{ "cells": [ { "cell_type": "code", "execution_count": 1, "id": "232987ca", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T05:09:10.323171Z", "iopub.status.busy": "2025-03-25T05:09:10.322935Z", "iopub.status.idle": "2025-03-25T05:09:10.493635Z", "shell.execute_reply": "2025-03-25T05:09:10.493307Z" } }, "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 = \"Epilepsy\"\n", "cohort = \"GSE273630\"\n", "\n", "# Input paths\n", "in_trait_dir = \"../../input/GEO/Epilepsy\"\n", "in_cohort_dir = \"../../input/GEO/Epilepsy/GSE273630\"\n", "\n", "# Output paths\n", "out_data_file = \"../../output/preprocess/Epilepsy/GSE273630.csv\"\n", "out_gene_data_file = \"../../output/preprocess/Epilepsy/gene_data/GSE273630.csv\"\n", "out_clinical_data_file = \"../../output/preprocess/Epilepsy/clinical_data/GSE273630.csv\"\n", "json_path = \"../../output/preprocess/Epilepsy/cohort_info.json\"\n" ] }, { "cell_type": "markdown", "id": "f7ea8321", "metadata": {}, "source": [ "### Step 1: Initial Data Loading" ] }, { "cell_type": "code", "execution_count": 2, "id": "32750019", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T05:09:10.495163Z", "iopub.status.busy": "2025-03-25T05:09:10.495011Z", "iopub.status.idle": "2025-03-25T05:09:10.510179Z", "shell.execute_reply": "2025-03-25T05:09:10.509866Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Background Information:\n", "!Series_title\t\"Dopamine-regulated biomarkers in peripheral blood of HIV+ Methamphetamine users\"\n", "!Series_summary\t\"HIV and Methamphetamine study - Translational Methamphetamine AIDS Research Center - Dopamine-regulated inflammatory biomarkers\"\n", "!Series_summary\t\"A digital transcript panel was custom-made based on Hs_NeuroPath_v1 (Nanostring) to accommodate dopamine-regulated inflammatory genes that were previously identified in vitro, and hypothesized to cluster HIV+ Methamphetamine users.\"\n", "!Series_overall_design\t\"Specimens were peripheral blood leukocytes isolated from participants that included adults enrolled by NIH-funded studies at the University of California San Diego’s HIV Neurobehavioral Research Program (HNRP) and Translational Methamphetamine Research Center (TMARC) under informed consent and approved protocols. The subset of PWH and PWoH selected for this study were by design males, between 35 – 44 years old, due to cohort characteristics and to increase statistical power. The participants were divided based on HIV serostatus (HIV+/-) and Meth use (METH+/-). METH+ was defined as meeting lifetime DSM-IV criteria for methamphetamine use or dependence, and METH dependence or abuse within 18 months (LT Methamphetamine Dx), with 8.2% urine toxicology positive/current METH users. A cross-sectional design assembled the following groups: HIV-METH- , HIV+METH- , HIV-METH+ , and HIV+METH+. Exclusion criteria were a history of non-HIV-related neurological, medical, or psychiatric disorders that affect brain function (e.g., schizophrenia, traumatic brain injury, epilepsy), learning disabilities, or dementia.\"\n", "Sample Characteristics Dictionary:\n", "{0: ['tissue: Peripheral blood cells']}\n" ] } ], "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": "f98a8c15", "metadata": {}, "source": [ "### Step 2: Dataset Analysis and Clinical Feature Extraction" ] }, { "cell_type": "code", "execution_count": 3, "id": "eff0591e", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T05:09:10.511150Z", "iopub.status.busy": "2025-03-25T05:09:10.511039Z", "iopub.status.idle": "2025-03-25T05:09:10.533866Z", "shell.execute_reply": "2025-03-25T05:09:10.533590Z" } }, "outputs": [ { "data": { "text/plain": [ "False" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Analyze the background information and sample characteristics\n", "import pandas as pd\n", "import os\n", "import json\n", "\n", "# 1. Gene Expression Data Availability\n", "# Based on the series summary, this appears to be a custom digital transcript panel for\n", "# dopamine-regulated inflammatory genes, which suggests gene expression data is available\n", "is_gene_available = True\n", "\n", "# 2. Variable Availability and Data Type Conversion\n", "# 2.1 Data Availability\n", "# From the sample characteristics dictionary, there's not much data visible,\n", "# but from the background information we can infer some