{ "cells": [ { "cell_type": "code", "execution_count": 1, "id": "92789792", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T08:31:26.480037Z", "iopub.status.busy": "2025-03-25T08:31:26.479585Z", "iopub.status.idle": "2025-03-25T08:31:26.643901Z", "shell.execute_reply": "2025-03-25T08:31:26.643580Z" } }, "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 = \"GSE216705\"\n", "\n", "# Input paths\n", "in_trait_dir = \"../../input/GEO/COVID-19\"\n", "in_cohort_dir = \"../../input/GEO/COVID-19/GSE216705\"\n", "\n", "# Output paths\n", "out_data_file = \"../../output/preprocess/COVID-19/GSE216705.csv\"\n", "out_gene_data_file = \"../../output/preprocess/COVID-19/gene_data/GSE216705.csv\"\n", "out_clinical_data_file = \"../../output/preprocess/COVID-19/clinical_data/GSE216705.csv\"\n", "json_path = \"../../output/preprocess/COVID-19/cohort_info.json\"\n" ] }, { "cell_type": "markdown", "id": "4e575dcb", "metadata": {}, "source": [ "### Step 1: Initial Data Loading" ] }, { "cell_type": "code", "execution_count": 2, "id": "07f964d6", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T08:31:26.645232Z", "iopub.status.busy": "2025-03-25T08:31:26.645100Z", "iopub.status.idle": "2025-03-25T08:31:26.742874Z", "shell.execute_reply": "2025-03-25T08:31:26.742589Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Background Information:\n", "!Series_title\t\"Loss of GM-CSF-dependent instruction of alveolar macrophages in COVID-19 provides a rationale for inhaled GM-CSF treatment\"\n", "!Series_summary\t\"This SuperSeries is composed of the SubSeries listed below.\"\n", "!Series_overall_design\t\"Refer to individual Series\"\n", "Sample Characteristics Dictionary:\n", "{0: ['strain: C57BL/6'], 1: ['metadata info: metaData_microarrays.txt']}\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": "cddfa4a5", "metadata": {}, "source": [ "### Step 2: Dataset Analysis and Clinical Feature Extraction" ] }, { "cell_type": "code", "execution_count": 3, "id": "785a9429", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T08:31:26.743921Z", "iopub.status.busy": "2025-03-25T08:31:26.743820Z", "iopub.status.idle": "2025-03-25T08:31:26.750213Z", "shell.execute_reply": "2025-03-25T08:31:26.749941Z" } }, "outputs": [ { "data": { "text/plain": [ "False" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Based on the background information and sample characteristics, let's analyze this dataset\n", "\n", "# 1. Gene Expression Data Availability\n", "# The background information about \"Loss of GM-CSF-dependent instruction of alveolar macrophages in COVID-19\"\n", "# suggests this is likely a gene expression dataset studying COVID-19's effects\n", "is_gene_available = True\n", "\n", "# 2. Variable Availability and Data Type Conversion\n", "# Looking at the sample characteristics dictionary, we don't see typical human clinical data\n", "# The dict shows 'strain: C57BL/6' which indicates this is likely a mouse model study, not human data\n", "# and 'metadata info: metaData_microarrays.txt' which refers to external metadata\n", "\n", "# 2.1 Data Availability\n", "# Since we don't see trait, age, or gender data in the sample characteristics,\n", "# we'll set all row identifiers to None\n", "trait_row = None # No COVID-19 status information in the sample characteristics\n", "age_row = None # No age information in the sample characteristics\n", "gender_row = None # No gender information in the sample characteristics\n", "\n", "# 2.2 Data Type Conversion\n", "# Define conversion functions in case they're needed, even though we don't have the data\n", "def convert_trait(value):\n", " if value is None:\n", " return None\n", " value = value.split(\":\")[-1].strip() if \":\" in value else value.strip()\n", " if value.lower() in [\"covid-19\", \"positive\", \"covid\", \"yes\"]:\n", " return 1\n", " elif value.lower() in [\"healthy\", \"control\", \"negative\", \"no\"]:\n", " return 0\n", " return None\n", "\n", "def convert_age(value):\n", " if value is None:\n", " return None\n", " value = value.split(\":\")[-1].strip() if \":\" in value else value.strip()\n", " try:\n", " return float(value)\n", " except ValueError:\n", " return None\n", "\n", "def convert_gender(value):\n", " if value is None:\n", " return None\n", " value = value.split(\":\")[-1].strip() if \":\" in value else value.strip()\n", " if value.lower() in [\"female\", \"f\"]:\n", " return 0\n", " elif value.lower() in [\"male\", \"m\"]:\n", " return 1\n", " return None\n", "\n", "# 3. Save Metadata\n", "# Trait data is not available since trait_row is None\n", "is_trait_available = trait_row is not None\n", "\n", "# Save initial usability information\n", "validate_and_save_cohort_info(\n", " is_final=False,\n", " cohort=cohort,\n", " info_path=json_path,\n", " is_gene_available=is_gene_available,\n", " is_trait_available=is_trait_available\n", ")\n", "\n", "# 4. Clinical Feature Extraction\n", "# Since trait_row is None, we'll skip this substep\n", "# If trait_row was not None, we would have executed:\n", "# 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", "# preview_df(clinical_df)\n", "# clinical_df.to_csv(out_clinical_data_file)\n" ] }, { "cell_type": "markdown", "id": "a619f55c", "metadata": {}, "source": [ "### Step 3: Gene Data Extraction" ] }, { "cell_type": "code", "execution_count": 4, "id": "61a1e12e", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T08:31:26.751206Z", "iopub.status.busy": "2025-03-25T08:31:26.751107Z", "iopub.status.idle": "2025-03-25T08:31:26.871768Z", "shell.execute_reply": "2025-03-25T08:31:26.871401Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "SOFT file: ../../input/GEO/COVID-19/GSE216705/GSE216705_family.soft.gz\n", "Matrix file: ../../input/GEO/COVID-19/GSE216705/GSE216705-GPL6246_series_matrix.txt.gz\n", "Found the matrix table marker at line 62\n", "Gene data shape: (35556, 27)\n", "First 20 gene/probe identifiers:\n", "['10338001', '10338002', '10338003', '10338004', '10338005', '10338006', '10338007', '10338008', '10338009', '10338010', '10338011', '10338012', '10338013', '10338014', '10338015', '10338016', '10338017', '10338018', '10338019', '10338020']\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": "7d3bf662", "metadata": {}, "source": [ "### Step 4: Gene Identifier Review" ] }, { "cell_type": "code", "execution_count": 5, "id": "109711f3", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T08:31:26.873008Z", "iopub.status.busy": "2025-03-25T08:31:26.872888Z", "iopub.status.idle": "2025-03-25T08:31:26.874784Z", "shell.execute_reply": "2025-03-25T08:31:26.874514Z" } }, "outputs": [], "source": [ "# Analyzing the gene identifiers provided in the previous output\n", "# The identifiers appear to be probe IDs (numeric format like '10338001') rather than standard human gene symbols\n", "# Human gene symbols typically follow patterns like \"BRCA1\", \"TP53\", etc.