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{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"id": "5728adab",
"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 = \"Arrhythmia\"\n",
"cohort = \"GSE53622\"\n",
"\n",
"# Input paths\n",
"in_trait_dir = \"../../input/GEO/Arrhythmia\"\n",
"in_cohort_dir = \"../../input/GEO/Arrhythmia/GSE53622\"\n",
"\n",
"# Output paths\n",
"out_data_file = \"../../output/preprocess/Arrhythmia/GSE53622.csv\"\n",
"out_gene_data_file = \"../../output/preprocess/Arrhythmia/gene_data/GSE53622.csv\"\n",
"out_clinical_data_file = \"../../output/preprocess/Arrhythmia/clinical_data/GSE53622.csv\"\n",
"json_path = \"../../output/preprocess/Arrhythmia/cohort_info.json\"\n"
]
},
{
"cell_type": "markdown",
"id": "8e0eab6e",
"metadata": {},
"source": [
"### Step 1: Initial Data Loading"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "f5eec03c",
"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": "2dc4220d",
"metadata": {},
"source": [
"### Step 2: Dataset Analysis and Clinical Feature Extraction"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "d6ad9df0",
"metadata": {},
"outputs": [],
"source": [
"I'll provide the correct solution for step 2:\n",
"\n",
"```python\n",
"# 1. Gene Expression Data Analysis\n",
"# Based on the background information, this dataset contains lncRNA expression profiles, not typical gene expression.\n",
"is_gene_available = False\n",
"\n",
"# 2. Data Availability and Type Conversion\n",
"# 2.1 Data Availability\n",
"\n",
"# Trait data - Arrhythmia\n",
"trait_row = 10 # 'arrhythmia: no', 'arrhythmia: yes'\n",
"\n",
"# Age data \n",
"age_row = 1 # 'age: 66.4602739726027', etc.\n",
"\n",
"# Gender data\n",
"gender_row = 2 # 'Sex: female', 'Sex: male'\n",
"\n",
"# 2.2 Data Type Conversion Functions\n",
"\n",
"def convert_trait(value):\n",
" \"\"\"Convert arrhythmia status to binary values.\"\"\"\n",
" if value is None:\n",
" return None\n",
" \n",
" if \":\" in value:\n",
" value = value.split(\":\", 1)[1].strip().lower()\n",
" \n",
" if value == \"yes\":\n",
" return 1\n",
" elif value == \"no\":\n",
" return 0\n",
" else:\n",
" return None\n",
"\n",
"def convert_age(value):\n",
" \"\"\"Convert age to a continuous numeric value.\"\"\"\n",
" if value is None:\n",
" return None\n",
" \n",
" if \":\" in value:\n",
" value = value.split(\":\", 1)[1].strip()\n",
" \n",
" try:\n",
" return float(value)\n",
" except (ValueError, TypeError):\n",
" return None\n",
"\n",
"def convert_gender(value):\n",
" \"\"\"Convert gender to binary (0: female, 1: male).\"\"\"\n",
" if value is None:\n",
" return None\n",
" \n",
" if \":\" in value:\n",
" value = value.split(\":\", 1)[1].strip().lower()\n",
" \n",
" if value == \"female\":\n",
" return 0\n",
" elif value == \"male\":\n",
" return 1\n",
" else:\n",
" return None\n",
"\n",
"# 3. Save metadata for initial filtering\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",
"if trait_row is not None:\n",
" # Create a DataFrame from the sample characteristics dictionary\n",
" sample_characteristics_dict = {\n",
" 0: ['patient id: ec302', 'patient id: ec303', 'patient id: ec305', 'patient id: ec306', 'patient id: ec325', 'patient id: ec326', 'patient id: ec330', 'patient id: ec331', 'patient id: ec308', 'patient id: ec309', 'patient id: ec311', 'patient id: ec312', 'patient id: ec315', 'patient id: ec316', 'patient id: ec317', 'patient id: ec318', 'patient id: ec319', 'patient id: ec321', 'patient id: ec322', 'patient id: ec324', 'patient id: ec333', 'patient id: ec334', 'patient id: ec337', 'patient id: ec338', 'patient id: ec340', 'patient id: ec341', 'patient id: ec342', 'patient id: ec347', 'patient id: ec353', 'patient id: ec355'],\n",
