{ "cells": [ { "cell_type": "code", "execution_count": 1, "id": "bfa6f42d", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T06:16:36.758391Z", "iopub.status.busy": "2025-03-25T06:16:36.758212Z", "iopub.status.idle": "2025-03-25T06:16:36.920561Z", "shell.execute_reply": "2025-03-25T06:16:36.920231Z" } }, "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 = \"Post-Traumatic_Stress_Disorder\"\n", "cohort = \"GSE81761\"\n", "\n", "# Input paths\n", "in_trait_dir = \"../../input/GEO/Post-Traumatic_Stress_Disorder\"\n", "in_cohort_dir = \"../../input/GEO/Post-Traumatic_Stress_Disorder/GSE81761\"\n", "\n", "# Output paths\n", "out_data_file = \"../../output/preprocess/Post-Traumatic_Stress_Disorder/GSE81761.csv\"\n", "out_gene_data_file = \"../../output/preprocess/Post-Traumatic_Stress_Disorder/gene_data/GSE81761.csv\"\n", "out_clinical_data_file = \"../../output/preprocess/Post-Traumatic_Stress_Disorder/clinical_data/GSE81761.csv\"\n", "json_path = \"../../output/preprocess/Post-Traumatic_Stress_Disorder/cohort_info.json\"\n" ] }, { "cell_type": "markdown", "id": "7edfe80d", "metadata": {}, "source": [ "### Step 1: Initial Data Loading" ] }, { "cell_type": "code", "execution_count": 2, "id": "14255d88", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T06:16:36.921976Z", "iopub.status.busy": "2025-03-25T06:16:36.921831Z", "iopub.status.idle": "2025-03-25T06:16:37.180168Z", "shell.execute_reply": "2025-03-25T06:16:37.179816Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Background Information:\n", "!Series_title\t\"Gene Expression Pathways Implicated in Posttraumatic Stress Disorder and Symptomatic Improvement\"\n", "!Series_summary\t\"Military service members often return from deployment with a multiplicity of injuries, including mild traumatic brain injury, depression, and sleep disorders, which obsures diagnosis of PTSD symptoms and complicates treatment of PTSD. In order to understand the biological mechanisms underlying PTSD, gene expression profiles of military service members with and without PTSD were compared. Additionally, gene expression was examined based on intrusion symptoms, a distinct subtype of PTSD symptoms, and on improvement of PTSD symptoms at a three month follow up. RNA was extracted from blood samples and hybridized to the HG-U133_Plus_2 Affymetrix chip.\"\n", "!Series_overall_design\t\"Gene expression of subjects with PTSD (n=39) were compared to controls without PTSD (n=27) at baseline. Further analysis of gene expression for subjects with PTSD at follow-up was based on improvement or lack of improvement in PTSD symtpoms. 109 Samples (not all subjects had follow-up data) were analyzed in total.\"\n", "Sample Characteristics Dictionary:\n", "{0: ['tissue: Peripheral blood'], 1: ['case/control: PTSD', 'case/control: No PTSD'], 2: ['ptsd subgroup: PTSD Improved', 'ptsd subgroup: No PTSD', 'ptsd subgroup: PTSD Not Improved', 'ptsd subgroup: No Follow Up Data'], 3: ['timepoint: Baseline', 'timepoint: Follow Up', 'timepoint: Follow-Up'], 4: ['Sex: Male', 'Sex: Female'], 5: ['age: 30', 'age: 38', 'age: 39', 'age: 23', 'age: 48', 'age: 49', 'age: 34', 'age: 33', 'age: 45', 'age: 25', 'age: 22', 'age: 46', 'age: 35', 'age: 36', 'age: 43', 'age: 26', 'age: 27', 'age: 28', 'age: 29', 'age: 41', 'age: 44', 'age: 31', 'age: 42', 'age: 21', 'age: 37', 'age: 52', 'age: 24', 'age: 32'], 6: ['race: Black', 'race: Caucasian', 'race: Asian', 'race: Native Hawaiian/Pacific Islander', 'race: Mixed Race', 'race: Other/Unknown', 'race: Native American'], 7: ['ethnicity: Hispanic', 'ethnicity: Not Hispanic']}\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": "f64c9ada", "metadata": {}, "source": [ "### Step 2: Dataset Analysis and Clinical Feature Extraction" ] }, { "cell_type": "code", "execution_count": 3, "id": "17296ee1", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T06:16:37.181504Z", "iopub.status.busy": "2025-03-25T06:16:37.181395Z", "iopub.status.idle": "2025-03-25T06:16:37.186716Z", "shell.execute_reply": "2025-03-25T06:16:37.186427Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Clinical data file not found at ../../input/GEO/Post-Traumatic_Stress_Disorder/GSE81761/clinical_data.csv\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", "# The background information indicates gene expression data is available:\n", "# \"RNA was extracted from blood samples and hybridized to the HG-U133_Plus_2 Affymetrix chip.\"\n", "# HG-U133_Plus_2 is a standard microarray for gene expression\n", "is_gene_available = True\n", "\n", "# 2.1 Data Availability\n", "# Review the sample characteristics dictionary to identify relevant rows\n", "# For PTSD (trait): Row 1 contains 'case/control: PTSD' and 'case/control: No PTSD'\n", "trait_row = 1\n", "\n", "# For Age: Row 5 contains age information\n", "age_row = 5\n", "\n", "# For Gender: Row 4 contains 'Sex: Male' and 'Sex: Female'\n", "gender_row = 4\n", "\n", "# 2.2 Data Type Conversion\n", "\n", "def convert_trait(value: str) -> int:\n", " \"\"\"Convert PTSD trait value to binary format (1 for PTSD, 0 for No PTSD).