\n", "# These numeric identifiers need to be mapped to human gene symbols for meaningful analysis\n", "\n", "requires_gene_mapping = True\n" ] }, { "cell_type": "markdown", "id": "d9a0110e", "metadata": {}, "source": [ "### Step 5: Gene Annotation" ] }, { "cell_type": "code", "execution_count": 6, "id": "95a5c320", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T08:31:26.875855Z", "iopub.status.busy": "2025-03-25T08:31:26.875753Z", "iopub.status.idle": "2025-03-25T08:31:29.190497Z", "shell.execute_reply": "2025-03-25T08:31:29.190121Z" } }, "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': ['10344614', '10344616', '10344618'], 'GB_LIST': ['AK145513,AK145782', nan, nan], 'SPOT_ID': ['chr1:3054233-3054733', 'chr1:3102016-3102125', 'chr1:3276323-3277348'], 'seqname': ['chr1', 'chr1', 'chr1'], 'RANGE_GB': ['NC_000067.6', 'NC_000067.6', 'NC_000067.6'], 'RANGE_STRAND': ['+', '+', '+'], 'RANGE_START': ['3054233', '3102016', '3276323'], 'RANGE_STOP': ['3054733', '3102125', '3277348'], 'total_probes': [33.0, 25.0, 25.0], 'gene_assignment': ['ENSMUST00000160944 // Gm16088 // predicted gene 16088 // --- // --- /// ENSMUST00000120800 // Gm14300 // predicted gene 14300 // --- // --- /// ENSMUST00000179907 // G430049J08Rik // RIKEN cDNA G430049J08 gene // --- // --- /// AK145513 // Gm2889 // predicted gene 2889 // 18 A1|18 // 100040658', 'ENSMUST00000082908 // Gm26206 // predicted gene, 26206 // --- // ---', '---'], 'mrna_assignment': ['ENSMUST00000160944 // ENSEMBL // havana:known chromosome:GRCm38:1:3054233:3054733:1 gene:ENSMUSG00000090025 gene_biotype:pseudogene transcript_biotype:unprocessed_pseudogene // chr1 // 100 // 100 // 33 // 33 // 0 /// ENSMUST00000120800 // ENSEMBL // havana:known chromosome:GRCm38:2:179612622:179613567:-1 gene:ENSMUSG00000083410 gene_biotype:pseudogene transcript_biotype:processed_pseudogene // chr1 // 30 // 100 // 10 // 33 // 0 /// ENSMUST00000179907 // ENSEMBL // ensembl:known chromosome:GRCm38:18:3471630:3474315:1 gene:ENSMUSG00000096528 gene_biotype:protein_coding transcript_biotype:protein_coding // chr1 // 42 // 100 // 14 // 33 // 0 /// AK145513 // GenBank HTC // Mus musculus blastocyst blastocyst cDNA, RIKEN full-length enriched library, clone:I1C0009C06 product:hypothetical DeoxyUTP pyrophosphatase/Aspartyl protease, retroviral-type family profile/Retrovirus capsid, C-terminal/Peptidase aspartic/Peptidase aspartic, active site containing protein, full insert sequence. // chr1 // 24 // 100 // 8 // 33 // 0 /// AK145782 // GenBank HTC // Mus musculus blastocyst blastocyst cDNA, RIKEN full-length enriched library, clone:I1C0042P10 product:hypothetical protein, full insert sequence. // chr1 // 52 // 100 // 17 // 33 // 0 /// KnowTID_00005135 // Luo lincRNA // Non-coding transcript identified by Luo, et al. // chr1 // 52 // 100 // 17 // 33 // 0 /// NONMMUT044096 // NONCODE // Non-coding transcript identified by NONCODE: Linc // chr1 // 52 // 100 // 17 // 33 // 0 /// AK139746 // GenBank HTC // Mus musculus 2 cells egg cDNA, RIKEN full-length enriched library, clone:B020014N01 product:hypothetical protein, full insert sequence. // chr1 // 42 // 100 // 14 // 33 // 0 /// AK145590 // GenBank HTC // Mus musculus blastocyst blastocyst cDNA, RIKEN full-length enriched library, clone:I1C0019N16 product:unclassifiable, full insert sequence. // chr1 // 42 // 100 // 14 // 33 // 0 /// AK145750 // GenBank HTC // Mus musculus blastocyst blastocyst cDNA, RIKEN full-length enriched library, clone:I1C0037K09 product:unclassifiable, full insert sequence. // chr1 // 36 // 85 // 10 // 28 // 0 /// AK165162 // GenBank HTC // Mus musculus 8 cells embryo 8 cells cDNA, RIKEN full-length enriched library, clone:E860009L19 product:unclassifiable, full insert sequence. // chr1 // 48 // 100 // 16 // 33 // 0 /// KnowTID_00001379 // Luo lincRNA // Non-coding transcript identified by Luo, et al. // chr1 // 42 // 100 // 14 // 33 // 0 /// KnowTID_00001380 // Luo lincRNA // Non-coding transcript identified by Luo, et al. // chr1 // 42 // 100 // 14 // 33 // 0 /// KnowTID_00002541 // Luo lincRNA // Non-coding transcript identified by Luo, et al. // chr1 // 36 // 85 // 10 // 28 // 0 /// KnowTID_00003768 // Luo lincRNA // Non-coding transcript identified by Luo, et al. // chr1 // 42 // 100 // 14 // 33 // 0 /// KnowTID_00005134 // Luo lincRNA // Non-coding transcript identified by Luo, et al. // chr1 // 45 // 100 // 15 // 33 // 0 /// NONMMUT013638 // NONCODE // Non-coding transcript identified by NONCODE: Linc // chr1 // 42 // 100 // 14 // 33 // 0 /// NONMMUT013641 // NONCODE // Non-coding transcript identified by NONCODE: Linc // chr1 // 42 // 100 // 14 // 33 // 0 /// NONMMUT021887 // NONCODE // Non-coding transcript identified by NONCODE: Linc // chr1 // 36 // 85 // 10 // 28 // 0 /// NONMMUT044095 // NONCODE // Non-coding transcript identified by NONCODE: Linc // chr1 // 45 // 100 // 15 // 33 // 0 /// NONMMUT046086 // NONCODE // Non-coding transcript identified by NONCODE: Linc // chr1 // 48 // 100 // 16 // 33 // 0 /// NONMMUT046087 // NONCODE // Non-coding transcript identified by NONCODE: Linc // chr1 // 48 // 100 // 16 // 33 // 0 /// AK145700 // GenBank HTC // Mus musculus blastocyst blastocyst cDNA, RIKEN full-length enriched library, clone:I1C0031F10 product:hypothetical protein, full insert sequence. // chr1 // 24 // 100 // 8 // 33 // 0 /// KnowTID_00003789 // Luo lincRNA // Non-coding transcript identified by Luo, et al. // chr1 // 24 // 100 // 8 // 33 // 0 /// NONMMUT031618 // NONCODE // Non-coding transcript identified by NONCODE: Linc // chr1 // 24 // 100 // 8 // 33 // 0 /// KnowTID_00002704 // Luo lincRNA // Non-coding transcript identified by Luo, et al. // chr1 // 24 // 24 // 8 // 33 // 1 /// NONMMUT023055 // NONCODE // Non-coding transcript identified by NONCODE: Linc // chr1 // 24 // 24 // 8 // 33 // 1', 'ENSMUST00000082908 // ENSEMBL // ncrna:known chromosome:GRCm38:1:3102016:3102125:1 gene:ENSMUSG00000064842 gene_biotype:snRNA transcript_biotype:snRNA // chr1 // 100 // 100 // 25 // 25 // 0 /// NONMMUT000002 // NONCODE // Non-coding transcript identified by NONCODE // chr1 // 100 // 100 // 25 // 25 // 0', '---'], 'category': ['main', 'main', 'main']}\n", "\n", "Examining 'gene_assignment' column examples:\n", "Example 1: ENSMUST00000160944 // Gm16088 // predicted gene 16088 // --- // --- /// ENSMUST00000120800 // Gm14300 // predicted gene 14300 // --- // --- /// ENSMUST00000179907 // G430049J08Rik // RIKEN cDNA G43004...\n", "Example 2: ENSMUST00000082908 // Gm26206 // predicted gene, 26206 // --- // ---\n", "Example 3: ---\n", "Example 4: AK140060 // Gm10568 // predicted gene 10568 // --- // 100038431\n", "Example 5: ---\n", "\n", "Gene assignment column completeness: 35556/995596 rows (3.57%)\n", "Probes without gene assignments: 8197/995596 rows (0.82%)\n", "\n", "Columns identified for gene mapping:\n", "- 'ID': Contains probe IDs (e.g., 7896736)\n", "- 'gene_assignment': Contains gene information that needs parsing to extract gene symbols\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", "# Examining the gene_assignment column which appears to contain gene symbol information\n", "print(\"\\nExamining 'gene_assignment' column examples:\")\n", "if 'gene_assignment' in gene_annotation.columns:\n", " # Display a few examples of the gene_assignment column to understand its format\n", " gene_samples = gene_annotation['gene_assignment'].head(5).tolist()\n", " for i, sample in enumerate(gene_samples):\n", " print(f\"Example {i+1}: {sample[:200]}...