{ "cells": [ { "cell_type": "code", "execution_count": 1, "id": "90b91c33", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T08:30:08.205938Z", "iopub.status.busy": "2025-03-25T08:30:08.205452Z", "iopub.status.idle": "2025-03-25T08:30:08.371351Z", "shell.execute_reply": "2025-03-25T08:30:08.371023Z" } }, "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 = \"COVID-19\"\n", "cohort = \"GSE185658\"\n", "\n", "# Input paths\n", "in_trait_dir = \"../../input/GEO/COVID-19\"\n", "in_cohort_dir = \"../../input/GEO/COVID-19/GSE185658\"\n", "\n", "# Output paths\n", "out_data_file = \"../../output/preprocess/COVID-19/GSE185658.csv\"\n", "out_gene_data_file = \"../../output/preprocess/COVID-19/gene_data/GSE185658.csv\"\n", "out_clinical_data_file = \"../../output/preprocess/COVID-19/clinical_data/GSE185658.csv\"\n", "json_path = \"../../output/preprocess/COVID-19/cohort_info.json\"\n" ] }, { "cell_type": "markdown", "id": "362d1a18", "metadata": {}, "source": [ "### Step 1: Initial Data Loading" ] }, { "cell_type": "code", "execution_count": 2, "id": "ebb746c0", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T08:30:08.372719Z", "iopub.status.busy": "2025-03-25T08:30:08.372580Z", "iopub.status.idle": "2025-03-25T08:30:08.483771Z", "shell.execute_reply": "2025-03-25T08:30:08.483483Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Background Information:\n", "!Series_title\t\"Rhinovirus-induced epithelial RIG-I inflammasome suppresses antiviral immunity and promotes inflammation in asthma and COVID-19\"\n", "!Series_summary\t\"Balanced immune responses in airways of patients with asthma are crucial to succesful clearance of viral infection and proper asthma control.\"\n", "!Series_summary\t\"We used microarrays to detail the global programme of gene expression data from bronchial brushings from control individuals and patients with asthma after rhinovirus infection in vivo.\"\n", "!Series_overall_design\t\"Bronchial brushings from control individuals and patients with asthma around two weeks before (day -14) and four days after (day 4) experimental in vivo rhinovirus infection were used for RNA isolation and hybrydyzation with Affymetric microarrays.\"\n", "Sample Characteristics Dictionary:\n", "{0: ['time: DAY14', 'time: DAY4'], 1: ['group: AsthmaHDM', 'group: AsthmaHDMNeg', 'group: Healthy'], 2: ['donor: DJ144', 'donor: DJ113', 'donor: DJ139', 'donor: DJ129', 'donor: DJ134', 'donor: DJ114', 'donor: DJ81', 'donor: DJ60', 'donor: DJ73', 'donor: DJ136', 'donor: DJ92', 'donor: DJ47', 'donor: DJ125', 'donor: DJ148', 'donor: DJ121', 'donor: DJ116', 'donor: DJ86', 'donor: DJ126', 'donor: DJ48', 'donor: DJ67', 'donor: DJ56', 'donor: DJ61', 'donor: DJ75', 'donor: DJ101']}\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": "4128a79e", "metadata": {}, "source": [ "### Step 2: Dataset Analysis and Clinical Feature Extraction" ] }, { "cell_type": "code", "execution_count": 3, "id": "753acb1a", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T08:30:08.484915Z", "iopub.status.busy": "2025-03-25T08:30:08.484810Z", "iopub.status.idle": "2025-03-25T08:30:08.489842Z", "shell.execute_reply": "2025-03-25T08:30:08.489574Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "A new JSON file was created at: ../../output/preprocess/COVID-19/cohort_info.json\n" ] }, { "data": { "text/plain": [ "False" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# 1. Gene Expression Data Availability\n", "# Based on the background information, this is microarray data from bronchial brushings\n", "# which indicates 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", "# After reviewing the data, it's clear this dataset is about asthma and rhinovirus, not COVID-19\n", "# Therefore, the COVID-19 trait we're interested in is not available in this dataset\n", "trait_row = None # COVID-19 trait information is not available\n", "age_row = None # Age information is not available\n", "gender_row = None # Gender information is not available\n", "\n", "# 2.2 Data Type Conversion\n", "def convert_trait(value):\n", " \"\"\"Convert trait information to binary values for COVID-19\"\"\"\n", " # Since the trait is not available, this function won't be used\n", " return None\n", "\n", "def convert_age(value):\n", " \"\"\"Convert age information to continuous values\"\"\"\n", " # Not applicable as age data is not available\n", " return None\n", "\n", "def convert_gender(value):\n", " \"\"\"Convert gender information to binary values\"\"\"\n", " # Not applicable as gender data is not available\n", " return None\n", "\n", "# 3. Save Metadata\n", "# Determine trait data availability\n", "is_trait_available = trait_row is not None\n", "\n", "# Conduct 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", "# Skip this step since trait_row is None (COVID-19 trait data is not available)\n" ] }, { "cell_type": "markdown", "id": "d74c5573", "metadata": {}, "source": [ "### Step 3: Gene Data Extraction" ] }, { "cell_type": "code", "execution_count": 4, "id": "df48e103", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T08:30:08.490923Z", "iopub.status.busy": "2025-03-25T08:30:08.490823Z", "iopub.status.idle": "2025-03-25T08:30:08.660942Z", "shell.execute_reply": "2025-03-25T08:30:08.660574Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "SOFT file: ../../input/GEO/COVID-19/GSE185658/GSE185658_family.soft.gz\n", "Matrix file: ../../input/GEO/COVID-19/GSE185658/GSE185658_series_matrix.txt.gz\n", "Found the matrix table marker at line 63\n", "Gene data shape: (32321, 48)\n", "First 20 gene/probe identifiers:\n", "['7892501', '7892502', '7892503', '7892504', '7892505', '7892506', '7892507', '7892508', '7892509', '7892510', '7892511', '7892512', '7892513', '7892514', '7892515', '7892516', '7892517', '7892518', '7892519', '7892520']\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", "marker_row = None\n", "try:\n", " with gzip.open(matrix_file, 'rt') as file:\n", " for i, line in enumerate(file):\n", " if \"!series_matrix_table_begin\" in line:\n", " found_marker = True\n", " marker_row = i\n", " print(f\"Found the matrix table marker at line {i}\")\n", " break\n", " \n", " if not found_marker:\n", " print(\"Warning: Could not find '!series_matrix_table_begin' marker in the file.\")\n", " is_gene_available = False\n", " \n", " # If marker was found, try to extract gene data\n", " if is_gene_available:\n", " try:\n", " # Try using the library function\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", " except Exception as e:\n", " print(f\"Error extracting gene data with get_genetic_data(): {e}\")\n", " is_gene_available = False\n", " \n", " # If gene data extraction failed, examine file content to diagnose\n", " if not is_gene_available:\n", " print(\"Examining file content to diagnose the issue:\")\n", " try:\n", " with gzip.open(matrix_file, 'rt') as file:\n", " # Print lines around the marker if found\n", " if marker_row is not None:\n", " for i, line in enumerate(file):\n", " if i >= marker_row - 2 and i <= marker_row + 10:\n", " print(f\"Line {i}: {line.strip()[:100]}...\")\n", " if i > marker_row + 10:\n", " break\n", " else:\n", " # If marker not found, print first 10 lines\n", " for i, line in enumerate(file):\n", " if i < 10:\n", " print(f\"Line {i}: {line.strip()[:100]}...\")\n", " else:\n", " break\n", " except Exception as e2:\n", " print(f\"Error examining file: {e2}\")\n", " \n", "except Exception as e:\n", " print(f\"Error processing file: {e}\")\n", " is_gene_available = False\n", "\n", "# Update validation information if gene data extraction failed\n", "if not is_gene_available:\n", " print(\"Gene expression data could not be successfully extracted from this dataset.