\"\"\"\n", " if not value or \":\" not in value:\n", " return None\n", " \n", " value = value.split(\":\", 1)[1].strip()\n", " if \"PTSD\" in value and \"No\" not in value:\n", " return 1\n", " elif \"No PTSD\" in value:\n", " return 0\n", " return None\n", "\n", "def convert_age(value: str) -> float:\n", " \"\"\"Convert age value to continuous format.\"\"\"\n", " if not value or \":\" not in value:\n", " return None\n", " \n", " value = value.split(\":\", 1)[1].strip()\n", " try:\n", " return float(value)\n", " except ValueError:\n", " return None\n", "\n", "def convert_gender(value: str) -> int:\n", " \"\"\"Convert gender value to binary format (0 for Female, 1 for Male).\"\"\"\n", " if not value or \":\" not in value:\n", " return None\n", " \n", " value = value.split(\":\", 1)[1].strip().lower()\n", " if \"male\" in value and \"fe\" not in value:\n", " return 1\n", " elif \"female\" in value:\n", " return 0\n", " return None\n", "\n", "# 3. Save Metadata - Initial validation\n", "# Trait data is available (trait_row is not None)\n", "is_trait_available = trait_row is not None\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 not None, we proceed with clinical feature extraction\n", "if trait_row is not None:\n", " # Load the clinical data\n", " clinical_data_path = os.path.join(in_cohort_dir, \"clinical_data.csv\")\n", " if os.path.exists(clinical_data_path):\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 extracted clinical features\n", " preview = preview_df(selected_clinical_df)\n", " print(\"Preview of extracted clinical features:\")\n", " print(preview)\n", " \n", " # Save the clinical data to the output file\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" ] }, { "cell_type": "markdown", "id": "d6650d7e", "metadata": {}, "source": [ "### Step 3: Gene Data Extraction" ] }, { "cell_type": "code", "execution_count": 4, "id": "7547a821", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T06:16:37.187950Z", "iopub.status.busy": "2025-03-25T06:16:37.187841Z", "iopub.status.idle": "2025-03-25T06:16:37.629554Z", "shell.execute_reply": "2025-03-25T06:16:37.629184Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "First 20 gene/probe identifiers:\n", "Index(['1007_s_at', '1053_at', '117_at', '121_at', '1255_g_at', '1294_at',\n", " '1316_at', '1320_at', '1405_i_at', '1431_at', '1438_at', '1487_at',\n", " '1494_f_at', '1552256_a_at', '1552257_a_at', '1552258_at', '1552261_at',\n", " '1552263_at', '1552264_a_at', '1552266_at'],\n", " dtype='object', name='ID')\n", "\n", "Gene data dimensions: 54675 genes × 109 samples\n" ] } ], "source": [ "# 1. Re-identify the SOFT and matrix files to ensure we have the correct paths\n", "soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)\n", "\n", "# 2. Extract the gene expression data from the matrix file\n", "gene_data = get_genetic_data(matrix_file)\n", "\n", "# 3. Print the first 20 row IDs (gene or probe identifiers)\n", "print(\"\\nFirst 20 gene/probe identifiers:\")\n", "print(gene_data.index[:20])\n", "\n", "# 4. Print the dimensions of the gene expression data\n", "print(f\"\\nGene data dimensions: {gene_data.shape[0]} genes × {gene_data.shape[1]} samples\")\n", "\n", "# Note: we keep is_gene_available as True since we successfully extracted gene expression data\n", "is_gene_available = True\n" ] }, { "cell_type": "markdown", "id": "65028b9b", "metadata": {}, "source": [ "### Step 4: Gene Identifier Review" ] }, { "cell_type": "code", "execution_count": 5, "id": "aaee2a75", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T06:16:37.630882Z", "iopub.status.busy": "2025-03-25T06:16:37.630771Z", "iopub.status.idle": "2025-03-25T06:16:37.632607Z", "shell.execute_reply": "2025-03-25T06:16:37.632335Z" } }, "outputs": [], "source": [ "# Looking at the gene identifiers like '1007_s_at', '1053_at', these appear to be Affymetrix probe IDs\n", "# from an Affymetrix microarray platform, not standard human gene symbols.\n", "# These probe IDs need to be mapped to gene symbols for proper biological interpretation.