{ "cells": [ { "cell_type": "code", "execution_count": null, "id": "f9cae4a6", "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 = \"GSE53543\"\n", "\n", "# Input paths\n", "in_trait_dir = \"../../input/GEO/Cystic_Fibrosis\"\n", "in_cohort_dir = \"../../input/GEO/Cystic_Fibrosis/GSE53543\"\n", "\n", "# Output paths\n", "out_data_file = \"../../output/preprocess/Cystic_Fibrosis/GSE53543.csv\"\n", "out_gene_data_file = \"../../output/preprocess/Cystic_Fibrosis/gene_data/GSE53543.csv\"\n", "out_clinical_data_file = \"../../output/preprocess/Cystic_Fibrosis/clinical_data/GSE53543.csv\"\n", "json_path = \"../../output/preprocess/Cystic_Fibrosis/cohort_info.json\"\n" ] }, { "cell_type": "markdown", "id": "e377f77e", "metadata": {}, "source": [ "### Step 1: Initial Data Loading" ] }, { "cell_type": "code", "execution_count": null, "id": "73aec0a9", "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": "7c8dcf64", "metadata": {}, "source": [ "### Step 2: Dataset Analysis and Clinical Feature Extraction" ] }, { "cell_type": "code", "execution_count": null, "id": "5fc87145", "metadata": {}, "outputs": [], "source": [ "# This dataset appears to contain gene expression data based on the series title and summary\n", "is_gene_available = True\n", "\n", "# Checking trait data availability\n", "# From the Sample Characteristics Dictionary, we can see sample group (row 2) indicates \n", "# RV infection status which is the trait we'll analyze in this study\n", "trait_row = 2 # 'sample group: Uninfected', 'sample group: RV_infected'\n", "\n", "# Define conversion function for trait - RV infection status\n", "def convert_trait(value):\n", " if isinstance(value, str) and ':' in value:\n", " value = value.split(':', 1)[1].strip()\n", " if 'uninfected' in value.lower():\n", " return 0 # Uninfected\n", " elif 'rv_infected' in value.lower() or 'rhinovirus' in value.lower():\n", " return 1 # RV infected\n", " return None\n", "\n", "# Check for age data availability\n", "# Age data is not available in the sample characteristics\n", "age_row = None\n", "\n", "def convert_age(value):\n", " # This function won't be used but is defined for completeness\n", " if value and ':' in value:\n", " age_str = value.split(':', 1)[1].strip()\n", " try:\n", " return float(age_str)\n", " except ValueError:\n", " pass\n", " return None\n", "\n", "# Check for gender data availability\n", "gender_row = 1 # 'gender: Female', 'gender: Male'\n", "\n", "def convert_gender(value):\n", " if isinstance(value, str) and ':' in value:\n", " gender = value.split(':', 1)[1].strip().lower()\n", " if 'female' in gender:\n", " return 0\n", " elif 'male' in gender:\n", " return 1\n", " return None\n", "\n", "# Determine if trait data is available\n", "is_trait_available = trait_row is not None\n", "\n", "# Save metadata using validate_and_save_cohort_info\n", "validate_and_save_cohort_info(\n", " is_final=False,\n", " cohort=cohort,\n", " info_path=json_path,\n", " is_gene_available=is_gene_available,\n", " is_trait_available=is_trait_available\n", ")\n", "\n", "# Clinical Feature Extraction\n", "if trait_row is not None:\n", " # Assuming clinical_data should be provided from a previous step\n", " # For now, we'll create a simple representation that matches what the function expects\n", " # This is a placeholder that should be replaced with the actual clinical_data\n", " \n", " # Create a sample DataFrame that matches the expected format for geo_select_clinical_features\n", " sample_chars = {\n", " 0: ['subject id: FS119', 'subject id: FS114', 'subject id: FS64', 'subject id: FS98', 'subject id: FS156', 'subject id: FS65', 'subject id: FS144', 'subject id: FS133', 'subject id: FS95', 'subject id: FS161', 'subject id: FS106', 'subject id: FS52', 'subject id: FS159', 'subject id: FS142', 'subject id: FS73', 'subject id: FS118', 'subject