{ "cells": [ { "cell_type": "code", "execution_count": 1, "id": "0f9b96ed", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T08:40:14.578918Z", "iopub.status.busy": "2025-03-25T08:40:14.578690Z", "iopub.status.idle": "2025-03-25T08:40:14.749331Z", "shell.execute_reply": "2025-03-25T08:40:14.748981Z" } }, "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 = \"Eczema\"\n", "cohort = \"GSE120899\"\n", "\n", "# Input paths\n", "in_trait_dir = \"../../input/GEO/Eczema\"\n", "in_cohort_dir = \"../../input/GEO/Eczema/GSE120899\"\n", "\n", "# Output paths\n", "out_data_file = \"../../output/preprocess/Eczema/GSE120899.csv\"\n", "out_gene_data_file = \"../../output/preprocess/Eczema/gene_data/GSE120899.csv\"\n", "out_clinical_data_file = \"../../output/preprocess/Eczema/clinical_data/GSE120899.csv\"\n", "json_path = \"../../output/preprocess/Eczema/cohort_info.json\"\n" ] }, { "cell_type": "markdown", "id": "1f21695d", "metadata": {}, "source": [ "### Step 1: Initial Data Loading" ] }, { "cell_type": "code", "execution_count": 2, "id": "122d100a", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T08:40:14.750651Z", "iopub.status.busy": "2025-03-25T08:40:14.750500Z", "iopub.status.idle": "2025-03-25T08:40:14.785152Z", "shell.execute_reply": "2025-03-25T08:40:14.784838Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Background Information:\n", "!Series_title\t\"A Phase 2 Randomized Trial of Apremilast in Patients With Atopic Dermatitis\"\n", "!Series_summary\t\"A phase 2, double-blind, placebo-controlled trial evaluated apremilast efficacy, safety, and pharmacodynamics in adults with moderate to severe atopic dermatitis (AD).\"\n", "!Series_overall_design\t\"Patients were randomized to placebo, apremilast 30 mg BID (APR30), or apremilast 40 mg BID (APR40) for 12 weeks. During Weeks 12–24, all patients received APR30 or APR40. A biopsy substudy evaluated AD-related biomarkers. Among 185 randomized intent-to-treat patients at Week 12, a dose-response relationship was observed; APR40 (n=63), but not APR30 (n=58), led to statistically significant improvements (vs. placebo [n=64]) in Eczema Area and Severity Index (mean [SD] percentage change from baseline: −31.6% [44.6] vs. −11.0% [71.2]; P<0.04; primary endpoint). mRNA expression of Th17/Th22-related markers (IL-17A, IL-22, S100A7/A8; P<0.05) showed the highest reductions with APR40, with minimal changes in other immune axes. Safety with APR30 was largely consistent with apremilast’s known profile (common adverse events [AEs]: nausea, diarrhea, headache, nasopharyngitis). With APR40, AEs were more frequent and cellulitis occurred (n=6). An independent safety monitoring committee discontinued the APR40 dose. APR40 demonstrated modest efficacy and decreased AD-related biomarkers in moderate to severe AD patients. AEs, including cellulitis, were more frequent with APR40, which was discontinued during the trial.\"\n", "Sample Characteristics Dictionary:\n", "{0: ['batch_date: 2016-02-01', 'batch_date: 2016-01-12', 'batch_date: 2016-01-20', 'batch_date: 2016-01-25'], 1: ['tissue: lesional skin', 'tissue: non-lesional skin', 'tissue: Normal'], 2: ['week: 0', 'week: 12', 'week: NA'], 3: ['treatment: APRMST-30', 'treatment: Placebo', 'treatment: APRMST-40', 'treatment: NA'], 4: ['patient id: 31007', 'patient id: 61001', 'patient id: 61007', 'patient id: 61013', 'patient id: 61015', 'patient id: 62012', 'patient id: 71001', 'patient id: 71004', 'patient id: 71005', 'patient id: 111002', 'patient id: 111005', 'patient id: 2011004', 'patient id: 2011005', 'patient id: 2011006', 'patient id: 2011014', 'patient id: 