{ "cells": [ { "cell_type": "code", "execution_count": 1, "id": "e96c83ee", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T04:01:04.364453Z", "iopub.status.busy": "2025-03-25T04:01:04.364157Z", "iopub.status.idle": "2025-03-25T04:01:04.533382Z", "shell.execute_reply": "2025-03-25T04:01:04.533021Z" } }, "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 = \"Stomach_Cancer\"\n", "cohort = \"GSE118916\"\n", "\n", "# Input paths\n", "in_trait_dir = \"../../input/GEO/Stomach_Cancer\"\n", "in_cohort_dir = \"../../input/GEO/Stomach_Cancer/GSE118916\"\n", "\n", "# Output paths\n", "out_data_file = \"../../output/preprocess/Stomach_Cancer/GSE118916.csv\"\n", "out_gene_data_file = \"../../output/preprocess/Stomach_Cancer/gene_data/GSE118916.csv\"\n", "out_clinical_data_file = \"../../output/preprocess/Stomach_Cancer/clinical_data/GSE118916.csv\"\n", "json_path = \"../../output/preprocess/Stomach_Cancer/cohort_info.json\"\n" ] }, { "cell_type": "markdown", "id": "0dd5bba1", "metadata": {}, "source": [ "### Step 1: Initial Data Loading" ] }, { "cell_type": "code", "execution_count": 2, "id": "abe1241b", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T04:01:04.534891Z", "iopub.status.busy": "2025-03-25T04:01:04.534738Z", "iopub.status.idle": "2025-03-25T04:01:04.671863Z", "shell.execute_reply": "2025-03-25T04:01:04.671526Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Files in the cohort directory:\n", "['GSE118916_family.soft.gz', 'GSE118916_series_matrix.txt.gz']\n", "Identified SOFT files: ['GSE118916_family.soft.gz']\n", "Identified matrix files: ['GSE118916_series_matrix.txt.gz']\n", "\n", "Background Information:\n", "!Series_title\t\"Expression data from human gastric tumor and human normal stomach tissues\"\n", "!Series_summary\t\"We identified several hub genes and key pathways associated with GAC initiation and progression by analysising the microarray data on DEGs, whcih provided a detailed molecular mechanism underlying GAC occurrence and progression.\"\n", "!Series_overall_design\t\"We analyzed the gene expression profile in GAC-associated tissues. 15 pairs of GAC tumor and adjacent non-tumor (normal) tissues were screened by microarray. Then differentially expressed genes (DEGs) was analysised by using the R bioconductor limma (Version 3.36.2). Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway and Gene Ontology (GO) analysis were used to annotate the unique biological significance and important pathways of enriched DEGs, which was identified by Fisher’s exact test (p＜0.05). To find the hub genes and key pathways, we constructed thre protein-protein interaction (PPI) network by Cytoscape and conducted KEGG enrichment analysis of the prime module extracted from the PPI network. We further applied the TCGA database to start the survival analysis of these hub genes by Kaplan-Meier estimates.\"\n", "\n", "Sample Characteristics Dictionary:\n", "{0: ['gender: female', 'gender: male']}\n" ] } ], "source": [ "# 1. Let's first list the directory contents to understand what files are available\n", "import os\n", "\n", "print(\"Files in the cohort directory:\")\n", "files = os.listdir(in_cohort_dir)\n", "print(files)\n", "\n", "# Adapt file identification to handle different naming patterns\n", "soft_files = [f for f in files if 'soft' in f.lower() or '.soft' in f.lower() or '_soft' in f.lower()]\n", "matrix_files = [f for f in files if 'matrix' in f.lower() or '.matrix' in f.lower() or '_matrix' in f.lower()]\n", "\n", "# If no files with these patterns are found, look for alternative file types\n", "if not soft_files:\n", " soft_files = [f for f in files if f.endswith('.txt') or f.endswith('.gz')]\n", "if not matrix_files:\n", " matrix_files = [f for f in files if f.endswith('.txt') or f.endswith('.gz')]\n", "\n", "print(\"Identified SOFT files:\", soft_files)\n", "print(\"Identified matrix files:\", matrix_files)\n", "\n", "# Use the first files found, if any\n", "if len(soft_files) > 0 and len(matrix_files) > 0:\n", " soft_file = os.path.join(in_cohort_dir, soft_files[0])\n", " matrix_file = os.path.join(in_cohort_dir, matrix_files[0])\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(\"\\nBackground Information:\")\n", " print(background_info)\n", " print(\"\\nSample Characteristics Dictionary:\")\n", " print(sample_characteristics_dict)\n", "else:\n", " print(\"No appropriate files found in the directory.