{ "cells": [ { "cell_type": "code", "execution_count": 1, "id": "9ab1cdd8", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T08:20:29.658035Z", "iopub.status.busy": "2025-03-25T08:20:29.657815Z", "iopub.status.idle": "2025-03-25T08:20:29.823792Z", "shell.execute_reply": "2025-03-25T08:20:29.823475Z" } }, "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 = \"Chronic_obstructive_pulmonary_disease_(COPD)\"\n", "cohort = \"GSE212331\"\n", "\n", "# Input paths\n", "in_trait_dir = \"../../input/GEO/Chronic_obstructive_pulmonary_disease_(COPD)\"\n", "in_cohort_dir = \"../../input/GEO/Chronic_obstructive_pulmonary_disease_(COPD)/GSE212331\"\n", "\n", "# Output paths\n", "out_data_file = \"../../output/preprocess/Chronic_obstructive_pulmonary_disease_(COPD)/GSE212331.csv\"\n", "out_gene_data_file = \"../../output/preprocess/Chronic_obstructive_pulmonary_disease_(COPD)/gene_data/GSE212331.csv\"\n", "out_clinical_data_file = \"../../output/preprocess/Chronic_obstructive_pulmonary_disease_(COPD)/clinical_data/GSE212331.csv\"\n", "json_path = \"../../output/preprocess/Chronic_obstructive_pulmonary_disease_(COPD)/cohort_info.json\"\n" ] }, { "cell_type": "markdown", "id": "7667d6d3", "metadata": {}, "source": [ "### Step 1: Initial Data Loading" ] }, { "cell_type": "code", "execution_count": 2, "id": "59e3b3aa", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T08:20:29.825132Z", "iopub.status.busy": "2025-03-25T08:20:29.824994Z", "iopub.status.idle": "2025-03-25T08:20:30.070099Z", "shell.execute_reply": "2025-03-25T08:20:30.069789Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Background Information:\n", "!Series_title\t\"Severity of lung function impairment drives transcriptional phenotypes of COPD and relates to immune and metabolic processes\"\n", "!Series_summary\t\"Gene expression profiles were generated from induced sputum samples in COPD and healthy controls. The study identified transcriptional phenotypes of COPD.\"\n", "!Series_overall_design\t\"This study used unsupervised hierarchical clustering of induced sputum gene expression profiles of 72 stable COPD patients and 15 healthy controls to identify distinct and clinically relevant transcriptional COPD phenotypes.\"\n", "Sample Characteristics Dictionary:\n", "{0: ['tissue: sputum'], 1: ['disease group: COPD', 'disease group: Healthy Control'], 2: ['gold stage: 2', 'gold stage: 3', 'gold stage: 4', 'gold stage: 1', 'gold stage: n/a'], 3: ['age: 75', 'age: 66', 'age: 83', 'age: 70', 'age: 61', 'age: 77', 'age: 64', 'age: 81', 'age: 60', 'age: 62', 'age: 80', 'age: 65', 'age: 74', 'age: 73', 'age: 54', 'age: 67', 'age: 72', 'age: 71', 'age: 82', 'age: 69', 'age: 63', 'age: 76', 'age: 68', 'age: 78', 'age: 84', 'age: 88', 'age: 79', 'age: 24', 'age: 21', 'age: 20'], 4: ['gender: Female', 'gender: Male']}\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": "16ab45cd", "metadata": {}, "source": [ "### Step 2: Dataset Analysis and Clinical Feature Extraction" ] }, { "cell_type": "code", "execution_count": 3, "id": "dbd7c2ae", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T08:20:30.071877Z", "iopub.status.busy": "2025-03-25T08:20:30.071744Z", "iopub.status.idle": "2025-03-25T08:20:30.082289Z", "shell.execute_reply": "2025-03-25T08:20:30.082013Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Preview of extracted clinical features:\n", "{0: [1.0, 75.0, 0.0]}\n", "Clinical data saved to: ../../output/preprocess/Chronic_obstructive_pulmonary_disease_(COPD)/clinical_data/GSE212331.csv\n" ] } ], "source": [ "# 1. Gene Expression Data Availability\n", "# Based on the background information, this dataset appears to contain gene expression data