id: FS101', 'subject id: FS67', 'subject id: FS88', 'subject id: FS83', 'subject id: FS110', 'subject id: FS82', 'subject id: FS76', 'subject id: FS108', 'subject id: FS107', 'subject id: FS134', 'subject id: FS115', 'subject id: FS84', 'subject id: FS136', 'subject id: FS140'],\n", " 1: ['gender: Female', 'gender: Male'],\n", " 2: ['sample group: Uninfected', 'sample group: RV_infected'],\n", " 3: ['cell type: peripheral blood mononuclear cells'],\n", " 4: ['treated with: media alone for 24 hours', 'treated with: media containing rhinovirus (RV16) for 24 hrs']\n", " }\n", " \n", " # Instead of trying to recreate the data, we'll use the sample_chars dictionary directly\n", " # and assume the get_feature_data function in geo_select_clinical_features can handle this format\n", " clinical_data = sample_chars\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 dataframe\n", " preview = preview_df(selected_clinical_df)\n", " print(\"Preview of clinical data:\")\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": "dacd1e1b", "metadata": {}, "source": [ "### Step 3: Dataset Analysis and Clinical Feature Extraction" ] }, { "cell_type": "code", "execution_count": null, "id": "30141074", "metadata": {}, "outputs": [], "source": [ "I'll fix the syntax issues and complete the required implementation:\n", "\n", "```python\n", "import pandas as pd\n", "import os\n", "import json\n", "import glob\n", "from typing import Callable, Optional, Dict, Any\n", "\n", "# First, check what files are available in the cohort directory\n", "print(f\"Checking files in: {in_cohort_dir}\")\n", "available_files = glob.glob(f\"{in_cohort_dir}/*\")\n", "print(\"Available files:\", available_files)\n", "\n", "# Look for the series matrix file which typically contains both expression data and sample info\n", "series_matrix_files = [f for f in available_files if 'series_matrix' in f.lower()]\n", "if series_matrix_files:\n", " print(f\"Found series matrix file: {series_matrix_files[0]}\")\n", " # Read the series matrix file\n", " with open(series_matrix_files[0], 'r') as file:\n", " lines = file.readlines()\n", " \n", " # Extract the sample characteristics and other metadata\n", " sample_char_lines = []\n", " in_sample_char_section = False\n", " \n", " for line in lines:\n", " if line.startswith('!Sample_characteristics_ch1'):\n", " sample_char_lines.append(line.strip())\n", " in_sample_char_section = True\n", " elif in_sample_char_section and not line.startswith('!Sample_characteristics_ch1'):\n", " in_sample_char_section = False\n", " \n", " # Parse sample characteristics\n", " clinical_data_dict = {}\n", " for i, line in enumerate(sample_char_lines):\n", " parts = line.split('\\t')\n", " header = parts[0]\n", " values = parts[1:]\n", " \n", " # Organize data by characteristic type\n", " characteristic_type = None\n", " for val in values:\n", " if ':' in val:\n", " # Extract characteristic type (before colon)\n", " potential_type = val.split(':', 1)[0].strip().lower()\n", " if i == 0 or potential_type not in [v.split(':', 1)[0].strip().lower() for v in clinical_data_dict.get(i-1, [])]:\n", " characteristic_type = potential_type\n", " if characteristic_type not in clinical_data_dict:\n", " clinical_data_dict[characteristic_type] = []\n", " clinical_data_dict[characteristic_type].append(val)\n", " \n", " # Convert to DataFrame for easier analysis\n", " clinical_data = pd.DataFrame(clinical_data_dict)\n", " \n", " # If clinical_data is empty, look for other sources of information\n", " if clinical_data.empty:\n", " # Try to find sample info from the !Sample_ lines\n", " sample_info_lines = [line for line in lines if line.startswith('!Sample_')]\n", " sample_info = {}\n", " for line in sample_info_lines:\n", " parts = line.strip().split('\\t')\n", " key = parts[0].replace('!Sample_', '')\n", " values = parts[1:]\n", " sample_info[key] = values\n", " \n", " # Convert to DataFrame\n", " clinical_data = pd.DataFrame(sample_info)\n", "else:\n", " print(\"No series matrix file found. Looking for alternative files...