\")\n" ] }, { "cell_type": "markdown", "id": "27083083", "metadata": {}, "source": [ "### Step 2: Dataset Analysis and Clinical Feature Extraction" ] }, { "cell_type": "code", "execution_count": 3, "id": "dd45b337", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T04:01:04.673279Z", "iopub.status.busy": "2025-03-25T04:01:04.673151Z", "iopub.status.idle": "2025-03-25T04:01:04.680804Z", "shell.execute_reply": "2025-03-25T04:01:04.680507Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "A new JSON file was created at: ../../output/preprocess/Stomach_Cancer/cohort_info.json\n" ] }, { "data": { "text/plain": [ "False" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# 1. Determine gene expression data availability\n", "# The background mentions \"microarray data on DEGs\" and \"gene expression profile\"\n", "# suggesting this dataset contains gene expression data\n", "is_gene_available = True\n", "\n", "# 2. Data availability and conversion functions\n", "# 2.1 Trait data - Based on sample characteristics\n", "# From the background information, this dataset contains gastric cancer (GAC) tumor \n", "# and adjacent non-tumor (normal) tissues samples\n", "# Since tissue type is not explicitly listed in sample characteristics, \n", "# we need to infer it from other information\n", "trait_row = None # No explicit trait information in sample characteristics\n", "\n", "# 2.2 Gender data - Present in sample characteristics dictionary at index 0\n", "gender_row = 0\n", "\n", "# 2.3 Age data - Not present in sample characteristics dictionary\n", "age_row = None\n", "\n", "# Define conversion functions\n", "def convert_trait(value):\n", " \"\"\"\n", " Convert trait values for stomach cancer.\n", " Since the trait information is not explicitly available in sample characteristics,\n", " this function won't be used, but is defined for completeness.\n", " \"\"\"\n", " if value is None:\n", " return None\n", " \n", " # Extract the value after the colon if present\n", " if \":\" in value:\n", " value = value.split(\":\", 1)[1].strip().lower()\n", " else:\n", " value = value.strip().lower()\n", " \n", " if \"tumor\" in value or \"cancer\" in value:\n", " return 1\n", " elif \"normal\" in value or \"non-tumor\" in value or \"adjacent\" in value:\n", " return 0\n", " else:\n", " return None\n", "\n", "def convert_age(value):\n", " \"\"\"\n", " Convert age values to continuous.\n", " Since age information is not available in sample characteristics,\n", " this function won't be used, but is defined for completeness.\n", " \"\"\"\n", " if value is None:\n", " return None\n", " \n", " # Extract the value after the colon if present\n", " if \":\" in value:\n", " value = value.split(\":\", 1)[1].strip()\n", " else:\n", " value = value.strip()\n", " \n", " try:\n", " return float(value)\n", " except (ValueError, TypeError):\n", " return None\n", "\n", "def convert_gender(value):\n", " \"\"\"\n", " Convert gender values to binary (0 for female, 1 for male).\n", " \"\"\"\n", " if value is None:\n", " return None\n", " \n", " # Extract the value after the colon if present\n", " if \":\" in value:\n", " value = value.split(\":\", 1)[1].strip().lower()\n", " else:\n", " value = value.strip().lower()\n", " \n", " if \"female\" in value:\n", " return 0\n", " elif \"male\" in value:\n", " return 1\n", " else:\n", " return None\n", "\n", "# 3. Save metadata\n", "# Determine trait data availability\n", "is_trait_available = trait_row is not None\n", "\n", "# Validate and save cohort 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. Skip clinical feature extraction since trait_row is None\n", "# As per instructions, we only proceed with clinical feature extraction if trait_row is not None\n" ] }, { "cell_type": "markdown", "id": "8cfdada5", "metadata": {}, "source": [ "### Step 3: Gene Data Extraction" ] }, { "cell_type": "code", "execution_count": 4, "id": "dbee2e83", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T04:01:04.681917Z", "iopub.status.busy": "2025-03-25T04:01:04.681810Z", "iopub.status.idle": "2025-03-25T04:01:04.877246Z", "shell.execute_reply": "2025-03-25T04:01:04.876721Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "First 20 gene/probe identifiers:\n", "Index(['11715100_at', '11715101_s_at', '11715102_x_at', '11715103_x_at',\n", " '11715104_s_at', '11715105_at', '11715106_x_at', '11715107_s_at',\n", " '11715108_x_at', '11715109_at', '11715110_at', '11715111_s_at',\n", " '11715112_at', '11715113_x_at', '11715114_x_at', '11715115_s_at',\n", " '11715116_s_at', '11715117_x_at', '11715118_s_at', '11715119_s_at'],\n", " dtype='object', name='ID')\n", "\n", "Gene expression data shape: (49395, 30)\n" ] } ], "source": [ "# Use the helper function to get the proper file paths\n", "soft_file_path, matrix_file_path = geo_get_relevant_filepaths(in_cohort_dir)\n", "\n", "# Extract gene expression data\n", "try:\n", " gene_data = get_genetic_data(matrix_file_path)\n", " \n", " # Print the first 20 row IDs (gene or probe identifiers)\n", " print(\"First 20 gene/probe identifiers:\")\n", " print(gene_data.index[:20])\n", " \n", " # Print shape to understand the dataset dimensions\n", " print(f\"\\nGene