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{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"id": "bc2f887d",
"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 = \"Amyotrophic_Lateral_Sclerosis\"\n",
"cohort = \"GSE68608\"\n",
"\n",
"# Input paths\n",
"in_trait_dir = \"../../input/GEO/Amyotrophic_Lateral_Sclerosis\"\n",
"in_cohort_dir = \"../../input/GEO/Amyotrophic_Lateral_Sclerosis/GSE68608\"\n",
"\n",
"# Output paths\n",
"out_data_file = \"../../output/preprocess/Amyotrophic_Lateral_Sclerosis/GSE68608.csv\"\n",
"out_gene_data_file = \"../../output/preprocess/Amyotrophic_Lateral_Sclerosis/gene_data/GSE68608.csv\"\n",
"out_clinical_data_file = \"../../output/preprocess/Amyotrophic_Lateral_Sclerosis/clinical_data/GSE68608.csv\"\n",
"json_path = \"../../output/preprocess/Amyotrophic_Lateral_Sclerosis/cohort_info.json\"\n"
]
},
{
"cell_type": "markdown",
"id": "e12b2c02",
"metadata": {},
"source": [
"### Step 1: Initial Data Loading"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "b7336d49",
"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": "734e077b",
"metadata": {},
"source": [
"### Step 2: Dataset Analysis and Clinical Feature Extraction"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "d18c70b5",
"metadata": {},
"outputs": [],
"source": [
"# 1. Gene Expression Data Availability\n",
"# Based on the background information about C9ORF72 ALS study with motor neurons\n",
"# This is likely a gene expression dataset looking at splicing dysregulation\n",
"is_gene_available = True\n",
"\n",
"# 2. Variable Availability and Data Type Conversion\n",
"# 2.1 Data Availability\n",
"# Looking at the sample characteristics dictionary\n",
"# For trait (ALS), row 1 contains \"patient group\" information\n",
"trait_row = 1\n",
"\n",
"# There's no information about age in the sample characteristics\n",
"age_row = None\n",
"\n",
"# No gender information in the sample characteristics\n",
"gender_row = None\n",
"\n",
"# 2.2 Data Type Conversion Functions\n",
"def convert_trait(value):\n",
" \"\"\"Convert ALS trait value to binary (1 for ALS, 0 for control)\"\"\"\n",
" if value is None:\n",
" return None\n",
" \n",
" # Extract the value after colon if present\n",
" if ':' in value:\n",
" value = value.split(':', 1)[1].strip()\n",
" \n",
" # Convert to binary\n",
" if 'ALS' in value or 'mutated C9ORF72' in value:\n",
" return 1\n",
" elif 'control' in value or 'healthy' in value:\n",
" return 0\n",
" else:\n",
" return None\n",
"\n",
"def convert_age(value):\n",
" \"\"\"Convert age value to continuous\"\"\"\n",
" # Not applicable as age data is not available\n",
" return None\n",
"\n",
"def convert_gender(value):\n",
" \"\"\"Convert gender value to binary (0 for female, 1 for male)\"\"\"\n",
" # Not applicable as gender data is not available\n",
" return None\n",
"\n",
"# 3. Save Metadata\n",
"# Check trait data availability\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",
" # Extract information from sample characteristics dictionary\n",
" sample_ids = []\n",
" for item in [s.split(\": \")[1] for s in sample_chars[0]]:\n",
" sample_ids.append(item)\n",
" \n",
" # Create a DataFrame with appropriate structure for geo_select_clinical_features\n",
" data = []\n",
" for sample_id in sample_ids:\n",
" if 'Patient' in sample_id:\n",
" # For patients\n",
" data.append({\n",
" 'ID_REF': sample_id,\n",
" trait_row: 'patient group: ALS due to mutated C9ORF72'\n",
" })\n",
" else:\n",
" # For controls\n",
" data.append({\n",
" 'ID_REF': sample_id,\n",
" trait_row: 'patient group: Neurologically healthy, non-disease control'\n",
" })\n",
" \n",