" 1: ['age: 66.4602739726027', 'age: 64.013698630137', 'age: 50.9123287671233', 'age: 46.3287671232877', 'age: 53.9972602739726', 'age: 67.8438356164384', 'age: 64.8794520547945', 'age: 45.2219178082192', 'age: 54.4794520547945', 'age: 56.2328767123288', 'age: 57.0986301369863', 'age: 44.6630136986301', 'age: 43.7698630136986', 'age: 67.2739726027397', 'age: 68.2904109589041', 'age: 60.5068493150685', 'age: 48.4027397260274', 'age: 54.2931506849315', 'age: 51.9890410958904', 'age: 58.3205479452055', 'age: 66.2712328767123', 'age: 72.241095890411', 'age: 64.7506849315069', 'age: 54.5753424657534', 'age: 62.4383561643836', 'age: 66.1479452054794', 'age: 53.7424657534247', 'age: 56.9643835616438', 'age: 71.9150684931507', 'age: 53.5643835616438'],\n",
" 2: ['Sex: female', 'Sex: male'],\n",
" 3: ['tobacco use: no', 'tobacco use: yes'],\n",
" 4: ['alcohol use: no', 'alcohol use: yes'],\n",
" 5: ['tumor loation: middle', 'tumor loation: lower', 'tumor loation: upper'],\n",
" 6: ['tumor grade: moderately', 'tumor grade: poorly', 'tumor grade: well'],\n",
" 7: ['t stage: T3', 't stage: T1', 't stage: T2', 't stage: T4'],\n",
" 8: ['n stage: N2', 'n stage: N0', 'n stage: N1', 'n stage: N3'],\n",
" 9: ['tnm stage: III', 'tnm stage: II', 'tnm stage: I'],\n",
" 10: ['arrhythmia: no', 'arrhythmia: yes'],\n",
" 11: ['pneumonia: no', 'pneumonia: yes'],\n",
" 12: ['anastomotic leak: no', 'anastomotic leak: yes'],\n",
" 13: ['adjuvant therapy: yes', 'adjuvant therapy: no', 'adjuvant therapy: unknown'],\n",
" 14: ['death at fu: yes', 'death at fu: no'],\n",
" 15: ['survival time(months): 11.6333333333333', 'survival time(months): 58.2', 'survival time(months): 39.1666666666667', 'survival time(months): 57.7333333333333', 'survival time(months): 11.0666666666667', 'survival time(months): 55.2666666666667', 'survival time(months): 29.7', 'survival time(months): 10.9666666666667', 'survival time(months): 57.6', 'survival time(months): 57.5333333333333', 'survival time(months): 57.3666666666667', 'survival time(months): 57.2666666666667', 'survival time(months): 57.2333333333333', 'survival time(months): 13', 'survival time(months): 5.7', 'survival time(months): 56.6333333333333', 'survival time(months): 56.1333333333333', 'survival time(months): 55.6666666666667', 'survival time(months): 6.26666666666667', 'survival time(months): 25.4666666666667', 'survival time(months): 10.2', 'survival time(months): 13.1666666666667', 'survival time(months): 54.7333333333333', 'survival time(months): 18.0666666666667', 'survival time(months): 36.3', 'survival time(months): 54.1', 'survival time(months): 16.0333333333333', 'survival time(months): 52.9333333333333', 'survival time(months): 52.7', 'survival time(months): 8.46666666666667'],\n",
" 16: ['disease state: esophageal squamous cell carcinoma'],\n",
" 17: ['tissue: cancer tissue from patient 302', 'tissue: normal tissue from patient 302', 'tissue: cancer tissue from patient 303', 'tissue: normal tissue from patient 303', 'tissue: cancer tissue\n"
]
},
{
"cell_type": "markdown",
"id": "c05abbb2",
"metadata": {},
"source": [
"### Step 3: Dataset Analysis and Clinical Feature Extraction"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "2ecca341",
"metadata": {},
"outputs": [],
"source": [
"I've analyzed the error and will provide a fixed and complete solution:\n",
"\n",
"```python\n",
"import pandas as pd\n",
"import os\n",
"import json\n",
"import numpy as np\n",
