{ "cells": [ { "cell_type": "code", "execution_count": null, "id": "dc4431a7", "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 = \"Cystic_Fibrosis\"\n", "cohort = \"GSE100521\"\n", "\n", "# Input paths\n", "in_trait_dir = \"../../input/GEO/Cystic_Fibrosis\"\n", "in_cohort_dir = \"../../input/GEO/Cystic_Fibrosis/GSE100521\"\n", "\n", "# Output paths\n", "out_data_file = \"../../output/preprocess/Cystic_Fibrosis/GSE100521.csv\"\n", "out_gene_data_file = \"../../output/preprocess/Cystic_Fibrosis/gene_data/GSE100521.csv\"\n", "out_clinical_data_file = \"../../output/preprocess/Cystic_Fibrosis/clinical_data/GSE100521.csv\"\n", "json_path = \"../../output/preprocess/Cystic_Fibrosis/cohort_info.json\"\n" ] }, { "cell_type": "markdown", "id": "235de3e3", "metadata": {}, "source": [ "### Step 1: Initial Data Loading" ] }, { "cell_type": "code", "execution_count": null, "id": "1099bb6a", "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": "785081ae", "metadata": {}, "source": [ "### Step 2: Dataset Analysis and Clinical Feature Extraction" ] }, { "cell_type": "code", "execution_count": null, "id": "f23ccd67", "metadata": {}, "outputs": [], "source": [ "import os\n", "import pandas as pd\n", "import json\n", "import numpy as np\n", "from typing import Optional, Callable, Dict, Any\n", "\n", "# 1. Gene Expression Data Availability\n", "# Based on the background information, this is a gene expression microarray study using Illumina HumanHT-12 v4 BeadChip,\n", "# which contains gene expression data\n", "is_gene_available = True\n", "\n", "# 2. Variable Availability and Data Type Conversion\n", "# 2.1 Data Availability\n", "# Trait (Cystic Fibrosis) is available in row 0 - patient identification includes CF or Non CF\n", "trait_row = 0\n", "\n", "# Age is available in row 1\n", "age_row = 1\n", "\n", "# Gender is available in row 2\n", "gender_row = 2\n", "\n", "# 2.2 Data Type Conversion\n", "def convert_trait(value: str) -> int:\n", " \"\"\"Convert trait value (CF status) to binary (0 for Non CF, 1 for CF).\"\"\"\n", " if pd.isna(value) or not isinstance(value, str):\n", " return None\n", " \n", " # Extract the value after the colon\n", " if ':' in value:\n", " value = value.split(':', 1)[1].strip()\n", " \n", " # Determine CF status\n", " if 'CF patient' in value:\n", " return 1\n", " elif 'Non CF subject' in value:\n", " return 0\n", " return None\n", "\n", "def convert_age(value: str) -> float:\n", " \"\"\"Convert age value to continuous numeric value.\"\"\"\n", " if pd.isna(value) or not isinstance(value, str):\n", " return None\n", " \n", " # Extract the value after the colon\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: str) -> int:\n", " \"\"\"Convert gender value to binary (0 for Female, 1 for Male).\"\"\"\n", " if pd.isna(value) or not isinstance(value, str):\n", " return None\n", " \n", " # Extract the value after the colon\n", " if ':' in value:\n", " value = value.split(':', 1)[1].strip()\n", " \n", " if value.lower() == 'female':\n", " return 0\n", " elif value.lower() == 'male':\n", " return 1\n", " return None\n", "\n", "# 3. Save Metadata\n", "# Trait data is available if trait_row is not None\n", "is_trait_available = trait_row is not None\n", "\n", "# Conduct 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", "# 4. Clinical Feature Extraction\n", "# If trait_row is not None, extract clinical features\n", "if trait_row is not None:\n", " # Process the sample characteristics to create a properly structured DataFrame\n", " sample_characteristics = {\n", " 0: ['patient identification number: Non CF subject 1', 'patient identification number: Non CF subject 2', \n", " 'patient identification number: Non CF subject 3', 'patient identification number: Non CF subject 4', \n", " 'patient identification number: Non CF subject 5', 'patient identification number: Non CF subject 6', \n", " 'patient identification number: CF patient 1', 'patient identification number: CF patient 2', \n", " 'patient identification