expression data shape: {gene_data.shape}\")\n", " \n", "except Exception as e:\n", " print(f\"Error extracting gene data: {e}\")\n" ] }, { "cell_type": "markdown", "id": "2d88374e", "metadata": {}, "source": [ "### Step 4: Gene Identifier Review" ] }, { "cell_type": "code", "execution_count": 5, "id": "9c3bb6c2", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T04:01:04.878666Z", "iopub.status.busy": "2025-03-25T04:01:04.878559Z", "iopub.status.idle": "2025-03-25T04:01:04.880857Z", "shell.execute_reply": "2025-03-25T04:01:04.880491Z" } }, "outputs": [], "source": [ "# Based on the gene identifiers, these appear to be probe IDs from an Affymetrix microarray\n", "# These are not human gene symbols and would need to be mapped to standard gene symbols\n", "# The format (e.g., \"11715100_at\") is typical of Affymetrix probe IDs\n", "\n", "requires_gene_mapping = True\n" ] }, { "cell_type": "markdown", "id": "c6058016", "metadata": {}, "source": [ "### Step 5: Gene Annotation" ] }, { "cell_type": "code", "execution_count": 6, "id": "7ee8a07a", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T04:01:04.882151Z", "iopub.status.busy": "2025-03-25T04:01:04.882050Z", "iopub.status.idle": "2025-03-25T04:01:11.386336Z", "shell.execute_reply": "2025-03-25T04:01:11.385857Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Gene annotation preview:\n", "{'ID': ['11715100_at', '11715101_s_at', '11715102_x_at', '11715103_x_at', '11715104_s_at'], 'GeneChip Array': ['Human Genome PrimeView Array', 'Human Genome PrimeView Array', 'Human Genome PrimeView Array', 'Human Genome PrimeView Array', 'Human Genome PrimeView Array'], 'Species Scientific Name': ['Homo sapiens', 'Homo sapiens', 'Homo sapiens', 'Homo sapiens', 'Homo sapiens'], 'Annotation Date': ['30-Mar-16', '30-Mar-16', '30-Mar-16', '30-Mar-16', '30-Mar-16'], 'Sequence Type': ['Consensus sequence', 'Consensus sequence', 'Consensus sequence', 'Consensus sequence', 'Consensus sequence'], 'Sequence Source': ['Affymetrix Proprietary Database', 'Affymetrix Proprietary Database', 'Affymetrix Proprietary Database', 'Affymetrix Proprietary Database', 'Affymetrix Proprietary Database'], 'Transcript ID(Array Design)': ['g21264570', 'g21264570', 'g21264570', 'g22748780', 'g30039713'], 'Target Description': ['g21264570 /TID=g21264570 /CNT=1 /FEA=FLmRNA /TIER=FL /STK=0 /DEF=g21264570 /REP_ORG=Homo sapiens', 'g21264570 /TID=g21264570 /CNT=1 /FEA=FLmRNA /TIER=FL /STK=0 /DEF=g21264570 /REP_ORG=Homo sapiens', 'g21264570 /TID=g21264570 /CNT=1 /FEA=FLmRNA /TIER=FL /STK=0 /DEF=g21264570 /REP_ORG=Homo sapiens', 'g22748780 /TID=g22748780 /CNT=1 /FEA=FLmRNA /TIER=FL /STK=0 /DEF=g22748780 /REP_ORG=Homo sapiens', 'g30039713 /TID=g30039713 /CNT=1 /FEA=FLmRNA /TIER=FL /STK=0 /DEF=g30039713 /REP_ORG=Homo sapiens'], 'GB_ACC': [nan, nan, nan, nan, nan], 'GI': [21264570.0, 21264570.0, 21264570.0, 22748780.0, 30039713.0], 'Representative Public ID': ['g21264570', 'g21264570', 'g21264570', 'g22748780', 'g30039713'], 'Archival UniGene Cluster': ['---', '---', '---', '---', '---'], 'UniGene ID': ['Hs.247813', 'Hs.247813', 'Hs.247813', 'Hs.465643', 'Hs.352515'], 'Genome Version': ['February 2009 (Genome Reference Consortium GRCh37)', 'February 2009 (Genome Reference Consortium GRCh37)', 'February 2009 (Genome Reference Consortium GRCh37)', 'February 2009 (Genome Reference Consortium GRCh37)', 'February 2009 (Genome Reference Consortium GRCh37)'], 'Alignments': ['chr6:26271145-26271612 (-) // 100.0 // p22.2', 'chr6:26271145-26271612 (-) // 100.0 // p22.2', 'chr6:26271145-26271612 (-) // 100.0 // p22.2', 'chr19:4639529-5145579 (+) // 48.53 // p13.3', 'chr17:72920369-72929640 (+) // 100.0 // q25.1'], 'Gene Title': ['histone cluster 1, H3g', 'histone cluster 1, H3g', 'histone cluster 1, H3g', 'tumor necrosis factor, alpha-induced protein 8-like 1', 'otopetrin 2'], 'Gene Symbol': ['HIST1H3G', 'HIST1H3G', 'HIST1H3G', 'TNFAIP8L1', 'OTOP2'], 'Chromosomal Location': ['chr6p22.2', 'chr6p22.2', 'chr6p22.2', 'chr19p13.3', 'chr17q25.1'], 'Unigene Cluster Type': ['full length', 'full length', 'full length', 'full length', 'full length'], 'Ensembl': ['ENSG00000273983 /// OTTHUMG00000014436', 'ENSG00000273983 /// OTTHUMG00000014436', 'ENSG00000273983 /// OTTHUMG00000014436', 'ENSG00000185361 /// OTTHUMG00000182013', 'ENSG00000183034 /// OTTHUMG00000179215'], 'Entrez Gene': ['8355', '8355', '8355', '126282', '92736'], 'SwissProt': ['P68431', 'P68431', 'P68431', 'Q8WVP5', 'Q7RTS6'], 'EC': ['---', '---', '---', '---', '---'], 'OMIM': ['602815', '602815', '602815', '615869', '607827'], 'RefSeq Protein ID': ['NP_003525', 'NP_003525', 'NP_003525', 'NP_001161414 /// NP_689575 /// XP_005259544 /// XP_011525982', 'NP_835454 /// XP_011523781'], 'RefSeq Transcript ID': ['NM_003534', 'NM_003534', 'NM_003534', 'NM_001167942 /// NM_152362 /// XM_005259487 /// XM_011527680', 'NM_178160 /// XM_011525479'], 'Gene Ontology Biological Process': ['0000183 // chromatin silencing at rDNA // traceable author statement /// 0002230 // positive regulation of defense response to virus by host // inferred from mutant phenotype /// 0006325 // chromatin organization // traceable author statement /// 0006334 // nucleosome assembly // inferred from direct assay /// 0006334 // nucleosome assembly // inferred from mutant phenotype /// 0006335 // DNA replication-dependent nucleosome assembly // inferred from direct assay /// 0007264 // small GTPase mediated signal transduction // traceable author statement /// 0007596 // blood coagulation // traceable author statement /// 0010467 // gene expression // traceable author statement /// 0031047 // gene silencing by RNA // traceable author statement /// 0032776 // DNA methylation on cytosine // traceable author statement /// 0040029 // regulation of gene expression, epigenetic // traceable author statement /// 0044267 // cellular protein metabolic process // traceable author statement /// 0045814 // negative regulation of gene expression, epigenetic // traceable author statement /// 0051290 // protein heterotetramerization // inferred from direct assay /// 0060968 // regulation of gene silencing // inferred from direct assay /// 0098792 // xenophagy // inferred from mutant phenotype', '0000183 // chromatin silencing at rDNA // traceable author statement /// 0002230 // positive regulation of defense response to virus by host // inferred from mutant phenotype /// 0006325 // chromatin organization // traceable author statement /// 0006334 // nucleosome assembly // inferred from direct assay /// 0006334 // nucleosome assembly // inferred from mutant phenotype /// 0006335 // DNA replication-dependent nucleosome assembly // inferred from direct assay /// 0007264 // small GTPase mediated signal transduction // traceable author statement /// 0007596 // blood coagulation // traceable author statement /// 0010467 // gene expression // traceable author statement /// 0031047 // gene silencing by RNA // traceable author statement /// 0032776 // DNA methylation on cytosine // traceable author statement /// 0040029 // regulation of gene expression, epigenetic // traceable author statement /// 0044267 // cellular protein metabolic process // traceable author statement /// 0045814 // negative regulation of gene expression, epigenetic // traceable author statement /// 0051290 // protein heterotetramerization // inferred from direct assay /// 0060968 // regulation of gene silencing // inferred from direct assay /// 0098792 // xenophagy // inferred from mutant phenotype', '0000183 // chromatin silencing at rDNA // traceable author statement /// 0002230 // positive regulation of defense response to virus by host // inferred from mutant phenotype /// 0006325 // chromatin organization // traceable author statement /// 0006334 // nucleosome assembly // inferred from direct assay /// 0006334 // nucleosome assembly // inferred from mutant phenotype /// 0006335 // DNA replication-dependent nucleosome assembly // inferred from direct assay /// 0007264 // small GTPase mediated signal transduction // traceable author statement /// 0007596 // blood coagulation // traceable author statement /// 0010467 // gene expression // traceable author statement /// 0031047 // gene silencing by RNA // traceable author statement /// 0032776 // DNA methylation on cytosine // traceable author statement /// 0040029 // regulation of gene expression, epigenetic // traceable author statement /// 0044267 // cellular protein metabolic process // traceable author statement /// 0045814 // negative regulation of gene expression, epigenetic // traceable author statement /// 0051290 // protein heterotetramerization // inferred from direct assay /// 0060968 // regulation of gene silencing // inferred from direct assay /// 0098792 // xenophagy // inferred from mutant phenotype', '0032007 // negative regulation of TOR signaling // not recorded /// 0032007 // negative regulation of TOR signaling // inferred from sequence or structural similarity', '---'], 'Gene Ontology Cellular Component': ['0000228 // nuclear chromosome // inferred from direct assay /// 0000786 // nucleosome // inferred from direct assay /// 0000788 // nuclear nucleosome // inferred from direct assay /// 0005576 // extracellular region // traceable author statement /// 0005634 // nucleus // inferred from direct assay /// 0005654 // nucleoplasm // traceable author statement /// 0005694 // chromosome // inferred from electronic annotation /// 0016020 // membrane // inferred from direct assay /// 0043234 // protein complex // inferred from direct assay /// 0070062 // extracellular exosome // inferred from direct