2012017', 'patient id: 2021002', 'patient id: 3091001', 'patient id: 3091003', 'patient id: 3101001', 'patient id: 3101002', 'patient id: N5', 'patient id: N8']}\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": "ca04a22d", "metadata": {}, "source": [ "### Step 2: Dataset Analysis and Clinical Feature Extraction" ] }, { "cell_type": "code", "execution_count": 3, "id": "3036d3d9", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T08:40:14.786389Z", "iopub.status.busy": "2025-03-25T08:40:14.786278Z", "iopub.status.idle": "2025-03-25T08:40:14.794268Z", "shell.execute_reply": "2025-03-25T08:40:14.793954Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Preview of clinical features: {'GSM3418016': [1.0], 'GSM3418017': [0.0], 'GSM3418018': [0.0], 'GSM3418019': [0.0], 'GSM3418020': [1.0], 'GSM3418021': [1.0], 'GSM3418022': [0.0], 'GSM3418023': [1.0], 'GSM3418024': [0.0], 'GSM3418025': [1.0], 'GSM3418026': [1.0], 'GSM3418027': [0.0], 'GSM3418028': [1.0], 'GSM3418029': [1.0], 'GSM3418030': [0.0], 'GSM3418031': [1.0], 'GSM3418032': [1.0], 'GSM3418033': [0.0], 'GSM3418034': [1.0], 'GSM3418035': [0.0], 'GSM3418036': [1.0], 'GSM3418037': [1.0], 'GSM3418038': [0.0], 'GSM3418039': [1.0], 'GSM3418040': [1.0], 'GSM3418041': [0.0], 'GSM3418042': [1.0], 'GSM3418043': [1.0], 'GSM3418044': [0.0], 'GSM3418045': [1.0], 'GSM3418046': [1.0], 'GSM3418047': [0.0], 'GSM3418048': [1.0], 'GSM3418049': [1.0], 'GSM3418050': [0.0], 'GSM3418051': [1.0], 'GSM3418052': [1.0], 'GSM3418053': [0.0], 'GSM3418054': [1.0], 'GSM3418055': [1.0], 'GSM3418056': [0.0], 'GSM3418057': [1.0], 'GSM3418058': [0.0], 'GSM3418059': [1.0], 'GSM3418060': [1.0], 'GSM3418061': [0.0], 'GSM3418062': [1.0], 'GSM3418063': [1.0], 'GSM3418064': [0.0], 'GSM3418065': [1.0], 'GSM3418066': [1.0], 'GSM3418067': [0.0], 'GSM3418068': [1.0], 'GSM3418069': [1.0], 'GSM3418070': [0.0], 'GSM3418071': [1.0], 'GSM3418072': [0.0], 'GSM3418073': [0.0]}\n", "Clinical features saved to ../../output/preprocess/Eczema/clinical_data/GSE120899.csv\n" ] } ], "source": [ "# 1. Gene Expression Data Availability\n", "# Based on the background information, this is a study evaluating Apremilast efficacy in atopic dermatitis\n", "# that includes mRNA expression data of various markers, indicating gene expression data is available\n", "is_gene_available = True\n", "\n", "# 2.1 Data Availability\n", "# trait_row: The trait (Eczema/Atopic Dermatitis) can be inferred from the \"tissue\" field (row 1)\n", "# where values indicate lesional skin (has Eczema) vs. non-lesional skin (no Eczema) or Normal skin\n", "trait_row = 1\n", "# age_row: Age information is not available in the sample characteristics\n", "age_row = None\n", "# gender_row: Gender information is not available in the sample characteristics\n", "gender_row = None\n", "\n", "# 2.2 Data Type Conversion\n", "def convert_trait(value):\n", " # Extract value after colon if present\n", " if \":\" in value:\n", " value = value.split(\":\", 1)[1].strip()\n", " \n", " # Convert to binary where lesional skin = 1 (has Eczema), non-lesional or normal = 0\n", " if value.lower() == \"lesional skin\":\n", " return 1\n", " elif value.lower() in [\"non-lesional skin\", \"normal\"]:\n", " return 0\n", " else:\n", " return None\n", "\n", "def convert_age(value):\n", " # Not applicable since age data is not available\n", " return None\n", "\n", "def convert_gender(value):\n", " # Not applicable since gender data is not available\n", " return None\n", "\n", "# 3. Save Metadata\n", "# trait_row is not None, so trait data is available\n", "is_trait_available = trait_row is not None\n", "\n", "# Validate