" # Create DataFrame\n",
" clinical_df = pd.DataFrame(data)\n",
" \n",
" # Extract clinical features\n",
" selected_clinical_df = geo_select_clinical_features(\n",
" clinical_df=clinical_df,\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 clinical features\n",
" preview = preview_df(selected_clinical_df)\n",
" print(\"Preview of selected clinical features:\")\n",
" print(preview)\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, index=False)\n",
" print(f\"Clinical data saved to {out_clinical_data_file}\")\n"
]
},
{
"cell_type": "markdown",
"id": "8cbce638",
"metadata": {},
"source": [
"### Step 3: Dataset Analysis and Clinical Feature Extraction"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "6e7b5886",
"metadata": {},
"outputs": [],
"source": [
"I'll implement the code for the current step with corrections to address the file parsing issue:\n",
"\n",
"```python\n",
"import pandas as pd\n",
"import numpy as np\n",
"import os\n",
"import json\n",
"import glob\n",
"import gzip\n",
"from typing import Optional, Callable, Dict, Any, List, Union\n",
"\n",
"# Initialize variables for validation\n",
"is_gene_available = False\n",
"is_trait_available = False\n",
"trait_row = None\n",
"age_row = None\n",
"gender_row = None\n",
"\n",
"try:\n",
" # Look for any series matrix file (compressed or not)\n",
" matrix_files = glob.glob(os.path.join(in_cohort_dir, \"*series_matrix*.txt*\"))\n",
" \n",
" if not matrix_files:\n",
" print(f\"No series matrix file found in {in_cohort_dir}\")\n",
" clinical_data = pd.DataFrame() # Empty DataFrame if no file found\n",
" else:\n",
" matrix_file = matrix_files[0] # Take the first matching file\n",
" print(f\"Found matrix file: {matrix_file}\")\n",
" \n",
" # First, let's examine the file structure\n",
" if matrix_file.endswith('.gz'):\n",
" with gzip.open(matrix_file, 'rt') as f:\n",
" header_lines = [next(f) for _ in range(40) if f]\n",
" else:\n",
" with open(matrix_file, 'rt') as f:\n",
" header_lines = [next(f) for _ in range(40) if f]\n",
" \n",
" # Print a few header lines to understand the structure\n",
" print(\"First few lines of the file:\")\n",
" for i, line in enumerate(header_lines[:5]):\n",
" print(f\"Line {i+1}: {line.strip()}\")\n",
" \n",
" # Read the file with flexible parsing to handle potential formatting issues\n",
" clinical_data = pd.read_csv(matrix_file, sep='\\t', comment='#', nrows=70, \n",
" on_bad_lines='skip', engine='python')\n",
" \n",
" # Check if the file has content\n",
" if clinical_data.empty:\n",
" print(\"Warning: The matrix file is empty or couldn't be parsed properly\")\n",
" else:\n",
" # Print the shape and first few columns to understand the structure\n",
" print(f\"Clinical data shape: {clinical_data.shape}\")\n",
" print(\"First column names:\", clinical_data.columns[:5].tolist())\n",
" \n",
" # Examine the first column which typically contains metadata identifiers\n",
" first_col = clinical_data.iloc[:,0].dropna().tolist()\n",
" print(\"\\nMetadata identifiers in first column:\")\n",
" for i, item in enumerate(first_col[:10]): # Print first 10 items\n",
" print(f\"{i}: {item}\")\n",
" \n",
" # Check if this contains gene expression data\n",
" # Look for platform information and other indicators\n",
" platform_entries = [item for item in first_col if 'platform' in str(item).lower()]\n",
" if platform_entries:\n",
" print(\"\\nPlatform information:\")\n",
" for entry in platform_entries:\n",
" print(entry)\n",
" # Typical gene expression platforms start with GPL\n",
" if any('GPL' in str(entry) for entry in platform_entries):\n",
" is_gene_available = True\n",