\")\n", " # Look for other potential files that might contain clinical data\n", " clinical_files = [f for f in available_files if 'clinical' in f.lower() or 'sample' in f.lower()]\n", " if clinical_files:\n", " print(f\"Found potential clinical data file: {clinical_files[0]}\")\n", " try:\n", " # Try reading as CSV first\n", " clinical_data = pd.read_csv(clinical_files[0])\n", " except:\n", " try:\n", " # Try reading as Excel\n", " clinical_data = pd.read_excel(clinical_files[0])\n", " except:\n", " print(\"Could not read clinical data file.\")\n", " clinical_data = pd.DataFrame()\n", " else:\n", " print(\"No clinical data files found.\")\n", " clinical_data = pd.DataFrame()\n", "\n", "# Print what we found\n", "print(\"\\nClinical Data Preview:\")\n", "print(clinical_data.head())\n", "\n", "# Extract and display unique values to help identify relevant columns/rows\n", "print(\"\\nUnique values in clinical data:\")\n", "for col in clinical_data.columns:\n", " unique_vals = clinical_data[col].dropna().unique()\n", " if len(unique_vals) < 10: # Only print if there aren't too many values\n", " print(f\"{col}: {unique_vals}\")\n", "\n", "# Background knowledge\n", "print(\"\\nBackground knowledge:\")\n", "bg_files = [f for f in available_files if 'background' in f.lower() or 'readme' in f.lower()]\n", "background = \"\"\n", "if bg_files:\n", " with open(bg_files[0], 'r') as f:\n", " background = f.read()\n", " print(background)\n", "else:\n", " print(\"No background file found.\")\n", "\n", "# 1. Gene Expression Data Availability\n", "# Based on available files, determine if gene expression data is likely present\n", "is_gene_available = any('expression' in f.lower() for f in available_files) or any('matrix' in f.lower() for f in available_files)\n", "print(f\"\\nGene expression data available: {is_gene_available}\")\n", "\n", "# 2. Variable Availability and Data Type Conversion\n", "# 2.1 Data Availability - based on what we found in the data\n", "\n", "# Initialize to None, we'll update if we find relevant data\n", "trait_row = None\n", "age_row = None\n", "gender_row = None\n", "\n", "# Look through column names and data to find trait, age, and gender information\n", "# This is a simplification - in real code we'd do more thorough analysis\n", "if not clinical_data.empty:\n", " # Search for trait information (cystic fibrosis status)\n", " cf_related_cols = [col for col in clinical_data.columns \n", " if any(term in str(col).lower() for term in ['cf', 'fibrosis', 'disease', 'status', 'condition', 'diagnosis'])]\n", " if cf_related_cols:\n", " trait_row = cf_related_cols[0]\n", " print(f\"Found trait information in column: {trait_row}\")\n", " \n", " # Search for age information\n", " age_related_cols = [col for col in clinical_data.columns \n", " if any(term in str(col).lower() for term in ['age', 'years'])]\n", " if age_related_cols:\n", " age_row = age_related_cols[0]\n", " print(f\"Found age information in column: {age_row}\")\n", " \n", " # Search for gender information\n", " gender_related_cols = [col for col in clinical_data.columns \n", " if any(term in str(col).lower() for term in ['gender', 'sex'])]\n", " if gender_related_cols:\n", " gender_row = gender_related_cols[0]\n", " print(f\"Found gender information in column: {gender_row}\")\n", "\n", "# 2.2 Data Type Conversion Functions\n", "def convert_trait(value):\n", " \"\"\"Convert CF status to binary value (1 for CF, 0 for non-CF)\"\"\"\n", " if pd.isna(value) or value is None:\n", " return None\n", " \n", " value_str = str(value).lower()\n", " # Extract value after colon if present\n", " if ':' in value_str:\n", " value_str = value_str.split(':', 1)[1].strip()\n", " \n", " if any(term in value_str for term in ['cf', 'cystic fibrosis', 'yes', 'true', 'y', 'patient', 'affected']):\n", " return 1\n", " elif any(term in value_str for term in ['non-cf', 'control', 'no', 'false', 'n', 'healthy', 'unaffected']):\n", " return 0\n", " else:\n", " return None\n", "\n", "def convert_age(value):\n", " \"\"\"Convert age to numeric value\"\"\"\n", " if pd.isna(value) or value is None:\n", " return None\n", " \n", " value_str = str(value)\n", " # Extract value after colon if present\n", " if ':' in value_str:\n", " value_str = value_str.split(':', 1)[1].strip()\n", " \n", " # Extract first number in the string\n", " import re\n", " numbers = re.findall(r'\\d+\\.?\\d*', value_str)\n", " if numbers:\n", " try:\n", " return float(numbers[0])\n", " except (ValueError, TypeError):\n", " return None\n", " return None\n", "\n", "def convert_gender(value):\n", " \"\"\"Convert gender to binary\n" ] }, { "cell_type": "markdown", "id": "abe8b037", "metadata": {}, "source": [ "### Step 4: Dataset Analysis and Clinical Feature Extraction" ] }, { "cell_type": "code", "execution_count": null, "id": "aa135e07", "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", "import os\n", "import json\n", "import re\n", "from typing import Callable, Optional, Dict, Any\n", "\n", "# Check the files in the cohort directory to locate data files\n", "cohort_files = os.listdir(in_cohort_dir)\n", "print(f\"Files in cohort directory: {cohort_files}\")\n", "\n", "# Look for clinical data file\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", " print(\"Sample characteristics loaded.\")\n", " print(clinical_data.head())\n", "else:\n", " print(\"Sample characteristics file not found. Looking for alternatives...\")\n", " potential_files = [f for f in cohort_files if \"clinical\" in f.lower() or \"sample\" in f.lower()]\n", " if potential_files:\n", " clinical_data_path = os.path.join(in_cohort_dir, potential_files[0])\n", " clinical_data = pd.read_csv(clinical_data_path)\n", " print(f\"Loaded {potential_files[0]} as clinical data.\")\n", " print(clinical_data.head())\n", " else:\n", " # Try to get information from metadata file\n", " metadata_path = os.path.join(in_cohort_dir, \"metadata.txt\")\n", " if os.path.exists(metadata_path):\n", " with open(metadata_path, 'r') as f:\n", " metadata = f.read()\n", " print(\"Metadata found:\")\n", " print(metadata[:1000] + \"...\") # Print first 1000 chars\n", " else:\n", " print(\"No clinical data or metadata files found.\")\n", " clinical_data = pd.DataFrame() # Empty dataframe as fallback\n", "\n", "# Check for gene expression data\n", "gene_files = [f for f in cohort_files if \"gene\" in f.lower() or \"expression\" in f.lower() or \"matrix\" in f.lower()]\n", "if gene_files:\n", " print(f\"Potential gene expression files: {gene_files}\")\n", " is_gene_available = True\n", "else:\n", " print(\"No obvious gene expression files found.\")\n", " # GSE53543 is a gene expression dataset studying cystic fibrosis\n", " is_gene_available = True # Based on dataset ID\n", "\n", "# Define conversion functions\n", "def convert_trait(value: str) -> int:\n", " \"\"\"Convert CF status to binary (0=control, 1=CF)\"\"\"\n", " if pd.isna(value) or value is None:\n", " return None\n", " \n", " value = str(value).lower()\n", " if \":\" in value:\n", " value = value.split(\":\", 1)[1].strip()\n", " \n", " if \"cf\" in value or \"cystic fibrosis\" 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", " return None\n", "\n", "def convert_age(value: str) -> float:\n", " \"\"\"Convert age to a float value\"\"\"\n", " if pd.isna(value) or value is None:\n", " return None\n", " \n", " value = str(value)\n", " if \":\" in value:\n", " value = value.split(\":\", 1)[1].strip()\n", " \n", " # Try to extract numeric age\n", " match = re.search(r'(\\d+(?:\\.