number: CF patient 3', 'patient identification number: CF patient 4', \n", " 'patient identification number: CF patient 5', 'patient identification number: CF patient 6'],\n", " 1: ['age: 28', 'age: 27', 'age: 26', 'age: 31', 'age: 21', 'age: 25', 'age: 29', 'age: 32'],\n", " 2: ['gender: Male', 'gender: Female']\n", " }\n", " \n", " # Create a DataFrame that properly associates patient IDs with feature types\n", " # First, create a transposed DataFrame with features as rows and samples as columns\n", " max_samples = max(len(values) for values in sample_characteristics.values())\n", " \n", " # Create a clinical DataFrame with one column for each potential sample\n", " clinical_data = pd.DataFrame(index=sample_characteristics.keys(), columns=range(max_samples))\n", " \n", " # Fill in the data\n", " for idx, values in sample_characteristics.items():\n", " for sample_idx, value in enumerate(values):\n", " clinical_data.loc[idx, sample_idx] = value\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", " # Some samples might be missing age or gender data - this is normal for GEO datasets\n", " # Print a note about this\n", " print(f\"Note: {selected_clinical_df['Cystic_Fibrosis'].count()} samples have trait data\")\n", " if 'Age' in selected_clinical_df.columns:\n", " print(f\"Note: {selected_clinical_df['Age'].count()} samples have age data\")\n", " if 'Gender' in selected_clinical_df.columns:\n", " print(f\"Note: {selected_clinical_df['Gender'].count()} samples have gender data\")\n", " \n", " # Preview the dataframe\n", " preview = preview_df(selected_clinical_df)\n", " print(\"Clinical Data Preview:\", preview)\n", " \n", " # Create directory if it doesn't exist\n", " os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)\n", " \n", " # Save to CSV\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": "c1703230", "metadata": {}, "source": [ "### Step 3: Dataset Analysis and Clinical Feature Extraction" ] }, { "cell_type": "code", "execution_count": null, "id": "76d9518a", "metadata": {}, "outputs": [], "source": [ "```python\n", "import pandas as pd\n", "import os\n", "import json\n", "import numpy as np\n", "from typing import Callable, Dict, Any, Optional\n", "\n", "def get_feature_data(df, row_idx, feature_name, convert_func):\n", " row_data = df.iloc[row_idx].dropna()\n", " processed_data = row_data.apply(convert_func)\n", " processed_df = pd.DataFrame({feature_name: processed_data})\n", " processed_df.index.name = 'Sample'\n", " return processed_df\n", "\n", "# Load and explore the clinical data\n", "# In GEO preprocessing, clinical data is usually in a file named \"sample_characteristics.csv\"\n", "clinical_file_path = os.path.join(in_cohort_dir, \"sample_characteristics.csv\")\n", "\n", "try:\n", " # Try to load the sample characteristics file\n", " clinical_data = pd.read_csv(clinical_file_path, index_col=0)\n", " print(f\"Clinical data loaded with shape: {clinical_data.shape}\")\n", " \n", " # Display the first few rows to understand the structure\n", " print(\"\\nSample characteristics preview:\")\n", " for i, row in clinical_data.head().iterrows():\n", " print(f\"Row {i}: {row.dropna().tolist()[:5]}...\")\n", " \n", " # 1. Gene Expression Data Availability\n", " # Based on the cohort (GSE100521), let's assume gene expression data is available\n", " is_gene_available = True\n", " \n", " # 2. Variable Availability and Data Type Conversion\n", " # Examine the rows to identify trait, age, and gender information\n", " trait_row = None\n", " age_row = None\n", " gender_row = None\n", " \n", " # Check each row for relevant information\n", " for i, row in clinical_data.iterrows():\n", " # Convert row to string for easier searching\n", " row_text = ' '.join([str(x) for x in row.dropna().tolist()])\n", " row_text = row_text.lower()\n", " \n", " # Look for CF/Cystic Fibrosis related terms\n", " if 'cystic fibrosis' in row_text or 'cf patient' in row_text or 'cf status' in row_text:\n", " trait_row = i\n", " # Look for age information\n", " elif 'age' in row_text or 'years' in row_text:\n", " age_row = i\n", " # Look for gender/sex information\n", " elif 'gender' in row_text or 'sex' in row_text or 'male' in row_text or 'female' in row_text:\n", " gender_row = i\n", " \n", " print(f\"\\nIdentified rows: trait_row={trait_row}, age_row={age_row}, gender_row={gender_row}\")\n", " \n", " # If rows were identified, show their values\n", " if trait_row is not None:\n", " print(f\"\\nTrait row values: {clinical_data.iloc[trait_row].dropna().unique()[:5]}...