" print(\"This appears to be gene expression data based on platform information\")\n",
" \n",
" # Look for sample characteristics entries to identify clinical features\n",
" sample_char_entries = [i for i, item in enumerate(first_col) \n",
" if 'sample_char' in str(item).lower()]\n",
" \n",
" if sample_char_entries:\n",
" print(\"\\nSample characteristic entries found at rows:\", sample_char_entries)\n",
" \n",
" # Examine each sample characteristic row\n",
" for idx in sample_char_entries:\n",
" row_content = str(clinical_data.iloc[idx, 0])\n",
" unique_values = set(clinical_data.iloc[idx, 1:].dropna())\n",
" print(f\"Row {idx}: {row_content}\")\n",
" print(f\"Unique values: {unique_values}\")\n",
" \n",
" # Identify trait, age, and gender information\n",
" row_content_lower = row_content.lower()\n",
" if ('disease' in row_content_lower or 'status' in row_content_lower or \n",
" 'diagnosis' in row_content_lower or 'als' in row_content_lower or\n",
" 'amyotrophic' in row_content_lower or 'control' in row_content_lower):\n",
" if trait_row is None and len(unique_values) > 1: # Ensure it's not a constant feature\n",
" trait_row = idx\n",
" print(f\"Identified trait row at {idx}\")\n",
" elif 'age' in row_content_lower:\n",
" if age_row is None and len(unique_values) > 1:\n",
" age_row = idx\n",
" print(f\"Identified age row at {idx}\")\n",
" elif 'gender' in row_content_lower or 'sex' in row_content_lower:\n",
" if gender_row is None and len(unique_values) > 1:\n",
" gender_row = idx\n",
" print(f\"Identified gender row at {idx}\")\n",
" \n",
" is_trait_available = trait_row is not None\n",
" \n",
" # Print final determinations\n",
" print(f\"\\nFinal determinations:\")\n",
" print(f\"Gene expression data available: {is_gene_available}\")\n",
" print(f\"Trait data available: {is_trait_available}\")\n",
" print(f\"Trait row: {trait_row}\")\n",
" print(f\"Age row: {age_row}\")\n",
" print(f\"Gender row: {gender_row}\")\n",
"\n",
"except Exception as e:\n",
" print(f\"Error processing file: {str(e)}\")\n",
" clinical_data = pd.DataFrame() # Empty DataFrame on error\n",
"\n",
"# Define conversion functions\n",
"def convert_trait(value: str) -> int:\n",
" \"\"\"Convert trait value to binary (0 for Control, 1 for ALS)\"\"\"\n",
" if value is None or pd.isna(value):\n",
" return None\n",
" value = value.lower() if isinstance(value, str) else str(value).lower()\n",
" if ':' in value:\n",
" value = value.split(':', 1)[1].strip()\n",
" if 'als' in value or 'amyotrophic lateral sclerosis' in value or 'patient' in value:\n",
" return 1\n",
" elif 'control' in value or 'normal' in value or 'healthy' in value:\n",
" return 0\n",
" return None\n",
"\n",
"def convert_age(value: str) -> float:\n",
" \"\"\"Convert age value to float\"\"\"\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",
" try:\n",
" # Extract numeric part if it contains non-numeric characters\n",
" import re\n",
" numeric_part = re.search(r'\\d+(\\.\\d+)?', value)\n",
" if numeric_part:\n",
" return float(numeric_part.group())\n",
" return None\n",
" except:\n",
" return None\n",
"\n",
"def convert_gender(value: str) -> int:\n",
" \"\"\"Convert gender value to binary (0 for Female, 1 for Male)\"\"\"\n",
" if value is None or pd.isna(value):\n",
" return None\n",
" value = value.lower() if isinstance(value, str) else str(value).lower()\n",
" if ':' in value:\n",
" value = value.split(':', 1)[1].strip()\n",
" if 'female' in value or 'f' == value or value.strip() == 'f':\n",
" return 0\n",
" elif 'male' in value or 'm' == value or value.strip() == 'm':\n",
" return 1\n",
" return None\n",
"\n",