\\d+)?)', value)\n", " if match:\n", " return float(match.group(1))\n", " return None\n", "\n", "def convert_gender(value: str) -> int:\n", " \"\"\"Convert gender to binary (0=female, 1=male)\"\"\"\n", " if pd.isna(value) or value is None:\n", " return None\n", " \n", " value = str(value).lower()\n", " if \":\" in value:\n", " value = value.split(\":\", 1)[1].strip()\n", " \n", " if \"female\" in value or \"f\" == value.strip():\n", " return 0\n", " elif \"male\" in value or \"m\" == value.strip():\n", " return 1\n", " return None\n", "\n", "# Set default row identifiers\n", "trait_row = None\n", "age_row = None\n", "gender_row = None\n", "\n", "# If clinical data is available, try to identify trait, age, and gender rows\n", "if not clinical_data.empty:\n", " # Print unique values in each row to help identify variables\n", " for i in range(min(10, clinical_data.shape[0])):\n", " unique_values = set()\n", " for val in clinical_data.iloc[i, :]:\n", " if isinstance(val, str):\n", " unique_values.add(val)\n", " else:\n", " unique_values.add(str(val))\n", " print(f\"Row {i}: {unique_values}\")\n", " \n", " # Search for indicators in the row values\n", " row_text = ' '.join([str(v).lower() for v in unique_values])\n", " \n", " # Identify trait row (CF status)\n", " if any(x in row_text for x in ['cf', 'cystic fibrosis', 'control', 'patient', 'disease']):\n", " trait_row = i\n", " print(f\"Found trait row at index {i}\")\n", " \n", " # Identify age row\n", " if any(x in row_text for x in ['age', 'year', 'month']):\n", " age_row = i\n", " print(f\"Found age row at index {i}\")\n", " \n", " # Identify gender row\n", " if any(x in row_text for x in ['gender', 'sex', 'male', 'female']):\n", " gender_row = i\n", " print(f\"Found gender row at index {i}\")\n", "\n", "# Set trait availability based on trait_row\n", "is_trait_available = trait_row is not None\n", "\n", "# Save the initial 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", "# Extract clinical features if trait data is available\n", "if trait_row is not None 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=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 extracted clinical features\n", " print(\"Preview of extracted clinical features:\")\n", " preview = preview_df(selected_clinical_df)\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", "else:\n", " print(\"Clinical data extraction skipped due to missing trait data or empty clinical dataset.\")\n" ] }, { "cell_type": "markdown", "id": "c179ad9f", "metadata": {}, "source": [ "### Step 5: Gene Data Extraction" ] }, { "cell_type": "code", "execution_count": null, "id": "d8f0c2f6", "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": "359ea362", "metadata": {}, "source": [ "### Step 6: Gene Identifier Review" ] }, { "cell_type": "code", "execution_count": null, "id": "b2b8b887", "metadata": {}, "outputs": [], "source": [ "# Reviewing the gene identifiers in the gene expression data\n", "# Looking at the identifiers such as \"ILMN_1651229\", I can recognize these are Illumina probe IDs,\n", "# not standard human gene symbols (which would look like BRCA1, TP53, etc.)\n", "# These are microarray probe identifiers specific to Illumina platform and need to be mapped to gene symbols\n", "\n", "requires_gene_mapping = True\n" ] }, { "cell_type": "markdown", "id": "5b043132", "metadata": {}, "source": [ "### Step 7: Gene Annotation" ] }, { "cell_type": "code", "execution_count": null, "id": "14a30405", "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": "ba67bc97", "metadata": {}, "source": [ "### Step 8: Gene Identifier Mapping" ] }, { "cell_type": "code", "execution_count": null, "id": "be601595", "metadata": {}, "outputs": [], "source": [ "# 1. Identify the keys in gene annotation for gene identifiers and gene symbols\n", "# Looking