and save initial 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", " # First, we need to define clinical_data\n", " # Assuming clinical_data was previously loaded and contains the sample characteristics\n", " try:\n", " # Extract clinical features\n", " clinical_features = 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(clinical_features)\n", " print(f\"Preview of clinical features: {preview}\")\n", " \n", " # Save the clinical features to CSV\n", " os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)\n", " clinical_features.to_csv(out_clinical_data_file)\n", " print(f\"Clinical features saved to {out_clinical_data_file}\")\n", " except NameError:\n", " print(\"Clinical data not available from previous steps.\")\n" ] }, { "cell_type": "markdown", "id": "0dd24812", "metadata": {}, "source": [ "### Step 3: Gene Data Extraction" ] }, { "cell_type": "code", "execution_count": 4, "id": "754663b5", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T08:40:14.795453Z", "iopub.status.busy": "2025-03-25T08:40:14.795344Z", "iopub.status.idle": "2025-03-25T08:40:14.845979Z", "shell.execute_reply": "2025-03-25T08:40:14.845663Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Matrix file found: ../../input/GEO/Eczema/GSE120899/GSE120899_series_matrix.txt.gz\n", "Gene data shape: (6854, 58)\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" ] } ], "source": [ "# 1. Get the SOFT and matrix file paths again \n", "soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)\n", "print(f\"Matrix file found: {matrix_file}\")\n", "\n", "# 2. Use the get_genetic_data function from the library to get the gene_data\n", "try:\n", " gene_data = get_genetic_data(matrix_file)\n", " print(f\"Gene data shape: {gene_data.shape}\")\n", " \n", " # 3. Print the first 20 row IDs (gene or probe identifiers)\n", " print(\"First 20 gene/probe identifiers:\")\n", " print(gene_data.index[:20])\n", "except Exception as e:\n", " print(f\"Error extracting gene data: {e}\")\n" ] }, { "cell_type": "markdown", "id": "22def7c7", "metadata": {}, "source": [ "### Step 4: Gene Identifier Review" ] }, { "cell_type": "code", "execution_count": 5, "id": "1bf3f187", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T08:40:14.847195Z", "iopub.status.busy": "2025-03-25T08:40:14.847083Z", "iopub.status.idle": "2025-03-25T08:40:14.848938Z", "shell.execute_reply": "2025-03-25T08:40:14.848634Z" } }, "outputs": [], "source": [ "# Based on examining the gene identifiers, these appear to be Affymetrix probe IDs, not human gene symbols.\n", "# These identifiers (like '1007_s_at', '1053_at') are typical Affymetrix microarray probe identifiers \n", "# and will need to be mapped to standard human gene symbols for analysis.\n", "\n", "requires_gene_mapping = True\n" ] }, { "cell_type": "markdown", "id": "6403f328", "metadata": {}, "source": [ "### Step 5: Gene Annotation" ] }, { "cell_type": "code", "execution_count": 6, "id": "614022a6", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T08:40:14.850110Z", "iopub.status.busy": "2025-03-25T08:40:14.849996Z", "iopub.status.idle": "2025-03-25T08:40:16.595374Z", "shell.execute_reply": "2025-03-25T08:40:16.594981Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Gene annotation preview:\n", "Columns in gene annotation: ['ID', 'GB_ACC', 'SPOT_ID', 'Species Scientific Name', 'Annotation Date', 'Sequence Type', 'Sequence Source', 'Target Description', 'Representative Public ID', 'Gene Title', 'Gene Symbol', 'ENTREZ_GENE_ID', 'RefSeq Transcript ID', 'Gene Ontology Biological Process', 'Gene Ontology Cellular Component', 'Gene Ontology Molecular Function']\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", "\n", "Exploring SOFT file more thoroughly for gene information:\n", "!Series_platform_id = GPL570\n", "!Platform_title = [HG-U133_Plus_2] Affymetrix Human Genome U133 Plus 2.0 Array\n", "\n", "Found gene-related patterns:\n", "!Platform_relation = Alternative to: GPL20182 (Gene Symbol Version)\n", "#Gene Symbol = A gene symbol, when one is available (from UniGene).