from induced sputum samples\n", "# The series title and summary explicitly mention \"gene expression profiles\" and not miRNA or methylation\n", "is_gene_available = True\n", "\n", "# 2. Variable Availability and Data Type Conversion\n", "# 2.1 Data Availability\n", "# Trait (COPD): Available in row 1 \"disease group\"\n", "trait_row = 1\n", "\n", "# Age: Available in row 3\n", "age_row = 3\n", "\n", "# Gender: Available in row 4\n", "gender_row = 4\n", "\n", "# 2.2 Data Type Conversion Functions\n", "def convert_trait(value):\n", " \"\"\"\n", " Convert COPD trait values to binary (0: Healthy Control, 1: COPD)\n", " \"\"\"\n", " if not isinstance(value, str):\n", " return None\n", " \n", " value = value.strip().lower()\n", " if \"disease group:\" in value:\n", " value = value.split(\"disease group:\")[1].strip().lower()\n", " \n", " if \"copd\" in value:\n", " return 1\n", " elif \"healthy control\" in value or \"control\" 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 not isinstance(value, str):\n", " return None\n", " \n", " value = value.strip().lower()\n", " if \"age:\" in value:\n", " value = value.split(\"age:\")[1].strip()\n", " \n", " try:\n", " return float(value)\n", " except (ValueError, TypeError):\n", " return None\n", "\n", "def convert_gender(value):\n", " \"\"\"\n", " Convert gender values to binary (0: Female, 1: Male)\n", " \"\"\"\n", " if not isinstance(value, str):\n", " return None\n", " \n", " value = value.strip().lower()\n", " if \"gender:\" in value:\n", " value = value.split(\"gender:\")[1].strip().lower()\n", " \n", " if \"female\" in value or \"f\" in value:\n", " return 0\n", " elif \"male\" in value or \"m\" in value:\n", " return 1\n", " else:\n", " return None\n", "\n", "# 3. Save Metadata\n", "# Determine trait availability based on trait_row\n", "is_trait_available = trait_row is not None\n", "\n", "# Initial filtering and saving 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", "# 4. Clinical Feature Extraction\n", "# We proceed with this step since trait_row is not None\n", "if trait_row is not None:\n", " # Create a DataFrame from the sample characteristics dictionary\n", " # The sample characteristics dictionary was shown in the previous output\n", " sample_chars = {\n", " 0: ['tissue: sputum'], \n", " 1: ['disease group: COPD', 'disease group: Healthy Control'], \n", " 2: ['gold stage: 2', 'gold stage: 3', 'gold stage: 4', 'gold stage: 1', 'gold stage: n/a'], \n", " 3: ['age: 75', 'age: 66', 'age: 83', 'age: 70', 'age: 61', 'age: 77', 'age: 64', 'age: 81', 'age: 60', \n", " 'age: 62', 'age: 80', 'age: 65', 'age: 74', 'age: 73', 'age: 54', 'age: 67', 'age: 72', 'age: 71', \n", " 'age: 82', 'age: 69', 'age: 63', 'age: 76', 'age: 68', 'age: 78', 'age: 84', 'age: 88', 'age: 79', \n", " 'age: 24', 'age: 21', 'age: 20'], \n", " 4: ['gender: Female', 'gender: Male']\n", " }\n", " \n", " # Convert this dictionary to a DataFrame format that geo_select_clinical_features can process\n", " # We need to create a DataFrame where each column is a sample and each row is a characteristic\n", " # First, create a DataFrame with appropriate row indices\n", " clinical_data = pd.DataFrame()\n", " \n", " # Populate the DataFrame with the sample characteristics\n", " for row_idx, values in sample_chars.items():\n", " row_data = pd.Series(values)\n", " clinical_data[row_idx] = row_data\n", " \n", " # Transpose so each row is a characteristic\n", " clinical_data = clinical_data.transpose()\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 extracted clinical features\n", " preview = preview_df(selected_clinical_df)\n", " print(\"Preview of extracted clinical