"import re\n",
"import glob\n",
"from typing import Dict, Any, Optional, Callable\n",
"\n",
"# --- Step 1: Explore available files in the directory\n",
"available_files = glob.glob(os.path.join(in_cohort_dir, \"*\"))\n",
"print(f\"Available files in {in_cohort_dir}:\")\n",
"for file in available_files:\n",
" print(f\" - {os.path.basename(file)}\")\n",
"\n",
"# Look for potential series matrix files with different patterns\n",
"series_files = [f for f in available_files if \"series_matrix\" in f.lower() or \"series\" in f.lower()]\n",
"if not series_files:\n",
" # Try to find any text files\n",
" series_files = [f for f in available_files if f.endswith('.txt') or f.endswith('.csv')]\n",
"\n",
"if not series_files:\n",
" print(\"No suitable data files found. Cannot proceed with analysis.\")\n",
" # If we have any files, let's try to examine the content of the first one\n",
" if available_files:\n",
" sample_file = available_files[0]\n",
" print(f\"Examining content of {os.path.basename(sample_file)}:\")\n",
" try:\n",
" with open(sample_file, 'r') as f:\n",
" content_preview = [next(f) for _ in range(min(10, os.path.getsize(sample_file)))]\n",
" for line in content_preview:\n",
" print(line.strip())\n",
" except Exception as e:\n",
" print(f\"Could not read file: {e}\")\n",
" \n",
" # Set variables to indicate data is not available\n",
" is_gene_available = False\n",
" is_trait_available = False\n",
"else:\n",
" # Use the first found file\n",
" matrix_file_path = series_files[0]\n",
" print(f\"Using file: {os.path.basename(matrix_file_path)}\")\n",
" \n",
" # Attempt to extract information from the file\n",
" try:\n",
" # Read file content\n",
" with open(matrix_file_path, 'r') as file:\n",
" # Read the first several lines to analyze header info\n",
" header_lines = []\n",
" line_count = 0\n",
" max_lines = 200 # Read more lines to ensure we capture sample characteristics\n",
" \n",
" for line in file:\n",
" header_lines.append(line)\n",
" line_count += 1\n",
" if line_count >= max_lines:\n",
" break\n",
" \n",
" # Check if this is likely gene expression data\n",
" platform_line = [line for line in header_lines if \"!Series_platform_id\" in line]\n",
" is_gene_available = True\n",
" \n",
" if platform_line:\n",
" platform_id = platform_line[0].split(\"=\")[1].strip().strip('\"')\n",
" print(f\"Platform ID: {platform_id}\")\n",
" # Check if platform suggests miRNA or methylation data\n",
" if \"mirna\" in platform_id.lower() or \"methylation\" in platform_id.lower():\n",
" print(\"Dataset appears to be miRNA or methylation data, not gene expression.\")\n",
" is_gene_available = False\n",
" \n",
" # Extract sample characteristics\n",
" sample_characteristics = {}\n",
" for i, line in enumerate(header_lines):\n",
" if line.startswith(\"!Sample_characteristics_ch1\"):\n",
" parts = line.strip().split('\\t')\n",
" if len(parts) > 1:\n",
" if i not in sample_characteristics:\n",
" sample_characteristics[i] = []\n",
" for part in parts[1:]:\n",
" sample_characteristics[i].append(part)\n",
" \n",
" # Check if sample characteristics were found\n",
" if not sample_characteristics:\n",
" print(\"No sample characteristics found in the file.\")\n",
" for i, line in enumerate(header_lines[:20]):\n",
" print(f\"Line {i}: {line.strip()}\")\n",
" is_trait_available = False\n",
" else:\n",
" # Print unique values for each row to help identify variables\n",
" print(\"\\nSample characteristics analysis:\")\n",
" for key, values in sample_characteristics.items():\n",
" unique_values = list(set(values))\n",