\")\n", " # Update the validation record since gene data isn't available\n", " is_trait_available = False # We already determined trait data isn't available in step 2\n", " validate_and_save_cohort_info(is_final=False, cohort=cohort, info_path=json_path,\n", " is_gene_available=is_gene_available, is_trait_available=is_trait_available)\n" ] }, { "cell_type": "markdown", "id": "1a30a9dc", "metadata": {}, "source": [ "### Step 4: Gene Identifier Review" ] }, { "cell_type": "code", "execution_count": 5, "id": "bbd75804", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T08:30:08.662213Z", "iopub.status.busy": "2025-03-25T08:30:08.662097Z", "iopub.status.idle": "2025-03-25T08:30:08.663939Z", "shell.execute_reply": "2025-03-25T08:30:08.663668Z" } }, "outputs": [], "source": [ "# These don't appear to be human gene symbols but rather probe identifiers from a microarray platform\n", "# They are numeric identifiers that likely need to be mapped to gene symbols\n", "# Based on my biomedical knowledge, human gene symbols are typically alphanumeric (like BRCA1, TP53, etc.)\n", "# These look like Illumina BeadChip probe IDs which require mapping to standard gene symbols\n", "\n", "requires_gene_mapping = True\n" ] }, { "cell_type": "markdown", "id": "9f5d354a", "metadata": {}, "source": [ "### Step 5: Gene Annotation" ] }, { "cell_type": "code", "execution_count": 6, "id": "20684cd7", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T08:30:08.665140Z", "iopub.status.busy": "2025-03-25T08:30:08.664954Z", "iopub.status.idle": "2025-03-25T08:30:11.838183Z", "shell.execute_reply": "2025-03-25T08:30:11.837861Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Gene annotation preview:\n", "Columns in gene annotation: ['ID', 'GB_LIST', 'SPOT_ID', 'seqname', 'RANGE_GB', 'RANGE_STRAND', 'RANGE_START', 'RANGE_STOP', 'total_probes', 'gene_assignment', 'mrna_assignment', 'category']\n", "{'ID': ['7896736', '7896738', '7896740'], 'GB_LIST': [nan, nan, 'NM_001004195,NM_001005240,NM_001005484,BC136848,BC136867,BC136907,BC136908'], 'SPOT_ID': ['chr1:53049-54936', 'chr1:63015-63887', 'chr1:69091-70008'], 'seqname': ['chr1', 'chr1', 'chr1'], 'RANGE_GB': ['NC_000001.10', 'NC_000001.10', 'NC_000001.10'], 'RANGE_STRAND': ['+', '+', '+'], 'RANGE_START': ['53049', '63015', '69091'], 'RANGE_STOP': ['54936', '63887', '70008'], 'total_probes': [7.0, 31.0, 24.0], 'gene_assignment': ['---', 'ENST00000328113 // OR4G2P // olfactory receptor, family 4, subfamily G, member 2 pseudogene // --- // --- /// ENST00000492842 // OR4G11P // olfactory receptor, family 4, subfamily G, member 11 pseudogene // --- // --- /// ENST00000588632 // OR4G1P // olfactory receptor, family 4, subfamily G, member 1 pseudogene // --- // ---', 'NM_001004195 // OR4F4 // olfactory receptor, family 4, subfamily F, member 4 // 15q26.3 // 26682 /// NM_001005240 // OR4F17 // olfactory receptor, family 4, subfamily F, member 17 // 19p13.3 // 81099 /// NM_001005484 // OR4F5 // olfactory receptor, family 4, subfamily F, member 5 // 1p36.33 // 79501 /// ENST00000318050 // OR4F17 // olfactory receptor, family 4, subfamily F, member 17 // 19p13.3 // 81099 /// ENST00000326183 // OR4F4 // olfactory receptor, family 4, subfamily F, member 4 // 15q26.3 // 26682 /// ENST00000335137 // OR4F5 // olfactory receptor, family 4, subfamily F, member 5 // 1p36.33 // 79501 /// ENST00000585993 // OR4F17 // olfactory receptor, family 4, subfamily F, member 17 // 19p13.3 // 81099 /// BC136848 // OR4F17 // olfactory receptor, family 4, subfamily F, member 17 // 19p13.3 // 81099 /// BC136867 // OR4F17 // olfactory receptor, family 4, subfamily F, member 17 // 19p13.3 // 81099 /// BC136907 // OR4F4 // olfactory receptor, family 4, subfamily F, member 4 // 15q26.3 // 26682 /// BC136908 // OR4F4 // olfactory receptor, family 4, subfamily F, member 4 // 15q26.3 // 26682'], 'mrna_assignment': ['NONHSAT060105 // NONCODE // Non-coding transcript identified by NONCODE // chr1 // 100 // 100 // 7 // 7 // 0', 'ENST00000328113 // ENSEMBL // havana:known chromosome:GRCh38:15:101926805:101927707:-1 gene:ENSG00000183909 gene_biotype:unprocessed_pseudogene transcript_biotype:unprocessed_pseudogene // chr1 // 100 // 100 // 31 // 31 // 0 /// ENST00000492842 // ENSEMBL // havana:known chromosome:GRCh38:1:62948:63887:1 gene:ENSG00000240361 gene_biotype:unprocessed_pseudogene transcript_biotype:unprocessed_pseudogene // chr1 // 100 // 100 // 31 // 31 // 0 /// ENST00000588632 // ENSEMBL // havana:known chromosome:GRCh38:19:104535:105471:1 gene:ENSG00000267310 gene_biotype:unprocessed_pseudogene transcript_biotype:unprocessed_pseudogene // chr1 // 100 // 100 // 31 // 31 // 0 /// NONHSAT000016 // NONCODE // Non-coding transcript identified by NONCODE: Linc // chr1 // 100 // 100 // 31 // 31 // 0 /// NONHSAT051704 // NONCODE // Non-coding transcript identified by NONCODE: Linc // chr1 // 100 // 100 // 31 // 31 // 0 /// NONHSAT060106 // NONCODE // Non-coding transcript identified by NONCODE: Linc // chr1 // 100 // 100 // 31 // 31 // 0', 'NM_001004195 // RefSeq // Homo sapiens olfactory receptor, family 4, subfamily F, member 4 (OR4F4), mRNA. // chr1 // 100 // 100 // 24 // 24 // 0 /// NM_001005240 // RefSeq // Homo sapiens olfactory receptor, family 4, subfamily F, member 17 (OR4F17), mRNA. // chr1 // 100 // 100 // 24 // 24 // 0 /// NM_001005484 // RefSeq // Homo sapiens olfactory receptor, family 4, subfamily F, member 5 (OR4F5), mRNA. // chr1 // 100 // 100 // 24 // 24 // 0 /// ENST00000318050 // ENSEMBL // ensembl:known chromosome:GRCh38:19:110643:111696:1 gene:ENSG00000176695 gene_biotype:protein_coding transcript_biotype:protein_coding // chr1 // 100 // 100 // 24 // 24 // 0 /// ENST00000326183 // ENSEMBL // ensembl_havana_transcript:known chromosome:GRCh38:15:101922042:101923095:-1 gene:ENSG00000177693 gene_biotype:protein_coding transcript_biotype:protein_coding // chr1 // 100 // 100 // 24 // 24 // 0 /// ENST00000335137 // ENSEMBL // ensembl_havana_transcript:known chromosome:GRCh38:1:69091:70008:1 gene:ENSG00000186092 gene_biotype:protein_coding transcript_biotype:protein_coding // chr1 // 100 // 100 // 24 // 24 // 0 /// ENST00000585993 // ENSEMBL // havana:known chromosome:GRCh38:19:107461:111696:1 gene:ENSG00000176695 gene_biotype:protein_coding transcript_biotype:protein_coding // chr1 // 100 // 100 // 24 // 24 // 0 /// BC136848 // GenBank // Homo sapiens olfactory receptor, family 4, subfamily F, member 17, mRNA (cDNA clone MGC:168462 IMAGE:9020839), complete cds. // chr1 // 100 // 100 // 24 // 24 // 0 /// BC136867 // GenBank // Homo sapiens olfactory receptor, family 4, subfamily F, member 17, mRNA (cDNA clone MGC:168481 IMAGE:9020858), complete cds. // chr1 // 100 // 100 // 24 // 24 // 0 /// BC136907 // GenBank // Homo sapiens olfactory receptor, family 4, subfamily F, member 4, mRNA (cDNA clone MGC:168521 IMAGE:9020898), complete cds. // chr1 // 100 // 100 // 24 // 24 // 0 /// BC136908 // GenBank // Homo sapiens olfactory receptor, family 4, subfamily F, member 4, mRNA (cDNA clone MGC:168522 IMAGE:9020899), complete cds. // chr1 // 100 // 100 // 24 // 24 // 0 /// ENST00000618231 // ENSEMBL // havana:known chromosome:GRCh38:19:110613:111417:1 gene:ENSG00000176695 gene_biotype:protein_coding transcript_biotype:retained_intron // chr1 // 100 // 88 // 21 // 21 // 0'], 'category': ['main', 'main', 'main']}\n", "\n", "Examining gene mapping columns:\n", "Column 'ID' examples:\n", "Example 1: 7896736\n", "Example 2: 7896738\n", "Example 3: 7896740\n", "Example 4: 7896742\n", "Example 5: 7896744\n", "\n", "Column 'gene_assignment' examples (contains gene symbols):\n", "Example 1: ---...