features:\")\n", " print(preview)\n", " \n", " # Create directories if they don't exist\n", " os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)\n", " \n", " # Save the clinical data to the specified output file\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": "d0034ca1", "metadata": {}, "source": [ "### Step 3: Gene Data Extraction" ] }, { "cell_type": "code", "execution_count": 4, "id": "c6088560", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T08:20:30.083904Z", "iopub.status.busy": "2025-03-25T08:20:30.083775Z", "iopub.status.idle": "2025-03-25T08:20:30.542216Z", "shell.execute_reply": "2025-03-25T08:20:30.541842Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Matrix file found: ../../input/GEO/Chronic_obstructive_pulmonary_disease_(COPD)/GSE212331/GSE212331_series_matrix.txt.gz\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Gene data shape: (47231, 87)\n", "First 20 gene/probe identifiers:\n", "Index(['ILMN_1343291', 'ILMN_1343295', 'ILMN_1651199', 'ILMN_1651209',\n", " 'ILMN_1651210', 'ILMN_1651221', 'ILMN_1651228', 'ILMN_1651229',\n", " 'ILMN_1651230', 'ILMN_1651232', 'ILMN_1651235', 'ILMN_1651236',\n", " 'ILMN_1651237', 'ILMN_1651238', 'ILMN_1651249', 'ILMN_1651253',\n", " 'ILMN_1651254', 'ILMN_1651259', 'ILMN_1651260', 'ILMN_1651262'],\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": "b0042e04", "metadata": {}, "source": [ "### Step 4: Gene Identifier Review" ] }, { "cell_type": "code", "execution_count": 5, "id": "613a5940", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T08:20:30.543624Z", "iopub.status.busy": "2025-03-25T08:20:30.543506Z", "iopub.status.idle": "2025-03-25T08:20:30.545347Z", "shell.execute_reply": "2025-03-25T08:20:30.545082Z" } }, "outputs": [], "source": [ "# These identifiers are Illumina probe IDs (ILMN_* format) and not human gene symbols\n", "# They need to be mapped to gene symbols for proper gene expression analysis\n", "\n", "requires_gene_mapping = True\n" ] }, { "cell_type": "markdown", "id": "d563a8be", "metadata": {}, "source": [ "### Step 5: Gene Annotation" ] }, { "cell_type": "code", "execution_count": 6, "id": "304dc5c9", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T08:20:30.547048Z", "iopub.status.busy": "2025-03-25T08:20:30.546897Z", "iopub.status.idle": "2025-03-25T08:20:39.371650Z", "shell.execute_reply": "2025-03-25T08:20:39.371333Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Gene annotation preview:\n", "Columns in gene annotation: ['ID', 'Species', 'Source', 'Search_Key', 'Transcript', 'ILMN_Gene', 'Source_Reference_ID', 'RefSeq_ID', 'Unigene_ID', 'Entrez_Gene_ID', 'GI', 'Accession', 'Symbol', 'Protein_Product', 'Probe_Id', '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_1343048', 'ILMN_1343049', 'ILMN_1343050', 'ILMN_1343052', 'ILMN_1343059'], 'Species': [nan, nan, nan, nan, nan], 'Source': [nan, nan, nan, nan, nan], 'Search_Key': [nan, nan, nan, nan, nan], 'Transcript': [nan, nan, nan, nan, nan], 'ILMN_Gene': [nan, nan, nan, nan, nan], 'Source_Reference_ID': [nan, nan, nan, nan, nan], 'RefSeq_ID': [nan, nan, nan, nan, nan], 'Unigene_ID': [nan, nan, nan, nan, nan], 'Entrez_Gene_ID': [nan, nan, nan, nan, nan], 'GI': [nan, nan, nan, nan, nan], 'Accession': [nan, nan, nan, nan, nan], 'Symbol': ['phage_lambda_genome', 'phage_lambda_genome', 'phage_lambda_genome:low', 'phage_lambda_genome:low', 'thrB'], 'Protein_Product': [nan, nan, nan, nan, 'thrB'], 'Probe_Id': [nan, nan, nan, nan, nan], 'Array_Address_Id': [5090180.0, 6510136.0, 7560739.0, 1450438.0, 1240647.0], 'Probe_Type': [nan, nan, nan, nan, nan], 'Probe_Start': [nan, nan, nan, nan, nan], 'SEQUENCE': ['GAATAAAGAACAATCTGCTGATGATCCCTCCGTGGATCTGATTCGTGTAA', 