"# 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",
"# Clinical Feature Extraction\n",
"if trait_row is not None and not clinical_data.empty:\n",
" try:\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"
]
},
{
"cell_type": "markdown",
"id": "330002a0",
"metadata": {},
"source": [
"### Step 4: Dataset Analysis and Clinical Feature Extraction"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "8cf8cea3",
"metadata": {},
"outputs": [],
"source": [
"I'll debug the code by addressing the syntax errors and completing the implementation properly.\n",
"\n",
"```python\n",
"import os\n",
"import pandas as pd\n",
"import numpy as np\n",
"import json\n",
"from typing import Dict, Any, Optional, Callable\n",
"import glob\n",
"\n",
"# Check if this dataset contains gene expression data\n",
"# Look for gene expression files in the directory\n",
"gene_expression_patterns = ['*_series_matrix.txt', '*gene*', '*expr*', '*.txt', '*.csv', '*.tsv', '*.gz']\n",
"gene_files = []\n",
"for pattern in gene_expression_patterns:\n",
" gene_files.extend(glob.glob(os.path.join(in_cohort_dir, pattern)))\n",
"\n",
"# Filter out files that might be clinical data\n",
"gene_files = [f for f in gene_files if 'clinical' not in f.lower() and 'phenotype' not in f.lower()]\n",
"is_gene_available = len(gene_files) > 0\n",
"\n",
"# Try to identify clinical data files using different patterns\n",
"clinical_data = None\n",
"clinical_file_patterns = ['*clinical*', '*phenotype*', '*meta*', '*sample*', '*char*', '*series_matrix.txt']\n",
"clinical_files = []\n",
"for pattern in clinical_file_patterns:\n",
" clinical_files.extend(glob.glob(os.path.join(in_cohort_dir, pattern)))\n",
"\n",
"# Load the first available clinical data file\n",
"for file_path in clinical_files:\n",
" try:\n",
" if file_path.endswith('.txt'):\n",
" # For series matrix files, we need to extract the sample characteristics\n",
" with open(file_path, 'r') as f:\n",
" lines = f.readlines()\n",
" \n",
" # Extract sample characteristic lines\n",
" sample_chars = []\n",
" for i, line in enumerate(lines):\n",
" if line.startswith('!Sample_characteristics_ch1'):\n",
" sample_chars.append(line.strip())\n",
" \n",
" if sample_chars:\n",
" # Process sample characteristics into a DataFrame\n",
" char_data = {}\n",
" for i, char in enumerate(sample_chars):\n",
" parts = char.split('\\t')\n",
" if i == 0:\n",
" # Initialize columns with sample IDs\n",
" samples = [p.replace('\"', '') for p in parts[1:]]\n",
" for sample in samples:\n",
" char_data[sample] = []\n",
" \n",
" # Add characteristics for each sample\n",
" values = [p.replace('\"', '').replace('!Sample_characteristics_ch1: ', '') for p in parts[1:]]\n",
" \n",
" # Check if this is a new type of characteristic\n",
" if len(values) > 0:\n",
" characteristic_type = values[0].split(':')[0] if ':' in values[0] else f'characteristic_{i}'\n",
" if characteristic_type not in char_data:\n",
" char_data[characteristic_type] = []\n",
" \n",
" # Add this characteristic to each sample\n",
" for j, value in enumerate(values):\n",
" if j < len(samples):\n",
" char_data[samples[j]].append(value)\n",
" \n",
" # Convert to DataFrame\n",
" clinical_data = pd.DataFrame(char_data)\n",
" break\n",
" else:\n",
" # Try standard CSV loading for other file types\n",
" clinical_data = pd.read_csv(file_path)\n",
" break\n",
" except Exception as e:\n",
" print(f\"Could not load {file_path}: {e}\")\n",
" continue\n",
"\n",
"# Display what we found for debugging\n",
"if clinical_data is not None:\n",
" print(\"Clinical data preview:\")\n",
" print(clinical_data.head())\n",
" print(\"\\nColumn names:\", clinical_data.columns.tolist())\n",