\n", "ID\tGB_ACC\tSPOT_ID\tSpecies Scientific Name\tAnnotation Date\tSequence Type\tSequence Source\tTarget Description\tRepresentative Public ID\tGene Title\tGene Symbol\tENTREZ_GENE_ID\tRefSeq Transcript ID\tGene Ontology Biological Process\tGene Ontology Cellular Component\tGene Ontology Molecular Function\n", "\n", "Analyzing ENTREZ_GENE_ID column:\n", "Number of entries where ENTREZ_GENE_ID differs from ID: 452265\n", "Some ENTREZ_GENE_ID values differ from probe IDs - this could be useful for mapping\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " ID GB_ACC SPOT_ID Species Scientific Name Annotation Date \\\n", "0 1007_s_at U48705 NaN Homo sapiens Oct 6, 2014 \n", "1 1053_at M87338 NaN Homo sapiens Oct 6, 2014 \n", "2 117_at X51757 NaN Homo sapiens Oct 6, 2014 \n", "3 121_at X69699 NaN Homo sapiens Oct 6, 2014 \n", "4 1255_g_at L36861 NaN Homo sapiens Oct 6, 2014 \n", "\n", " Sequence Type Sequence Source \\\n", "0 Exemplar sequence Affymetrix Proprietary Database \n", "1 Exemplar sequence GenBank \n", "2 Exemplar sequence Affymetrix Proprietary Database \n", "3 Exemplar sequence GenBank \n", "4 Exemplar sequence Affymetrix Proprietary Database \n", "\n", " Target Description Representative Public ID \\\n", "0 U48705 /FEATURE=mRNA /DEFINITION=HSU48705 Huma... U48705 \n", "1 M87338 /FEATURE= /DEFINITION=HUMA1SBU Human re... M87338 \n", "2 X51757 /FEATURE=cds /DEFINITION=HSP70B Human h... X51757 \n", "3 X69699 /FEATURE= /DEFINITION=HSPAX8A H.sapiens... X69699 \n", "4 L36861 /FEATURE=expanded_cds /DEFINITION=HUMGC... L36861 \n", "\n", " Gene Title Gene Symbol \\\n", "0 discoidin domain receptor tyrosine kinase 1 //... DDR1 /// MIR4640 \n", "1 replication factor C (activator 1) 2, 40kDa RFC2 \n", "2 heat shock 70kDa protein 6 (HSP70B') HSPA6 \n", "3 paired box 8 PAX8 \n", "4 guanylate cyclase activator 1A (retina) GUCA1A \n", "\n", " ENTREZ_GENE_ID RefSeq Transcript ID \\\n", "0 780 /// 100616237 NM_001202521 /// NM_001202522 /// NM_001202523... \n", "1 5982 NM_001278791 /// NM_001278792 /// NM_001278793... \n", "2 3310 NM_002155 \n", "3 7849 NM_003466 /// NM_013951 /// NM_013952 /// NM_0... \n", "4 2978 NM_000409 /// XM_006715073 \n", "\n", " Gene Ontology Biological Process \\\n", "0 0001558 // regulation of cell growth // inferr... \n", "1 0000278 // mitotic cell cycle // traceable aut... \n", "2 0000902 // cell morphogenesis // inferred from... \n", "3 0001655 // urogenital system development // in... \n", "4 0007165 // signal transduction // non-traceabl... \n", "\n", " Gene Ontology Cellular Component \\\n", "0 0005576 // extracellular region // inferred fr... \n", "1 0005634 // nucleus // inferred from electronic... \n", "2 0005737 // cytoplasm // inferred from direct a... \n", "3 0005634 // nucleus // inferred from direct ass... \n", "4 0001750 // photoreceptor outer segment // infe... \n", "\n", " Gene Ontology Molecular Function \n", "0 0000166 // nucleotide binding // inferred from... \n", "1 0000166 // nucleotide binding // inferred from... \n", "2 0000166 // nucleotide binding // inferred from... \n", "3 0000979 // RNA polymerase II core promoter seq... \n", "4 0005509 // calcium ion binding // inferred fro... \n", "\n", "Looking for alternative annotation approaches:\n", "- Checking for platform ID or accession number in SOFT file\n", "Found platform GEO accession: GPL570\n", "\n", "Preparing provisional gene mapping using ENTREZ_GENE_ID:\n", "Provisional mapping data shape: (452265, 2)\n", "{'ID': ['1007_s_at', '1053_at', '117_at', '121_at', '1255_g_at'], 'Gene': ['780 /// 100616237', '5982', '3310', '7849', '2978']}\n" ] } ], "source": [ "# 1. 