\n", "\n", "requires_gene_mapping = True\n" ] }, { "cell_type": "markdown", "id": "29e83e51", "metadata": {}, "source": [ "### Step 5: Gene Annotation" ] }, { "cell_type": "code", "execution_count": 6, "id": "46efdef4", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T06:16:37.633795Z", "iopub.status.busy": "2025-03-25T06:16:37.633695Z", "iopub.status.idle": "2025-03-25T06:16:45.432185Z", "shell.execute_reply": "2025-03-25T06:16:45.431804Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Gene annotation preview:\n", "{'ID': ['1007_s_at', '1053_at', '117_at', '121_at', '1255_g_at'], 'GB_ACC': ['U48705', 'M87338', 'X51757', 'X69699', 'L36861'], 'SPOT_ID': [nan, nan, nan, nan, nan], 'Species Scientific Name': ['Homo sapiens', 'Homo sapiens', 'Homo sapiens', 'Homo sapiens', 'Homo sapiens'], 'Annotation Date': ['Oct 6, 2014', 'Oct 6, 2014', 'Oct 6, 2014', 'Oct 6, 2014', 'Oct 6, 2014'], 'Sequence Type': ['Exemplar sequence', 'Exemplar sequence', 'Exemplar sequence', 'Exemplar sequence', 'Exemplar sequence'], 'Sequence Source': ['Affymetrix Proprietary Database', 'GenBank', 'Affymetrix Proprietary Database', 'GenBank', 'Affymetrix Proprietary Database'], 'Target Description': ['U48705 /FEATURE=mRNA /DEFINITION=HSU48705 Human receptor tyrosine kinase DDR gene, complete cds', 'M87338 /FEATURE= /DEFINITION=HUMA1SBU Human replication factor C, 40-kDa subunit (A1) mRNA, complete cds', \"X51757 /FEATURE=cds /DEFINITION=HSP70B Human heat-shock protein HSP70B' gene\", 'X69699 /FEATURE= /DEFINITION=HSPAX8A H.sapiens Pax8 mRNA', 'L36861 /FEATURE=expanded_cds /DEFINITION=HUMGCAPB Homo sapiens guanylate cyclase activating protein (GCAP) gene exons 1-4, complete cds'], 'Representative Public ID': ['U48705', 'M87338', 'X51757', 'X69699', 'L36861'], 'Gene Title': ['discoidin domain receptor tyrosine kinase 1 /// microRNA 4640', 'replication factor C (activator 1) 2, 40kDa', \"heat shock 70kDa protein 6 (HSP70B')\", 'paired box 8', 'guanylate cyclase activator 1A (retina)'], 'Gene Symbol': ['DDR1 /// MIR4640', 'RFC2', 'HSPA6', 'PAX8', 'GUCA1A'], 'ENTREZ_GENE_ID': ['780 /// 100616237', '5982', '3310', '7849', '2978'], 'RefSeq Transcript ID': ['NM_001202521 /// NM_001202522 /// NM_001202523 /// NM_001954 /// NM_013993 /// NM_013994 /// NR_039783 /// XM_005249385 /// XM_005249386 /// XM_005249387 /// XM_005249389 /// XM_005272873 /// XM_005272874 /// XM_005272875 /// XM_005272877 /// XM_005275027 /// XM_005275028 /// XM_005275030 /// XM_005275031 /// XM_005275162 /// XM_005275163 /// XM_005275164 /// XM_005275166 /// XM_005275457 /// XM_005275458 /// XM_005275459 /// XM_005275461 /// XM_006715185 /// XM_006715186 /// XM_006715187 /// XM_006715188 /// XM_006715189 /// XM_006715190 /// XM_006725501 /// XM_006725502 /// XM_006725503 /// XM_006725504 /// XM_006725505 /// XM_006725506 /// XM_006725714 /// XM_006725715 /// XM_006725716 /// XM_006725717 /// XM_006725718 /// XM_006725719 /// XM_006725720 /// XM_006725721 /// XM_006725722 /// XM_006725827 /// XM_006725828 /// XM_006725829 /// XM_006725830 /// XM_006725831 /// XM_006725832 /// XM_006726017 /// XM_006726018 /// XM_006726019 /// XM_006726020 /// XM_006726021 /// XM_006726022 /// XR_427836 /// XR_430858 /// XR_430938 /// XR_430974 /// XR_431015', 'NM_001278791 /// NM_001278792 /// NM_001278793 /// NM_002914 /// NM_181471 /// XM_006716080', 'NM_002155', 'NM_003466 /// NM_013951 /// NM_013952 /// NM_013953 /// NM_013992', 'NM_000409 /// XM_006715073'], 'Gene Ontology Biological Process': ['0001558 // regulation of cell growth // inferred from electronic annotation /// 0001952 // regulation of cell-matrix adhesion // inferred from electronic annotation /// 0006468 // protein phosphorylation // inferred from electronic annotation /// 0007155 // cell adhesion // traceable author statement /// 0007169 // transmembrane receptor protein tyrosine kinase signaling pathway // inferred from electronic annotation /// 0007565 // female pregnancy // inferred from electronic annotation /// 0007566 // embryo implantation // inferred from electronic annotation /// 0007595 // lactation // inferred from electronic annotation /// 0008285 // negative regulation of cell proliferation // inferred from electronic annotation /// 0010715 // regulation of extracellular matrix disassembly // inferred from mutant phenotype /// 0014909 // smooth muscle cell migration // inferred from mutant phenotype /// 0016310 // phosphorylation // inferred from electronic annotation /// 0018108 // peptidyl-tyrosine phosphorylation // inferred from electronic annotation /// 0030198 // extracellular matrix organization // traceable author statement /// 0038063 // collagen-activated tyrosine kinase receptor signaling pathway // inferred from direct assay /// 0038063 // collagen-activated tyrosine kinase receptor signaling pathway // inferred from mutant phenotype /// 0038083 // peptidyl-tyrosine autophosphorylation // inferred from direct assay /// 0043583 // ear development // inferred from electronic annotation /// 0044319 // wound healing, spreading of cells // inferred from mutant phenotype /// 0046777 // protein autophosphorylation // inferred from direct assay /// 0060444 // branching