details\n", "\n", "# For trait (Epilepsy), the background mentions exclusion criteria including epilepsy,\n", "# which suggests all participants do NOT have epilepsy\n", "trait_row = None # No information about epilepsy status for each individual\n", "\n", "# For age, the background mentions participants were between 35-44 years old\n", "# But this seems to be a selection criteria rather than variable data per participant\n", "age_row = None # No individual-level age data\n", "\n", "# For gender, the background mentions participants were males\n", "# But this seems to be constant across all participants\n", "gender_row = None # No individual-level gender data (all males)\n", "\n", "# 2.2 Data Type Conversion Functions\n", "# Even though we don't have these variables available, we'll define conversion functions\n", "# in case they're needed for other processing\n", "\n", "def convert_trait(value):\n", " if value is None:\n", " return None\n", " \n", " # Extract value after colon if present\n", " if ':' in value:\n", " value = value.split(':', 1)[1].strip()\n", " \n", " # For epilepsy trait\n", " value = value.lower()\n", " if 'epilepsy' in value or 'seizure' 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", " \n", " # Extract value after colon if present\n", " if ':' in value:\n", " value = value.split(':', 1)[1].strip()\n", " \n", " # Try to convert to float\n", " try:\n", " age = float(value)\n", " return age\n", " except:\n", " return None\n", "\n", "def convert_gender(value):\n", " if value is None:\n", " return None\n", " \n", " # Extract value after colon if present\n", " if ':' in value:\n", " value = value.split(':', 1)[1].strip()\n", " \n", " # Convert to binary: female=0, male=1\n", " value = value.lower()\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", "# 3. Save Metadata\n", "# Determine trait data availability (is_trait_available)\n", "is_trait_available = trait_row is not None\n", "\n", "# Save initial filtering results\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", "# Skip this step since trait_row is None, which means clinical data is not available\n" ] }, { "cell_type": "markdown", "id": "6a4d2c4c", "metadata": {}, "source": [ "### Step 3: Gene Data Extraction" ] }, { "cell_type": "code", "execution_count": 4, "id": "1de0382d", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T05:09:10.534932Z", "iopub.status.busy": "2025-03-25T05:09:10.534827Z", "iopub.status.idle": "2025-03-25T05:09:10.555804Z", "shell.execute_reply": "2025-03-25T05:09:10.555510Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "SOFT file: ../../input/GEO/Epilepsy/GSE273630/GSE273630_family.soft.gz\n", "Matrix file: ../../input/GEO/Epilepsy/GSE273630/GSE273630_series_matrix.txt.gz\n", "Found the matrix table marker in the file.\n", "Gene data shape: (780, 99)\n", "First 20 gene/probe identifiers:\n", "['ABAT', 'ABL1', 'ACAA1', 'ACHE', 'ACIN1', 'ACTN1', 'ACVRL1', 'ADAM10', 'ADCY5', 'ADCY8', 'ADCY9', 'ADCYAP1', 'ADORA1', 'ADORA2A', 'ADRA2A', 'ADRB2', 'AGER', 'AIF1', 'AKT1', 'AKT1S1']\n" ] } ], "source": [ "# 1. Identify the paths to the SOFT file and the matrix file\n", "soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)\n", "print(f\"SOFT file: {soft_file}\")\n", "print(f\"Matrix file: {matrix_file}\")\n", "\n", "# Set gene availability flag\n", "is_gene_available = True # Initially assume gene data is available\n", "\n", "# First check if the matrix file contains the expected marker\n", "found_marker = False\n", "try:\n", " with gzip.open(matrix_file, 'rt') as file:\n", " for line in file:\n", " if \"!series_matrix_table_begin\" in line:\n", " found_marker = True\n", " break\n", " \n", " if found_marker:\n", " print(\"Found the matrix table marker in the file.\")\n", " else:\n", " print(\"Warning: Could not find '!series_matrix_table_begin' marker in the file.\")\n", " \n", " # Try to extract gene data from the matrix file\n", " gene_data = get_genetic_data(matrix_file)\n", " \n", " if gene_data.shape[0] == 0:\n", " print(\"Warning: Extracted gene data has 0 rows.