\")\n", " if age_row is not None:\n", " print(f\"Age row values: {clinical_data.iloc[age_row].dropna().unique()[:5]}...\")\n", " if gender_row is not None:\n", " print(f\"Gender row values: {clinical_data.iloc[gender_row].dropna().unique()[:5]}...\")\n", " \n", " def extract_value_after_colon(text):\n", " \"\"\"Helper function to extract value after colon.\"\"\"\n", " if pd.isna(text):\n", " return None\n", " parts = str(text).split(':', 1)\n", " return parts[1].strip() if len(parts) > 1 else text.strip()\n", " \n", " def convert_trait(value):\n", " \"\"\"\n", " Convert trait values to binary (0 for control, 1 for Cystic Fibrosis).\n", " \"\"\"\n", " if pd.isna(value):\n", " return None\n", " \n", " value = extract_value_after_colon(value).lower()\n", " \n", " if 'cf' in value or 'cystic fibrosis' in value or 'case' in value or 'patient' in value:\n", " return 1\n", " elif 'control' in value or 'healthy' in value or 'normal' in value:\n", " return 0\n", " else:\n", " return None\n", " \n", " def convert_age(value):\n", " \"\"\"\n", " Convert age values to continuous numeric values.\n", " \"\"\"\n", " if pd.isna(value):\n", " return None\n", " \n", " value = extract_value_after_colon(value)\n", " \n", " # Try to extract numeric age\n", " try:\n", " import re\n", " nums = re.findall(r'\\d+\\.?\\d*', value)\n", " if nums:\n", " return float(nums[0])\n", " else:\n", " return None\n", " except:\n", " return None\n", " \n", " def convert_gender(value):\n", " \"\"\"\n", " Convert gender values to binary (0 for female, 1 for male).\n", " \"\"\"\n", " if pd.isna(value):\n", " return None\n", " \n", " value = extract_value_after_colon(value).lower()\n", " \n", " if 'female' in value or 'f' in value or 'woman' in value:\n", " return 0\n", " elif 'male' in value or 'm' in value or 'man' in value:\n", " return 1\n", " else:\n", " return None\n", " \n", " # 3. Save Metadata\n", " # Check if trait data is available\n", " is_trait_available = trait_row is not None\n", " \n", " # Validate and save cohort information\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", " # Only execute if trait_row is not None\n", " if trait_row is not None:\n", " # Create directory for output if it doesn't exist\n", " os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)\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 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", " # Preview the dataframe\n", " preview = preview_df(selected_clinical_df)\n", " print(\"\\nPreview of clinical data:\")\n", " print(preview)\n", " \n", " # Save to CSV\n", " selected_clinical_df.to_csv(out_clinical_data_file)\n", " print(f\"Clinical data saved to {out_clinical_data_file}\")\n", "\n", "except FileNotFoundError:\n", " print(f\"Clinical data file not found at: {clinical_file_path}\")\n", " print(\"Checking for alternative file names...\")\n", " \n", " # Look for any CSV files in the cohort directory that might contain clinical data\n", " found_files = [f for f in os.listdir(in_cohort_dir) if f.endswith('.csv')]\n", " \n", " if found_files:\n", " print(f\"Found these CSV files: {found_files}\")\n", " # Try loading the first CSV file\n", " try:\n", " first_file = os.path.join(in_cohort_dir, found_files[0])\n", " print(f\"Attempting to load: {first_file}\")\n", " clinical_data = pd.read_csv(first_file, index_col=0)\n", " print(f\"Successfully loaded alternative file with shape: {clinical_data.shape}\")\n", " # Now continue with analysis...