\" if isinstance(sample, str) and len(sample) > 200 else f\"Example {i+1}: {sample}\")\n", " \n", " # Check the quality and completeness of the gene_assignment column\n", " non_null_assignments = gene_annotation['gene_assignment'].notna().sum()\n", " total_rows = len(gene_annotation)\n", " print(f\"\\nGene assignment column completeness: {non_null_assignments}/{total_rows} rows ({non_null_assignments/total_rows:.2%})\")\n", " \n", " # Check for probe IDs without gene assignments (typically '---' entries)\n", " missing_assignments = gene_annotation[gene_annotation['gene_assignment'] == '---'].shape[0]\n", " print(f\"Probes without gene assignments: {missing_assignments}/{total_rows} rows ({missing_assignments/total_rows:.2%})\")\n", " \n", " # Identify the columns needed for gene mapping\n", " print(\"\\nColumns identified for gene mapping:\")\n", " print(\"- 'ID': Contains probe IDs (e.g., 7896736)\")\n", " print(\"- 'gene_assignment': Contains gene information that needs parsing to extract gene symbols\")\n", "else:\n", " print(\"Error: 'gene_assignment' column not found in annotation data.\")\n", " print(\"Available columns:\", gene_annotation.columns.tolist())\n" ] }, { "cell_type": "markdown", "id": "e9b7fd0f", "metadata": {}, "source": [ "### Step 6: Gene Identifier Mapping" ] }, { "cell_type": "code", "execution_count": 7, "id": "b706a199", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T08:31:29.191822Z", "iopub.status.busy": "2025-03-25T08:31:29.191707Z", "iopub.status.idle": "2025-03-25T08:31:30.356745Z", "shell.execute_reply": "2025-03-25T08:31:30.356378Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Gene expression data shape: (35556, 27)\n", "Sample gene expression IDs: ['10338001', '10338002', '10338003', '10338004', '10338005']\n", "Creating gene mapping dataframe...\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Mapping dataframe shape: (290501, 2)\n", "Sample mapping entries:\n", " ID Gene\n", "0 10344614 Gm16088\n", "0 10344614 Gm14300\n", "0 10344614 G430049J08Rik\n", "0 10344614 Gm2889\n", "1 10344616 Gm26206\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Gene expression data after mapping: (290, 27)\n", "Sample gene symbols: ['A330087I24', 'A730034C02', 'AA066038', 'AA386476', 'AA388235', 'AA414768', 'AA415398', 'AA467197', 'AA474408', 'AA667203']\n", "Gene expression data saved to ../../output/preprocess/COVID-19/gene_data/GSE216705.csv\n" ] } ], "source": [ "# Check the format of IDs in our gene expression data\n", "soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)\n", "gene_data = get_genetic_data(matrix_file)\n", "print(f\"Gene expression data shape: {gene_data.shape}\")\n", "print(f\"Sample gene expression IDs: {gene_data.index[:5].tolist()}\")\n", "\n", "# Extract the mapping between probe IDs and gene symbols\n", "# Based on the previous output, we need the 'ID' column (probe identifiers) and 'gene_assignment' column (gene symbols)\n", "print(\"Creating gene mapping dataframe...\")\n", "\n", "# Create a function to extract gene symbols from gene_assignment string\n", "def extract_gene_symbols(gene_assignment_str):\n", " if not isinstance(gene_assignment_str, str) or gene_assignment_str == '---':\n", " return []\n", " \n", " # The format appears to be \"ENSMUST... // GeneName // description // --- // ---\"\n", " # We want to extract the gene names (second element after '//')\n", " genes = []\n", " assignments = gene_assignment_str.split('///')\n", " for assignment in assignments:\n", " parts = assignment.strip().split('//')\n", " if len(parts) >= 2:\n", " gene_symbol = parts[1].strip()\n", " if gene_symbol and gene_symbol != '---':\n", " genes.append(gene_symbol)\n", " return genes\n", "\n", "# Apply extraction function to create mapping dataframe\n", "gene_annotation['Genes'] = gene_annotation['gene_assignment'].apply(extract_gene_symbols)\n", "valid_rows = gene_annotation['Genes'].apply(len) > 0\n", "mapping_df = gene_annotation.loc[valid_rows, ['ID', 'Genes']]\n", "mapping_df = mapping_df.explode('Genes')\n", "mapping_df = mapping_df.rename(columns={'Genes': 'Gene'})\n", "print(f\"Mapping dataframe shape: {mapping_df.shape}\")\n", "print(\"Sample mapping entries:\")\n", "print(mapping_df.head())\n", "\n", "# Apply gene mapping to convert probe-level measurements to gene expression data\n", "gene_data = apply_gene_mapping(gene_data, mapping_df)\n", "print(f\"Gene expression data after mapping: {gene_data.shape}\")\n", "print(f\"Sample gene symbols: {gene_data.index[:10].tolist()}\")\n", "\n", "# Save the gene expression data\n", "if gene_data.shape[0] > 0:\n", " # Create directory if it doesn't exist\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", "else:\n", " print(\"No gene expression data to save after mapping.\")\n" ] }, { "cell_type": "markdown", "id": "d3de0ce4", "metadata": {}, "source": [ "### Step 7: Data Normalization and Linking" ] }, { "cell_type": "code", "execution_count": 8, "id": "d50710f1", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T08:31:30.358105Z", "iopub.status.busy": "2025-03-25T08:31:30.357987Z", "iopub.status.idle": "2025-03-25T08:31:30.432674Z", "shell.execute_reply": "2025-03-25T08:31:30.432306Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Gene data shape after normalization: (34, 27)\n", "Normalized gene data saved to ../../output/preprocess/COVID-19/gene_data/GSE216705.csv\n", "No clinical features available, skipping clinical data processing.\n", "Abnormality detected in the cohort: GSE216705. Preprocessing failed.\n", "Linked data not saved due to quality issues or missing trait information.\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", "# Determine if trait data is available\n", "is_trait_available = trait_row is not None\n", "\n", "# Skip clinical processing if trait_row is None\n", "if is_trait_available:\n", " # 2. Extract clinical features using the previously identified feature rows\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", " # Create directory for clinical data output\n", " os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)\n", " \n", " # Save the clinical features\n", " clinical_features.to_csv(out_clinical_data_file)\n", " print(f\"Clinical features saved to {out_clinical_data_file}\")\n", " \n", " # Preview the clinical features\n", " clinical_features_preview = preview_df(clinical_features.T)\n", " print(\"Clinical features preview:\")\n", " print(clinical_features_preview)\n", " \n", " # 3. Link clinical and genetic data\n", " linked_data = geo_link_clinical_genetic_data(clinical_features, normalized_gene_data)\n", " print(f\"Linked data shape: {linked_data.shape}\")\n", " \n", " # 4. Handle missing values in the linked data\n", " linked_data = handle_missing_values(linked_data, trait)\n", " print(f\"Linked data shape after handling missing values: {linked_data.shape}\")\n", " \n", " # 5. Determine if trait and demographic features are biased\n", " is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)\n", "else:\n", " print(\"No clinical features available, skipping clinical data processing.\")\n", " # Create a minimal DataFrame with the trait column\n", " linked_data = pd.DataFrame({trait: []})\n", " is_biased = True # Set to True because without trait data, it's unusable\n", "\n", "# 6. Validate and save cohort info\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 mouse gene expression data but lacks human clinical annotations for COVID-19.\"\n", ")\n", "\n", "# 7. Save the linked data if it's usable\n", "if is_usable and is_trait_available:\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 or missing trait information.\")" ] } ], "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 }