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{
"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",
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"source": [
"### Step 8: Data Normalization and Linking"
]
},
{
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"id": "a531181d",
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"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.\")"
]
}
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