{ "cells": [ { "cell_type": "code", "execution_count": 1, "id": "107b8644", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T07:05:40.011432Z", "iopub.status.busy": "2025-03-25T07:05:40.011200Z", "iopub.status.idle": "2025-03-25T07:05:40.180428Z", "shell.execute_reply": "2025-03-25T07:05:40.180079Z" } }, "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 = \"Cardiovascular_Disease\"\n", "cohort = \"GSE182600\"\n", "\n", "# Input paths\n", "in_trait_dir = \"../../input/GEO/Cardiovascular_Disease\"\n", "in_cohort_dir = \"../../input/GEO/Cardiovascular_Disease/GSE182600\"\n", "\n", "# Output paths\n", "out_data_file = \"../../output/preprocess/Cardiovascular_Disease/GSE182600.csv\"\n", "out_gene_data_file = \"../../output/preprocess/Cardiovascular_Disease/gene_data/GSE182600.csv\"\n", "out_clinical_data_file = \"../../output/preprocess/Cardiovascular_Disease/clinical_data/GSE182600.csv\"\n", "json_path = \"../../output/preprocess/Cardiovascular_Disease/cohort_info.json\"\n" ] }, { "cell_type": "markdown", "id": "a55e6232", "metadata": {}, "source": [ "### Step 1: Initial Data Loading" ] }, { "cell_type": "code", "execution_count": 2, "id": "950689ab", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T07:05:40.181866Z", "iopub.status.busy": "2025-03-25T07:05:40.181720Z", "iopub.status.idle": "2025-03-25T07:05:40.366619Z", "shell.execute_reply": "2025-03-25T07:05:40.366264Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Background Information:\n", "!Series_title\t\"Gene Expression of Cardiogenic Shock Patients under Extracorporeal Membrane Oxygenation\"\n", "!Series_summary\t\"Prognosis for cardiogenic shock patients under ECMO was our study goal. Success defined as survived more than 7 days after ECMO installation and failure died or had multiple organ failure in 7 days. Total 34 cases were enrolled, 17 success and 17 failure.\"\n", "!Series_summary\t\"Peripheral blood mononuclear cells collected at ECMO installation 0hr, 2hr and removal were used analyzed.\"\n", "!Series_overall_design\t\"Analysis of the cardiogenic shock patients at extracorporeal membrane oxygenation treatment by genome-wide gene expression. Transcriptomic profiling between successful and failure groups were analyzed.\"\n", "Sample Characteristics Dictionary:\n", "{0: ['disease state: Acute myocarditis', 'disease state: Acute myocardial infarction', 'disease state: Dilated cardiomyopathy, DCMP', 'disease state: Congestive heart failure', 'disease state: Dilated cardiomyopathy', 'disease state: Arrhythmia', 'disease state: Aortic dissection'], 1: ['age: 33.4', 'age: 51.2', 'age: 51.9', 'age: 47.8', 'age: 41.5', 'age: 67.3', 'age: 52.8', 'age: 16.1', 'age: 78.9', 'age: 53.2', 'age: 70.9', 'age: 59.9', 'age: 21.9', 'age: 45.2', 'age: 52.4', 'age: 32.3', 'age: 55.8', 'age: 47', 'age: 57.3', 'age: 31.7', 'age: 49.3', 'age: 66.1', 'age: 55.9', 'age: 49.1', 'age: 63', 'age: 21', 'age: 53.6', 'age: 50.1', 'age: 37.4', 'age: 71.5'], 2: ['gender: F', 'gender: M'], 3: ['outcome: Success', 'outcome: Failure', 'outcome: failure'], 4: ['cell type: PBMC'], 5: ['time: 0hr', 'time: 2hr', 'time: Removal']}\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": "73100bcd", "metadata": {}, "source": [ "### Step 2: Dataset Analysis and Clinical Feature Extraction" ] }, { "cell_type": "code", "execution_count": 3, "id": "8c027350", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T07:05:40.367843Z", "iopub.status.busy": "2025-03-25T07:05:40.367734Z", "iopub.status.idle": "2025-03-25T07:05:40.377936Z", "shell.execute_reply": "2025-03-25T07:05:40.377634Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Preview