLY\" in value.upper():\n",
" return 1\n",
" elif \"INTERMEDIATE\" in value.upper() or \"LATE\" in value.upper():\n",
" return 0\n",
" else:\n",
" return None\n",
"\n",
"def convert_gender(value):\n",
" \"\"\"Convert gender to binary values.\"\"\"\n",
" if value is None:\n",
" return None\n",
" \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 value.lower() == \"female\":\n",
" return 0\n",
" elif value.lower() == \"male\":\n",
" return 1\n",
" else:\n",
" return None\n",
"\n",
"# Since age_row is None, we don't need to define convert_age\n",
"\n",
"# 3. Save Metadata\n",
"# Trait data is available since trait_row is not None\n",
"is_trait_available = trait_row is not None\n",
"\n",
"# Conduct initial filtering and save cohort info\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 proceed with clinical feature extraction\n",
"try:\n",
" # Load clinical data if available\n",
" if os.path.exists(f\"{in_cohort_dir}/clinical_data.csv\"):\n",
" clinical_data = pd.read_csv(f\"{in_cohort_dir}/clinical_data.csv\", index_col=0)\n",
" \n",
" # Extract clinical features using the imported 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",
" gender_row=gender_row,\n",
" convert_gender=convert_gender\n",
" )\n",
" \n",
" # Preview the processed clinical data\n",
" preview = preview_df(selected_clinical_df)\n",
" print(\"Clinical Data Preview:\")\n",
" print(preview)\n",
" \n",
" # Save the processed 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\"Saved clinical data to {out_clinical_data_file}\")\n",
" else:\n",
" print(f\"Clinical data file not found at {in_cohort_dir}/clinical_data.csv\")\n",
"except Exception as e:\n",
" print(f\"Error processing clinical data: {e}\")\n"
]
},
{
"cell_type": "markdown",
"id": "7c9d059a",
"metadata": {},
"source": [
"### Step 3: Gene Data Extraction"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "8ff1c980",
"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",
"marker_row = None\n",
"try:\n",
" with gzip.open(matrix_file, 'rt') as file:\n",
" for i, line in enumerate(file):\n",
" if \"!series_matrix_table_begin\" in line:\n",
" found_marker = True\n",
" marker_row = i\n",
" print(f\"Found the matrix table marker at line {i}\")\n",
" break\n",
" \n",
" if not found_marker:\n",
" print(\"Warning: Could not find '!series_matrix_table_begin' marker in the file.\")\n",
" is_gene_available = False\n",
" \n",
" # If marker was found, try to extract gene data\n",
" if is_gene_available:\n",
" try:\n",
" # Try using the library function\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",
" except Exception as e:\n",
" print(f\"Error extracting gene data with get_genetic_data(): {e}\")\n",
" is_gene_available = False\n",
" \n",
" # If gene data extraction failed, examine file content to diagnose\n",
" if not is_gene_available:\n",
" print(\"Examining file content to diagnose the issue:\")\n",
" try:\n",
" with gzip.open(matrix_file, 'rt') as file:\n",
" # Print lines around the marker if found\n",
" if marker_row is not None:\n",
" for i, line in enumerate(file):\n",
" if i >= marker_row - 2 and i <= marker_row + 10:\n",
" print(f\"Line {i}: {line.strip()[:100]}...\")\n",
" if i > marker_row + 10:\n",
" break\n",
" else:\n",
" # If marker not found, print first 10 lines\n",
" for i, line in enumerate(file):\n",
" if i < 10:\n",
" print(f\"Line {i}: {line.strip()[:100]}...\")\n",
" else:\n",
" break\n",
" except Exception as e2:\n",
" print(f\"Error examining file: {e2}\")\n",
" \n",
"except Exception as e:\n",
" print(f\"Error processing file: {e}\")\n",
" is_gene_available = False\n",
"\n",
"# Update validation information if gene data extraction failed\n",
"if not is_gene_available:\n",
" print(\"Gene expression data could not be successfully extracted from this dataset.\")\n",
" # Update the validation record since gene data isn't available\n",
" is_trait_available = False # We already determined trait data isn't available in step 2\n",
" validate_and_save_cohort_info(is_final=False, cohort=cohort, info_path=json_path,\n",