" print(f\"Row {key}: {unique_values[:5]}\")\n",
" if len(unique_values) > 5:\n",
" print(f\" ...and {len(unique_values)-5} more unique values\")\n",
" \n",
" # Load clinical data\n",
" clinical_data = pd.DataFrame()\n",
" for key, values in sample_characteristics.items():\n",
" clinical_data[key] = values\n",
" \n",
" # Based on inspection of sample characteristics, identify rows for trait, age, and gender\n",
" trait_row = None\n",
" age_row = None\n",
" gender_row = None\n",
" \n",
" # Look for row containing Arrhythmia information\n",
" for key, values in sample_characteristics.items():\n",
" unique_str = ' '.join(set([str(v).lower() for v in values]))\n",
" \n",
" # Check for trait information (Arrhythmia)\n",
" if any(term in unique_str for term in ['arrhythmia', 'disease', 'condition', 'patient', 'control', 'case']):\n",
" trait_row = key\n",
" print(f\"Found potential trait information in row {key}: {list(set(values))[:5]}\")\n",
" \n",
" # Check for age information\n",
" if any(term in unique_str for term in ['age', 'years']):\n",
" age_row = key\n",
" print(f\"Found potential age information in row {key}: {list(set(values))[:5]}\")\n",
" \n",
" # Check for gender information\n",
" if any(term in unique_str for term in ['gender', 'sex', 'male', 'female']):\n",
" gender_row = key\n",
" print(f\"Found potential gender information in row {key}: {list(set(values))[:5]}\")\n",
" \n",
" # Define conversion functions based on the identified data\n",
" def convert_trait(value):\n",
" \"\"\"Convert trait values to binary (0/1)\"\"\"\n",
" if value is None or pd.isna(value):\n",
" return None\n",
" value = str(value).lower()\n",
" if ':' in value:\n",
" value = value.split(':', 1)[1].strip()\n",
" \n",
" # Adapt based on the actual values in the dataset\n",
" if any(term in value for term in ['control', 'normal', 'healthy', 'no', 'negative']):\n",
" return 0\n",
" elif any(term in value for term in ['arrhythmia', 'disease', 'patient', 'yes', 'positive']):\n",
" return 1\n",
" else:\n",
" return None\n",
" \n",
" def convert_age(value):\n",
" \"\"\"Convert age values to continuous numeric form\"\"\"\n",
" if value is None or pd.isna(value):\n",
" return None\n",
" value = str(value)\n",
" if ':' in value:\n",
" value = value.split(':', 1)[1].strip()\n",
" \n",
" # Extract numeric age using regex\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",
" \"\"\"Convert gender values to binary (0 for female, 1 for male)\"\"\"\n",
" if value is None or pd.isna(value):\n",
" return None\n",
" value = str(value).lower()\n",
" if ':' in value:\n",
" value = value.split(':', 1)[1].strip()\n",
" \n",
" if any(term in value for term in ['female', 'f']):\n",
" return 0\n",
" elif any(term in value for term in ['male', 'm']):\n",
" return 1\n",
" else:\n",
" return None\n",
" \n",
" is_trait_available = trait_row is not None\n",
" \n",
" # Save metadata for initial filtering\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",
" selected_clinical_df = geo_select_clinical_features(\n",
" clinical_df=clinical_data,\n",
" trait=trait,\n",
" trait_\n"
]
},
{
"cell_type": "markdown",
"id": "0acb2393",
"metadata": {},
"source": [
"### Step 4: Gene Data Extraction"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "dc77b2d0",
"metadata": {},
"outputs": [],
"source": [
"# 1. Get the SOFT and matrix file paths again \n",
"soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)\n",
"print(f\"Matrix file found: {matrix_file}\")\n",
"\n",
"# 2. Use the get_genetic_data function from the library to get the gene_data\n",
"try:\n",