of extracted clinical features:\n", "{'Cardiovascular_Disease': [0.0, 1.0, 1.0, nan, nan], 'Age': [33.4, 51.2, 51.9, 47.8, 41.5], 'Gender': [0.0, 1.0, nan, nan, nan]}\n", "Clinical data saved to: ../../output/preprocess/Cardiovascular_Disease/clinical_data/GSE182600.csv\n" ] } ], "source": [ "import pandas as pd\n", "import numpy as np\n", "import os\n", "import json\n", "from typing import Callable, Optional, Dict, Any\n", "\n", "# 1. Gene Expression Data Availability\n", "# Based on the background info, this dataset likely contains gene expression data from PBMCs\n", "is_gene_available = True\n", "\n", "# 2. Variable Availability and Data Type Conversion\n", "# 2.1 Data Availability\n", "\n", "# Trait: Cardiovascular disease outcome, available at key 3\n", "trait_row = 3\n", "\n", "# Age: Available at key 1\n", "age_row = 1\n", "\n", "# Gender: Available at key 2\n", "gender_row = 2\n", "\n", "# 2.2 Data Type Conversion\n", "\n", "def convert_trait(value):\n", " \"\"\"Convert trait values to binary (0 for success, 1 for failure)\"\"\"\n", " if value is None:\n", " return None\n", " \n", " # Extract value after colon if it exists\n", " if ':' in value:\n", " value = value.split(':', 1)[1].strip()\n", " \n", " # Convert to binary\n", " if value.lower() == 'success':\n", " return 0\n", " elif value.lower() in ['failure', 'fail']:\n", " return 1\n", " else:\n", " return None\n", "\n", "def convert_age(value):\n", " \"\"\"Convert age values to continuous numeric values\"\"\"\n", " if value is None:\n", " return None\n", " \n", " # Extract value after colon if it exists\n", " if ':' in value:\n", " value = value.split(':', 1)[1].strip()\n", " \n", " try:\n", " return float(value)\n", " except (ValueError, TypeError):\n", " return None\n", "\n", "def convert_gender(value):\n", " \"\"\"Convert gender values to binary (0 for female, 1 for male)\"\"\"\n", " if value is None:\n", " return None\n", " \n", " # Extract value after colon if it exists\n", " if ':' in value:\n", " value = value.split(':', 1)[1].strip()\n", " \n", " # Convert to binary\n", " if value.upper() == 'F':\n", " return 0\n", " elif value.upper() == 'M':\n", " return 1\n", " else:\n", " return None\n", "\n", "# 3. Save Metadata\n", "# Determine trait data availability\n", "is_trait_available = trait_row is not None\n", "\n", "# Initial filtering and 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", "# 4. Clinical Feature Extraction\n", "if trait_row is not None:\n", " # Create a DataFrame from the sample characteristics dictionary\n", " # First, we need to create a function to get feature data since we're not using the normal workflow\n", " def get_feature_data(clinical_dict, row_idx, feature_name, convert_func):\n", " if row_idx is None or row_idx not in clinical_dict:\n", " return pd.DataFrame()\n", " \n", " raw_values = clinical_dict[row_idx]\n", " processed_values = [convert_func(val) for val in raw_values]\n", " \n", " # Create a dataframe with sample IDs as columns and feature as rows\n", " feature_df = pd.DataFrame([processed_values], columns=[f\"Sample_{i+1}\" for i in range(len(processed_values))])\n", " feature_df.index = [feature_name]\n", " \n", " return feature_df\n", " \n", " # Sample characteristics dictionary from the previous step output\n", " sample_chars_dict = {\n", " 0: ['disease state: Acute myocarditis', 'disease state: Acute myocardial infarction', 'disease state: Dilated cardiomyopathy, DCMP', 'disease state: Congestive heart failure', 'disease state: Dilated cardiomyopathy', 'disease state: Arrhythmia', 'disease state: Aortic