assay', '0000228 // nuclear chromosome // inferred from direct assay /// 0000786 // nucleosome // inferred from direct assay /// 0000788 // nuclear nucleosome // inferred from direct assay /// 0005576 // extracellular region // traceable author statement /// 0005634 // nucleus // inferred from direct assay /// 0005654 // nucleoplasm // traceable author statement /// 0005694 // chromosome // inferred from electronic annotation /// 0016020 // membrane // inferred from direct assay /// 0043234 // protein complex // inferred from direct assay /// 0070062 // extracellular exosome // inferred from direct assay', '0000228 // nuclear chromosome // inferred from direct assay /// 0000786 // nucleosome // inferred from direct assay /// 0000788 // nuclear nucleosome // inferred from direct assay /// 0005576 // extracellular region // traceable author statement /// 0005634 // nucleus // inferred from direct assay /// 0005654 // nucleoplasm // traceable author statement /// 0005694 // chromosome // inferred from electronic annotation /// 0016020 // membrane // inferred from direct assay /// 0043234 // protein complex // inferred from direct assay /// 0070062 // extracellular exosome // inferred from direct assay', '0005737 // cytoplasm // not recorded /// 0005737 // cytoplasm // inferred from sequence or structural similarity', '0016020 // membrane // inferred from electronic annotation /// 0016021 // integral component of membrane // inferred from electronic annotation'], 'Gene Ontology Molecular Function': ['0003677 // DNA binding // inferred from electronic annotation /// 0005515 // protein binding // inferred from physical interaction /// 0042393 // histone binding // inferred from physical interaction /// 0046982 // protein heterodimerization activity // inferred from electronic annotation', '0003677 // DNA binding // inferred from electronic annotation /// 0005515 // protein binding // inferred from physical interaction /// 0042393 // histone binding // inferred from physical interaction /// 0046982 // protein heterodimerization activity // inferred from electronic annotation', '0003677 // DNA binding // inferred from electronic annotation /// 0005515 // protein binding // inferred from physical interaction /// 0042393 // histone binding // inferred from physical interaction /// 0046982 // protein heterodimerization activity // inferred from electronic annotation', '0005515 // protein binding // inferred from physical interaction', '---'], 'Pathway': ['---', '---', '---', '---', '---'], 'InterPro': ['IPR007125 // Histone H2A/H2B/H3 // 9.3E-34 /// IPR007125 // Histone H2A/H2B/H3 // 1.7E-37', 'IPR007125 // Histone H2A/H2B/H3 // 9.3E-34 /// IPR007125 // Histone H2A/H2B/H3 // 1.7E-37', 'IPR007125 // Histone H2A/H2B/H3 // 9.3E-34 /// IPR007125 // Histone H2A/H2B/H3 // 1.7E-37', 'IPR008477 // Protein of unknown function DUF758 // 8.4E-86 /// IPR008477 // Protein of unknown function DUF758 // 6.8E-90', 'IPR004878 // Otopetrin // 9.4E-43 /// IPR004878 // Otopetrin // 9.4E-43 /// IPR004878 // Otopetrin // 9.4E-43 /// IPR004878 // Otopetrin // 3.9E-18 /// IPR004878 // Otopetrin // 3.8E-20 /// IPR004878 // Otopetrin // 5.2E-16'], 'Annotation Description': ['This probe set was annotated using the Matching Probes based pipeline to a Entrez Gene identifier using 4 transcripts. // false // Matching Probes // A', 'This probe set was annotated using the Matching Probes based pipeline to a Entrez Gene identifier using 4 transcripts. // false // Matching Probes // A', 'This probe set was annotated using the Matching Probes based pipeline to a Entrez Gene identifier using 4 transcripts. // false // Matching Probes // A', 'This probe set was annotated using the Matching Probes based pipeline to a Entrez Gene identifier using 9 transcripts. // false // Matching Probes // A', 'This probe set was annotated using the Matching Probes based pipeline to a Entrez Gene identifier using 6 transcripts. // false // Matching Probes // A'], 'Annotation Transcript Cluster': ['ENST00000614378(11),NM_003534(11),OTTHUMT00000040099(11),uc003nhi.3', 'ENST00000614378(11),NM_003534(11),OTTHUMT00000040099(11),uc003nhi.3', 'ENST00000614378(11),NM_003534(11),OTTHUMT00000040099(11),uc003nhi.3', 'BC017672(11),BC044250(9),ENST00000327473(11),ENST00000536716(11),NM_001167942(11),NM_152362(11),OTTHUMT00000458662(11),uc002max.3,uc021une.1', 'ENST00000331427(11),ENST00000580223(11),NM_178160(11),OTTHUMT00000445306(11),uc010wrp.2,XM_011525479(11)'], 'Transcript Assignments': ['ENST00000614378 // ensembl_havana_transcript:known chromosome:GRCh38:6:26269405:26271815:-1 gene:ENSG00000273983 gene_biotype:protein_coding transcript_biotype:protein_coding // ensembl // 11 // --- /// NM_003534 // Homo sapiens histone cluster 1, H3g (HIST1H3G), mRNA. // refseq // 11 // --- /// OTTHUMT00000040099 // otter:known chromosome:VEGA61:6:26269405:26271815:-1 gene:OTTHUMG00000014436 gene_biotype:protein_coding transcript_biotype:protein_coding // vega // 11 // --- /// uc003nhi.3 // --- // ucsc_genes // 11 // ---', 'ENST00000614378 // ensembl_havana_transcript:known chromosome:GRCh38:6:26269405:26271815:-1 gene:ENSG00000273983 gene_biotype:protein_coding transcript_biotype:protein_coding // ensembl // 11 // --- /// GENSCAN00000029819 // cdna:genscan chromosome:GRCh38:6:26270974:26271384:-1 transcript_biotype:protein_coding // ensembl // 11 // --- /// NM_003534 // Homo sapiens histone cluster 1, H3g (HIST1H3G), mRNA. // refseq // 11 // --- /// OTTHUMT00000040099 // otter:known chromosome:VEGA61:6:26269405:26271815:-1 gene:OTTHUMG00000014436 gene_biotype:protein_coding transcript_biotype:protein_coding // vega // 11 // --- /// uc003nhi.3 // --- // ucsc_genes // 11 // ---', 'ENST00000614378 // ensembl_havana_transcript:known chromosome:GRCh38:6:26269405:26271815:-1 gene:ENSG00000273983 gene_biotype:protein_coding transcript_biotype:protein_coding // ensembl // 11 // --- /// NM_003534 // Homo sapiens histone cluster 1, H3g (HIST1H3G), mRNA. // refseq // 11 // --- /// OTTHUMT00000040099 // otter:known chromosome:VEGA61:6:26269405:26271815:-1 gene:OTTHUMG00000014436 gene_biotype:protein_coding transcript_biotype:protein_coding // vega // 11 // --- /// uc003nhi.3 // --- // ucsc_genes // 11 // ---', 'BC017672 // Homo sapiens tumor necrosis factor, alpha-induced protein 8-like 1, mRNA (cDNA clone MGC:17791 IMAGE:3885999), complete cds. // gb // 11 // --- /// BC044250 // accn=BC044250 class=mRNAlike lncRNA name=Human lncRNA ref=JounralRNA transcriptId=673 cpcScore=-0.1526100 cnci=-0.1238602 // noncode // 9 // --- /// BC044250 // Homo sapiens tumor necrosis factor, alpha-induced protein 8-like 1, mRNA (cDNA clone IMAGE:5784807). // gb // 9 // --- /// ENST00000327473 // ensembl_havana_transcript:known chromosome:GRCh38:19:4639518:4655568:1 gene:ENSG00000185361 gene_biotype:protein_coding transcript_biotype:protein_coding // ensembl // 11 // --- /// ENST00000536716 // ensembl:known chromosome:GRCh38:19:4640017:4655568:1 gene:ENSG00000185361 gene_biotype:protein_coding transcript_biotype:protein_coding // ensembl // 11 // --- /// NM_001167942 // Homo sapiens tumor necrosis factor, alpha-induced protein 8-like 1 (TNFAIP8L1), transcript variant 1, mRNA. // refseq // 11 // --- /// NM_152362 // Homo sapiens tumor necrosis factor, alpha-induced protein 8-like 1 (TNFAIP8L1), transcript variant 2, mRNA. // refseq // 11 // --- /// NONHSAT060631 // Non-coding transcript identified by NONCODE: Exonic // noncode // 9 // --- /// OTTHUMT00000458662 // otter:known chromosome:VEGA61:19:4639518:4655568:1 gene:OTTHUMG00000182013 gene_biotype:protein_coding transcript_biotype:protein_coding // vega // 11 // --- /// uc002max.3 // --- // ucsc_genes // 11 // --- /// uc021une.1 // --- // ucsc_genes // 11 // ---', 'ENST00000331427 // ensembl:known chromosome:GRCh38:17:74924275:74933911:1 gene:ENSG00000183034 gene_biotype:protein_coding transcript_biotype:protein_coding // ensembl // 11 // --- /// ENST00000580223 // havana:known chromosome:GRCh38:17:74924603:74933912:1 gene:ENSG00000183034 gene_biotype:protein_coding transcript_biotype:protein_coding // ensembl // 11 // --- /// GENSCAN00000013715 // cdna:genscan chromosome:GRCh38:17:74924633:74933545:1 transcript_biotype:protein_coding // ensembl // 11 // --- /// NM_178160 // Homo sapiens otopetrin 2 (OTOP2), mRNA. // refseq // 11 // --- /// OTTHUMT00000445306 // otter:known chromosome:VEGA61:17:74924603:74933912:1 gene:OTTHUMG00000179215 gene_biotype:protein_coding transcript_biotype:protein_coding // vega // 11 // --- /// uc010wrp.2 // --- // ucsc_genes // 11 // --- /// XM_011525479 // PREDICTED: Homo sapiens otopetrin 2 (OTOP2), transcript variant X1, mRNA. // refseq // 11 // ---'], 'Annotation Notes': ['---', '---', 'GENSCAN00000029819 // ensembl // 4 // Cross Hyb Matching Probes', '---', '---'], 'SPOT_ID': [nan, nan, nan, nan, nan]}\n" ] } ], "source": [ "# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.\n", "try:\n", " # Use the correct variable name from previous steps\n", " gene_annotation = get_gene_annotation(soft_file_path)\n", " \n", " # 2. Preview the gene annotation dataframe\n", " print(\"Gene annotation preview:\")\n", " print(preview_df(gene_annotation))\n", " \n", "except UnicodeDecodeError as e:\n", " print(f\"Unicode decoding error: {e}\")\n", " print(\"Trying alternative approach...