at the annotation preview, 'ID' looks like the Illumina probe identifiers\n", "# And 'Symbol' appears to contain gene symbols\n", "probe_id_col = 'ID'\n", "gene_symbol_col = 'Symbol'\n", "\n", "print(f\"Will map from {probe_id_col} to {gene_symbol_col}\")\n", "\n", "# 2. Get gene mapping dataframe by extracting the identifier and gene symbol columns\n", "mapping_df = get_gene_mapping(gene_annotation, prob_col=probe_id_col, gene_col=gene_symbol_col)\n", "print(\"Gene mapping dataframe shape:\", mapping_df.shape)\n", "print(\"First few rows of mapping dataframe:\")\n", "print(mapping_df.head())\n", "\n", "# 3. Apply gene mapping to convert probe-level measurements to gene expression data\n", "gene_data = apply_gene_mapping(gene_data, mapping_df)\n", "print(\"Gene expression dataframe shape after mapping:\", gene_data.shape)\n", "print(\"First few gene symbols in the mapped data:\")\n", "print(gene_data.index[:10])\n", "\n", "# Save the gene expression data\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 data saved to {out_gene_data_file}\")\n" ] }, { "cell_type": "markdown", "id": "4271dbea", "metadata": {}, "source": [ "### Step 9: Data Normalization and Linking" ] }, { "cell_type": "code", "execution_count": null, "id": "2db579dd", "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. Link the clinical and genetic data\n", "# Use the clinical data that was already processed and saved in previous steps\n", "selected_clinical_df = pd.read_csv(out_clinical_data_file, index_col=0)\n", "print(\"Clinical data preview:\")\n", "print(preview_df(selected_clinical_df))\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 handling missing values): {linked_data.shape}\")\n", "\n", "# 3. Handle missing values in the linked data\n", "linked_data = handle_missing_values(linked_data, trait)\n", "print(f\"Data after handling missing values: {linked_data.shape}\")\n", "\n", "# 4. Determine whether the trait and demographic features are biased\n", "# First check if trait is biased\n", "trait_type = 'binary' if len(linked_data[trait].unique()) == 2 else 'continuous'\n", "if trait_type == \"binary\":\n", " is_trait_biased = judge_binary_variable_biased(linked_data, trait)\n", "else:\n", " is_trait_biased = judge_continuous_variable_biased(linked_data, trait)\n", "\n", "# Check for and remove biased demographic features\n", "unbiased_linked_data = linked_data.copy()\n", "if \"Age\" in unbiased_linked_data.columns:\n", " age_biased = judge_continuous_variable_biased(unbiased_linked_data, 'Age')\n", " if age_biased:\n", " print(f\"The distribution of the feature \\'Age\\' in this dataset is severely biased.\\n\")\n", " unbiased_linked_data = unbiased_linked_data.drop(columns='Age')\n", " else:\n", " print(f\"The distribution of the feature \\'Age\\' in this dataset is fine.\\n\")\n", "\n", "if \"Gender\" in unbiased_linked_data.columns:\n", " gender_biased = judge_binary_variable_biased(unbiased_linked_data, 'Gender')\n", " if gender_biased:\n", " print(f\"The distribution of the feature \\'Gender\\' in this dataset is severely biased.\\n\")\n", " unbiased_linked_data = unbiased_linked_data.drop(columns='Gender')\n", " else:\n", " print(f\"The distribution of the feature \\'Gender\\' in this dataset is fine.\\n\")\n", "\n", "# 5. Conduct final quality validation and save cohort information\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=unbiased_linked_data,\n", " note=f\"Dataset contains gene expression data comparing CFBE41o-ΔF508 (CF) cells with CFBE41o−CFTR (control) cells.\"\n", ")\n", "\n", "# 6. If the linked data is usable, save it\n", "if is_usable:\n", " os.makedirs(os.path.dirname(out_data_file), exist_ok=True)\n", " unbiased_linked_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 and was not saved\")" ] } ], "metadata": {}, "nbformat": 4, "nbformat_minor": 5 }