" gene_data = get_genetic_data(matrix_file)\n",
" print(f\"Gene data shape: {gene_data.shape}\")\n",
" \n",
" # 3. Print the first 20 row IDs (gene or probe identifiers)\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"
]
},
{
"cell_type": "markdown",
"id": "0273c6ce",
"metadata": {},
"source": [
"### Step 5: Gene Identifier Review"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "e9893739",
"metadata": {},
"outputs": [],
"source": [
"# Review the gene identifiers shown in the output\n",
"# These appear to be numeric identifiers (1, 2, 24, 25...) rather than standard human gene symbols\n",
"# Standard human gene symbols would typically be alphabetic like BRCA1, TP53, etc.\n",
"# Therefore, these identifiers need to be mapped to gene symbols\n",
"\n",
"requires_gene_mapping = True\n"
]
},
{
"cell_type": "markdown",
"id": "cbcc3900",
"metadata": {},
"source": [
"### Step 6: Gene Annotation"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "306ef120",
"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. 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",
"# Check for gene information in the SPOT_ID.1 column which appears to contain gene names\n",
"print(\"\\nAnalyzing SPOT_ID.1 column for gene symbols:\")\n",
"if 'SPOT_ID.1' in gene_annotation.columns:\n",
" # Extract a few sample values\n",
" sample_values = gene_annotation['SPOT_ID.1'].head(3).tolist()\n",
" for i, value in enumerate(sample_values):\n",
" print(f\"Sample {i+1} excerpt: {value[:200]}...\") # Print first 200 chars\n",
" # Test the extract_human_gene_symbols function on these values\n",
" symbols = extract_human_gene_symbols(value)\n",
" print(f\" Extracted gene symbols: {symbols}\")\n",
"\n",
"# Try to find the probe IDs in the gene annotation\n",
"gene_data_id_prefix = gene_data.index[0].split('_')[0] # Get prefix of first gene ID\n",
"print(f\"\\nGene data ID prefix: {gene_data_id_prefix}\")\n",
"\n",
"# Look for columns that might match the gene data IDs\n",
"for col in gene_annotation.columns:\n",
" if gene_annotation[col].astype(str).str.contains(gene_data_id_prefix).any():\n",
" print(f\"Column '{col}' contains values matching gene data ID pattern\")\n",
"\n",
"# Check if there's any column that might contain transcript or gene IDs\n",
"print(\"\\nChecking for columns containing transcript or gene related terms:\")\n",
"for col in gene_annotation.columns:\n",
" if any(term in col.upper() for term in ['GENE', 'TRANSCRIPT', 'SYMBOL', 'NAME', 'DESCRIPTION']):\n",
" print(f\"Column '{col}' may contain gene-related information\")\n",
" # Show sample values\n",
" print(f\"Sample values: {gene_annotation[col].head(3).tolist()}\")\n"
]
},
{
"cell_type": "markdown",
"id": "72559510",
"metadata": {},
"source": [
"### Step 7: Gene Identifier Mapping"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "9d097843",
"metadata": {},
"outputs": [],
"source": [
"# Analyze the structure of the gene data identifiers\n",
"print(\"Gene data index structure:\")\n",
"print(gene_data.index[:5].tolist())\n",
"\n",
"# First, we need to determine if the SPOT_ID column in gene_annotation might contain gene symbols\n",
"# Check if there's a relationship between gene_data index and gene_annotation ID\n",
"gene_id_set = set(gene_data.index.astype(str))\n",
"annotation_id_set = set(gene_annotation['ID'].astype(str))\n",
"\n",
"# Check the overlap between gene_data IDs and gene_annotation IDs\n",
"overlap = gene_id_set.intersection(annotation_id_set)\n",
"print(f\"\\nOverlap between gene data IDs and gene annotation IDs: {len(overlap)} IDs\")\n",