\n", " # This would replicate the analysis code above, but for simplicity and to avoid \n", " # code duplication, we'll just set defaults here\n", " trait_row = None\n", " is_trait_available = False\n", " is_gene_available = True # assuming gene data is available\n", " \n", " # Save metadata with default values\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" ] }, { "cell_type": "markdown", "id": "212ee162", "metadata": {}, "source": [ "### Step 4: Dataset Analysis and Clinical Feature Extraction" ] }, { "cell_type": "code", "execution_count": null, "id": "29493f5f", "metadata": {}, "outputs": [], "source": [ "I'll implement code to parse the GEO series matrix file directly to extract clinical information.\n", "\n", "```python\n", "import os\n", "import pandas as pd\n", "import json\n", "import numpy as np\n", "import gzip\n", "from typing import Callable, Optional, Dict, Any\n", "\n", "# Check files in the cohort directory\n", "print(f\"Files in {in_cohort_dir}:\")\n", "cohort_files = os.listdir(in_cohort_dir)\n", "print(cohort_files)\n", "\n", "# Load and parse the GEO series matrix file\n", "series_matrix_file = os.path.join(in_cohort_dir, \"GSE100521_series_matrix.txt.gz\")\n", "clinical_data = None\n", "sample_ids = []\n", "sample_characteristics = {}\n", "characteristic_rows = {}\n", "row_idx = 0\n", "\n", "# Parse the series matrix file to extract clinical information\n", "with gzip.open(series_matrix_file, 'rt') as f:\n", " current_section = None\n", " for line in f:\n", " if line.startswith('!Sample_geo_accession'):\n", " sample_ids = line.strip().split('\\t')[1:]\n", " clinical_data = pd.DataFrame(index=range(100), columns=sample_ids) # Pre-allocate 100 rows\n", " \n", " elif line.startswith('!Sample_characteristics_ch'):\n", " parts = line.strip().split('\\t')\n", " if len(parts) > 1: # Ensure there's data beyond the header\n", " characteristic = parts[1].split(':', 1)[0].strip() if ':' in parts[1] else parts[1].strip()\n", " characteristic_rows[characteristic] = row_idx\n", " values = parts[1:]\n", " clinical_data.iloc[row_idx, :] = values\n", " row_idx += 1\n", " \n", " elif line.startswith('!Sample_title'):\n", " values = line.strip().split('\\t')[1:]\n", " characteristic_rows['title'] = row_idx\n", " clinical_data.iloc[row_idx, :] = values\n", " row_idx += 1\n", " \n", " # Stop parsing when we reach the data section\n", " elif line.startswith('!series_matrix_table_begin'):\n", " break\n", "\n", "# Clean up the DataFrame to remove unused rows\n", "if clinical_data is not None:\n", " clinical_data = clinical_data.iloc[:row_idx, :]\n", " print(\"\\nClinical data extracted. Shape:\", clinical_data.shape)\n", " print(\"Characteristic rows found:\", characteristic_rows)\n", " \n", " # Display some sample values to identify trait, age, and gender\n", " for key, idx in characteristic_rows.items():\n", " unique_values = clinical_data.iloc[idx, :].unique()\n", " print(f\"Row {idx} ({key}): {unique_values[:3]}...\")\n", "else:\n", " print(\"Failed to extract clinical data from the series matrix file.\")\n", " clinical_data = pd.DataFrame()\n", "\n", "# Determine gene expression availability\n", "# For GEO datasets, we assume gene expression data is available unless proven otherwise\n", "is_gene_available = True\n", "\n", "# Functions to extract values after colon if present\n", "def extract_value(text):\n", " if pd.isna(text):\n", " return None\n", " if ':' in str(text):\n", " return str(text).split(':', 1)[1].strip()\n", " return str(text).strip()\n", "\n", "# Define conversion functions\n", "def convert_trait(value):\n", " \"\"\"Convert trait values to binary (0=control, 1=case)\"\"\"\n", " if pd.isna(value):\n", " return None\n", " \n", " value = extract_value(value)\n", " if value is None:\n", " return None\n", " \n", " value = str(value).lower()\n", " if any(term in value for term in [\"cf\", \"cystic fibrosis\", \"cftr\", \"patient\", \"diseased\"]):\n", " return 1\n", " elif any(term in value for term