" \n",
" # Check for trait, age, and gender information\n",
" trait_row = None\n",
" age_row = None\n",
" gender_row = None\n",
" \n",
" # Analyze each column for clinical information\n",
" for col in clinical_data.columns:\n",
" values = clinical_data[col].astype(str).str.lower()\n",
" unique_values = values.unique()\n",
" \n",
" # Look for trait information (ALS vs control)\n",
" if (any(['als' in str(v) for v in unique_values]) or \n",
" any(['amyotrophic' in str(v) for v in unique_values])) and \\\n",
" (any(['control' in str(v) for v in unique_values]) or \n",
" any(['healthy' in str(v) for v in unique_values])):\n",
" print(f\"Found trait information in column: {col}\")\n",
" print(f\"Unique values: {unique_values}\")\n",
" trait_row = clinical_data.columns.get_loc(col)\n",
" \n",
" # Look for age information\n",
" if any(['age' in str(v) for v in unique_values]) or \\\n",
" any([str(v).replace('.', '', 1).isdigit() for v in unique_values if v != 'nan']):\n",
" print(f\"Found potential age information in column: {col}\")\n",
" print(f\"Unique values: {unique_values}\")\n",
" age_row = clinical_data.columns.get_loc(col)\n",
" \n",
" # Look for gender information\n",
" if any(['male' in str(v) for v in unique_values]) or \\\n",
" any(['female' in str(v) for v in unique_values]) or \\\n",
" any(['gender' in str(v) for v in unique_values]) or \\\n",
" any(['sex' in str(v) for v in unique_values]):\n",
" print(f\"Found gender information in column: {col}\")\n",
" print(f\"Unique values: {unique_values}\")\n",
" gender_row = clinical_data.columns.get_loc(col)\n",
"else:\n",
" print(\"No clinical data found in the directory.\")\n",
" trait_row = None\n",
" age_row = None\n",
" gender_row = None\n",
"\n",
"# Define conversion functions based on observed data patterns\n",
"def convert_trait(value):\n",
" if value is None:\n",
" return None\n",
" if isinstance(value, str) and \":\" in value:\n",
" value = value.split(\":\", 1)[1].strip()\n",
" \n",
" value = str(value).lower() if value is not None else \"\"\n",
" if \"als\" in value or \"amyotrophic\" in value or \"disease\" in value:\n",
" return 1\n",
" elif \"control\" in value or \"healthy\" in value or \"normal\" in value:\n",
" return 0\n",
" return None\n",
"\n",
"def convert_age(value):\n",
" if value is None:\n",
" return None\n",
" if isinstance(value, str) and \":\" in value:\n",
" value = value.split(\":\", 1)[1].strip()\n",
" \n",
" # Try to extract the numeric age\n",
" if isinstance(value, str):\n",
" # Extract digits from the string\n",
" import re\n",
" digits = re.findall(r'\\d+\\.?\\d*', value)\n",
" if digits:\n",
" return float(digits[0])\n",
" elif isinstance(value, (int, float)):\n",
" return float(value)\n",
" return None\n",
"\n",
"def convert_gender(value):\n",
" if value is None:\n",
" return None\n",
" if isinstance(value, str) and \":\" in value:\n",
" value = value.split(\":\", 1)[1].strip()\n",
" \n",
" value = str(value).lower() if value is not None else \"\"\n",
" if \"female\" in value or \"f\" == value:\n",
" return 0\n",
" elif \"male\" in value or \"m\" == value:\n",
" return 1\n",
" return None\n",
"\n",
"# Save initial metadata\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",
"# Extract clinical features if trait data is available\n",
"if is_trait_available and clinical_data is not None:\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 selected clinical data\n",
" print(\"Selected clinical data preview:\")\n",
" preview = preview_df(selected_clinical_df)\n",
" print(preview)\n",
" \n",
" # Save to file\n",
" os.makedirs(os.path.dirname(out\n"
]
},
{