\n", "Example 2: ENST00000328113 // OR4G2P // olfactory receptor, family 4, subfamily G, member 2 pseudogene // --- // --- /// ENST00000492842 // OR4G11P // olfactory receptor, family 4, subfamily G, member 11 pseudog...\n", "Example 3: NM_001004195 // OR4F4 // olfactory receptor, family 4, subfamily F, member 4 // 15q26.3 // 26682 /// NM_001005240 // OR4F17 // olfactory receptor, family 4, subfamily F, member 17 // 19p13.3 // 81099 ...\n", "\n", "Extracted gene symbols from gene_assignment:\n", "Example 1 extracted symbols: []\n", "Example 2 extracted symbols: ['OR4G2P', 'OR4G11P', 'OR4G1P']\n", "Example 3 extracted symbols: ['OR4F4', 'OR4F17', 'OR4F5', 'BC136848', 'BC136867', 'BC136907', 'BC136908']\n", "\n", "Columns identified for gene mapping:\n", "- 'ID': Contains probe IDs\n", "- 'gene_assignment': Contains gene information from which symbols can be extracted\n" ] } ], "source": [ "# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.\n", "soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)\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=3))\n", "\n", "# Examine the columns to find gene information\n", "print(\"\\nExamining gene mapping columns:\")\n", "print(\"Column 'ID' examples:\")\n", "id_samples = gene_annotation['ID'].head(5).tolist()\n", "for i, sample in enumerate(id_samples):\n", " print(f\"Example {i+1}: {sample}\")\n", "\n", "# Look at gene_assignment column which contains gene symbols embedded in text\n", "print(\"\\nColumn 'gene_assignment' examples (contains gene symbols):\")\n", "if 'gene_assignment' in gene_annotation.columns:\n", " # Display a few examples of the gene_assignment column\n", " gene_samples = gene_annotation['gene_assignment'].head(3).tolist()\n", " for i, sample in enumerate(gene_samples):\n", " print(f\"Example {i+1}: {sample[:200]}...\") # Show first 200 chars\n", " \n", " # Extract some gene symbols to verify\n", " print(\"\\nExtracted gene symbols from gene_assignment:\")\n", " for i, sample in enumerate(gene_samples[:3]):\n", " symbols = extract_human_gene_symbols(sample)\n", " print(f\"Example {i+1} extracted symbols: {symbols}\")\n", " \n", " # Identify the columns needed for gene mapping\n", " print(\"\\nColumns identified for gene mapping:\")\n", " print(\"- 'ID': Contains probe IDs\")\n", " print(\"- 'gene_assignment': Contains gene information from which symbols can be extracted\")\n", "else:\n", " print(\"Error: 'gene_assignment' column not found in annotation data.\")\n" ] }, { "cell_type": "markdown", "id": "702952b7", "metadata": {}, "source": [ "### Step 6: Gene Identifier Mapping" ] }, { "cell_type": "code", "execution_count": 7, "id": "4e59efc4", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T08:30:11.839495Z", "iopub.status.busy": "2025-03-25T08:30:11.839369Z", "iopub.status.idle": "2025-03-25T08:30:15.668497Z", "shell.execute_reply": "2025-03-25T08:30:15.668165Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Sample gene mapping (before filtering):\n", " ID Gene\n", "0 7896736 []\n", "1 7896738 [OR4G2P, OR4G11P, OR4G1P]\n", "2 7896740 [OR4F4, OR4F17, OR4F5, OR4F17, OR4F4, OR4F5, O...\n", "3 7896742 [LOC728323, LOC101060626, LOC101060626, LOC101...\n", "4 7896744 [OR4F29, OR4F3, OR4F16, OR4F21, OR4F21, OR4F3,...