involved in mammary gland duct morphogenesis // inferred from electronic annotation /// 0060749 // mammary gland alveolus development // inferred from electronic annotation /// 0061302 // smooth muscle cell-matrix adhesion // inferred from mutant phenotype', '0000278 // mitotic cell cycle // traceable author statement /// 0000722 // telomere maintenance via recombination // traceable author statement /// 0000723 // telomere maintenance // traceable author statement /// 0006260 // DNA replication // traceable author statement /// 0006271 // DNA strand elongation involved in DNA replication // traceable author statement /// 0006281 // DNA repair // traceable author statement /// 0006283 // transcription-coupled nucleotide-excision repair // traceable author statement /// 0006289 // nucleotide-excision repair // traceable author statement /// 0006297 // nucleotide-excision repair, DNA gap filling // traceable author statement /// 0015979 // photosynthesis // inferred from electronic annotation /// 0015995 // chlorophyll biosynthetic process // inferred from electronic annotation /// 0032201 // telomere maintenance via semi-conservative replication // traceable author statement', '0000902 // cell morphogenesis // inferred from electronic annotation /// 0006200 // ATP catabolic process // inferred from direct assay /// 0006950 // response to stress // inferred from electronic annotation /// 0006986 // response to unfolded protein // traceable author statement /// 0034605 // cellular response to heat // inferred from direct assay /// 0042026 // protein refolding // inferred from direct assay /// 0070370 // cellular heat acclimation // inferred from mutant phenotype', '0001655 // urogenital system development // inferred from sequence or structural similarity /// 0001656 // metanephros development // inferred from electronic annotation /// 0001658 // branching involved in ureteric bud morphogenesis // inferred from expression pattern /// 0001822 // kidney development // inferred from expression pattern /// 0001823 // mesonephros development // inferred from sequence or structural similarity /// 0003337 // mesenchymal to epithelial transition involved in metanephros morphogenesis // inferred from expression pattern /// 0006351 // transcription, DNA-templated // inferred from direct assay /// 0006355 // regulation of transcription, DNA-templated // inferred from electronic annotation /// 0007275 // multicellular organismal development // inferred from electronic annotation /// 0007417 // central nervous system development // inferred from expression pattern /// 0009653 // anatomical structure morphogenesis // traceable author statement /// 0030154 // cell differentiation // inferred from electronic annotation /// 0030878 // thyroid gland development // inferred from expression pattern /// 0030878 // thyroid gland development // inferred from mutant phenotype /// 0038194 // thyroid-stimulating hormone signaling pathway // traceable author statement /// 0039003 // pronephric field specification // inferred from sequence or structural similarity /// 0042472 // inner ear morphogenesis // inferred from sequence or structural similarity /// 0042981 // regulation of apoptotic process // inferred from sequence or structural similarity /// 0045893 // positive regulation of transcription, DNA-templated // inferred from direct assay /// 0045893 // positive regulation of transcription, DNA-templated // inferred from sequence or structural similarity /// 0045944 // positive regulation of transcription from RNA polymerase II promoter // inferred from direct assay /// 0048793 // pronephros development // inferred from sequence or structural similarity /// 0071371 // cellular response to gonadotropin stimulus // inferred from direct assay /// 0071599 // otic vesicle development // inferred from expression pattern /// 0072050 // S-shaped body morphogenesis // inferred from electronic annotation /// 0072073 // kidney epithelium development // inferred from electronic annotation /// 0072108 // positive regulation of mesenchymal to epithelial transition involved in metanephros morphogenesis // inferred from sequence or structural similarity /// 0072164 // mesonephric tubule development // inferred from electronic annotation /// 0072207 // metanephric epithelium development // inferred from expression pattern /// 0072221 // metanephric distal convoluted tubule development // inferred from sequence or structural similarity /// 0072278 // metanephric comma-shaped body morphogenesis // inferred from expression pattern /// 0072284 // metanephric S-shaped body morphogenesis // inferred from expression pattern /// 0072289 // metanephric nephron tubule formation // inferred from sequence or structural similarity /// 