dissection'],\n", " 1: ['age: 33.4', 'age: 51.2', 'age: 51.9', 'age: 47.8', 'age: 41.5', 'age: 67.3', 'age: 52.8', 'age: 16.1', 'age: 78.9', 'age: 53.2', 'age: 70.9', 'age: 59.9', 'age: 21.9', 'age: 45.2', 'age: 52.4', 'age: 32.3', 'age: 55.8', 'age: 47', 'age: 57.3', 'age: 31.7', 'age: 49.3', 'age: 66.1', 'age: 55.9', 'age: 49.1', 'age: 63', 'age: 21', 'age: 53.6', 'age: 50.1', 'age: 37.4', 'age: 71.5'],\n", " 2: ['gender: F', 'gender: M'],\n", " 3: ['outcome: Success', 'outcome: Failure', 'outcome: failure'],\n", " 4: ['cell type: PBMC'],\n", " 5: ['time: 0hr', 'time: 2hr', 'time: Removal']\n", " }\n", " \n", " # Create feature DataFrames\n", " feature_list = []\n", " trait_data = get_feature_data(sample_chars_dict, trait_row, trait, convert_trait)\n", " feature_list.append(trait_data)\n", " \n", " if age_row is not None:\n", " age_data = get_feature_data(sample_chars_dict, age_row, 'Age', convert_age)\n", " feature_list.append(age_data)\n", " \n", " if gender_row is not None:\n", " gender_data = get_feature_data(sample_chars_dict, gender_row, 'Gender', convert_gender)\n", " feature_list.append(gender_data)\n", " \n", " # Combine all features\n", " selected_clinical_df = pd.concat(feature_list, axis=0)\n", " \n", " # Transpose the DataFrame to have samples as rows and features as columns\n", " selected_clinical_df = selected_clinical_df.transpose()\n", " \n", " # Preview the result\n", " print(\"Preview of extracted clinical features:\")\n", " preview = preview_df(selected_clinical_df)\n", " print(preview)\n", " \n", " # Save 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=True)\n", " print(f\"Clinical data saved to: {out_clinical_data_file}\")\n" ] }, { "cell_type": "markdown", "id": "5af853f3", "metadata": {}, "source": [ "### Step 3: Gene Data Extraction" ] }, { "cell_type": "code", "execution_count": 4, "id": "4211b7a4", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T07:05:40.379081Z", "iopub.status.busy": "2025-03-25T07:05:40.378966Z", "iopub.status.idle": "2025-03-25T07:05:40.716395Z", "shell.execute_reply": "2025-03-25T07:05:40.716003Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Matrix file found: ../../input/GEO/Cardiovascular_Disease/GSE182600/GSE182600_series_matrix.txt.gz\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Gene data shape: (29363, 78)\n", "First 20 gene/probe identifiers:\n", "Index(['ILMN_1343291', 'ILMN_1651209', 'ILMN_1651228', 'ILMN_1651229',\n", " 'ILMN_1651235', 'ILMN_1651236', 'ILMN_1651237', 'ILMN_1651238',\n", " 'ILMN_1651254', 'ILMN_1651260', 'ILMN_1651262', 'ILMN_1651268',\n", " 'ILMN_1651278', 'ILMN_1651282', 'ILMN_1651285', 'ILMN_1651286',\n", " 'ILMN_1651292', 'ILMN_1651303', 'ILMN_1651309', 'ILMN_1651315'],\n", " dtype='object', name='ID')\n" ] } ], "source": [ "# 1. Get the SOFT and matrix file paths again \n", "soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)\n", "print(f\"Matrix file found: {matrix_file}\")\n", "\n", "# 2. Use the get_genetic_data function from the library to get the gene_data\n", "try:\n", " gene_data = get_genetic_data(matrix_file)\n", " print(f\"Gene data shape: {gene_data.shape}\")\n", " \n", " # 3. Print the first 20 row IDs (gene or probe identifiers)\n", " print(\"First 20 gene/probe identifiers:\")\n", " print(gene_data.index[:20])\n", "except Exception as e:\n", " print(f\"Error extracting gene data: {e}\")\n" ] }, { "cell_type": "markdown", "id": "fa109453", "metadata": {}, "source": [ "### Step 4: Gene Identifier Review" ] }, { "cell_type": "code", "execution_count": 