" is_gene_available=is_gene_available, is_trait_available=is_trait_available)\n"
]
},
{
"cell_type": "markdown",
"id": "cdd548e4",
"metadata": {},
"source": [
"### Step 4: Gene Identifier Review"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "e40127f5",
"metadata": {},
"outputs": [],
"source": [
"# These are not standard human gene symbols but rather appear to be probe identifiers from a microarray\n",
"# or similar platform. They have a specific format (TC0100006432.hg.1) that suggests they need to be\n",
"# mapped to actual human gene symbols.\n",
"\n",
"requires_gene_mapping = True\n"
]
},
{
"cell_type": "markdown",
"id": "755144f1",
"metadata": {},
"source": [
"### Step 5: Gene Annotation"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "6a5c1323",
"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=3))\n",
"\n",
"# Looking at the output, the gene_assignment column contains gene symbols\n",
"print(\"\\nExamining ID and gene_assignment columns format (first 3 rows):\")\n",
"if 'ID' in gene_annotation.columns and 'gene_assignment' in gene_annotation.columns:\n",
" for i in range(min(3, len(gene_annotation))):\n",
" print(f\"Row {i}: ID={gene_annotation['ID'].iloc[i]}, gene_assignment={gene_annotation['gene_assignment'].iloc[i]}\")\n",
"\n",
" # Check the quality and completeness of the mapping\n",
" non_null_symbols = gene_annotation['gene_assignment'].notna().sum()\n",
" total_rows = len(gene_annotation)\n",
" print(f\"\\ngene_assignment column completeness: {non_null_symbols}/{total_rows} rows ({non_null_symbols/total_rows:.2%})\")\n"
]
},
{
"cell_type": "markdown",
"id": "27ecdc35",
"metadata": {},
"source": [
"### Step 6: Gene Identifier Mapping"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "3847e24a",
"metadata": {},
"outputs": [],
"source": [
"# Extract the probe identifier (ID) and gene symbol (gene_assignment) columns\n",
"# Looking at the output, we can see:\n",
"# 1. The ID column in gene_annotation matches the probe IDs in gene_data\n",
"# 2. The gene_assignment column contains gene symbol information but in a complex format\n",
"\n",
"# 1. First, get the gene expression data\n",
"gene_data = get_genetic_data(matrix_file)\n",
"\n",
"# 2. Extract the mapping between probe IDs and gene symbols\n",
"mapping_df = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='gene_assignment')\n",
"\n",
"# 3. Convert probe-level measurements to gene expression data\n",
"gene_data = apply_gene_mapping(gene_data, mapping_df)\n",
"\n",
"# Check the shape of the resulting gene expression dataframe\n",
"print(f\"Converted gene expression data shape: {gene_data.shape}\")\n",
"\n",
"# Display a few gene symbols (indexes) in the gene expression data\n",
"print(\"First 10 gene symbols in converted gene expression data:\")\n",
"print(gene_data.index[:10].tolist())\n",
"\n",
"# Check for any issues with the data\n",
"print(f\"Number of gene symbols after mapping: {len(gene_data.index)}\")\n",
"print(f\"Number of samples: {gene_data.shape[1]}\")\n"
]
},
{
"cell_type": "markdown",
"id": "f684299d",
"metadata": {},
"source": [
"### Step 7: Data Normalization and Linking"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "acd4fe52",
"metadata": {},
"outputs": [],
"source": [
"```python\n",
"# 1. Normalize gene symbols in the gene expression data\n",
"try:\n",
" # Make sure the directory exists\n",
" os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)\n",
" \n",
" # Use the gene_data variable from the previous step (don't try to load it from file)\n",
" print(f\"Gene data shape before normalization: {gene_data.shape}\")\n",
" \n",
" # Apply normalization to gene symbols\n",
" normalized_gene_data = normalize_gene_symbols_in_index(gene_data)\n",
" print(f\"Gene data shape after normalization: {normalized_gene_data.shape}\")\n",
" \n",
" # Save the normalized gene data\n",
" normalized_gene_data.to_csv(out_gene_data_file)\n",
" print(f\"Normalized gene data saved to {out_gene_data_file}\")\n",