"print(f\"Example overlapping IDs: {list(overlap)[:5] if overlap else 'None'}\")\n",
"\n",
"# Since we don't have clear gene symbols in our annotation, we'll check if the SPOT_ID column might contain information\n",
"# that can be parsed to extract gene symbols\n",
"print(\"\\nAnalyzing SPOT_ID column for potential gene information:\")\n",
"spot_id_examples = gene_annotation['SPOT_ID'].dropna().head(10).tolist()\n",
"print(f\"SPOT_ID examples: {spot_id_examples}\")\n",
"\n",
"# Try to extract gene symbols from SPOT_ID values\n",
"gene_symbols_extracted = [extract_human_gene_symbols(str(id_val)) for id_val in spot_id_examples]\n",
"print(f\"Extracted gene symbols from SPOT_ID: {gene_symbols_extracted}\")\n",
"\n",
"# If we can't find clear gene symbols from the annotation, we need another approach\n",
"# This is a special case where we might need to use the SPOT_ID as a temporary gene identifier\n",
"# and advise that proper gene mapping would require additional annotation data\n",
"\n",
"# Since we have limited information in the annotation, we'll create a simple mapping using SPOT_ID\n",
"# assuming it might contain some biological information even if not standard gene symbols\n",
"prob_col = 'ID'\n",
"gene_col = 'SPOT_ID' # Using SPOT_ID as our best available option\n",
"\n",
"# Create a mapping dataframe - the best we can do with available annotation\n",
"mapping_df = gene_annotation[[prob_col, gene_col]].dropna()\n",
"mapping_df = mapping_df.rename(columns={gene_col: 'Gene'})\n",
"mapping_df = mapping_df.astype({'ID': 'str'})\n",
"\n",
"print(\"\\nMapping dataframe created:\")\n",
"print(f\"Shape: {mapping_df.shape}\")\n",
"print(mapping_df.head())\n",
"\n",
"# Check if mapping covers gene_data IDs\n",
"coverage = len(set(mapping_df['ID']).intersection(gene_id_set)) / len(gene_id_set) * 100\n",
"print(f\"Mapping covers {coverage:.2f}% of gene_data IDs\")\n",
"\n",
"# Apply the gene mapping to convert probe-level measurements to gene expression\n",
"try:\n",
" gene_data = apply_gene_mapping(gene_data, mapping_df)\n",
" print(\"\\nGene expression data created:\")\n",
" print(f\"Shape: {gene_data.shape}\")\n",
" print(f\"First few gene symbols: {gene_data.index[:5].tolist()}\")\n",
"except Exception as e:\n",
" print(f\"Error during gene mapping: {e}\")\n",
" \n",
" # Alternative approach if the mapping fails - create a simple one-to-one mapping\n",
" # This is not ideal but allows the pipeline to continue\n",
" print(\"\\nFalling back to simplified mapping approach...\")\n",
" \n",
" # Create a mapping where each probe ID maps to itself (preserving the data)\n",
" # This is a workaround when proper gene symbols aren't available\n",
" fallback_mapping = pd.DataFrame({'ID': gene_data.index, 'Gene': gene_data.index})\n",
" gene_data = apply_gene_mapping(gene_data, fallback_mapping)\n",
" print(f\"Fallback gene expression data shape: {gene_data.shape}\")\n",
" print(f\"First few identifiers: {gene_data.index[:5].tolist()}\")\n",
" \n",
" # Add a note about the limitations of this mapping\n",
" print(\"NOTE: Unable to map to standard gene symbols. Using probe IDs as gene identifiers.\")\n",
"\n",
"# Print a summary of the gene data\n",
"print(\"\\nSummary of mapped gene expression data:\")\n",
"print(f\"Number of genes: {gene_data.shape[0]}\")\n",
"print(f\"Number of samples: {gene_data.shape[1]}\")\n"
]
},
{
"cell_type": "markdown",
"id": "ebf53e1f",
"metadata": {},
"source": [
"### Step 8: Data Normalization and Linking"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "593dcfe0",
"metadata": {},
"outputs": [],
"source": [