"cell_type": "markdown",
"id": "6096451a",
"metadata": {},
"source": [
"### Step 5: Gene Data Extraction"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "68426b85",
"metadata": {},
"outputs": [],
"source": [
"# 1. Re-identify the SOFT and matrix files to ensure we have the correct paths\n",
"soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)\n",
"\n",
"# 2. Extract the gene expression data from the matrix file\n",
"gene_data = get_genetic_data(matrix_file)\n",
"\n",
"# 3. Print the first 20 row IDs (gene or probe identifiers)\n",
"print(\"\\nFirst 20 gene/probe identifiers:\")\n",
"print(gene_data.index[:20])\n",
"\n",
"# 4. Print the dimensions of the gene expression data\n",
"print(f\"\\nGene data dimensions: {gene_data.shape[0]} genes × {gene_data.shape[1]} samples\")\n",
"\n",
"# Note: we keep is_gene_available as True since we successfully extracted gene expression data\n",
"is_gene_available = True\n"
]
},
{
"cell_type": "markdown",
"id": "4be61ba5",
"metadata": {},
"source": [
"### Step 6: Gene Identifier Review"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "a6c8c199",
"metadata": {},
"outputs": [],
"source": [
"# These identifiers are in Affymetrix HG-U133 Plus 2.0 format (e.g., \"1007_s_at\"), \n",
"# which are probe IDs rather than human gene symbols. \n",
"# They need to be mapped to standard gene symbols.\n",
"\n",
"requires_gene_mapping = True\n"
]
},
{
"cell_type": "markdown",
"id": "501900bd",
"metadata": {},
"source": [
"### Step 7: Gene Annotation"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "b0fff948",
"metadata": {},
"outputs": [],
"source": [
"# 1. First get the file paths using geo_get_relevant_filepaths function\n",
"soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)\n",
"\n",
"# 2. 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",
"# 3. 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": "ac9afc3b",
"metadata": {},
"source": [
"### Step 8: Gene Identifier Mapping"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "8a0c335c",
"metadata": {},
"outputs": [],
"source": [
"# 1. Identify the appropriate columns for gene identifiers and gene symbols\n",
"# From the preview, we can see:\n",
"# - The 'ID' column contains probe identifiers (e.g., '1007_s_at') matching the gene expression data\n",
"# - The 'Gene Symbol' column contains the human gene symbols we need to map to\n",
"\n",
"# 2. Get the gene mapping dataframe using the library function\n",
"gene_mapping = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='Gene Symbol')\n",
"\n",
"# Print a preview of the mapping\n",
"print(\"\\nGene mapping preview (first 5 rows):\")\n",
"print(gene_mapping.head())\n",
"\n",
"# 3. Apply the gene mapping to convert probe-level data to gene-level data\n",
"gene_data = apply_gene_mapping(expression_df=gene_data, mapping_df=gene_mapping)\n",
"\n",
"# Print information about the result\n",
"print(f\"\\nAfter mapping: {gene_data.shape[0]} genes × {gene_data.shape[1]} samples\")\n",
"print(\"\\nFirst 10 gene symbols after mapping:\")\n",
"print(gene_data.index[:10])\n",
"\n",
"# Normalize gene symbols to handle synonyms and aggregate duplicate genes\n",
"gene_data = normalize_gene_symbols_in_index(gene_data)\n",
"print(f\"\\nAfter normalization: {gene_data.shape[0]} genes × {gene_data.shape[1]} samples\")\n",
"\n",
"# Save the gene expression data to a file\n",
"os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)\n",
"gene_data.to_csv(out_gene_data_file)\n",
"print(f\"\\nGene expression data saved to {out_gene_data_file}\")\n"
]
},
{
"cell_type": "markdown",
"id": "86ae4c11",
"metadata": {},
"source": [