" \n",
" # Use the normalized data for further processing\n",
" gene_data = normalized_gene_data\n",
" is_gene_available = True\n",
"except Exception as e:\n",
" print(f\"Error normalizing gene data: {e}\")\n",
" is_gene_available = False\n",
"\n",
"# 2. Load clinical data - respecting the analysis from Step 2\n",
"# From Step 2, we determined:\n",
"trait_row = 2 # loco-regional recurrence\n",
"age_row = None # Age information is not available\n",
"gender_row = 0 # Gender information is available\n",
"is_trait_available = trait_row is not None\n",
"\n",
"# Define converter functions as done in Step 2\n",
"def convert_trait(value):\n",
" \"\"\"Convert loco-regional recurrence timing to binary values.\"\"\"\n",
" if value is None:\n",
" return None\n",
" \n",
" # Extract value after colon if present\n",
" if \":\" in value:\n",
" value = value.split(\":\", 1)[1].strip()\n",
" \n",
" # Convert to binary: early (< 2 yrs) = 1, others = 0\n",
" if \"EARLY\" in value.upper():\n",
" return 1\n",
" elif \"INTERMEDIATE\" in value.upper() or \"LATE\" in value.upper():\n",
" return 0\n",
" else:\n",
" return None\n",
"\n",
"def convert_gender(value):\n",
" \"\"\"Convert gender to binary values.\"\"\"\n",
" if value is None:\n",
" return None\n",
" \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 value.lower() == \"female\":\n",
" return 0\n",
" elif value.lower() == \"male\":\n",
" return 1\n",
" else:\n",
" return None\n",
"\n",
"# Skip clinical feature extraction when trait_row is None\n",
"if is_trait_available:\n",
" try:\n",
" # Load the clinical data from 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 clinical features - note we don't include age_row and convert_age since age_row is None\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",
" gender_row=gender_row,\n",
" convert_gender=convert_gender\n",
" )\n",
" \n",
" print(f\"Extracted clinical data shape: {clinical_features.shape}\")\n",
" print(\"Preview of clinical data (first 5 samples):\")\n",
" print(clinical_features.iloc[:, :5])\n",
" \n",
" # Save the properly extracted clinical data\n",
" os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)\n",
" clinical_features.to_csv(out_clinical_data_file)\n",
" print(f\"Clinical data saved to {out_clinical_data_file}\")\n",
" except Exception as e:\n",
" print(f\"Error extracting clinical data: {e}\")\n",
" is_trait_available = False\n",
"else:\n",
" print(f\"No trait data ({trait}) available in this dataset based on previous analysis.\")\n",
"\n",
"# 3. Link clinical and genetic data if both are available\n",
"if is_trait_available and is_gene_available:\n",
" try:\n",
" # Debug the column names to ensure they match\n",
" print(f\"Gene data columns (first 5): {gene_data.columns[:5].tolist()}\")\n",
" print(f\"Clinical data columns (first 5): {clinical_features.columns[:5].tolist()}\")\n",
" \n",
" # Check for common sample IDs\n",
" common_samples = set(gene_data.columns).intersection(clinical_features.columns)\n",
" print(f\"Found {len(common_samples)} common samples between gene and clinical data\")\n",
" \n",
" if len(common_samples) > 0:\n",
" # Link the clinical and genetic data\n",
" linked_data = geo_link_clinical_genetic_data(clinical_features, gene_data)\n",
" print(f\"Initial linked data shape: {linked_data.shape}\")\n",
" \n",
" # Debug the trait values before handling missing values\n",
" print(\"Preview of linked data (first 5 rows, first 5 columns):\")\n",
" print(linked_data.iloc[:5, :5])\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",
" if linked_data.shape[0] > 0:\n",
" # Check for bias in trait and demographic features\n",
" is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)\n",
" \n",
" # Validate the data quality and save cohort info\n",
" note = \"Dataset contains gene expression from luminal breast cancer patients with different loco-regional recurrence timing (early, intermediate, late).\"\n",
" is_usable = validate_and_save_cohort_info(\n",
" is_final=True,\n",
" cohort=cohort,\n",