\n", "Mapping entries with gene symbols: 25293\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Number of probe IDs in mapping that match expression data: 24520\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Original expression data shape: (32321, 48)\n", "Gene mapping entries: 25293\n", "Resulting gene expression data shape: (25745, 48)\n", "First 10 gene symbols: ['MT-TM', 'FAM87B', 'FAM87A', 'LINC01128', 'SAMD11', 'KLHL17', 'PLEKHN1', 'ISG15', 'AGRN', 'MIR200B']\n" ] } ], "source": [ "# 1. Analyze the gene identifiers in the expression data and gene annotation data\n", "# Based on the preview, the 'ID' column in gene_annotation corresponds to the probe IDs in gene_data\n", "# The gene symbols are in the 'gene_assignment' column and need to be extracted\n", "\n", "# Define a more specific extraction function for this dataset format\n", "def extract_genes_from_assignment(text):\n", " \"\"\"Extract gene symbols from gene_assignment field with specific format handling for this dataset\"\"\"\n", " if not isinstance(text, str) or text == '---':\n", " return []\n", " \n", " genes = []\n", " # Gene symbols appear after '//' in the format \"ID // GENE // description\"\n", " parts = text.split('///')\n", " for part in parts:\n", " subparts = part.split('//')\n", " if len(subparts) > 1 and len(subparts[1].strip()) > 0:\n", " gene = subparts[1].strip()\n", " if gene != '---':\n", " genes.append(gene)\n", " return genes\n", "\n", "# 2. Create the gene mapping dataframe\n", "# We'll use the 'ID' column and extract gene symbols from 'gene_assignment' column\n", "mapping_df = gene_annotation[['ID', 'gene_assignment']].copy()\n", "\n", "# Process the mapping dataframe\n", "mapping_df = mapping_df.dropna(subset=['gene_assignment']) # Drop rows without gene assignments\n", "\n", "# Use our custom extraction function instead of the generic one\n", "mapping_df['Gene'] = mapping_df['gene_assignment'].apply(extract_genes_from_assignment)\n", "\n", "# Check intermediate results\n", "print(\"Sample gene mapping (before filtering):\")\n", "print(mapping_df[['ID', 'Gene']].head(5))\n", "\n", "# Only keep rows that have at least one gene symbol\n", "mapping_df = mapping_df[mapping_df['Gene'].apply(len) > 0]\n", "print(f\"Mapping entries with gene symbols: {len(mapping_df)}\")\n", "\n", "# Make sure IDs are strings\n", "mapping_df['ID'] = mapping_df['ID'].astype(str)\n", "\n", "# 3. Apply gene mapping to convert probe-level measurements to gene expression data\n", "soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)\n", "expression_df = get_genetic_data(matrix_file)\n", "\n", "# Check if our probe IDs match the expression data index\n", "common_ids = set(mapping_df['ID']) & set(expression_df.index.astype(str))\n", "print(f\"Number of probe IDs in mapping that match expression data: {len(common_ids)}\")\n", "\n", "# Create a custom mapping function for debugging\n", "def custom_map_probes_to_genes():\n", " # Dictionary to store summed expression values for each gene\n", " gene_expr = {}\n", " \n", " # Process each probe\n", " for idx, row in mapping_df.iterrows():\n", " probe_id = row['ID']\n", " genes = row['Gene']\n", " \n", " # Skip if probe not in expression data\n", " if probe_id not in expression_df.index:\n", " continue\n", " \n", " # Skip if no genes to map to\n", " if len(genes) == 0:\n", " continue\n", " \n", " # Get probe expression values\n", " probe_values = expression_df.loc[probe_id].to_dict()\n", " \n", " # Distribute expression values among genes\n", " weight = 1.0 / len(genes)\n", " for gene in genes:\n", " if gene not in gene_expr:\n", " gene_expr[gene] = {col: 0 for col in expression_df.columns}\n", " \n", " # Add weighted expression to each gene\n", " for col, val in probe_values.items():\n", " gene_expr[gene][col] += val * weight\n", " \n", " # Convert to dataframe\n", " result = pd.DataFrame.from_dict(gene_expr, orient='index')\n", " return result\n", "\n", "# Try our custom mapping approach for more control over the process\n", "gene_data = custom_map_probes_to_genes()\n", "\n", "# Print information about the mapping and resulting gene expression data\n", "print(f\"Original expression data shape: {expression_df.shape}\")\n", "print(f\"Gene mapping entries: {len(mapping_df)}\")\n", "print(f\"Resulting gene expression data shape: {gene_data.shape}\")\n", "if gene_data.shape[0] > 0:\n", " print(f\"First 10 gene symbols: {gene_data.index[:10].tolist()}\")\n", "else:\n", " print(\"No gene symbols were mapped successfully.