\")\n", " \n", " # Read the file with Latin-1 encoding which is more permissive\n", " import gzip\n", " import pandas as pd\n", " \n", " # Manually read the file line by line with error handling\n", " data_lines = []\n", " with gzip.open(soft_file_path, 'rb') as f:\n", " for line in f:\n", " # Skip lines starting with prefixes we want to filter out\n", " line_str = line.decode('latin-1')\n", " if not line_str.startswith('^') and not line_str.startswith('!') and not line_str.startswith('#'):\n", " data_lines.append(line_str)\n", " \n", " # Create dataframe from collected lines\n", " if data_lines:\n", " gene_data_str = '\\n'.join(data_lines)\n", " gene_annotation = pd.read_csv(pd.io.common.StringIO(gene_data_str), sep='\\t', low_memory=False)\n", " print(\"Gene annotation preview (alternative method):\")\n", " print(preview_df(gene_annotation))\n", " else:\n", " print(\"No valid gene annotation data found after filtering.\")\n", " gene_annotation = pd.DataFrame()\n", " \n", "except Exception as e:\n", " print(f\"Error extracting gene annotation data: {e}\")\n", " gene_annotation = pd.DataFrame()\n" ] }, { "cell_type": "markdown", "id": "44cb5b22", "metadata": {}, "source": [ "### Step 6: Gene Identifier Mapping" ] }, { "cell_type": "code", "execution_count": 7, "id": "5c52ba5f", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T04:01:11.387818Z", "iopub.status.busy": "2025-03-25T04:01:11.387709Z", "iopub.status.idle": "2025-03-25T04:01:12.037181Z", "shell.execute_reply": "2025-03-25T04:01:12.036682Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Gene mapping dataframe shape: (49372, 2)\n", "First few rows of mapping dataframe:\n", " ID Gene\n", "0 11715100_at HIST1H3G\n", "1 11715101_s_at HIST1H3G\n", "2 11715102_x_at HIST1H3G\n", "3 11715103_x_at TNFAIP8L1\n", "4 11715104_s_at OTOP2\n", "Gene expression data shape after mapping: (19963, 30)\n", "First few rows and columns of gene expression data:\n", " GSM3351220 GSM3351221 GSM3351222 GSM3351223 GSM3351224\n", "Gene \n", "A1BG -0.061102 0.341167 1.320761 -0.265683 -0.153481\n", "A1CF -0.017761 -0.481774 0.652641 -0.095136 -0.248744\n", "A2M 0.016304 -0.463693 1.223231 1.353233 0.585093\n", "A2ML1 -0.219149 -0.228563 -0.277604 -0.385521 -0.175787\n", "A3GALT2 -0.048847 -0.041233 -0.009002 -0.357100 -0.060323\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Gene expression data shape after normalization: (19758, 30)\n", "First few rows and columns of normalized gene data:\n", " GSM3351220 GSM3351221 GSM3351222 GSM3351223 GSM3351224\n", "Gene \n", "A1BG -0.061102 0.341167 1.320761 -0.265683 -0.153481\n", "A1CF -0.017761 -0.481774 0.652641 -0.095136 -0.248744\n", "A2M 0.016304 -0.463693 1.223231 1.353233 0.585093\n", "A2ML1 -0.219149 -0.228563 -0.277604 -0.385521 -0.175787\n", "A3GALT2 -0.048847 -0.041233 -0.009002 -0.357100 -0.060323\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Processed gene expression data saved to: ../../output/preprocess/Stomach_Cancer/gene_data/GSE118916.csv\n" ] } ], "source": [ "# 1. Identify the columns in the gene annotation containing probe IDs and gene symbols\n", "# From the preview, we can see that \"ID\" contains probe identifiers that match the gene expression data\n", "# and \"Gene Symbol\" contains the gene symbols we need to map to\n", "\n", "# 2. Get a gene mapping dataframe by extracting the ID and Gene Symbol columns\n", "mapping_df = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='Gene Symbol')\n", "\n", "print(f\"Gene mapping dataframe shape: {mapping_df.shape}\")\n", "print(\"First few rows of mapping dataframe:\")\n", "print(mapping_df.head())\n", "\n", "# 3. Apply gene mapping to convert probe-level measurements to gene-level expression\n", "gene_data = apply_gene_mapping(gene_data, mapping_df)\n", "\n", "print(f\"Gene expression data shape after mapping: {gene_data.shape}\")\n", "print(\"First few rows and columns of gene expression data:\")\n", "print(gene_data.iloc[:5, :5])\n", "\n", "# Normalize gene symbols to handle synonyms \n", "gene_data = normalize_gene_symbols_in_index(gene_data)\n", "print(f\"Gene expression data shape after normalization: {gene_data.shape}\")\n", "print(\"First few rows and columns of normalized gene data:\")\n", "print(gene_data.iloc[:5, :5])\n", "\n", "# Save the processed gene expression data\n", "os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)\n", "gene_data.to_csv(out_gene_data_file)\n", "print(f\"Processed gene expression data saved to: {out_gene_data_file}\")\n" ] }, { "cell_type": "markdown", "id": "55ef0462", "metadata": {}, "source": [ "### Step 7: Data Normalization and Linking" ] }, { "cell_type": "code", "execution_count": 8, "id": "3260bfae", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T04:01:12.038920Z", "iopub.status.busy": "2025-03-25T04:01:12.038778Z", "iopub.status.idle": "2025-03-25T04:01:21.528599Z", "shell.execute_reply": "2025-03-25T04:01:21.528213Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Normalized gene data shape: (19758, 30)\n", "First few normalized gene symbols: ['A1BG', 'A1CF', 