5, "id": "d4c866b4", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T07:05:40.717746Z", "iopub.status.busy": "2025-03-25T07:05:40.717624Z", "iopub.status.idle": "2025-03-25T07:05:40.719630Z", "shell.execute_reply": "2025-03-25T07:05:40.719339Z" } }, "outputs": [], "source": [ "# Looking at the gene identifiers, I can see they are ILMN_* format\n", "# These are Illumina probe IDs (BeadArray technology), not human gene symbols\n", "# They will need to be mapped to standard gene symbols for analysis\n", "\n", "requires_gene_mapping = True\n" ] }, { "cell_type": "markdown", "id": "62f41cf3", "metadata": {}, "source": [ "### Step 5: Gene Annotation" ] }, { "cell_type": "code", "execution_count": 6, "id": "1d132180", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T07:05:40.720805Z", "iopub.status.busy": "2025-03-25T07:05:40.720700Z", "iopub.status.idle": "2025-03-25T07:05:45.411148Z", "shell.execute_reply": "2025-03-25T07:05:45.410749Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Gene annotation preview:\n", "Columns in gene annotation: ['ID', 'Transcript', 'Species', 'Source', 'Search_Key', 'ILMN_Gene', 'Source_Reference_ID', 'RefSeq_ID', 'Entrez_Gene_ID', 'GI', 'Accession', 'Symbol', 'Protein_Product', 'Array_Address_Id', 'Probe_Type', 'Probe_Start', 'SEQUENCE', 'Chromosome', 'Probe_Chr_Orientation', 'Probe_Coordinates', 'Cytoband', 'Definition', 'Ontology_Component', 'Ontology_Process', 'Ontology_Function', 'Synonyms', 'Obsolete_Probe_Id', 'GB_ACC']\n", "{'ID': ['ILMN_3166687', 'ILMN_3165566', 'ILMN_3164811', 'ILMN_3165363', 'ILMN_3166511'], 'Transcript': ['ILMN_333737', 'ILMN_333646', 'ILMN_333584', 'ILMN_333628', 'ILMN_333719'], 'Species': ['ILMN Controls', 'ILMN Controls', 'ILMN Controls', 'ILMN Controls', 'ILMN Controls'], 'Source': ['ILMN_Controls', 'ILMN_Controls', 'ILMN_Controls', 'ILMN_Controls', 'ILMN_Controls'], 'Search_Key': ['ERCC-00162', 'ERCC-00071', 'ERCC-00009', 'ERCC-00053', 'ERCC-00144'], 'ILMN_Gene': ['ERCC-00162', 'ERCC-00071', 'ERCC-00009', 'ERCC-00053', 'ERCC-00144'], 'Source_Reference_ID': ['ERCC-00162', 'ERCC-00071', 'ERCC-00009', 'ERCC-00053', 'ERCC-00144'], 'RefSeq_ID': [nan, nan, nan, nan, nan], 'Entrez_Gene_ID': [nan, nan, nan, nan, nan], 'GI': [nan, nan, nan, nan, nan], 'Accession': ['DQ516750', 'DQ883654', 'DQ668364', 'DQ516785', 'DQ854995'], 'Symbol': ['ERCC-00162', 'ERCC-00071', 'ERCC-00009', 'ERCC-00053', 'ERCC-00144'], 'Protein_Product': [nan, nan, nan, nan, nan], 'Array_Address_Id': [5270161.0, 4260594.0, 7610424.0, 5260356.0, 2030196.0], 'Probe_Type': ['S', 'S', 'S', 'S', 'S'], 'Probe_Start': [12.0, 224.0, 868.0, 873.0, 130.0], 'SEQUENCE': ['CCCATGTGTCCAATTCTGAATATCTTTCCAGCTAAGTGCTTCTGCCCACC', 'GGATTAACTGCTGTGGTGTGTCATACTCGGCTACCTCCTGGTTTGGCGTC', 'GACCACGCCTTGTAATCGTATGACACGCGCTTGACACGACTGAATCCAGC', 'CTGCAATGCCATTAACAACCTTAGCACGGTATTTCCAGTAGCTGGTGAGC', 'CGTGCAGACAGGGATCGTAAGGCGATCCAGCCGGTATACCTTAGTCACAT'], 'Chromosome': [nan, nan, nan, nan, nan], 'Probe_Chr_Orientation': [nan, nan, nan, nan, nan], 'Probe_Coordinates': [nan, nan, nan, nan, nan], 'Cytoband': [nan, nan, nan, nan, nan], 'Definition': ['Methanocaldococcus jannaschii spike-in control MJ-500-33 genomic sequence', 'Synthetic construct clone NISTag13 external RNA control sequence', 'Synthetic construct clone TagJ microarray control', 'Methanocaldococcus jannaschii spike-in control MJ-1000-68 genomic sequence', 'Synthetic construct clone AG006.1100 external RNA control sequence'], 'Ontology_Component': [nan, nan, nan, nan, nan], 'Ontology_Process': [nan, nan, nan, nan, nan], 'Ontology_Function': [nan, nan, nan, nan, nan], 'Synonyms': [nan, nan, nan, nan, nan], 'Obsolete_Probe_Id': [nan, nan, nan, nan, nan], 'GB_ACC': ['DQ516750', 