"# 1. Normalize gene symbols in the gene expression data\n",
"# Use normalize_gene_symbols_in_index to standardize gene symbols\n",
"normalized_gene_data = normalize_gene_symbols_in_index(gene_data)\n",
"print(f\"Gene data shape before normalization: {gene_data.shape}\")\n",
"print(f\"Gene data shape after normalization: {normalized_gene_data.shape}\")\n",
"\n",
"# Save the normalized gene data to file\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 expression data saved to {out_gene_data_file}\")\n",
"\n",
"# Load the actual clinical data from the matrix file that was previously obtained in Step 1\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",
"# Get preview of clinical data to understand its structure\n",
"print(\"Original clinical data preview:\")\n",
"print(clinical_data.head())\n",
"\n",
"# 2. If we have trait data available, proceed with linking\n",
"if trait_row is not None:\n",
" # Extract clinical features using the original clinical data\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",
" print(f\"Selected clinical data shape: {selected_clinical_df.shape}\")\n",
" print(\"Clinical data preview:\")\n",
" print(selected_clinical_df.head())\n",
"\n",
" # Link the clinical and genetic data\n",
" linked_data = geo_link_clinical_genetic_data(selected_clinical_df, normalized_gene_data)\n",
" print(f\"Linked data shape before processing: {linked_data.shape}\")\n",
" print(\"Linked data preview (first 5 rows, 5 columns):\")\n",
" print(linked_data.iloc[:5, :5] if not linked_data.empty else \"Empty dataframe\")\n",
"\n",
" # 3. Handle missing values\n",
" try:\n",
" linked_data = handle_missing_values(linked_data, trait)\n",
" print(f\"Data shape after handling missing values: {linked_data.shape}\")\n",
" except Exception as e:\n",
" print(f\"Error handling missing values: {e}\")\n",
" linked_data = pd.DataFrame() # Create empty dataframe if error occurs\n",
"\n",
" # 4. Check for bias in features\n",
" if not linked_data.empty and linked_data.shape[0] > 0:\n",
" is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)\n",
" print(f\"Data shape after removing biased features: {linked_data.shape}\")\n",
" else:\n",
" is_biased = True\n",
" print(\"Cannot check for bias as dataframe is empty or has no rows after missing value handling\")\n",
"\n",
" # 5. Validate and save cohort information\n",
" note = \"\"\n",
" if linked_data.empty or linked_data.shape[0] == 0:\n",
" note = \"Dataset contains gene expression data related to liver fibrosis progression, but linking clinical and genetic data failed, possibly due to mismatched sample IDs.\"\n",
" else:\n",
" note = \"Dataset contains gene expression data for liver fibrosis progression, which is relevant to liver cirrhosis research.\"\n",
" \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 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(\"Dataset is not usable for analysis. No linked data file saved.\")\n",
"else:\n",
" # If no trait data available, validate with trait_available=False\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=False,\n",
" is_biased=True, # Set to True since we can't use data without trait\n",
" df=pd.DataFrame(), # Empty DataFrame\n",
" note=\"Dataset contains gene expression data but lacks proper clinical trait information for liver cirrhosis analysis.\"\n",
" )\n",
" \n",
" print(\"Dataset is not usable for liver cirrhosis analysis due to lack of clinical trait data. No linked data file saved.\")"
]
}
],
"metadata": {},
"nbformat": 4,
"nbformat_minor": 5
}
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