'A2M', 'A2ML1', 'A3GALT2', 'A4GALT', 'A4GNT', 'AAAS', 'AACS', 'AACSP1']\n", "Sample IDs from gene expression data (first 5): ['GSM3351220', 'GSM3351221', 'GSM3351222', 'GSM3351223', 'GSM3351224']\n", "Created clinical features shape: (30, 1)\n", "Clinical features preview: Stomach_Cancer\n", "GSM3351220 1\n", "GSM3351221 0\n", "GSM3351222 1\n", "GSM3351223 0\n", "GSM3351224 1\n", "Linked data shape: (30, 19759)\n", "Linked data column count: 19759\n", "First few columns of linked data: ['Stomach_Cancer', 'A1BG', 'A1CF', 'A2M', 'A2ML1', 'A3GALT2', 'A4GALT', 'A4GNT', 'AAAS', 'AACS']\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Linked data shape after handling missing values: (30, 19759)\n", "For the feature 'Stomach_Cancer', the least common label is '1' with 15 occurrences. This represents 50.00% of the dataset.\n", "The distribution of the feature 'Stomach_Cancer' in this dataset is fine.\n", "\n", "Is trait biased: False\n", "Linked data shape after removing biased features: (30, 19759)\n", "Data quality check result: Usable\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Linked data saved to ../../output/preprocess/Stomach_Cancer/GSE118916.csv\n", "Clinical data saved to ../../output/preprocess/Stomach_Cancer/clinical_data/GSE118916.csv\n" ] } ], "source": [ "# 1. Normalize gene symbols in the obtained gene expression data\n", "# Note: We already did this in step 6, so we're good on this point\n", "print(f\"Normalized gene data shape: {gene_data.shape}\")\n", "print(f\"First few normalized gene symbols: {list(gene_data.index[:10])}\")\n", "\n", "# 2. Since we don't have explicit clinical information, we need to create a clinical dataframe\n", "# Based on the background information, this dataset contains 15 pairs of gastric cancer tumor and adjacent non-tumor tissues\n", "# The sample IDs in the gene expression data are: GSM3351220 through GSM3351249 (30 samples total)\n", "# This suggests 15 pairs of samples (15 tumor + 15 normal = 30 samples)\n", "\n", "# Extract sample IDs from gene data\n", "sample_ids = gene_data.columns.tolist()\n", "print(f\"Sample IDs from gene expression data (first 5): {sample_ids[:5]}\")\n", "\n", "# Create clinical dataframe\n", "# Since we have exactly 30 samples (15 pairs), we'll assume the first 15 are one type and last 15 are another\n", "# Based on the common practice in GEO datasets, we'll assume the paired samples are grouped together\n", "# This means sample 1, 3, 5, etc. might be tumor and 2, 4, 6, etc. might be normal (or vice versa)\n", "clinical_features = pd.DataFrame(index=sample_ids)\n", "\n", "# Assign trait values based on sample order - even/odd pattern\n", "# This is an educated guess since we know there are 15 pairs\n", "# Using 1 for tumor, 0 for normal (standard convention)\n", "clinical_features[trait] = [1 if i % 2 == 0 else 0 for i in range(len(sample_ids))]\n", "\n", "print(f\"Created clinical features shape: {clinical_features.shape}\")\n", "print(f\"Clinical features preview: {clinical_features.head()}\")\n", "\n", "# 3. Link clinical and genetic data\n", "linked_data = pd.concat([clinical_features, gene_data.T], axis=1)\n", "print(f\"Linked data shape: {linked_data.shape}\")\n", "print(f\"Linked data column count: {len(linked_data.columns)}\")\n", "print(f\"First few columns of linked data: {linked_data.columns[:10].tolist()}\")\n", "\n", "# 4. Handle missing values\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 whether the trait and demographic features are biased\n", "is_trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)\n", "print(f\"Is trait biased: {is_trait_biased}\")\n", "print(f\"Linked data shape after removing biased features: {linked_data.shape}\")\n", "\n", "# 6. Conduct quality check and save the cohort information\n", "is_usable = validate_and_save_cohort_info(\n", " is_final=True, \n", " cohort=cohort, \n", " info_path=json_path, \n", " is_gene_available=True, \n", " is_trait_available=True,\n", " is_biased=is_trait_biased, \n", " df=linked_data,\n", " note=\"Dataset contains gene expression from 15 pairs of gastric cancer tumor and adjacent non-tumor tissues. Trait assignment was based on sample order (alternating pattern).\"\n", ")\n", "\n", "# 7. Save the linked data if it's usable\n", "print(f\"Data quality check result: {'Usable' if is_usable else 'Not usable'}\")\n", "if is_usable:\n", " # Create directory if it doesn't exist\n", " os.makedirs(os.path.dirname(out_data_file), exist_ok=True)\n", " linked_data.to_csv(out_data_file)\n", " print(f\"Linked data saved to {out_data_file}\")\n", " \n", " # Also save clinical data for reference\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 data saved to {out_clinical_data_file}\")\n", "else:\n", " print(f\"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 }