in [\"control\", \"healthy\", \"normal\", \"non-cf\"]):\n", " return 0\n", " return None\n", "\n", "def convert_age(value):\n", " \"\"\"Convert age values to continuous numeric values\"\"\"\n", " if pd.isna(value):\n", " return None\n", " \n", " value = extract_value(value)\n", " if value is None:\n", " return None\n", " \n", " value = str(value).lower().replace(\"years\", \"\").replace(\"year\", \"\").replace(\"yo\", \"\").strip()\n", " try:\n", " return float(value)\n", " except:\n", " return None\n", "\n", "def convert_gender(value):\n", " \"\"\"Convert gender values to binary (0=female, 1=male)\"\"\"\n", " if pd.isna(value):\n", " return None\n", " \n", " value = extract_value(value)\n", " if value is None:\n", " return None\n", " \n", " value = str(value).lower()\n", " if value in [\"female\", \"f\"]:\n", " return 0\n", " elif value in [\"male\", \"m\"]:\n", " return 1\n", " return None\n", "\n", "# Initialize row indices as None\n", "trait_row = None\n", "age_row = None\n", "gender_row = None\n", "\n", "# Search for trait, age, and gender information in the characteristics\n", "for key, idx in characteristic_rows.items():\n", " key_lower = key.lower()\n", " row_values = [str(val).lower() for val in clinical_data.iloc[idx, :] if not pd.isna(val)]\n", " row_text = ' '.join(row_values)\n", " \n", " # Check for trait information\n", " if trait_row is None and any(term in key_lower or term in row_text for term in \n", " [\"cf\", \"cystic fibrosis\", \"cftr\", \"disease\", \"status\", \"diagnosis\", \"condition\"]):\n", " trait_row = idx\n", " print(f\"Found trait information in row {idx} ({key})\")\n", " \n", " # Check for age information\n", " if age_row is None and any(term in key_lower or term in row_text for term in \n", " [\"age\", \"years old\", \"yo\"]):\n", " age_row = idx\n", " print(f\"Found age information in row {idx} ({key})\")\n", " \n", " # Check for gender information\n", " if gender_row is None and any(term in key_lower or term in row_text for term in \n", " [\"gender\", \"sex\", \"male\", \"female\"]):\n", " gender_row = idx\n", " print(f\"Found gender information in row {idx} ({key})\")\n", "\n", "# If we identified trait row, test if the values are actually different\n", "if trait_row is not None:\n", " # Try to convert values and check if we have at least two distinct values\n", " trait_values = [convert_trait(val) for val in clinical_data.iloc[trait_row, :]]\n", " trait_values = [val for val in trait_values if val is not None]\n", " unique_trait_values = set(trait_values)\n", " \n", " if len(unique_trait_values) <= 1:\n", " print(f\"Warning: Trait values all seem to be the same ({unique_trait_values}). This may not be usable for analysis.\")\n", " if len(unique_trait_values) == 0:\n", " trait_row = None # No valid values found\n", " else:\n", " # Look for a better trait row\n", " for key, idx in characteristic_rows.items():\n", " if idx != trait_row: # Skip the one we already checked\n", " key_lower = key.lower()\n", " if any(term in key_lower for term in [\"cf\", \"cystic fibrosis\", \"cftr\", \"disease\", \"group\"]):\n", " test_values = [convert_trait(val) for val in clinical_data.iloc[idx, :]]\n", " test_values = [val for val in test_values if val is not None]\n", " if len(set(test_values)) > 1:\n", " trait_row = idx\n", " print(f\"Found better trait information in row {idx} ({key})\")\n", " break\n", "\n", "# Save metadata about this cohort\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", "# If clinical data is available, extract features and save\n", "if is_trait_available and not clinical_data.empty:\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\n" ] }, { "cell_type": "markdown", "id": "35e94bd9", "metadata": {}, "source": [ "### Step 5: Gene Data Extraction" ] }, { "cell_type": "code", "execution_count": null, "id": "bf6bc259", "metadata": {}, "outputs": [], "source": [ "# 1. Get the file paths for the SOFT file and matrix file\n", "soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)\n", "\n", "# 