\")\n", " is_gene_available = False\n", " else:\n", " print(f\"Gene data shape: {gene_data.shape}\")\n", " # Print the first 20 gene/probe identifiers\n", " print(\"First 20 gene/probe identifiers:\")\n", " print(gene_data.index[:20].tolist())\n", " \n", "except Exception as e:\n", " print(f\"Error extracting gene data: {e}\")\n", " is_gene_available = False\n", " \n", " # Try to diagnose the file format\n", " print(\"Examining file content to diagnose the issue:\")\n", " try:\n", " with gzip.open(matrix_file, 'rt') as file:\n", " for i, line in enumerate(file):\n", " if i < 10: # Print first 10 lines to diagnose\n", " print(f\"Line {i}: {line.strip()[:100]}...\") # Print first 100 chars of each line\n", " else:\n", " break\n", " except Exception as e2:\n", " print(f\"Error examining file: {e2}\")\n", "\n", "if not is_gene_available:\n", " print(\"Gene expression data could not be successfully extracted from this dataset.\")\n" ] }, { "cell_type": "markdown", "id": "a7157c31", "metadata": {}, "source": [ "### Step 4: Gene Identifier Review" ] }, { "cell_type": "code", "execution_count": 5, "id": "9da08290", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T05:09:10.556768Z", "iopub.status.busy": "2025-03-25T05:09:10.556658Z", "iopub.status.idle": "2025-03-25T05:09:10.558397Z", "shell.execute_reply": "2025-03-25T05:09:10.558109Z" } }, "outputs": [], "source": [ "# Based on the gene identifiers observed in the gene expression data, \n", "# they appear to be standard human gene symbols (like ABAT, ABL1, etc.)\n", "# which are the official symbols used in human genetics, not probe IDs or other identifiers.\n", "\n", "requires_gene_mapping = False\n" ] }, { "cell_type": "markdown", "id": "290d5516", "metadata": {}, "source": [ "### Step 5: Data Normalization and Linking" ] }, { "cell_type": "code", "execution_count": 6, "id": "0da1561d", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T05:09:10.559429Z", "iopub.status.busy": "2025-03-25T05:09:10.559326Z", "iopub.status.idle": "2025-03-25T05:09:10.691187Z", "shell.execute_reply": "2025-03-25T05:09:10.690783Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Gene data shape before normalization: (780, 99)\n", "Gene data shape after normalization: (780, 99)\n", "Normalized gene data saved to ../../output/preprocess/Epilepsy/gene_data/GSE273630.csv\n", "No trait data (Epilepsy) available in this dataset based on previous analysis.\n", "Cannot proceed with data linking due to missing trait or gene data.\n", "Abnormality detected in the cohort: GSE273630. Preprocessing failed.\n" ] } ], "source": [ "# 1. Normalize gene symbols in the gene expression data\n", "try:\n", " # Make sure the directory exists\n", " os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)\n", " \n", " # Use the gene_data variable from the previous step (don't try to load it from file)\n", " print(f\"Gene data shape before normalization: {gene_data.shape}\")\n", " \n", " # Apply normalization to gene symbols\n", " normalized_gene_data = normalize_gene_symbols_in_index(gene_data)\n", " print(f\"Gene data shape after normalization: {normalized_gene_data.shape}\")\n", " \n", " # Save the normalized gene data\n", " normalized_gene_data.to_csv(out_gene_data_file)\n", " print(f\"Normalized gene data saved to {out_gene_data_file}\")\n", " \n", " # Use the normalized data for further processing\n", " gene_data = normalized_gene_data\n", " is_gene_available = True\n", "except Exception as e:\n", " print(f\"Error normalizing gene data: {e}\")\n", " is_gene_available = False\n", "\n", "# 2. Load clinical data - respecting the analysis from Step 2\n", "# From Step 2, we determined:\n", "# trait_row = None # No Epilepsy data available\n", "# age_row = None\n", "# gender_row = None\n", "is_trait_available = trait_row is not None\n", "\n", "# Skip clinical feature extraction when trait_row is None\n", "if is_trait_available:\n", " try:\n", " # Load the clinical data from file\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", " # 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", " gender_row=gender_row,\n", " convert_gender=convert_gender,\n", " age_row=age_row,\n", " convert_age=convert_age\n", " )\n", " \n", " print(f\"Extracted clinical data shape: {clinical_features.shape}\")\n", " print(\"Preview of clinical data (first 5 samples):\")\n", " print(clinical_features.iloc[:, :5])\n", " \n", " # Save the properly extracted clinical data\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 data saved to {out_clinical_data_file}\")\n", " except Exception as e:\n", " print(f\"Error extracting clinical data: {e}\")\n", " is_trait_available = False\n", "else:\n", " print(\"No trait data (Epilepsy) available in this dataset based on previous analysis.