{ "cells": [ { "cell_type": "code", "execution_count": 1, "id": "0e12b627", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T08:30:57.804937Z", "iopub.status.busy": "2025-03-25T08:30:57.804641Z", "iopub.status.idle": "2025-03-25T08:30:57.970947Z", "shell.execute_reply": "2025-03-25T08:30:57.970579Z" } }, "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 = \"GSE212866\"\n", "\n", "# Input paths\n", "in_trait_dir = \"../../input/GEO/COVID-19\"\n", "in_cohort_dir = \"../../input/GEO/COVID-19/GSE212866\"\n", "\n", "# Output paths\n", "out_data_file = \"../../output/preprocess/COVID-19/GSE212866.csv\"\n", "out_gene_data_file = \"../../output/preprocess/COVID-19/gene_data/GSE212866.csv\"\n", "out_clinical_data_file = \"../../output/preprocess/COVID-19/clinical_data/GSE212866.csv\"\n", "json_path = \"../../output/preprocess/COVID-19/cohort_info.json\"\n" ] }, { "cell_type": "markdown", "id": "a218e494", "metadata": {}, "source": [ "### Step 1: Initial Data Loading" ] }, { "cell_type": "code", "execution_count": 2, "id": "3b5ec58d", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T08:30:57.972440Z", "iopub.status.busy": "2025-03-25T08:30:57.972290Z", "iopub.status.idle": "2025-03-25T08:30:58.299882Z", "shell.execute_reply": "2025-03-25T08:30:58.299544Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Background Information:\n", "!Series_title\t\"Dynamics of gene expression profiling by microarrays and identification of high-risk patients for severe COVID-19\"\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: ['disease state: Control', 'disease state: Covid19', 'disease state: Covid19_SDRA'], 1: ['time: NA', 'time: D0', 'time: D7'], 2: ['tissue: peripheral blood']}\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": "0ec76a0c", "metadata": {}, "source": [ "### Step 2: Dataset Analysis and Clinical Feature Extraction" ] }, { "cell_type": "code", "execution_count": 3, "id": "506622fa", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T08:30:58.301156Z", "iopub.status.busy": "2025-03-25T08:30:58.301032Z", "iopub.status.idle": "2025-03-25T08:30:58.306444Z", "shell.execute_reply": "2025-03-25T08:30:58.306119Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Clinical data file not found at ../../input/GEO/COVID-19/GSE212866/clinical_data.csv\n", "This is a SuperSeries (GSE212866) that may not have standalone clinical data files at this directory level.\n", "Clinical feature extraction will be skipped.\n" ] } ], "source": [ "import pandas as pd\n", "import os\n", "import json\n", "from typing import Optional, Callable, Dict, Any\n", "\n", "# 1. Gene Expression Data Availability\n", "# Based on the Series title, this appears to be microarray gene expression data\n", "is_gene_available = True\n", "\n", "# 2. Variable Availability and Data Type Conversion\n", "\n", "# 2.1 Data Availability\n", "# Trait - The disease state is in row 0\n", "trait_row = 0\n", "\n", "# Age - Not available in the sample characteristics\n", "age_row = None\n", "\n", "# Gender - Not available in the sample characteristics\n", "gender_row = None\n", "\n", "# 2.2 Data Type Conversion\n", "def convert_trait(value):\n", " \"\"\"Convert COVID-19 disease state to binary (0: Control, 1: COVID-19)\"\"\"\n", " if value is None:\n", " return None\n", " \n", " # Extract the value after the colon\n", " if ':' in value:\n", " value = value.split(':', 1)[1].strip()\n", " \n", " if value.lower() == 'control':\n", " return 0\n", " elif value.lower() in ['covid19', 'covid19_sdra']:\n", " return 1\n", " else:\n", " return None\n", "\n", "def convert_age(value):\n", " \"\"\"Convert age to a continuous value\"\"\"\n", " # Not used in this dataset\n", " return None\n", "\n", "def convert_gender(value):\n", " \"\"\"Convert gender to binary (0: Female, 1: Male)\"\"\"\n", " # Not used in this dataset\n", " return None\n", "\n", "# 3. Save Metadata\n", "# Determine if trait data is available\n", "is_trait_available = trait_row is not None\n", "\n", "# Validate and save 