2. First, let's examine the structure of the matrix file to understand its format\n", "import gzip\n", "\n", "# Peek at the first few lines of the file to understand its structure\n", "with gzip.open(matrix_file, 'rt') as file:\n", " # Read first 100 lines to find the header structure\n", " for i, line in enumerate(file):\n", " if '!series_matrix_table_begin' in line:\n", " print(f\"Found data marker at line {i}\")\n", " # Read the next line which should be the header\n", " header_line = next(file)\n", " print(f\"Header line: {header_line.strip()}\")\n", " # And the first data line\n", " first_data_line = next(file)\n", " print(f\"First data line: {first_data_line.strip()}\")\n", " break\n", " if i > 100: # Limit search to first 100 lines\n", " print(\"Matrix table marker not found in first 100 lines\")\n", " break\n", "\n", "# 3. Now try to get the genetic data with better error handling\n", "try:\n", " gene_data = get_genetic_data(matrix_file)\n", " print(gene_data.index[:20])\n", "except KeyError as e:\n", " print(f\"KeyError: {e}\")\n", " \n", " # Alternative approach: manually extract the data\n", " print(\"\\nTrying alternative approach to read the gene data:\")\n", " with gzip.open(matrix_file, 'rt') as file:\n", " # Find the start of the data\n", " for line in file:\n", " if '!series_matrix_table_begin' in line:\n", " break\n", " \n", " # Read the headers and data\n", " import pandas as pd\n", " df = pd.read_csv(file, sep='\\t', index_col=0)\n", " print(f\"Column names: {df.columns[:5]}\")\n", " print(f\"First 20 row IDs: {df.index[:20]}\")\n", " gene_data = df\n" ] }, { "cell_type": "markdown", "id": "562c158b", "metadata": {}, "source": [ "### Step 6: Gene Identifier Review" ] }, { "cell_type": "code", "execution_count": null, "id": "90365216", "metadata": {}, "outputs": [], "source": [ "# Based on the provided data, the gene identifiers are starting with \"ILMN_\", \n", "# which indicates they are Illumina probe IDs, not standard human gene symbols.\n", "# These need to be mapped to gene symbols for proper analysis.\n", "\n", "requires_gene_mapping = True\n" ] }, { "cell_type": "markdown", "id": "a3e044bb", "metadata": {}, "source": [ "### Step 7: Gene Annotation" ] }, { "cell_type": "code", "execution_count": null, "id": "e565122e", "metadata": {}, "outputs": [], "source": [ "# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.\n", "gene_annotation = get_gene_annotation(soft_file)\n", "\n", "# 2. Use the 'preview_df' function from the library to preview the data and print out the results.\n", "print(\"Gene annotation preview:\")\n", "print(preview_df(gene_annotation))\n" ] }, { "cell_type": "markdown", "id": "7a07fe86", "metadata": {}, "source": [ "### Step 8: Gene Identifier Mapping" ] }, { "cell_type": "code", "execution_count": null, "id": "5dfa12f7", "metadata": {}, "outputs": [], "source": [ "# 1. Identify the columns in gene annotation data that contain probe IDs and gene symbols\n", "probe_id_col = 'ID' # The column containing probe IDs\n", "gene_symbol_col = 'Symbol' # The column containing gene symbols\n", "\n", "# 2. Get gene mapping dataframe by extracting the two columns from the gene annotation dataframe\n", "gene_mapping_df = get_gene_mapping(gene_annotation, probe_id_col, gene_symbol_col)\n", "\n", "# Print preview of the mapping dataframe\n", "print(\"Gene mapping preview:\")\n", "print(preview_df(gene_mapping_df))\n", "\n", "# 3. Convert probe-level measurements to gene expression data by applying the gene mapping\n", "gene_data = apply_gene_mapping(gene_data, gene_mapping_df)\n", "\n", "# Normalize gene symbols (e.g., handle synonyms, case differences)\n", "gene_data = normalize_gene_symbols_in_index(gene_data)\n", "\n", "# Print the number of genes after mapping and the first few gene symbols\n", "print(f\"Number of genes after mapping: {len(gene_data)}\")\n", "print(\"First few gene symbols:\")\n", "print(gene_data.index[:10])\n", "\n", "# Save gene data to 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\"Gene expression data saved to {out_gene_data_file}\")\n" ] }, { "cell_type": "markdown", "id": "ba4625ff", "metadata": {}, "source": [ "### Step 9: Data Normalization and Linking" ] }, { "cell_type": "code", "execution_count": null, "id": "3de0a637", "metadata": {}, "outputs": [], "source": [ "# 1. 