\")\n", "\n", "# 3. Link clinical and genetic data if both are available\n", "if is_trait_available and is_gene_available:\n", " try:\n", " # Debug the column names to ensure they match\n", " print(f\"Gene data columns (first 5): {gene_data.columns[:5].tolist()}\")\n", " print(f\"Clinical data columns (first 5): {clinical_features.columns[:5].tolist()}\")\n", " \n", " # Check for common sample IDs\n", " common_samples = set(gene_data.columns).intersection(clinical_features.columns)\n", " print(f\"Found {len(common_samples)} common samples between gene and clinical data\")\n", " \n", " if len(common_samples) > 0:\n", " # Link the clinical and genetic data\n", " linked_data = geo_link_clinical_genetic_data(clinical_features, gene_data)\n", " print(f\"Initial linked data shape: {linked_data.shape}\")\n", " \n", " # Debug the trait values before handling missing values\n", " print(\"Preview of linked data (first 5 rows, first 5 columns):\")\n", " print(linked_data.iloc[:5, :5])\n", " \n", " # Handle missing values\n", " linked_data = handle_missing_values(linked_data, trait)\n", " print(f\"Linked data shape after handling missing values: {linked_data.shape}\")\n", " \n", " if linked_data.shape[0] > 0:\n", " # Check for bias in trait and demographic features\n", " is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)\n", " \n", " # Validate the data quality and save cohort info\n", " note = \"Dataset contains gene expression data from GBM cell cultures, but no epilepsy phenotype 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=is_gene_available,\n", " is_trait_available=is_trait_available,\n", " is_biased=is_biased,\n", " df=linked_data,\n", " note=note\n", " )\n", " \n", " # Save the linked data if it's usable\n", " if is_usable:\n", " os.makedirs(os.path.dirname(out_data_file), exist_ok=True)\n", " linked_data.to_csv(out_data_file)\n", " print(f\"Linked data saved to {out_data_file}\")\n", " else:\n", " print(\"Data not usable for the trait study - not saving final linked data.\")\n", " else:\n", " print(\"After handling missing values, no samples remain.\")\n", " validate_and_save_cohort_info(\n", " is_final=True,\n", " cohort=cohort,\n", " info_path=json_path,\n", " is_gene_available=is_gene_available,\n", " is_trait_available=is_trait_available,\n", " is_biased=True,\n", " df=pd.DataFrame(),\n", " note=\"No valid samples after handling missing values.\"\n", " )\n", " else:\n", " print(\"No common samples found between gene expression and clinical data.\")\n", " validate_and_save_cohort_info(\n", " is_final=True,\n", " cohort=cohort,\n", " info_path=json_path,\n", " is_gene_available=is_gene_available,\n", " is_trait_available=is_trait_available,\n", " is_biased=True,\n", " df=pd.DataFrame(),\n", " note=\"No common samples between gene expression and clinical data.\"\n", " )\n", " except Exception as e:\n", " print(f\"Error linking or processing data: {e}\")\n", " validate_and_save_cohort_info(\n", " is_final=True,\n", " cohort=cohort,\n", " info_path=json_path,\n", " is_gene_available=is_gene_available,\n", " is_trait_available=is_trait_available,\n", " is_biased=True, # Assume biased if there's an error\n", " df=pd.DataFrame(), # Empty dataframe for metadata\n", " note=f\"Error in data processing: {str(e)}\"\n", " )\n", "else:\n", " # Create an empty DataFrame for metadata purposes\n", " empty_df = pd.DataFrame()\n", " \n", " # We can't proceed with linking if either trait or gene data is missing\n", " print(\"Cannot proceed with data linking due to missing trait or gene data.\")\n", " validate_and_save_cohort_info(\n", " is_final=True,\n", " cohort=cohort,\n", " info_path=json_path,\n", " is_gene_available=is_gene_available,\n", " is_trait_available=is_trait_available,\n", " is_biased=True, # Data is unusable if we're missing components\n", " df=empty_df, # Empty dataframe for metadata\n", " note=\"Missing essential data components for linking: dataset contains gene expression data from GBM cell cultures, but no epilepsy phenotype information.\"\n", " )" ] } ], "metadata": { "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.10.16" } }, "nbformat": 4, "nbformat_minor": 5 }