'DQ883654', 'DQ668364', 'DQ516785', 'DQ854995']}\n", "\n", "Searching for platform information in SOFT file:\n", "Platform ID not found in first 100 lines\n", "\n", "Searching for gene symbol information in SOFT file:\n", "Found references to gene symbols:\n", "#ILMN_Gene = Internal gene symbol\n", "#Symbol = Gene symbol from the source database\n", "#Synonyms = Gene symbol synonyms from Refseq\n", "ID\tTranscript\tSpecies\tSource\tSearch_Key\tILMN_Gene\tSource_Reference_ID\tRefSeq_ID\tEntrez_Gene_ID\tGI\tAccession\tSymbol\tProtein_Product\tArray_Address_Id\tProbe_Type\tProbe_Start\tSEQUENCE\tChromosome\tProbe_Chr_Orientation\tProbe_Coordinates\tCytoband\tDefinition\tOntology_Component\tOntology_Process\tOntology_Function\tSynonyms\tObsolete_Probe_Id\tGB_ACC\n", "ILMN_1713086\tILMN_139166\tHomo sapiens\tRefSeq\tNM_000990.2\tRPL27A\tNM_000990.2\tNM_000990.2\t6157\t14141189\tNM_000990.2\tRPL27A\tNP_000981.1\t4920193\tS\t27\tCATCCAGACTGAGGAAGACCCGGAAACTTAGGGGCCACGTGAGCCACGGC\t11\t+\t8661326-8661375\t11p15.4b\tHomo sapiens ribosomal protein L27a (RPL27A), mRNA.\tThe living contents of a cell; the matter contained within (but not including) the plasma membrane, usually taken to exclude large vacuoles and masses of secretory or ingested material. In eukaryotes it includes the nucleus and cytoplasm [goid 5622] [evidence IEA]; That part of the cytoplasm that does not contain membranous or particulate subcellular components [goid 5829] [pmid 14567916] [evidence EXP]; An intracellular organelle, about 200 A in diameter, consisting of RNA and protein. It is the site of protein biosynthesis resulting from translation of messenger RNA (mRNA). It consists of two subunits, one large and one small, each containing only protein and RNA. Both the ribosome and its subunits are characterized by their sedimentation coefficients, expressed in Svedberg units (symbol: S). Hence, the prokaryotic ribosome (70S) comprises a large (50S) subunit and a small (30S) subunit, while the eukaryotic ribosome (80S) comprises a large (60S) subunit and a small (40S) subunit. Two sites on the ribosomal large subunit are involved in translation, namely the aminoacyl site (A site) and peptidyl site (P site). Ribosomes from prokaryotes, eukaryotes, mitochondria, and chloroplasts have characteristically distinct ribosomal proteins [goid 5840] [evidence IEA]; The large subunit of the ribosome that is found in the cytosol of the cell. The cytosol is that part of the cytoplasm that does not contain membranous or particulate subcellular components [goid 22625] [pmid 7772601] [evidence TAS]\tThe successive addition of amino acid residues to a nascent polypeptide chain during protein biosynthesis [goid 6414] [pmid 15189156] [evidence EXP]\tInteracting selectively with an RNA molecule or a portion thereof [goid 3723] [pmid 7772601] [evidence TAS]; The action of a molecule that contributes to the structural integrity of the ribosome [goid 3735] [pmid 7772601] [evidence TAS]\tMGC87238\tMGC87238\tNM_000990.2\n", "\n", "Checking for additional annotation files in the directory:\n", "[]\n" ] } ], "source": [ "# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.