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", "if trait_row is not None:\n", " # Check if clinical data file exists\n", " clinical_data_path = os.path.join(in_cohort_dir, \"clinical_data.csv\")\n", " \n", " if os.path.exists(clinical_data_path):\n", " # Load clinical data\n", " clinical_data = pd.read_csv(clinical_data_path)\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 selected clinical features\n", " preview = preview_df(selected_clinical_df)\n", " print(\"Preview of selected clinical features:\")\n", " print(preview)\n", " \n", " # Save the selected clinical features\n", " os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)\n", " selected_clinical_df.to_csv(out_clinical_data_file, index=False)\n", " print(f\"Clinical data saved to {out_clinical_data_file}\")\n", " else:\n", " print(f\"Clinical data file not found at {clinical_data_path}\")\n", " print(f\"This is a SuperSeries (GSE212866) that may not have standalone clinical data files at this directory level.\")\n", " print(f\"Clinical feature extraction will be skipped.\")\n" ] }, { "cell_type": "markdown", "id": "6b4c1148", "metadata": {}, "source": [ "### Step 3: Gene Data Extraction" ] }, { "cell_type": "code", "execution_count": 4, "id": "31716a75", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T08:30:58.307686Z", "iopub.status.busy": "2025-03-25T08:30:58.307566Z", "iopub.status.idle": "2025-03-25T08:30:58.848861Z", "shell.execute_reply": "2025-03-25T08:30:58.848474Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "SOFT file: ../../input/GEO/COVID-19/GSE212866/GSE212866_family.soft.gz\n", "Matrix file: ../../input/GEO/COVID-19/GSE212866/GSE212866-GPL23159_series_matrix.txt.gz\n", "Found the matrix table marker at line 59\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Gene data shape: (27189, 137)\n", "First 20 gene/probe identifiers:\n", "['23064070', '23064071', '23064072', '23064073', '23064074', '23064075', '23064076', '23064077', '23064078', '23064079', '23064080', '23064081', '23064083', '23064084', '23064085', '23064086', '23064087', '23064088', '23064089', '23064090']\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": "cec3f6ca", "metadata": {}, "source": [ "### Step 4: Gene Identifier Review" ] }, { "cell_type": "code", "execution_count": 5, "id": "9f889e92", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T08:30:58.850303Z", "iopub.status.busy": "2025-03-25T08:30:58.850166Z", "iopub.status.idle": "2025-03-25T08:30:58.852226Z", "shell.execute_reply": "2025-03-25T08:30:58.851882Z" } }, "outputs": [], "source": [ "# These appear to be probe IDs from a microarray platform (GPL23159)\n", "# They are not standard human gene symbols like BRCA1, TP53, etc.\n", "# These are numeric identifiers that need to be mapped to actual gene symbols\n", "\n", "requires_gene_mapping = True\n" ] }, { "cell_type": "markdown", "id": "ba7f7124", "metadata": {}, "source": [ "### Step 5: Gene Annotation" ] }, { "cell_type": "code", "execution_count": 6, "id": "4e694970", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T08:30:58.853482Z", "iopub.status.busy": "2025-03-25T08:30:58.853360Z", "iopub.status.idle": "2025-03-25T08:31:05.887630Z", "shell.execute_reply": "2025-03-25T08:31:05.887103Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Gene annotation preview:\n", "Columns in gene annotation: ['ID', 'probeset_id', 'seqname', 'strand', 'start', 'stop', 'total_probes', 'category', 'SPOT_ID', 'SPOT_ID.1']\n", "{'ID': ['TC0100006437.hg.1', 'TC0100006476.hg.1', 'TC0100006479.hg.1'], 'probeset_id': ['TC0100006437.hg.1', 'TC0100006476.hg.1', 'TC0100006479.hg.1'], 'seqname': ['chr1', 'chr1', 'chr1'], 'strand': ['+', '+', '+'], 'start': ['69091', '924880', '960587'], 'stop': ['70008', '944581', '965719'], 'total_probes': [10.0, 10.0, 10.0], 'category': ['main', 'main', 'main'], 'SPOT_ID': ['Coding', 'Multiple_Complex', 'Multiple_Complex'], 'SPOT_ID.1': ['NM_001005484 // RefSeq // Homo sapiens olfactory receptor, family 4, subfamily F, member 5 (OR4F5), mRNA. // chr1 // 100 // 100 // 0 // --- // 0 /// ENST00000335137 // ENSEMBL // olfactory receptor, family 4, subfamily F, member 5 [gene_biotype:protein_coding transcript_biotype:protein_coding] // chr1 // 100 // 100 // 0 // --- // 0 /// OTTHUMT00000003223 // Havana transcript // olfactory receptor, family 4, subfamily F, member 5[gene_biotype:protein_coding transcript_biotype:protein_coding] // chr1 // 100 // 100 // 0 // --- // 0 /// uc001aal.1 // UCSC Genes // Homo sapiens olfactory receptor, family 4, subfamily F, member 5 (OR4F5), mRNA. // chr1 // 100 // 100 // 0 // --- // 0 /// CCDS30547.1 // ccdsGene // olfactory receptor, family 4, subfamily F, member 5 [Source:HGNC Symbol;Acc:HGNC:14825] // chr1 // 100 // 100 // 0 // --- // 0', 'NM_152486 // RefSeq // Homo sapiens sterile alpha motif domain containing 11 (SAMD11), mRNA. // chr1 // 100 // 100 // 0 // --- // 0 /// ENST00000341065 // ENSEMBL // sterile alpha motif domain containing 11 [gene_biotype:protein_coding transcript_biotype:protein_coding] // chr1 // 100 // 100 // 0 // --- // 0 /// ENST00000342066 // ENSEMBL // sterile alpha motif domain containing 11 [gene_biotype:protein_coding transcript_biotype:protein_coding] // chr1 // 100 // 100 // 0 // --- // 0 /// ENST00000420190 // ENSEMBL // sterile alpha motif domain containing 11 [gene_biotype:protein_coding transcript_biotype:protein_coding] // chr1 // 100 // 100 // 0 // --- // 0 /// ENST00000437963 // ENSEMBL // sterile alpha motif domain containing 11 [gene_biotype:protein_coding transcript_biotype:protein_coding] // chr1 // 100 // 100 // 0 // --- // 0 /// ENST00000455979 // ENSEMBL // sterile alpha motif domain containing 11 [gene_biotype:protein_coding transcript_biotype:protein_coding] // chr1 // 100 // 100 // 0 // --- // 0 /// ENST00000464948 // ENSEMBL // sterile alpha motif domain containing 11 [gene_biotype:protein_coding transcript_biotype:retained_intron] // chr1 // 100 // 100 // 0 // --- // 0 /// ENST00000466827 // ENSEMBL // sterile alpha motif domain containing 11 [gene_biotype:protein_coding transcript_biotype:retained_intron] // chr1 // 100 // 100 // 0 // --- // 0 /// ENST00000474461 // ENSEMBL // sterile alpha motif domain containing 11 [gene_biotype:protein_coding transcript_biotype:retained_intron] // chr1 // 100 // 100 // 0 // --- // 0 /// ENST00000478729 // ENSEMBL // sterile alpha motif domain containing 11 [gene_biotype:protein_coding transcript_biotype:processed_transcript] // chr1 // 100 // 100 // 0 // --- // 0 /// ENST00000616016 // ENSEMBL // sterile alpha motif domain containing 11 [gene_biotype:protein_coding transcript_biotype:protein_coding] // chr1 // 100 // 100 // 0 // --- // 0 /// ENST00000616125 // ENSEMBL // sterile alpha motif domain containing 11 [gene_biotype:protein_coding transcript_biotype:protein_coding] // chr1 // 100 // 100 // 0 // --- // 0 /// ENST00000617307 // ENSEMBL // sterile alpha motif domain containing 11 [gene_biotype:protein_coding transcript_biotype:protein_coding] // chr1 // 100 // 100 // 0 // --- // 0 /// ENST00000618181 // ENSEMBL // sterile alpha motif domain containing 11 [gene_biotype:protein_coding transcript_biotype:protein_coding] // chr1 // 100 // 100 // 0 // --- // 0 /// ENST00000618323 // ENSEMBL // sterile alpha motif domain containing 11 [gene_biotype:protein_coding transcript_biotype:protein_coding] // chr1 // 100 // 100 // 0 // --- // 0 /// ENST00000618779 // ENSEMBL // sterile alpha motif domain containing 11 [gene_biotype:protein_coding transcript_biotype:protein_coding] // chr1 // 100 // 100 // 0 // --- // 0 /// ENST00000620200 // ENSEMBL // sterile alpha motif domain containing 11 [gene_biotype:protein_coding transcript_biotype:protein_coding] // chr1 // 100 // 100 // 0 // --- // 0 /// ENST00000622503 // ENSEMBL // sterile alpha motif domain containing 11 [gene_biotype:protein_coding transcript_biotype:protein_coding] // chr1 // 100 // 100 // 0 // --- // 0 /// BC024295 // GenBank // Homo sapiens sterile alpha motif domain containing 11, mRNA (cDNA clone MGC:39333 IMAGE:3354502), complete cds. // chr1 // 100 // 100 // 0 // --- // 0 /// BC033213 // GenBank // Homo sapiens sterile alpha motif domain containing 11, mRNA (cDNA clone MGC:45873 IMAGE:5014368), complete cds. // chr1 // 100 // 100 // 0 // --- // 0 /// OTTHUMT00000097860 // Havana transcript // sterile alpha motif domain containing 11[gene_biotype:protein_coding transcript_biotype:protein_coding] // chr1 // 100 // 100 // 0 // --- // 0 /// OTTHUMT00000097862 // Havana transcript // sterile alpha motif domain containing 11[gene_biotype:protein_coding transcript_biotype:protein_coding] // chr1 // 100 // 100 // 0 // --- // 0 /// OTTHUMT00000097863 // Havana transcript // sterile alpha motif domain containing 11[gene_biotype:protein_coding transcript_biotype:protein_coding] // chr1 // 100 // 100 // 0 // --- // 0 /// OTTHUMT00000097865 // Havana transcript // sterile alpha motif domain containing 11[gene_biotype:protein_coding transcript_biotype:processed_transcript] // chr1 // 100 // 100 // 0 // --- // 0 /// OTTHUMT00000097866 // Havana transcript // sterile alpha motif domain containing 11[gene_biotype:protein_coding transcript_biotype:retained_intron] // chr1 // 100 // 100 // 0 // --- // 0 /// OTTHUMT00000097867 // Havana transcript // sterile alpha motif domain containing 11[gene_biotype:protein_coding transcript_biotype:retained_intron] // chr1 // 100 // 100 // 0 // --- // 0 /// OTTHUMT00000097868 // Havana transcript // sterile alpha motif domain containing 11[gene_biotype:protein_coding transcript_biotype:retained_intron] // chr1 // 100 // 100 // 0 // --- // 0 /// OTTHUMT00000276866 // Havana transcript // sterile alpha motif domain containing 11[gene_biotype:protein_coding transcript_biotype:protein_coding] // chr1 // 100 // 100 // 0 // --- // 0 /// OTTHUMT00000316521 // Havana transcript // sterile alpha motif domain containing 11[gene_biotype:protein_coding transcript_biotype:protein_coding] // chr1 // 100 // 100 // 0 // --- // 0 /// CCDS2.2 // ccdsGene // sterile alpha motif domain containing 11 [Source:HGNC Symbol;Acc:HGNC:28706] // chr1 // 100 // 100 // 0 // --- // 0 /// hsa_circ_0009185 // circbase // Salzman2013 ANNOTATED, CDS, coding, INTERNAL, OVCODE, OVERLAPTX, OVEXON best transcript NM_152486 // chr1 // 100 // 100 // 0 // --- // 0 /// hsa_circ_0009186 // circbase // Salzman2013 ANNOTATED, CDS, coding, INTERNAL, OVCODE, OVERLAPTX, OVEXON best transcript NM_152486 // chr1 // 100 // 100 // 0 // --- // 0 /// hsa_circ_0009187 // circbase // Salzman2013 ANNOTATED, CDS, coding, INTERNAL, OVCODE, OVEXON best transcript NM_152486 // chr1 // 100 // 100 // 0 // --- // 0 /// hsa_circ_0009188 // circbase // Salzman2013 ANNOTATED, CDS, coding, INTERNAL, OVCODE, OVEXON best transcript NM_152486 // chr1 // 100 // 100 // 0 // --- // 0 /// hsa_circ_0009189 // circbase // Salzman2013 ALT_DONOR, CDS, coding, INTERNAL, OVCODE, OVEXON best transcript NM_152486 // chr1 // 100 // 100 // 0 // --- // 0 /// hsa_circ_0009190 // circbase // Salzman2013 ANNOTATED, CDS, coding, INTERNAL, OVCODE, OVEXON best transcript NM_152486 // chr1 // 100 // 100 // 0 // --- // 0 /// hsa_circ_0009191 // circbase // Salzman2013 ANNOTATED, CDS, coding, INTERNAL, OVCODE, OVEXON best transcript NM_152486 // chr1 // 100 // 100 // 0 // --- // 0 /// hsa_circ_0009192 // circbase // Salzman2013 ANNOTATED, CDS, coding, INTERNAL, OVCODE, OVERLAPTX, OVEXON best transcript NM_152486 // chr1 // 100 // 100 // 0 // --- // 0 /// hsa_circ_0009193 // circbase // Salzman2013 ANNOTATED, CDS, coding, INTERNAL, OVCODE, OVERLAPTX, OVEXON best transcript NM_152486 // chr1 // 100 // 100 // 0 // --- // 0 /// hsa_circ_0009194 // circbase // Salzman2013 ANNOTATED, CDS, coding, OVCODE, OVERLAPTX, OVEXON, UTR3 best transcript NM_152486 // chr1 // 100 // 100 // 0 // --- // 0 /// hsa_circ_0009195 // circbase // Salzman2013 ANNOTATED, CDS, coding, INTERNAL, OVCODE, OVERLAPTX, OVEXON best transcript NM_152486 // chr1 // 100 // 100 // 0 // --- // 0 /// uc001abw.2 // UCSC Genes // sterile alpha motif domain containing 11 [Source:HGNC Symbol;Acc:HGNC:28706] // chr1 // 100 // 100 // 0 // --- // 0 /// uc031pjt.2 // UCSC Genes // sterile alpha motif domain containing 11 [Source:HGNC Symbol;Acc:HGNC:28706] // chr1 // 100 // 100 // 0 // --- // 0 /// uc031pju.2 // UCSC Genes // sterile alpha motif domain containing 11 [Source:HGNC Symbol;Acc:HGNC:28706] // chr1 // 100 // 100 // 0 // --- // 0 /// uc031pkg.2 // UCSC Genes // sterile alpha motif domain containing 11 [Source:HGNC Symbol;Acc:HGNC:28706] // chr1 // 100 // 100 // 0 // --- // 0 /// uc031pkh.2 // UCSC Genes // sterile alpha motif domain containing 11 [Source:HGNC Symbol;Acc:HGNC:28706] // chr1 // 100 // 100 // 0 // --- // 0 /// uc031pkk.2 // UCSC Genes // sterile alpha motif domain containing 11 [Source:HGNC Symbol;Acc:HGNC:28706] // chr1 // 100 // 100 // 0 // --- // 0 /// uc031pkm.2 // UCSC Genes // sterile alpha motif domain containing 11 [Source:HGNC Symbol;Acc:HGNC:28706] // chr1 // 100 // 100 // 0 // --- // 0 /// uc031pko.2 // UCSC Genes // sterile alpha motif domain containing 11 [Source:HGNC Symbol;Acc:HGNC:28706] // chr1 // 100 // 100 // 0 // --- // 0 /// uc057axs.1 // UCSC Genes // sterile alpha motif domain containing 11 [Source:HGNC Symbol;Acc:HGNC:28706] // chr1 // 100 // 100 // 0 // --- // 0 /// uc057axt.1 // UCSC Genes // sterile alpha motif domain containing 11 [Source:HGNC Symbol;Acc:HGNC:28706] // chr1 // 100 // 100 // 0 // --- // 0 /// uc057axu.1 // UCSC Genes // sterile alpha motif domain containing 11 [Source:HGNC Symbol;Acc:HGNC:28706] // chr1 // 100 // 100 // 0 // --- // 0 /// uc057axv.1 // UCSC Genes // sterile alpha motif domain containing 11 [Source:HGNC Symbol;Acc:HGNC:28706] // chr1 // 100 // 100 // 0 // --- // 0 /// uc057axw.1 // UCSC Genes // sterile alpha motif domain containing 11 [Source:HGNC Symbol;Acc:HGNC:28706] // chr1 // 100 // 100 // 0 // --- // 0 /// uc057axx.1 // UCSC Genes // sterile alpha motif domain containing 11 [Source:HGNC Symbol;Acc:HGNC:28706] // chr1 // 100 // 100 // 0 // --- // 0 /// uc057axy.1 // UCSC Genes // sterile alpha motif domain containing 11 [Source:HGNC Symbol;Acc:HGNC:28706] // chr1 // 100 // 100 // 0 // --- // 0 /// uc057axz.1 // UCSC Genes // sterile alpha motif domain containing 11 [Source:HGNC Symbol;Acc:HGNC:28706] // chr1 // 100 // 100 // 0 // --- // 0 /// uc057aya.1 // UCSC Genes // sterile alpha motif domain containing 11 [Source:HGNC Symbol;Acc:HGNC:28706] // chr1 // 100 // 100 // 0 // --- // 0 /// NONHSAT000212 // lncRNAWiki // Non-coding transcript identified by NONCODE // chr1 // 100 // 100 // 0 // --- // 0 /// NONHSAT000212 // NONCODE // Non-coding transcript identified by NONCODE: Exonic // chr1 // 100 // 100 // 0 // --- // 0 /// NONHSAT000213 // lncRNAWiki // Non-coding transcript identified by NONCODE // chr1 // 100 // 100 // 0 // --- // 0 /// NONHSAT000213 // NONCODE // Non-coding transcript identified by NONCODE: Exonic // chr1 // 100 // 100 // 0 // --- // 0', 'NM_198317 // RefSeq // Homo sapiens kelch-like family member 17 (KLHL17), mRNA. // chr1 // 100 // 100 // 0 // --- // 0 /// ENST00000338591 // ENSEMBL // kelch-like family member 17 [gene_biotype:protein_coding transcript_biotype:protein_coding] // chr1 // 100 // 100 // 0 // --- // 0 /// ENST00000463212 // ENSEMBL // kelch-like family member 17 [gene_biotype:protein_coding transcript_biotype:retained_intron] // chr1 // 100 // 100 // 0 // --- // 0 /// ENST00000466300 // ENSEMBL // kelch-like family member 17 [gene_biotype:protein_coding transcript_biotype:nonsense_mediated_decay] // chr1 // 100 // 100 // 0 // --- // 0 /// ENST00000481067 // ENSEMBL // kelch-like family member 17 [gene_biotype:protein_coding transcript_biotype:retained_intron] // chr1 // 100 // 100 // 0 // --- // 0 /// ENST00000622660 // ENSEMBL // kelch-like family member 17 [gene_biotype:protein_coding transcript_biotype:protein_coding] // chr1 // 100 // 100 // 0 // --- // 0 /// OTTHUMT00000097875 // Havana transcript // kelch-like 17 (Drosophila)[gene_biotype:protein_coding transcript_biotype:protein_coding] // chr1 // 100 // 100 // 0 // --- // 0 /// OTTHUMT00000097877 // Havana transcript // kelch-like 17 (Drosophila)[gene_biotype:protein_coding transcript_biotype:retained_intron] // chr1 // 100 // 100 // 0 // --- // 0 /// OTTHUMT00000097878 // Havana transcript // kelch-like 17 (Drosophila)[gene_biotype:protein_coding transcript_biotype:nonsense_mediated_decay] // chr1 // 100 // 100 // 0 // --- // 0 /// OTTHUMT00000097931 // Havana transcript // kelch-like 17 (Drosophila)[gene_biotype:protein_coding transcript_biotype:retained_intron] // chr1 // 100 // 100 // 0 // --- // 0 /// BC166618 // GenBank // Synthetic construct Homo sapiens clone IMAGE:100066344, MGC:195481 kelch-like 17 (Drosophila) (KLHL17) mRNA, encodes complete protein. // chr1 // 100 // 100 // 0 // --- // 0 /// CCDS30550.1 // ccdsGene // kelch-like family member 17 [Source:HGNC Symbol;Acc:HGNC:24023] // chr1 // 100 // 100 // 0 // --- // 0 /// hsa_circ_0009209 // circbase // Salzman2013 ANNOTATED, CDS, coding, INTERNAL, OVCODE, OVEXON best transcript NM_198317 // chr1 // 100 // 100 // 0 // --- // 0 /// uc001aca.3 // UCSC Genes // kelch-like family member 17 [Source:HGNC Symbol;Acc:HGNC:24023] // chr1 // 100 // 100 // 0 // --- // 0 /// uc001acb.2 // UCSC Genes // kelch-like family member 17 [Source:HGNC Symbol;Acc:HGNC:24023] // chr1 // 100 // 100 // 0 // --- // 0 /// uc057ayg.1 // UCSC Genes // kelch-like family member 17 [Source:HGNC Symbol;Acc:HGNC:24023] // chr1 // 100 // 100 // 0 // --- // 0 /// uc057ayh.1 // UCSC Genes // kelch-like family member 17 [Source:HGNC Symbol;Acc:HGNC:24023] // chr1 // 100 // 100 // 0 // --- // 0 /// uc057ayi.1 // UCSC Genes // kelch-like family member 17 [Source:HGNC Symbol;Acc:HGNC:24023] // chr1 // 100 // 100 // 0 // --- // 0 /// uc057ayj.1 // UCSC Genes // N/A // chr1 // 100 // 100 // 0 // --- // 0 /// ENST00000617073 // ENSEMBL // ncrna:novel chromosome:GRCh38:1:965110:965166:1 gene:ENSG00000277294 gene_biotype:miRNA transcript_biotype:miRNA // chr1 // 100 // 100 // 0 // --- // 0 /// NONHSAT000216 // lncRNAWiki // Non-coding transcript identified by NONCODE // chr1 // 100 // 100 // 0 // --- // 0 /// NONHSAT000216 // NONCODE // Non-coding transcript identified by NONCODE: Exonic // chr1 // 100 // 100 // 0 // --- // 0']}\n", "\n", "Examining gene mapping columns:\n", "Column 'ID' examples:\n", "Example 1: TC0100006437.hg.1\n", "Example 2: TC0100006476.hg.1\n", "Example 3: TC0100006479.hg.1\n", "Example 4: TC0100006480.hg.1\n", "Example 5: TC0100006483.hg.1\n", "\n", "Column 'SPOT_ID.1' examples (contains gene symbols):\n", "Example 1: NM_001005484 // RefSeq // Homo sapiens olfactory receptor, family 4, subfamily F, member 5 (OR4F5), mRNA. // chr1 // 100 // 100 // 0 // --- // 0 /// ENST00000335137 // ENSEMBL // olfactory receptor, f...\n", "Example 2: NM_152486 // RefSeq // Homo sapiens sterile alpha motif domain containing 11 (SAMD11), mRNA. // chr1 // 100 // 100 // 0 // --- // 0 /// ENST00000341065 // ENSEMBL // sterile alpha motif domain contain...\n", "Example 3: NM_198317 // RefSeq // Homo sapiens kelch-like family member 17 (KLHL17), mRNA. // chr1 // 100 // 100 // 0 // --- // 0 /// ENST00000338591 // ENSEMBL // kelch-like family member 17 [gene_biotype:prote...\n", "\n", "Extracted gene symbols from SPOT_ID.1:\n", "Example 1 extracted symbols: ['OR4F5', 'ENSEMBL', 'UCSC', 'CCDS30547', 'HGNC']\n", "Example 2 extracted symbols: ['SAMD11', 'ENSEMBL', 'BC024295', 'MGC', 'IMAGE', 'BC033213', 'CCDS2', 'HGNC', 'ANNOTATED', 'CDS', 'INTERNAL', 'OVCODE', 'OVERLAPTX', 'OVEXON', 'UTR3', 'UCSC', 'NONCODE']\n", "Example 3 extracted symbols: ['KLHL17', 'ENSEMBL', 'BC166618', 'IMAGE', 'MGC', 'CCDS30550', 'HGNC', 'ANNOTATED', 'CDS', 'INTERNAL', 'OVCODE', 'OVEXON', 'UCSC', 'NONCODE']\n", "\n", "Columns identified for gene mapping:\n", "- 'ID': Contains probe IDs\n", "- 'SPOT_ID.1': 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 SPOT_ID.1 column which contains gene information embedded in text\n", "print(\"\\nColumn 'SPOT_ID.1' examples (contains gene symbols):\")\n", "if 'SPOT_ID.1' in gene_annotation.columns:\n", " # Display a few examples of the SPOT_ID.1 column\n", " spot_samples = gene_annotation['SPOT_ID.1'].head(3).tolist()\n", " for i, sample in enumerate(spot_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 SPOT_ID.1:\")\n", " for i, sample in enumerate(spot_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(\"- 'SPOT_ID.1': Contains gene information from which symbols can be extracted\")\n", "else:\n", " print(\"Error: 'SPOT_ID.1' column not found in annotation data.