0072305 // negative regulation of mesenchymal cell apoptotic process involved in metanephric nephron morphogenesis // inferred from sequence or structural similarity /// 0072307 // regulation of metanephric nephron tubule epithelial cell differentiation // inferred from sequence or structural similarity /// 0090190 // positive regulation of branching involved in ureteric bud morphogenesis // inferred from sequence or structural similarity /// 1900212 // negative regulation of mesenchymal cell apoptotic process involved in metanephros development // inferred from sequence or structural similarity /// 1900215 // negative regulation of apoptotic process involved in metanephric collecting duct development // inferred from sequence or structural similarity /// 1900218 // negative regulation of apoptotic process involved in metanephric nephron tubule development // inferred from sequence or structural similarity /// 2000594 // positive regulation of metanephric DCT cell differentiation // inferred from sequence or structural similarity /// 2000611 // positive regulation of thyroid hormone generation // inferred from mutant phenotype /// 2000612 // regulation of thyroid-stimulating hormone secretion // inferred from mutant phenotype', '0007165 // signal transduction // non-traceable author statement /// 0007601 // visual perception // inferred from electronic annotation /// 0007602 // phototransduction // inferred from electronic annotation /// 0007603 // phototransduction, visible light // traceable author statement /// 0016056 // rhodopsin mediated signaling pathway // traceable author statement /// 0022400 // regulation of rhodopsin mediated signaling pathway // traceable author statement /// 0030828 // positive regulation of cGMP biosynthetic process // inferred from electronic annotation /// 0031282 // regulation of guanylate cyclase activity // inferred from electronic annotation /// 0031284 // positive regulation of guanylate cyclase activity // inferred from electronic annotation /// 0050896 // response to stimulus // inferred from electronic annotation'], 'Gene Ontology Cellular Component': ['0005576 // extracellular region // inferred from electronic annotation /// 0005615 // extracellular space // inferred from direct assay /// 0005886 // plasma membrane // traceable author statement /// 0005887 // integral component of plasma membrane // traceable author statement /// 0016020 // membrane // inferred from electronic annotation /// 0016021 // integral component of membrane // inferred from electronic annotation /// 0043235 // receptor complex // inferred from direct assay /// 0070062 // extracellular vesicular exosome // inferred from direct assay', '0005634 // nucleus // inferred from electronic annotation /// 0005654 // nucleoplasm // traceable author statement /// 0005663 // DNA replication factor C complex // inferred from direct assay', '0005737 // cytoplasm // inferred from direct assay /// 0005814 // centriole // inferred from direct assay /// 0005829 // cytosol // inferred from direct assay /// 0008180 // COP9 signalosome // inferred from direct assay /// 0070062 // extracellular vesicular exosome // inferred from direct assay /// 0072562 // blood microparticle // inferred from direct assay', '0005634 // nucleus // inferred from direct assay /// 0005654 // nucleoplasm // inferred from sequence or structural similarity /// 0005730 // nucleolus // inferred from direct assay', '0001750 // photoreceptor outer segment // inferred from electronic annotation /// 0001917 // photoreceptor inner segment // inferred from electronic annotation /// 0005578 // proteinaceous extracellular matrix // inferred from electronic annotation /// 0005886 // plasma membrane // inferred from direct assay /// 0016020 // membrane // inferred from electronic annotation /// 0097381 // photoreceptor disc membrane // traceable author statement'], 'Gene Ontology Molecular Function': ['0000166 // nucleotide binding // inferred from electronic annotation /// 0004672 // protein kinase activity // inferred from electronic annotation /// 0004713 // protein tyrosine kinase activity // inferred from electronic annotation /// 0004714 // transmembrane receptor protein tyrosine kinase activity // traceable author statement /// 0005515 // protein binding // inferred from physical interaction /// 0005518 // collagen binding // inferred from direct assay /// 0005518 // collagen binding // inferred from mutant phenotype /// 0005524 // ATP binding // inferred from electronic annotation /// 0016301 // kinase activity // inferred from electronic annotation /// 0016740 // transferase activity // inferred from electronic annotation /// 0016772 // transferase activity, transferring phosphorus-containing groups // inferred