\")\n", "\n", "# If custom mapping failed, try using the library function with explicit exploded mapping\n", "if gene_data.shape[0] == 0:\n", " print(\"Trying alternative mapping approach with exploded mapping dataframe...\")\n", " \n", " # Create an exploded version with one gene per row\n", " exploded_df = mapping_df.explode('Gene')\n", " exploded_df = exploded_df[exploded_df['Gene'] != ''] # Remove empty strings\n", " \n", " # Apply the gene mapping using the library function\n", " gene_data = apply_gene_mapping(expression_df, exploded_df[['ID', 'Gene']])\n", " \n", " print(f\"Resulting gene expression data shape with exploded mapping: {gene_data.shape}\")\n", " if gene_data.shape[0] > 0:\n", " print(f\"First 10 gene symbols: {gene_data.index[:10].tolist()}\")\n" ] }, { "cell_type": "markdown", "id": "e989347a", "metadata": {}, "source": [ "### Step 7: Data Normalization and Linking" ] }, { "cell_type": "code", "execution_count": 8, "id": "b80d42b3", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T08:30:15.669829Z", "iopub.status.busy": "2025-03-25T08:30:15.669713Z", "iopub.status.idle": "2025-03-25T08:30:16.550758Z", "shell.execute_reply": "2025-03-25T08:30:16.550380Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Gene data shape after normalization: (24776, 48)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Normalized gene data saved to ../../output/preprocess/COVID-19/gene_data/GSE185658.csv\n", "No clinical data available for the trait of interest.\n", "Abnormality detected in the cohort: GSE185658. Preprocessing failed.\n", "Linked data not saved due to quality issues.\n" ] } ], "source": [ "# 1. Normalize gene symbols in the gene expression data\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", "# Create output directory if it doesn't exist\n", "os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)\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", "# 2. Check if clinical data is available before trying to extract features\n", "if trait_row is not None:\n", " # Extract clinical features only if trait_row exists\n", " clinical_features = geo_select_clinical_features(\n", " 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", " # Save clinical features\n", " os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)\n", " clinical_features.to_csv(out_clinical_data_file)\n", " print(f\"Clinical features saved to {out_clinical_data_file}\")\n", " \n", " # Link clinical and genetic data\n", " linked_data = geo_link_clinical_genetic_data(clinical_features, normalized_gene_data)\n", " \n", " # Handle missing values and check for bias\n", " linked_data = handle_missing_values(linked_data, trait)\n", " is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)\n", "else:\n", " # No clinical data available\n", " print(\"No clinical data available for the trait of interest.\")\n", " linked_data = pd.DataFrame() # Empty dataframe\n", " is_biased = True # Dataset is biased since we have no trait data\n", "\n", "# 6. Validate and save cohort info\n", "is_trait_available = trait_row is not None\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=\"Dataset contains gene expression data but lacks COVID-19 trait information.\"\n", ")\n", "\n", "# 7. Save the linked data if it's usable\n", "if is_usable:\n", " # Create output directory if it doesn't exist\n", " os.makedirs(os.path.dirname(out_data_file), exist_ok=True)\n", " \n", " # Save the linked data\n", " linked_data.to_csv(out_data_file)\n", " print(f\"Linked data saved to {out_data_file}\")\n", "else:\n", " print(\"Linked data not saved due to quality issues.\")" ] } ], "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 }