" info_path=json_path,\n",
" is_gene_available=is_gene_available,\n",
" is_trait_available=is_trait_available,\n",
" is_biased=is_biased,\n",
" df=linked_data,\n",
" note=note\n",
" )\n",
" \n",
" # 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.\")\n",
" else:\n",
" print(\"After handling missing values, no samples remain.\")\n",
" validate_and_save_cohort_info(\n",
" is_final=True,\n",
" cohort=cohort,\n",
" info_path=json_path,\n",
" is_gene_available=is_gene_available,\n",
" is_trait_available=is_trait_available,\n",
" is_biased=True,\n",
" df=pd.DataFrame(),\n",
" note=\"No valid samples after handling missing values.\"\n",
" )\n",
" else:\n",
" print(\"No common samples found between gene expression and clinical data.\")\n",
" validate_and_save_cohort_info(\n",
" is_final=True,\n",
" cohort=cohort,\n",
" info_path=json_path,\n",
" is_gene_available=is_gene_available,\n",
" is_trait_available=is_trait_available,\n",
" is_biased=True,\n",
" df=pd.DataFrame(),\n",
" note=\"No common samples between gene expression and clinical data.\"\n",
" )\n",
" except Exception as e:\n",
" print(f\"Error linking or processing data: {e}\")\n",
" validate_and_save_cohort_info(\n",
" is_final=True,\n",
" cohort=cohort,\n",
" info_path=json_path,\n",
" is_gene_available=is_gene_available,\n",
" is_trait_available=is_trait_available,\n",
" is_biased=True, # Assume biased if there's an error\n",
" df=pd.DataFrame(), # Empty dataframe for metadata\n",
" note=f\"Error in data processing: {str(e)}\"\n",
" )\n",
"else:\n",
" # Create an empty DataFrame for metadata purposes\n",
" empty_df = pd.DataFrame()\n",
" \n",
" # We can't procee\n"
]
},
{
"cell_type": "markdown",
"id": "f7f25be6",
"metadata": {},
"source": [
"### Step 8: Data Normalization and Linking"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "505a6c10",
"metadata": {},
"outputs": [],
"source": [
"# 1. Normalize gene symbols in the gene expression data\n",
"try:\n",
" # Make sure the directory exists\n",
" os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)\n",
" \n",
" # Use the gene_data variable from the previous step (don't try to load it from file)\n",
" print(f\"Gene data shape before normalization: {gene_data.shape}\")\n",
" \n",
" # Apply normalization to gene symbols\n",
" normalized_gene_data = normalize_gene_symbols_in_index(gene_data)\n",
" print(f\"Gene data shape after normalization: {normalized_gene_data.shape}\")\n",
" \n",
" # Save the normalized gene data\n",
" normalized_gene_data.to_csv(out_gene_data_file)\n",
" print(f\"Normalized gene data saved to {out_gene_data_file}\")\n",
" \n",
" # Use the normalized data for further processing\n",
" gene_data = normalized_gene_data\n",
" is_gene_available = True\n",
"except Exception as e:\n",
" print(f\"Error normalizing gene data: {e}\")\n",
" is_gene_available = False\n",
"\n",
"# 2. Load clinical data - respecting the analysis from Step 2\n",
"# From Step 2, we determined:\n",
"# trait_row = None # No Breast Cancer subtype data available\n",
"# age_row = 2\n",
"# gender_row = None\n",
"is_trait_available = trait_row is not None\n",
"\n",
"# Skip clinical feature extraction when trait_row is None\n",
"if is_trait_available:\n",
" try:\n",
" # Load the clinical data from 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 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",
" gender_row=gender_row,\n",
" convert_gender=convert_gender,\n",
" age_row=age_row,\n",
" convert_age=convert_age\n",
" )\n",
" \n",
" print(f\"Extracted clinical data shape: {clinical_features.shape}\")\n",
" print(\"Preview of clinical data (first 5 samples):\")\n",
" print(clinical_features.iloc[:, :5])\n",
" \n",
" # Save the properly extracted clinical data\n",
" os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)\n",
" clinical_features.to_csv(out_clinical_data_file)\n",
" print(f\"Clinical data saved to {out_clinical_data_file}\")\n",
" except Exception as e:\n",
" print(f\"Error extracting clinical data: {e}\")\n",