"### Step 9: Data Normalization and Linking"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "e23178e2",
"metadata": {},
"outputs": [],
"source": [
"# 1. Normalize gene symbols in the index of gene expression data\n",
"normalized_gene_data = normalize_gene_symbols_in_index(gene_data)\n",
"print(f\"Gene data shape after normalization: {normalized_gene_data.shape}\")\n",
"print(f\"First 5 gene symbols after normalization: {normalized_gene_data.index[:5]}\")\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. Check if clinical data was properly loaded\n",
"# First, reload the clinical_data to make sure we're using the original data\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",
"# Print the sample IDs to understand the data structure\n",
"print(\"Sample IDs in clinical data:\")\n",
"print(clinical_data.columns[:5], \"...\") # Show first 5 sample IDs\n",
"\n",
"# Print the sample IDs in gene expression data\n",
"print(\"Sample IDs in gene expression data:\")\n",
"print(normalized_gene_data.columns[:5], \"...\") # Show first 5 sample IDs\n",
"\n",
"# Extract clinical features using the actual sample IDs\n",
"is_trait_available = trait_row is not None\n",
"linked_data = None\n",
"\n",
"if is_trait_available:\n",
" # Extract clinical features with proper sample IDs\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 if age_row is not None else None,\n",
" gender_row=gender_row,\n",
" convert_gender=convert_gender if gender_row is not None else None\n",
" )\n",
" \n",
" print(f\"Clinical data shape: {selected_clinical_df.shape}\")\n",
" print(f\"Clinical data preview: {preview_df(selected_clinical_df, n=3)}\")\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",
" \n",
" # Link clinical and genetic data\n",
" # Make sure both dataframes have compatible indices/columns\n",
" linked_data = geo_link_clinical_genetic_data(selected_clinical_df, normalized_gene_data)\n",
" print(f\"Linked data shape before handling missing values: {linked_data.shape}\")\n",
" \n",
" if linked_data.shape[0] == 0:\n",
" print(\"WARNING: No samples matched between clinical and genetic data!\")\n",
" # Create a sample dataset for demonstration\n",
" print(\"Using gene data with artificial trait values for demonstration\")\n",
" is_trait_available = False\n",
" is_biased = True\n",
" linked_data = pd.DataFrame(index=normalized_gene_data.columns)\n",
" linked_data[trait] = 1 # Placeholder\n",
" else:\n",
" # 3. Handle missing values\n",
" linked_data = handle_missing_values(linked_data, trait)\n",
" print(f\"Data shape after handling missing values: {linked_data.shape}\")\n",
" \n",
" # 4. Determine if trait and demographic features are biased\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",
" print(\"Trait data was determined to be unavailable in previous steps.\")\n",
" is_biased = True # Set to True since we can't evaluate without trait data\n",
" linked_data = pd.DataFrame(index=normalized_gene_data.columns)\n",
" linked_data[trait] = 1 # Add a placeholder trait column\n",
" print(f\"Using placeholder data due to missing trait information, shape: {linked_data.shape}\")\n",
"\n",
"# 5. Validate and save cohort info\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=is_trait_available,\n",
" is_biased=is_biased,\n",
" df=linked_data,\n",
" note=\"Dataset contains gene expression data from multiple sclerosis patients, but there were issues linking clinical and genetic data.\"\n",
")\n",
"\n",
"# 6. Save 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 deemed not usable for associational studies.\")"
]
}
],
"metadata": {},
"nbformat": 4,
"nbformat_minor": 5
}
|