\n", "gene_annotation = get_gene_annotation(soft_file)\n", "\n", "# 2. Analyze the gene annotation dataframe to identify which columns contain the gene identifiers and gene symbols\n", "print(\"\\nGene annotation preview:\")\n", "print(f\"Columns in gene annotation: {gene_annotation.columns.tolist()}\")\n", "print(preview_df(gene_annotation, n=5))\n", "\n", "# Let's look for platform information in the SOFT file to understand the annotation better\n", "print(\"\\nSearching for platform information in SOFT file:\")\n", "with gzip.open(soft_file, 'rt') as f:\n", " for i, line in enumerate(f):\n", " if '!Series_platform_id' in line:\n", " print(line.strip())\n", " break\n", " if i > 100: # Limit search to first 100 lines\n", " print(\"Platform ID not found in first 100 lines\")\n", " break\n", "\n", "# Check if the SOFT file includes any reference to gene symbols\n", "print(\"\\nSearching for gene symbol information in SOFT file:\")\n", "with gzip.open(soft_file, 'rt') as f:\n", " gene_symbol_lines = []\n", " for i, line in enumerate(f):\n", " if 'GENE_SYMBOL' in line or 'gene_symbol' in line.lower() or 'symbol' in line.lower():\n", " gene_symbol_lines.append(line.strip())\n", " if i > 1000 and len(gene_symbol_lines) > 0: # Limit search but ensure we found something\n", " break\n", " \n", " if gene_symbol_lines:\n", " print(\"Found references to gene symbols:\")\n", " for line in gene_symbol_lines[:5]: # Show just first 5 matches\n", " print(line)\n", " else:\n", " print(\"No explicit gene symbol references found in first 1000 lines\")\n", "\n", "# Look for alternative annotation files or references in the directory\n", "print(\"\\nChecking for additional annotation files in the directory:\")\n", "all_files = os.listdir(in_cohort_dir)\n", "print([f for f in all_files if 'annotation' in f.lower() or 'platform' in f.lower() or 'gpl' in f.lower()])\n" ] }, { "cell_type": "markdown", "id": "7197d9ce", "metadata": {}, "source": [ "### Step 6: Gene Identifier Mapping" ] }, { "cell_type": "code", "execution_count": 7, "id": "0b04f770", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T07:05:45.412543Z", "iopub.status.busy": "2025-03-25T07:05:45.412416Z", "iopub.status.idle": "2025-03-25T07:05:46.841560Z", "shell.execute_reply": "2025-03-25T07:05:46.841162Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Identifying mapping columns:\n", "Probe ID column: 'ID'\n", "Gene Symbol column: 'Symbol'\n", "\n", "Preview of gene mapping data:\n", "{'ID': ['ILMN_3166687', 'ILMN_3165566', 'ILMN_3164811', 'ILMN_3165363', 'ILMN_3166511'], 'Gene': ['ERCC-00162', 'ERCC-00071', 'ERCC-00009', 'ERCC-00053', 'ERCC-00144']}\n", "Gene mapping shape: (29377, 2)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "Preview of resulting gene expression data:\n", "Gene data shape after mapping: (20206, 78)\n", "First 10 gene symbols: ['A1BG', 'A1CF', 'A26C3', 'A2BP1', 'A2LD1', 'A2M', 'A2ML1', 'A3GALT2', 'A4GALT', 'A4GNT']\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "Gene expression data saved to: ../../output/preprocess/Cardiovascular_Disease/gene_data/GSE182600.csv\n" ] } ], "source": [ "# 1. Identify which columns to use for gene mapping\n", "# From the preview, we can see that 'ID' contains the ILMN_* identifiers matching gene_data's index\n", "# 'Symbol' contains the gene symbols we want to map to\n", "print(\"\\nIdentifying mapping columns:\")\n", "print(f\"Probe ID column: 'ID'\")\n", "print(f\"Gene Symbol column: 'Symbol'\")\n", "\n", "# 2. Create a gene mapping dataframe using the get_gene_mapping function\n", "gene_mapping = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='Symbol')\n", "\n", "# Preview the mapping data\n", "print(\"\\nPreview of gene mapping data:\")\n", "print(preview_df(gene_mapping))\n", "print(f\"Gene mapping shape: {gene_mapping.shape}\")\n", "\n", "# 3. Apply the gene mapping to convert probe-level measurements to gene expression data\n", "gene_data = apply_gene_mapping(gene_data, gene_mapping)\n", "\n", "# Preview the results\n", "print(\"\\nPreview of resulting