from electronic annotation /// 0038062 // protein tyrosine kinase collagen receptor activity // inferred from direct assay /// 0046872 // metal ion binding // inferred from electronic annotation', '0000166 // nucleotide binding // inferred from electronic annotation /// 0003677 // DNA binding // inferred from electronic annotation /// 0005515 // protein binding // inferred from physical interaction /// 0005524 // ATP binding // inferred from electronic annotation /// 0016851 // magnesium chelatase activity // inferred from electronic annotation /// 0017111 // nucleoside-triphosphatase activity // inferred from electronic annotation', '0000166 // nucleotide binding // inferred from electronic annotation /// 0005524 // ATP binding // inferred from electronic annotation /// 0019899 // enzyme binding // inferred from physical interaction /// 0031072 // heat shock protein binding // inferred from physical interaction /// 0042623 // ATPase activity, coupled // inferred from direct assay /// 0051082 // unfolded protein binding // inferred from direct assay', '0000979 // RNA polymerase II core promoter sequence-specific DNA binding // inferred from direct assay /// 0003677 // DNA binding // inferred from direct assay /// 0003677 // DNA binding // inferred from mutant phenotype /// 0003700 // sequence-specific DNA binding transcription factor activity // inferred from direct assay /// 0004996 // thyroid-stimulating hormone receptor activity // traceable author statement /// 0005515 // protein binding // inferred from physical interaction /// 0044212 // transcription regulatory region DNA binding // inferred from direct assay', '0005509 // calcium ion binding // inferred from electronic annotation /// 0008048 // calcium sensitive guanylate cyclase activator activity // inferred from electronic annotation /// 0030249 // guanylate cyclase regulator activity // inferred from electronic annotation /// 0046872 // metal ion binding // inferred from electronic annotation']}\n" ] } ], "source": [ "# 1. First get the file paths using geo_get_relevant_filepaths function\n", "soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)\n", "\n", "# 2. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.\n", "gene_annotation = get_gene_annotation(soft_file)\n", "\n", "# 3. Use the 'preview_df' function from the library to preview the data and print out the results.\n", "print(\"Gene annotation preview:\")\n", "print(preview_df(gene_annotation))\n" ] }, { "cell_type": "markdown", "id": "18c0d0c6", "metadata": {}, "source": [ "### Step 6: Gene Identifier Mapping" ] }, { "cell_type": "code", "execution_count": 7, "id": "2045ff2d", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T06:16:45.433529Z", "iopub.status.busy": "2025-03-25T06:16:45.433400Z", "iopub.status.idle": "2025-03-25T06:16:45.877633Z", "shell.execute_reply": "2025-03-25T06:16:45.877252Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Gene mapping preview (first 5 rows):\n", " ID Gene\n", "0 1007_s_at DDR1 /// MIR4640\n", "1 1053_at RFC2\n", "2 117_at HSPA6\n", "3 121_at PAX8\n", "4 1255_g_at GUCA1A\n", "\n", "Gene data after mapping dimensions: 21278 genes × 109 samples\n", "First 10 gene symbols after mapping:\n", "Index(['A1BG', 'A1BG-AS1', 'A1CF', 'A2M', 'A2M-AS1', 'A2ML1', 'A2MP1',\n", " 'A4GALT', 'A4GNT', 'AA06'],\n", " dtype='object', name='Gene')\n" ] } ], "source": [ "# 1. Observe the gene identifiers and decide which columns to use\n", "# From the annotation preview, we can see:\n", "# - 'ID' column contains probe IDs like '1007_s_at' which matches gene_data.index\n", "# - 'Gene Symbol' column contains the gene symbols we need\n", "\n", "# 2. Get the gene mapping dataframe\n", "gene_mapping = get_gene_mapping(gene_annotation, 'ID', 'Gene Symbol')\n", "\n", "# Preview the mapping to confirm\n", "print(\"Gene mapping preview (first 5 rows):\")\n", "print(gene_mapping.head())\n", "\n", "# 3. Apply the gene mapping to convert probe-level measurements to gene expression data\n", "gene_data = apply_gene_mapping(gene_data, gene_mapping)\n", "\n", "# Print statistics about the gene data after mapping\n", "print(f\"\\nGene data after mapping dimensions: {gene_data.shape[0]} genes × {gene_data.shape[1]} samples\")\n", "print(\"First 10 gene symbols after mapping:\")\n", "print(gene_data.index[:10])\n" ] }, { "cell_type": "markdown", "id": "0cf6c6c3", "metadata": {}, "source": [ "### Step 7: Data Normalization and Linking" ] }, { "cell_type": "code", "execution_count": 8, "id": "e414d532", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T06:16:45.879043Z", "iopub.status.busy": "2025-03-25T06:16:45.878914Z", "iopub.status.idle": "2025-03-25T06:17:09.207891Z", "shell.execute_reply": "2025-03-25T06:17:09.207350Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Normalizing gene symbols...