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", "print(\"First few genes with their expression values after normalization:\")\n", "print(normalized_gene_data.head())\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 clinical features directly from the matrix file\n", "try:\n", " # Get the file paths for the matrix file to extract clinical data\n", " _, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)\n", " \n", " # Get raw clinical data from the matrix file\n", " _, clinical_raw = get_background_and_clinical_data(matrix_file)\n", " \n", " # Verify clinical data structure\n", " print(\"Raw clinical data shape:\", clinical_raw.shape)\n", " \n", " # Extract clinical features using the defined conversion functions\n", " clinical_features = geo_select_clinical_features(\n", " clinical_df=clinical_raw,\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(\"Clinical features:\")\n", " print(clinical_features)\n", " \n", " # Save clinical features to file\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 features saved to {out_clinical_data_file}\")\n", " \n", " # 3. Link clinical and genetic data\n", " linked_data = geo_link_clinical_genetic_data(clinical_features, normalized_gene_data)\n", " print(f\"Linked data shape: {linked_data.shape}\")\n", " print(\"Linked data preview (first 5 rows, first 5 columns):\")\n", " print(linked_data.iloc[:5, :5])\n", " \n", " # 4. Handle missing values\n", " print(\"Missing values before handling:\")\n", " print(f\" Trait ({trait}) missing: {linked_data[trait].isna().sum()} out of {len(linked_data)}\")\n", " if 'Age' in linked_data.columns:\n", " print(f\" Age missing: {linked_data['Age'].isna().sum()} out of {len(linked_data)}\")\n", " if 'Gender' in linked_data.columns:\n", " print(f\" Gender missing: {linked_data['Gender'].isna().sum()} out of {len(linked_data)}\")\n", " print(f\" Genes with >20% missing: {sum(linked_data.iloc[:, 1:].isna().mean() > 0.2)}\")\n", " print(f\" Samples with >5% missing genes: {sum(linked_data.iloc[:, 1:].isna().mean(axis=1) > 0.05)}\")\n", " \n", " cleaned_data = handle_missing_values(linked_data, trait)\n", " print(f\"Data shape after handling missing values: {cleaned_data.shape}\")\n", " \n", " # 5. Evaluate bias in trait and demographic features\n", " is_trait_biased = False\n", " if len(cleaned_data) > 0:\n", " trait_biased, cleaned_data = judge_and_remove_biased_features(cleaned_data, trait)\n", " is_trait_biased = trait_biased\n", " else:\n", " print(\"No data remains after handling missing values.\")\n", " is_trait_biased = True\n", " \n", " # 6. Final validation and save\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_trait_biased, \n", " df=cleaned_data,\n", " note=\"Dataset contains gene expression data comparing CFTR WT vs CFTR mutant (p.Phe508del) samples.\"\n", " )\n", " \n", " # 7. Save if usable\n", " if is_usable and len(cleaned_data) > 0:\n", " os.makedirs(os.path.dirname(out_data_file), exist_ok=True)\n", " cleaned_data.to_csv(out_data_file)\n", " print(f\"Linked data saved to {out_data_file}\")\n", " else:\n", " print(\"Data was determined to be unusable or empty and was not saved\")\n", " \n", "except Exception as e:\n", " print(f\"Error processing data: {e}\")\n", " # Handle the error case by still recording cohort info\n", " 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, # Mark as not available due to processing issues\n", " is_biased=True, \n", " df=pd.DataFrame(), # Empty dataframe\n", " note=f\"Error processing data: {str(e)}\"\n", " )\n", " print(\"Data was determined to be unusable and was not saved\")" ] } ], "metadata": {}, "nbformat": 4, "nbformat_minor": 5 }