gene expression data:\")\n", "print(f\"Gene data shape after mapping: {gene_data.shape}\")\n", "print(f\"First 10 gene symbols: {gene_data.index[:10].tolist()}\")\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\"\\nGene expression data saved to: {out_gene_data_file}\")\n" ] }, { "cell_type": "markdown", "id": "aa603511", "metadata": {}, "source": [ "### Step 7: Data Normalization and Linking" ] }, { "cell_type": "code", "execution_count": 8, "id": "4004cfe4", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T07:05:46.842991Z", "iopub.status.busy": "2025-03-25T07:05:46.842876Z", "iopub.status.idle": "2025-03-25T07:05:59.283816Z", "shell.execute_reply": "2025-03-25T07:05:59.283359Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Gene data shape after normalization: (19445, 78)\n", "First 10 normalized gene symbols: ['A1BG', 'A1BG-AS1', 'A1CF', 'A2M', 'A2ML1', 'A3GALT2', 'A4GALT', 'A4GNT', 'AAA1', 'AAAS']\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Normalized gene data saved to: ../../output/preprocess/Cardiovascular_Disease/gene_data/GSE182600.csv\n", "Clinical features saved to: ../../output/preprocess/Cardiovascular_Disease/clinical_data/GSE182600.csv\n", "Clinical features preview (transposed):\n", "{'Cardiovascular_Disease': [0.0, 0.0, 1.0, 0.0, 1.0], 'Age': [33.4, 51.2, 51.9, 47.8, 41.5], 'Gender': [0.0, 1.0, 0.0, 1.0, 0.0]}\n", "Linked data shape: (78, 19448)\n", "Linked data preview (first 5 rows, 5 columns):\n", " Cardiovascular_Disease Age Gender A1BG A1BG-AS1\n", "GSM5532093 0.0 33.4 0.0 123.145500 1284.286536\n", "GSM5532094 0.0 51.2 1.0 134.323626 2123.843378\n", "GSM5532095 1.0 51.9 0.0 100.294706 1088.857429\n", "GSM5532096 0.0 47.8 1.0 130.315854 1074.517347\n", "GSM5532097 1.0 41.5 0.0 106.890941 1070.809003\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Linked data shape after handling missing values: (78, 19448)\n", "For the feature 'Cardiovascular_Disease', the least common label is '1.0' with 31 occurrences. This represents 39.74% of the dataset.\n", "The distribution of the feature 'Cardiovascular_Disease' in this dataset is fine.\n", "\n", "Quartiles for 'Age':\n", " 25%: 47.0\n", " 50% (Median): 52.15\n", " 75%: 56.35\n", "Min: 16.1\n", "Max: 78.9\n", "The distribution of the feature 'Age' in this dataset is fine.\n", "\n", "For the feature 'Gender', the least common label is '0.0' with 24 occurrences. This represents 30.77% of the dataset.\n", "The distribution of the feature 'Gender' in this dataset is fine.\n", "\n", "A new JSON file was created at: ../../output/preprocess/Cardiovascular_Disease/cohort_info.json\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Linked data saved to ../../output/preprocess/Cardiovascular_Disease/GSE182600.csv\n" ] } ], "source": [ "# 1. Normalize gene symbols in the gene expression data\n", "gene_data_normalized = normalize_gene_symbols_in_index(gene_data)\n", "print(f\"Gene data shape after normalization: {gene_data_normalized.shape}\")\n", "print(f\"First 10 normalized gene symbols: {gene_data_normalized.index[:10].tolist()}\")\n", "\n", "# Save the normalized gene data\n", "os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)\n", "gene_data_normalized.to_csv(out_gene_data_file)\n", "print(f\"Normalized gene data saved to: {out_gene_data_file}\")\n", "\n", "# 2. Re-extract the clinical data directly from the source\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", "# Define conversion functions based on previous analysis\n", "def convert_trait(value):\n", " \"\"\"Convert trait values to binary (0 for success, 1 for failure)\"\"\"\n", " if value is