\n", "Gene data shape after normalization: (19845, 109)\n", "First 5 normalized gene symbols: ['A1BG', 'A1BG-AS1', 'A1CF', 'A2M', 'A2M-AS1']\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Normalized gene data saved to ../../output/preprocess/Post-Traumatic_Stress_Disorder/gene_data/GSE81761.csv\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Preview of clinical data:\n", "{'GSM2175165': [1.0, 30.0, 1.0], 'GSM2175166': [0.0, 38.0, 1.0], 'GSM2175167': [1.0, 39.0, 1.0], 'GSM2175168': [1.0, 38.0, 1.0], 'GSM2175169': [1.0, 23.0, 1.0], 'GSM2175170': [0.0, 48.0, 1.0], 'GSM2175171': [0.0, 49.0, 1.0], 'GSM2175172': [0.0, 34.0, 1.0], 'GSM2175173': [1.0, 33.0, 1.0], 'GSM2175174': [1.0, 45.0, 1.0], 'GSM2175175': [1.0, 25.0, 1.0], 'GSM2175176': [0.0, 25.0, 1.0], 'GSM2175177': [1.0, 30.0, 1.0], 'GSM2175178': [1.0, 39.0, 1.0], 'GSM2175179': [1.0, 23.0, 1.0], 'GSM2175180': [1.0, 22.0, 1.0], 'GSM2175181': [1.0, 46.0, 1.0], 'GSM2175182': [0.0, 35.0, 1.0], 'GSM2175183': [0.0, 22.0, 1.0], 'GSM2175184': [1.0, 23.0, 1.0], 'GSM2175185': [0.0, 48.0, 1.0], 'GSM2175186': [1.0, 23.0, 1.0], 'GSM2175187': [0.0, 49.0, 1.0], 'GSM2175188': [1.0, 38.0, 1.0], 'GSM2175189': [1.0, 25.0, 1.0], 'GSM2175190': [1.0, 33.0, 1.0], 'GSM2175191': [1.0, 30.0, 1.0], 'GSM2175192': [0.0, 36.0, 1.0], 'GSM2175193': [0.0, 43.0, 1.0], 'GSM2175194': [0.0, 34.0, 1.0], 'GSM2175195': [1.0, 22.0, 1.0], 'GSM2175196': [0.0, 26.0, 1.0], 'GSM2175197': [1.0, 46.0, 1.0], 'GSM2175198': [1.0, 39.0, 0.0], 'GSM2175199': [1.0, 27.0, 1.0], 'GSM2175200': [1.0, 23.0, 1.0], 'GSM2175201': [0.0, 28.0, 1.0], 'GSM2175202': [0.0, 22.0, 1.0], 'GSM2175203': [0.0, 29.0, 1.0], 'GSM2175204': [1.0, 41.0, 1.0], 'GSM2175205': [0.0, 25.0, 1.0], 'GSM2175206': [0.0, 39.0, 1.0], 'GSM2175207': [0.0, 38.0, 1.0], 'GSM2175208': [1.0, 25.0, 1.0], 'GSM2175209': [0.0, 46.0, 1.0], 'GSM2175210': [0.0, 35.0, 1.0], 'GSM2175211': [0.0, 44.0, 1.0], 'GSM2175212': [0.0, 34.0, 1.0], 'GSM2175213': [1.0, 23.0, 1.0], 'GSM2175214': [0.0, 46.0, 1.0], 'GSM2175215': [0.0, 43.0, 1.0], 'GSM2175216': [1.0, 26.0, 1.0], 'GSM2175217': [0.0, 34.0, 1.0], 'GSM2175218': [0.0, 31.0, 0.0], 'GSM2175219': [1.0, 30.0, 1.0], 'GSM2175220': [0.0, 36.0, 1.0], 'GSM2175221': [1.0, 23.0, 1.0], 'GSM2175222': [1.0, 25.0, 1.0], 'GSM2175223': [1.0, 28.0, 1.0], 'GSM2175224': [1.0, 27.0, 1.0], 'GSM2175225': [1.0, 39.0, 0.0], 'GSM2175226': [0.0, 26.0, 1.0], 'GSM2175227': [0.0, 38.0, 1.0], 'GSM2175228': [0.0, 25.0, 1.0], 'GSM2175229': [1.0, 31.0, 1.0], 'GSM2175230': [0.0, 39.0, 1.0], 'GSM2175231': [0.0, 29.0, 1.0], 'GSM2175232': [0.0, 34.0, 1.0], 'GSM2175233': [1.0, 23.0, 1.0], 'GSM2175234': [0.0, 42.0, 1.0], 'GSM2175235': [0.0, 44.0, 1.0], 'GSM2175236': [0.0, 46.0, 1.0], 'GSM2175237': [1.0, 21.0, 1.0], 'GSM2175238': [0.0, 35.0, 1.0], 'GSM2175239': [0.0, 44.0, 1.0], 'GSM2175240': [0.0, 34.0, 1.0], 'GSM2175241': [1.0, 28.0, 1.0], 'GSM2175242': [1.0, 37.0, 1.0], 'GSM2175243': [1.0, 23.0, 1.0], 'GSM2175244': [1.0, 26.0, 1.0], 'GSM2175245': [1.0, 28.0, 1.0], 'GSM2175246': [1.0, 52.0, 1.0], 'GSM2175247': [1.0, 31.0, 1.0], 'GSM2175248': [0.0, 36.0, 1.0], 'GSM2175249': [1.0, 41.0, 0.0], 'GSM2175250': [1.0, 21.0, 1.0], 'GSM2175251': [0.0, 42.0, 1.0], 'GSM2175252': [1.0, 30.0, 1.0], 'GSM2175253': [1.0, 24.0, 1.0], 'GSM2175254': [1.0, 41.0, 1.0], 'GSM2175255': [0.0, 35.0, 1.0], 'GSM2175256': [0.0, 44.0, 1.0], 'GSM2175257': [1.0, 26.0, 1.0], 'GSM2175258': [0.0, 27.0, 1.0], 'GSM2175259': [1.0, 37.0, 1.0], 'GSM2175260': [1.0, 52.0, 1.0], 'GSM2175261': [0.0, 36.0, 1.0], 'GSM2175262': [1.0, 41.0, 0.0], 'GSM2175263': [1.0, 24.0, 1.0], 'GSM2175264': [1.0, 26.0, 1.0], 'GSM2175265': [0.0, 27.0, 1.0], 'GSM2175266': [1.0, 26.0, 1.0], 'GSM2175267': [1.0, 37.0, 1.0], 'GSM2175268': [1.0, 22.0, 1.0], 'GSM2175269': [1.0, 32.0, 1.0], 'GSM2175270': [1.0, 45.0, 1.0], 'GSM2175271': [1.0, 23.0, 1.0], 'GSM2175272': [1.0, 35.0, 1.0], 'GSM2175273': [1.0, 27.0, 1.0]}\n", "Clinical data saved to ../../output/preprocess/Post-Traumatic_Stress_Disorder/clinical_data/GSE81761.csv\n", "Linked data shape: (109, 19848)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Data shape after handling missing values: (109, 19848)\n", "\n", "Checking for bias in the trait variable:\n", "For the feature 'Post-Traumatic_Stress_Disorder', the least common label is '0.0' with 47 occurrences. This represents 43.12% of the dataset.\n", "The distribution of the feature 'Post-Traumatic_Stress_Disorder' in this dataset is fine.\n", "\n", "Quartiles for 'Age':\n", " 25%: 26.0\n", " 50% (Median): 33.0\n", " 75%: 39.0\n", "Min: 21.0\n", "Max: 52.0\n", "The distribution of the feature 'Age' in this dataset is fine.\n", "\n", "For the feature 'Gender', the least common label is '0.0' with 5 occurrences. This represents 4.59% of the dataset.