" is_trait_available = False\n",
"else:\n",
" print(f\"No trait data ({trait}) available in this dataset based on previous analysis.\")\n",
"\n",
"# 3. Link clinical and genetic data if both are available\n",
"if is_trait_available and is_gene_available:\n",
" try:\n",
" # Debug the column names to ensure they match\n",
" print(f\"Gene data columns (first 5): {gene_data.columns[:5].tolist()}\")\n",
" print(f\"Clinical data columns (first 5): {clinical_features.columns[:5].tolist()}\")\n",
" \n",
" # Check for common sample IDs\n",
" common_samples = set(gene_data.columns).intersection(clinical_features.columns)\n",
" print(f\"Found {len(common_samples)} common samples between gene and clinical data\")\n",
" \n",
" if len(common_samples) > 0:\n",
" # Link the clinical and genetic data\n",
" linked_data = geo_link_clinical_genetic_data(clinical_features, gene_data)\n",
" print(f\"Initial linked data shape: {linked_data.shape}\")\n",
" \n",
" # Debug the trait values before handling missing values\n",
" print(\"Preview of linked data (first 5 rows, first 5 columns):\")\n",
" print(linked_data.iloc[:5, :5])\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",
" if linked_data.shape[0] > 0:\n",
" # Check for bias in trait and demographic features\n",
" is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)\n",
" \n",
" # Validate the data quality and save cohort info\n",
" note = \"Dataset contains gene expression data from triple negative breast cancer vs. luminal tumors, but no explicit breast cancer subtype labels in the sample characteristics.\"\n",
" is_usable = validate_and_save_cohort_info(\n",
" is_final=True,\n",
" cohort=cohort,\n",
" info_path=json_path,\n",
" is_gene_available=is_gene_available,\n",
" is_trait_available=is_trait_available,\n",
" is_biased=is_biased,\n",
" df=linked_data,\n",
" note=note\n",
" )\n",
" \n",
" # 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.\")\n",
" else:\n",
" print(\"After handling missing values, no samples remain.\")\n",
" validate_and_save_cohort_info(\n",
" is_final=True,\n",
" cohort=cohort,\n",
" info_path=json_path,\n",
" is_gene_available=is_gene_available,\n",
" is_trait_available=is_trait_available,\n",
" is_biased=True,\n",
" df=pd.DataFrame(),\n",
" note=\"No valid samples after handling missing values.\"\n",
" )\n",
" else:\n",
" print(\"No common samples found between gene expression and clinical data.\")\n",
" validate_and_save_cohort_info(\n",
" is_final=True,\n",
" cohort=cohort,\n",
" info_path=json_path,\n",
" is_gene_available=is_gene_available,\n",
" is_trait_available=is_trait_available,\n",
" is_biased=True,\n",
" df=pd.DataFrame(),\n",
" note=\"No common samples between gene expression and clinical data.\"\n",
" )\n",
" except Exception as e:\n",
" print(f\"Error linking or processing data: {e}\")\n",
" validate_and_save_cohort_info(\n",
" is_final=True,\n",
" cohort=cohort,\n",
" info_path=json_path,\n",
" is_gene_available=is_gene_available,\n",
" is_trait_available=is_trait_available,\n",
" is_biased=True, # Assume biased if there's an error\n",
" df=pd.DataFrame(), # Empty dataframe for metadata\n",
" note=f\"Error in data processing: {str(e)}\"\n",
" )\n",
"else:\n",
" # Create an empty DataFrame for metadata purposes\n",
" empty_df = pd.DataFrame()\n",
" \n",
" # We can't proceed with linking if either trait or gene data is missing\n",
" print(\"Cannot proceed with data linking due to missing trait or gene data.\")\n",
" validate_and_save_cohort_info(\n",
" is_final=True,\n",
" cohort=cohort,\n",
" info_path=json_path,\n",
" is_gene_available=is_gene_available,\n",
" is_trait_available=is_trait_available,\n",
" is_biased=True, # Data is unusable if we're missing components\n",
" df=empty_df, # Empty dataframe for metadata\n",
" note=\"Dataset contains gene expression data from triple negative breast cancer vs. luminal tumors, but no explicit breast cancer subtype labels in the sample characteristics.\"\n",
" )"
]
}
],
"metadata": {},
"nbformat": 4,
"nbformat_minor": 5
}
|