\")\n" ] }, { "cell_type": "markdown", "id": "3e9218bd", "metadata": {}, "source": [ "### Step 6: Gene Identifier Mapping" ] }, { "cell_type": "code", "execution_count": 7, "id": "84348d68", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T08:31:05.889258Z", "iopub.status.busy": "2025-03-25T08:31:05.889127Z", "iopub.status.idle": "2025-03-25T08:31:10.361804Z", "shell.execute_reply": "2025-03-25T08:31:10.361410Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Gene expression data shape: (27189, 137)\n", "Gene expression index type: object\n", "First few gene IDs: ['23064070', '23064071', '23064072', '23064073', '23064074']\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Created mapping dataframe with 21447 rows.\n", "Preview of mapping:\n", " ID Gene\n", "0 TC0100006437.hg.1 [OR4F5, ENSEMBL, UCSC, CCDS30547, HGNC]\n", "1 TC0100006476.hg.1 [SAMD11, ENSEMBL, BC024295, MGC, IMAGE, BC0332...\n", "2 TC0100006479.hg.1 [KLHL17, ENSEMBL, BC166618, IMAGE, MGC, CCDS30...\n", "Number of probes in expression data: 27189\n", "Number of probes in mapping data: 21447\n", "Number of overlapping probes: 21447\n", "Applying gene mapping with 21447 mapped probes...\n", "Resulting gene expression data shape: (0, 137)\n", "Sample of gene symbols: []\n", "Normalizing gene symbols...\n", "Final gene expression data shape after normalization: (0, 137)\n", "Final sample of gene symbols: []\n", "Gene expression data saved to ../../output/preprocess/COVID-19/gene_data/GSE212866.csv\n" ] } ], "source": [ "# 1. Step 1: Observe the gene identifiers in the gene expression data and find corresponding columns in gene annotation\n", "# From the previous steps, we can see:\n", "# - Gene expression data has numeric IDs starting with numbers like '23064070'\n", "# - Gene annotation data has alphanumeric IDs in the 'ID' column like 'TC0100006437.hg.1'\n", "\n", "# Unfortunately, the probe IDs in the expression data don't directly match the IDs in the annotation data.\n", "# We need to check if there's a way to map between them.\n", "\n", "# Extract expression data again to verify its structure\n", "gene_data = get_genetic_data(matrix_file)\n", "print(f\"Gene expression data shape: {gene_data.shape}\")\n", "print(f\"Gene expression index type: {gene_data.index.dtype}\")\n", "print(f\"First few gene IDs: {gene_data.index[:5].tolist()}\")\n", "\n", "# We'll create a better mapping by extracting gene symbols from SPOT_ID.1\n", "# Create a new mapping dataframe with ID and extracted gene symbols\n", "mapping_df = pd.DataFrame()\n", "mapping_df['ID'] = gene_annotation['ID']\n", "mapping_df['Gene'] = gene_annotation['SPOT_ID.1'].apply(extract_human_gene_symbols)\n", "\n", "# Filter out entries with empty gene lists\n", "mapping_df = mapping_df[mapping_df['Gene'].apply(len) > 0]\n", "\n", "print(f\"Created mapping dataframe with {len(mapping_df)} rows.\")\n", "print(\"Preview of mapping:\")\n", "print(mapping_df.head(3))\n", "\n", "# Check the overlap between probe IDs in expression data and mapping data\n", "expression_probes = set(gene_data.index)\n", "mapping_probes = set(mapping_df['ID'])\n", "overlap = expression_probes.intersection(mapping_probes)\n", "\n", "print(f\"Number of probes in expression data: {len(expression_probes)}\")\n", "print(f\"Number of probes in mapping data: {len(mapping_probes)}\")\n", "print(f\"Number of overlapping probes: {len(overlap)}\")\n", "\n", "# There seems to be a mismatch between probe IDs in expression data and gene annotation.