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", "# 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=5))\n", "\n", "# Let's explore the SOFT file more thoroughly to find gene symbols\n", "print(\"\\nExploring SOFT file more thoroughly for gene information:\")\n", "gene_info_patterns = []\n", "entrez_to_symbol = {}\n", "\n", "with gzip.open(soft_file, 'rt') as f:\n", " for i, line in enumerate(f):\n", " if i < 1000: # Check header section for platform info\n", " if '!Series_platform_id' in line or '!Platform_title' in line:\n", " print(line.strip())\n", " \n", " # Look for gene-related columns and patterns in the file\n", " if 'GENE_SYMBOL' in line or 'gene_symbol' in line or 'Symbol' in line:\n", " gene_info_patterns.append(line.strip())\n", " \n", " # Extract a mapping using ENTREZ_GENE_ID if available\n", " if len(gene_info_patterns) < 2 and 'ENTREZ_GENE_ID' in line and '\\t' in line:\n", " parts = line.strip().split('\\t')\n", " if len(parts) >= 2:\n", " try:\n", " # Attempt to add to mapping - assuming ENTREZ_GENE_ID could help with lookup\n", " entrez_id = parts[1]\n", " probe_id = parts[0]\n", " if entrez_id.isdigit() and entrez_id != probe_id:\n", " entrez_to_symbol[probe_id] = entrez_id\n", " except:\n", " pass\n", " \n", " if i > 10000 and len(gene_info_patterns) > 0: # Limit search but ensure we found something\n", " break\n", "\n", "# Show some of the patterns found\n", "if gene_info_patterns:\n", " print(\"\\nFound gene-related patterns:\")\n", " for pattern in gene_info_patterns[:5]:\n", " print(pattern)\n", "else:\n", " print(\"\\nNo explicit gene info patterns found\")\n", "\n", "# Let's try to match the ENTREZ_GENE_ID to the probe IDs\n", "print(\"\\nAnalyzing ENTREZ_GENE_ID column:\")\n", "if 'ENTREZ_GENE_ID' in gene_annotation.columns:\n", " # Check if ENTREZ_GENE_ID contains actual Entrez IDs (different from probe IDs)\n", " gene_annotation['ENTREZ_GENE_ID'] = gene_annotation['ENTREZ_GENE_ID'].astype(str)\n", " different_ids = (gene_annotation['ENTREZ_GENE_ID'] != gene_annotation['ID']).sum()\n", " print(f\"Number of entries where ENTREZ_GENE_ID differs from ID: {different_ids}\")\n", " \n", " if different_ids > 0:\n", " print(\"Some ENTREZ_GENE_ID values differ from probe IDs - this could be useful for mapping\")\n", " # Show examples of differing values\n", " diff_examples = gene_annotation[gene_annotation['ENTREZ_GENE_ID'] != gene_annotation['ID']].head(5)\n", " print(diff_examples)\n", " else:\n", " print(\"ENTREZ_GENE_ID appears to be identical to probe ID - not useful for mapping\")\n", "\n", "# Search for additional annotation information in the dataset\n", "print(\"\\nLooking for alternative annotation approaches:\")\n", "print(\"- Checking for platform ID or accession number in SOFT file\")\n", "\n", "platform_id = None\n", "with gzip.open(soft_file, 'rt') as f:\n", " for i, line in enumerate(f):\n", " if '!Platform_geo_accession' in line:\n", " platform_id = line.split('=')[1].strip().strip('\"')\n", " print(f\"Found platform GEO accession: {platform_id}\")\n", " break\n", " if i > 200:\n", " break\n", "\n", "# If we don't find proper gene symbol mappings, prepare to use the