\n", "The distribution of the feature 'Gender' in this dataset is fine.\n", "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Linked data saved to ../../output/preprocess/Post-Traumatic_Stress_Disorder/GSE81761.csv\n" ] } ], "source": [ "# 1. Re-extract the gene expression data to ensure we have it properly defined\n", "soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)\n", "gene_data = get_genetic_data(matrix_file)\n", "\n", "# Extract gene annotation data for mapping\n", "gene_annotation = get_gene_annotation(soft_file)\n", "\n", "# Based on previous output in Step 5, we can see the column is 'Gene Symbol'\n", "gene_mapping = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='Gene Symbol')\n", "\n", "# Apply gene mapping to convert probe-level data to gene expression data\n", "gene_data = apply_gene_mapping(gene_data, gene_mapping)\n", "\n", "# 1. Normalize gene symbols in the gene expression data\n", "print(\"Normalizing gene symbols...\")\n", "normalized_gene_data = normalize_gene_symbols_in_index(gene_data)\n", "print(f\"Gene data shape after normalization: {normalized_gene_data.shape}\")\n", "print(f\"First 5 normalized gene symbols: {normalized_gene_data.index[:5].tolist() if len(normalized_gene_data) > 0 else 'No genes after normalization'}\")\n", "\n", "# Save the normalized gene data\n", "os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)\n", "normalized_gene_data.to_csv(out_gene_data_file)\n", "print(f\"Normalized gene data saved to {out_gene_data_file}\")\n", "\n", "# 2. Re-extract clinical 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", "# From Step 2, we identified the correct rows\n", "trait_row = 1 # 'case/control: PTSD', 'case/control: No PTSD'\n", "age_row = 5 # 'age: 30', 'age: 38', etc.\n", "gender_row = 4 # 'Sex: Male', 'Sex: Female'\n", "\n", "def convert_trait(value):\n", " \"\"\"Convert PTSD information to binary (0: No PTSD, 1: PTSD)\"\"\"\n", " if value is None or pd.isna(value):\n", " return None\n", " if ':' in value:\n", " value = value.split(':', 1)[1].strip()\n", " if \"PTSD\" in value and \"No\" not in value:\n", " return 1\n", " elif \"No PTSD\" in value:\n", " return 0\n", " return None\n", "\n", "def convert_age(value):\n", " \"\"\"Convert age information to numeric (continuous) values\"\"\"\n", " if value is None or pd.isna(value):\n", " return None\n", " if ':' in value:\n", " value = value.split(':', 1)[1].strip()\n", " try:\n", " return float(value)\n", " except ValueError:\n", " return None\n", "\n", "def convert_gender(value):\n", " \"\"\"Convert gender information to binary (0: female, 1: male)\"\"\"\n", " if value is None or pd.isna(value):\n", " return None\n", " if ':' in value:\n", " value = value.split(':', 1)[1].strip().lower()\n", " if \"female\" in value:\n", " return 0\n", " elif \"male\" in value and \"fe\" not in value:\n", " return 1\n", " return None\n", "\n", "# Extract clinical features with correct row indices and conversion functions\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", "# Debug: Show preview of clinical data\n", "print(\"Preview of clinical data:\")\n", "print(preview_df(selected_clinical_df))\n", "\n", "# Save clinical data\n", "os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)\n", "selected_clinical_df.to_csv(out_clinical_data_file)\n", "print(f\"Clinical data saved to {out_clinical_data_file}\")\n", "\n", "# 3. Link clinical and genetic data\n", "linked_data = geo_link_clinical_genetic_data(selected_clinical_df, normalized_gene_data)\n", "print(f\"Linked data shape: {linked_data.shape}\")\n", "\n", "# 4. Handle missing values\n", "linked_data = handle_missing_values(linked_data, trait_col=trait)\n", "print(f\"Data shape after handling missing values: {linked_data.shape}\")\n", "\n", "# 5. Determine if trait is biased\n", "print(\"\\nChecking for bias in the trait variable:\")\n", "# The trait in this dataset is binary (PTSD vs No PTSD)\n", "is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)\n", "\n", "# 6. Conduct final quality validation\n", "is_trait_available = True # We confirmed trait data is available in Step 2\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=is_trait_available,\n", " is_biased=is_biased,\n", " df=linked_data,\n", " note=\"Dataset studies blood gene expression profiles in PTSD patients compared to controls, with assessment of symptom improvement at follow-up.\"\n", ")\n", "\n", "# 7. Save linked data if usable\n", "if is_usable:\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", "else:\n", " print(\"Dataset deemed not usable for trait association studies, linked data not saved.\")" ] } ], "metadata": { "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.10.16" } }, "nbformat": 4, "nbformat_minor": 5 }