\n", "# This is a common issue in GEO datasets. We need to try an alternative approach.\n", "\n", "# Let's try to directly map using positions if there's a 1:1 correspondence\n", "# This assumes the order of probes in gene annotation matches the order in expression data\n", "if len(gene_data) == len(gene_annotation) or abs(len(gene_data) - len(gene_annotation)) < 100:\n", " print(\"Attempting to map by position due to ID mismatch...\")\n", " # Create a mapping from position to gene symbol\n", " position_mapping = gene_annotation['SPOT_ID.1'].apply(extract_human_gene_symbols)\n", " \n", " # Get probe IDs from expression data in their original order\n", " probe_ids = gene_data.index.tolist()\n", " \n", " # Create position-based mapping dataframe\n", " position_mapping_df = pd.DataFrame({\n", " 'ID': probe_ids[:len(position_mapping)],\n", " 'Gene': position_mapping[:len(probe_ids)]\n", " })\n", " \n", " # Filter out entries with empty gene lists\n", " position_mapping_df = position_mapping_df[position_mapping_df['Gene'].apply(len) > 0]\n", " \n", " print(f\"Created position-based mapping with {len(position_mapping_df)} rows.\")\n", " print(\"Preview of position-based mapping:\")\n", " print(position_mapping_df.head(3))\n", " \n", " # Use this mapping instead if it has more entries\n", " if len(position_mapping_df) > len(mapping_df):\n", " mapping_df = position_mapping_df\n", " print(\"Using position-based mapping as it has more entries.\")\n", "\n", "# If we still don't have a proper mapping or the overlap is too small,\n", "# let's create a custom mapping based on the ID ranges\n", "if len(overlap) < 1000:\n", " print(\"Creating custom mapping based on probe ID patterns...\")\n", " # In GSE212866, looking at the IDs from gene_data vs gene_annotation:\n", " # Gene expression data has numeric IDs (e.g., '23064070')\n", " # Gene annotation has different format IDs (e.g., 'TC0100006437.hg.1')\n", " \n", " # Check if there's a pattern in the SPOT_ID.1 column that contains both numeric IDs and gene symbols\n", " print(\"Checking for ID patterns in SPOT_ID.1...\")\n", " \n", " # Given the apparent mismatch, we can create a direct mapping based on the row position\n", " # Assuming the probe order is preserved between the two files\n", " # This is risky but may be our best option in this specific dataset\n", " \n", " # Extract gene symbols from each annotation entry\n", " gene_symbols = gene_annotation['SPOT_ID.1'].apply(extract_human_gene_symbols)\n", " \n", " # Create a dataframe with expression IDs and corresponding gene symbols\n", " # Taking the minimum length to avoid index errors\n", " min_length = min(len(gene_data.index), len(gene_symbols))\n", " \n", " # Create a mapping from expression IDs to gene symbols\n", " position_mapping_df = pd.DataFrame({\n", " 'ID': gene_data.index[:min_length],\n", " 'Gene': gene_symbols[:min_length]\n", " })\n", " \n", " # Filter rows with empty gene symbols\n", " position_mapping_df = position_mapping_df[position_mapping_df['Gene'].apply(len) > 0]\n", " \n", " print(f\"Created position-based mapping with {len(position_mapping_df)} rows\")\n", " mapping_df = position_mapping_df\n", "\n", "# 3. Apply the gene mapping to convert probe-level measurements to gene-level expression\n", "print(f\"Applying gene mapping with {len(mapping_df)} mapped probes...\")\n", "gene_data = apply_gene_mapping(gene_data, mapping_df)\n", "\n", "print(f\"Resulting gene expression data shape: {gene_data.shape}\")\n", "print(f\"Sample of gene symbols: {list(gene_data.index[:5])}\")\n", "\n", "# Normalize gene symbols to handle synonyms and aggregate redundant rows\n", "print(\"Normalizing gene symbols...\")\n", "gene_data = normalize_gene_symbols_in_index(gene_data)\n", "\n", "print(f\"Final gene expression data shape after normalization: {gene_data.shape}\")\n", "print(f\"Final sample of gene symbols: {list(gene_data.index[:5])}\")\n", "\n", "# Save the gene data to 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}\")" ] } ], "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 }