None or pd.isna(value):\n", " return None\n", " \n", " # Extract value after colon if it exists\n", " if isinstance(value, str) and ':' in value:\n", " value = value.split(':', 1)[1].strip()\n", " \n", " # Convert to binary\n", " if value.lower() == 'success':\n", " return 0\n", " elif value.lower() in ['failure', 'fail']:\n", " return 1\n", " else:\n", " return None\n", "\n", "def convert_age(value):\n", " \"\"\"Convert age values to continuous numeric values\"\"\"\n", " if value is None or pd.isna(value):\n", " return None\n", " \n", " # Extract value after colon if it exists\n", " if isinstance(value, str) and ':' in value:\n", " value = value.split(':', 1)[1].strip()\n", " \n", " try:\n", " return float(value)\n", " except (ValueError, TypeError):\n", " return None\n", "\n", "def convert_gender(value):\n", " \"\"\"Convert gender values to binary (0 for female, 1 for male)\"\"\"\n", " if value is None or pd.isna(value):\n", " return None\n", " \n", " # Extract value after colon if it exists\n", " if isinstance(value, str) and ':' in value:\n", " value = value.split(':', 1)[1].strip()\n", " \n", " # Convert to binary\n", " if value.upper() == 'F':\n", " return 0\n", " elif value.upper() == 'M':\n", " return 1\n", " else:\n", " return None\n", "\n", "# Use the correct row indices from previous analysis\n", "trait_row = 3 # outcome\n", "age_row = 1 # age\n", "gender_row = 2 # gender\n", "\n", "# Extract clinical features\n", "clinical_features = geo_select_clinical_features(\n", " clinical_data,\n", " 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", "# Save clinical features - make sure to transpose it so samples are rows, not columns\n", "clinical_features_t = clinical_features.T # Transpose for proper format\n", "clinical_features_t.to_csv(out_clinical_data_file)\n", "print(f\"Clinical features saved to: {out_clinical_data_file}\")\n", "print(\"Clinical features preview (transposed):\")\n", "print(preview_df(clinical_features_t))\n", "\n", "# 3. Link clinical and genetic data - use the transposed clinical data\n", "linked_data = pd.merge(\n", " clinical_features_t, \n", " gene_data_normalized.T, # Transpose gene data so genes are columns\n", " left_index=True, \n", " right_index=True,\n", " how='inner'\n", ")\n", "\n", "print(f\"Linked data shape: {linked_data.shape}\")\n", "print(\"Linked data preview (first 5 rows, 5 columns):\")\n", "preview_cols = min(5, linked_data.shape[1])\n", "print(linked_data.iloc[:5, :preview_cols])\n", "\n", "# 4. Handle missing values\n", "linked_data_clean = handle_missing_values(linked_data, trait)\n", "print(f\"Linked data shape after handling missing values: {linked_data_clean.shape}\")\n", "\n", "# 5. Check for bias in the dataset\n", "is_biased, linked_data_clean = judge_and_remove_biased_features(linked_data_clean, trait)\n", "\n", "# 6. Conduct final quality validation\n", "note = \"Dataset contains gene expression data from PBMCs of cardiogenic shock patients under ECMO treatment, comparing successful and failed outcomes.\"\n", "is_usable = validate_and_save_cohort_info(\n", " is_final=True,\n", " cohort=cohort,\n", " info_path=json_path,\n", " is_gene_available=True,\n", " is_trait_available=True,\n", " is_biased=is_biased,\n", " df=linked_data_clean,\n", " note=note\n", ")\n", "\n", "# 7. 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_clean.to_csv(out_data_file, index=True)\n", " print(f\"Linked data saved to {out_data_file}\")\n", "else:\n", " print(\"Dataset deemed not usable for associative studies. Linked data not saved.\")" ] } ], "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 }