'CCATGTGATACGAGGGCGCGTAGTTTGCATTATCGTTTTTATCGTTTCAA', 'CCGACAGATGTATGTAAGGCCAACGTGCTCAAATCTTCATACAGAAAGAT', 'TCTGTCACTGTCAGGAAAGTGGTAAAACTGCAACTCAATTACTGCAATGC', 'CTTGTGCCTGAGCTGTCAAAAGTAGAGCACGTCGCCGAGATGAAGGGCGC'], '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': [nan, nan, nan, nan, nan], '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': [nan, nan, nan, nan, nan]}\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\tSpecies\tSource\tSearch_Key\tTranscript\tILMN_Gene\tSource_Reference_ID\tRefSeq_ID\tUnigene_ID\tEntrez_Gene_ID\tGI\tAccession\tSymbol\tProtein_Product\tProbe_Id\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_1651228\tHomo sapiens\tRefSeq\tNM_001031.4\tILMN_992\tRPS28\tNM_001031.4\tNM_001031.4\t\t6234\t71565158\tNM_001031.4\tRPS28\tNP_001022.1\tILMN_1651228\t650349\tS\t329\tCGCCACACGTAACTGAGATGCTCCTTTAAATAAAGCGTTTGTGTTTCAAG\t19\t+\t8293227-8293276\t19p13.2d\t\"Homo sapiens ribosomal protein S28 (RPS28), mRNA.\"\t\"The 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 12588972] [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 small 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 22627] [pmid 15883184] [evidence IDA]\"\tThe successive addition of amino acid residues to a nascent polypeptide chain during protein biosynthesis [goid 6414] [pmid 15189156] [evidence EXP]\tThe action of a molecule that contributes to the structural integrity of the ribosome [goid 3735] [pmid 15883184] [evidence IDA]; Interacting selectively with any protein or protein complex (a complex of two or more proteins that may include other nonprotein molecules) [goid 5515] [pmid 17353931] [evidence IPI]\t\t\tNM_001031.4\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": "50c931cb", "metadata": {}, "source": [ "### Step 6: Gene Identifier Mapping" ] }, { "cell_type": "code", "execution_count": 7, "id": "cc36bcae", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T08:20:39.373531Z", "iopub.status.busy": "2025-03-25T08:20:39.373382Z", "iopub.status.idle": "2025-03-25T08:20:40.834339Z", "shell.execute_reply": "2025-03-25T08:20:40.834014Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Gene mapping dataframe shape: (44837, 2)\n", "Sample of gene mapping:\n", " ID Gene\n", "0 ILMN_1343048 phage_lambda_genome\n", "1 ILMN_1343049 phage_lambda_genome\n", "2 ILMN_1343050 phage_lambda_genome:low\n", "3 ILMN_1343052 phage_lambda_genome:low\n", "4 ILMN_1343059 thrB\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Gene-level expression data shape: (21372, 87)\n", "First few gene symbols:\n", "Index(['A1BG', 'A1CF', 'A26C3', 'A2BP1', 'A2LD1', 'A2M', 'A2ML1', 'A3GALT2',\n", " 'A4GALT', 'A4GNT'],\n", " dtype='object', name='Gene')\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "After normalizing gene symbols, expression data shape: (20259, 87)\n", "First few normalized gene symbols:\n", "Index(['A1BG', 'A1BG-AS1', 'A1CF', 'A2M', 'A2ML1', 'A3GALT2', 'A4GALT',\n", " 'A4GNT', 'AAA1', 'AAAS'],\n", " dtype='object', name='Gene')\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Gene expression data saved to: ../../output/preprocess/Chronic_obstructive_pulmonary_disease_(COPD)/gene_data/GSE212331.csv\n" ] } ], "source": [ "# 1. Identify columns for gene identifiers and gene symbols in the annotation data\n", "# Based on the preview, we can see:\n", "# - 'ID' column contains Illumina probe IDs (ILMN_*) which match our gene expression data indices\n", "# - 'Symbol' column contains gene symbols \n", "\n", "# 2. Get gene mapping dataframe by extracting the relevant columns\n", "gene_mapping = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='Symbol')\n", "print(f\"Gene mapping dataframe shape: {gene_mapping.shape}\")\n", "print(\"Sample of gene mapping:\")\n", "print(gene_mapping.head())\n", "\n", "# 3. Apply gene mapping to convert probe-level measurements to gene-level expression\n", "gene_data = apply_gene_mapping(gene_data, gene_mapping)\n", "print(f\"Gene-level expression data shape: {gene_data.shape}\")\n", "print(\"First few gene symbols:\")\n", "print(gene_data.index[:10])\n", "\n", "# Normalize gene symbols to handle synonyms and standardize\n", "gene_data = normalize_gene_symbols_in_index(gene_data)\n", "print(f\"After normalizing gene symbols, expression data shape: {gene_data.shape}\")\n", "print(\"First few normalized gene symbols:\")\n", "print(gene_data.index[:10])\n", "\n", "# Save the gene data to the specified output 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": "c4f676f5", "metadata": {}, "source": [ "### Step 7: Data Normalization and Linking" ] }, { "cell_type": "code", "execution_count": 8, "id": "14c941be", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T08:20:40.836183Z", "iopub.status.busy": "2025-03-25T08:20:40.836030Z", "iopub.status.idle": "2025-03-25T08:20:54.889084Z", "shell.execute_reply": "2025-03-25T08:20:54.888706Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Normalized gene data shape: (20259, 87)\n", "Gene data column names (sample IDs):\n", "Index(['GSM6524456', 'GSM6524458', 'GSM6524459', 'GSM6524460', 'GSM6524462'], dtype='object')\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "Raw clinical data structure:\n", "Clinical data shape: (5, 88)\n", "Clinical data columns: Index(['!Sample_geo_accession', 'GSM6524456', 'GSM6524458', 'GSM6524459',\n", " 'GSM6524460'],\n", " dtype='object')\n", "\n", "Sample characteristics dictionary:\n", "{0: ['tissue: sputum'], 1: ['disease group: COPD', 'disease group: Healthy Control'], 2: ['gold stage: 2', 'gold stage: 3', 'gold stage: 4', 'gold stage: 1', 'gold stage: n/a'], 3: ['age: 75', 'age: 66', 'age: 83', 'age: 70', 'age: 61', 'age: 77', 'age: 64', 'age: 81', 'age: 60', 'age: 62', 'age: 80', 'age: 65', 'age: 74', 'age: 73', 'age: 54', 'age: 67', 'age: 72', 'age: 71', 'age: 82', 'age: 69', 'age: 63', 'age: 76', 'age: 68', 'age: 78', 'age: 84', 'age: 88', 'age: 79', 'age: 24', 'age: 21', 'age: 20'], 4: ['gender: Female', 'gender: Male']}\n", "\n", "Values in trait row:\n", "['!Sample_characteristics_ch1' 'disease group: COPD' 'disease group: COPD'\n", " 'disease group: COPD' 'disease group: COPD']\n", "\n", "Created clinical features dataframe:\n", "Shape: (1, 87)\n", " GSM6524456 GSM6524458 GSM6524459 \\\n", "Chronic_obstructive_pulmonary_disease_(COPD) 1 1 1 \n", "\n", " GSM6524460 GSM6524462 \n", "Chronic_obstructive_pulmonary_disease_(COPD) 1 1 \n", "\n", "Linked data shape before handling missing values: (87, 20260)\n", "Actual trait column in linked data: Chronic_obstructive_pulmonary_disease_(COPD)\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/media/techt/DATA/GenoAgent/tools/preprocess.py:455: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n", " df[gene_cols] = df[gene_cols].fillna(df[gene_cols].mean())\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Linked data shape after handling missing values: (87, 20260)\n", "For the feature 'Chronic_obstructive_pulmonary_disease_(COPD)', the least common label is '0' with 15 occurrences. This represents 17.24% of the dataset.\n", "The distribution of the feature 'Chronic_obstructive_pulmonary_disease_(COPD)' in this dataset is fine.\n", "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Linked data saved to ../../output/preprocess/Chronic_obstructive_pulmonary_disease_(COPD)/GSE212331.csv\n" ] } ], "source": [ "# 1. Normalize gene symbols in the gene expression data \n", "# (This was already done in the previous step, so no need to repeat)\n", "print(f\"Normalized gene data shape: {gene_data.shape}\")\n", "\n", "# 2. Examine the sample IDs in the gene expression data to understand the structure\n", "print(\"Gene data column names (sample IDs):\")\n", "print(gene_data.columns[:5]) # Print first 5 for brevity\n", "\n", "# Inspect the clinical data format from the matrix file directly\n", "background_info, clinical_data = get_background_and_clinical_data(matrix_file)\n", "print(\"\\nRaw clinical data structure:\")\n", "print(f\"Clinical data shape: {clinical_data.shape}\")\n", "print(f\"Clinical data columns: {clinical_data.columns[:5]}\")\n", "\n", "# Get the sample characteristics to re-extract the disease information\n", "sample_characteristics_dict = get_unique_values_by_row(clinical_data)\n", "print(\"\\nSample characteristics dictionary:\")\n", "print(sample_characteristics_dict)\n", "\n", "# 3. Directly create clinical features from the raw data again\n", "# Verify trait row contains the disease information (OA vs RA)\n", "print(\"\\nValues in trait row:\")\n", "trait_values = clinical_data.iloc[trait_row].values\n", "print(trait_values[:5])\n", "\n", "# Create clinical dataframe with proper structure\n", "# First get the sample IDs from gene data as these are our actual sample identifiers\n", "sample_ids = gene_data.columns.tolist()\n", "\n", "# Create the clinical features dataframe with those sample IDs\n", "clinical_features = pd.DataFrame(index=[trait], columns=sample_ids)\n", "\n", "# Fill the clinical features with our trait values by mapping GSM IDs to actual values\n", "for col in clinical_data.columns:\n", " if col in sample_ids:\n", " # Extract the disease value and convert it\n", " disease_val = clinical_data.iloc[trait_row][col]\n", " clinical_features.loc[trait, col] = convert_trait(disease_val)\n", "\n", "print(\"\\nCreated clinical features dataframe:\")\n", "print(f\"Shape: {clinical_features.shape}\")\n", "print(clinical_features.iloc[:, :5]) # Show first 5 columns\n", "\n", "# 4. Link clinical and genetic data\n", "linked_data = geo_link_clinical_genetic_data(clinical_features, gene_data)\n", "print(f\"\\nLinked data shape before handling missing values: {linked_data.shape}\")\n", "\n", "# 5. Handle missing values - we need to use the actual column name, not the trait variable\n", "# First identify the actual trait column name in the linked data\n", "trait_column = clinical_features.index[0] # This should be 'Osteoarthritis'\n", "print(f\"Actual trait column in linked data: {trait_column}\")\n", "\n", "# Now handle missing values with the correct column name\n", "linked_data_clean = handle_missing_values(linked_data, trait_column)\n", "print(f\"Linked data shape after handling missing values: {linked_data_clean.shape}\")\n", "\n", "# 6. Evaluate bias in trait and demographic features\n", "is_biased, linked_data_clean = judge_and_remove_biased_features(linked_data_clean, trait_column)\n", "\n", "# 7. Conduct final quality validation\n", "note = \"Dataset contains gene expression data from synovial fibroblasts of RA and OA patients. Data includes high serum and low serum responses.\"\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=(linked_data_clean.shape[0] > 0),\n", " is_biased=is_biased,\n", " df=linked_data_clean,\n", " note=note\n", ")\n", "\n", "# 8. Save linked data if 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)\n", " print(f\"Linked data saved to {out_data_file}\")\n", "else:\n", " print(\"Dataset deemed not usable due to quality issues - 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 }