ENTREZ_GENE_ID as is\n", "if 'ENTREZ_GENE_ID' in gene_annotation.columns:\n", " print(\"\\nPreparing provisional gene mapping using ENTREZ_GENE_ID:\")\n", " mapping_data = gene_annotation[['ID', 'ENTREZ_GENE_ID']].copy()\n", " mapping_data.rename(columns={'ENTREZ_GENE_ID': 'Gene'}, inplace=True)\n", " print(f\"Provisional mapping data shape: {mapping_data.shape}\")\n", " print(preview_df(mapping_data, n=5))\n", "else:\n", " print(\"\\nWarning: No suitable mapping column found for gene symbols\")\n" ] }, { "cell_type": "markdown", "id": "128cca11", "metadata": {}, "source": [ "### Step 6: Gene Identifier Mapping" ] }, { "cell_type": "code", "execution_count": 7, "id": "332c14a9", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T08:40:16.597157Z", "iopub.status.busy": "2025-03-25T08:40:16.597025Z", "iopub.status.idle": "2025-03-25T08:40:16.867952Z", "shell.execute_reply": "2025-03-25T08:40:16.867562Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Gene mapping dataframe shape: (45782, 2)\n", "Gene mapping preview:\n", "{'ID': ['1007_s_at', '1053_at', '117_at', '121_at', '1255_g_at'], 'Gene': ['DDR1 /// MIR4640', 'RFC2', 'HSPA6', 'PAX8', 'GUCA1A']}\n", "Gene expression data shape after mapping: (4014, 58)\n", "Gene expression data preview (first 5 genes and 5 samples):\n", "{'GSM3418016': [2.092183039, 7.971652466, 3.546681119, 6.299075065, 6.939140418], 'GSM3418017': [2.092183039, 8.218329849, 2.56706382, 6.808457269, 6.004497419], 'GSM3418018': [2.092183039, 6.388610006, 2.555676168, 7.6624577160000005, 5.736624633], 'GSM3418019': [2.092183039, 6.60390926, 2.620776054, 12.454928539, 5.67115637], 'GSM3418020': [2.092183039, 6.978084306, 3.032865135, 15.321492221, 6.561169036]}\n", "Gene expression data shape after normalization: (3714, 58)\n", "Gene expression data preview after normalization (first 5 genes and 5 samples):\n", "{'GSM3418016': [2.092183039, 7.971652466, 3.546681119, 6.299075065, 6.939140418], 'GSM3418017': [2.092183039, 8.218329849, 2.56706382, 6.808457269, 6.004497419], 'GSM3418018': [2.092183039, 6.388610006, 2.555676168, 7.6624577160000005, 5.736624633], 'GSM3418019': [2.092183039, 6.60390926, 2.620776054, 12.454928539, 5.67115637], 'GSM3418020': [2.092183039, 6.978084306, 3.032865135, 15.321492221, 6.561169036]}\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Gene expression data saved to ../../output/preprocess/Eczema/gene_data/GSE120899.csv\n" ] } ], "source": [ "# Based on the previews, we can see:\n", "# 1. In gene expression data, the IDs are probe IDs like '1007_s_at'\n", "# 2. In gene annotation, the 'ID' column contains these probe IDs\n", "# 3. The 'Gene Symbol' column contains the gene symbols we need\n", "\n", "# 1. Decide which columns to use for mapping\n", "prob_col = 'ID' # This is the probe ID column in gene_annotation\n", "gene_col = 'Gene Symbol' # This is the gene symbol column in gene_annotation\n", "\n", "# 2. Get a gene mapping dataframe using the get_gene_mapping function\n", "gene_mapping = get_gene_mapping(gene_annotation, prob_col, gene_col)\n", "print(f\"Gene mapping dataframe shape: {gene_mapping.shape}\")\n", "print(\"Gene mapping preview:\")\n", "print(preview_df(gene_mapping, n=5))\n", "\n", "# 3. Apply the gene mapping to convert probe-level measurements to gene-level expression\n", "gene_data = apply_gene_mapping(gene_data, gene_mapping)\n", "print(f\"Gene expression data shape after mapping: {gene_data.shape}\")\n", "print(\"Gene expression data preview (first 5 genes and 5 samples):\")\n", "print(preview_df(gene_data.iloc[:5, :5], n=5))\n", "\n", "# Normalize gene symbols\n", "gene_data = normalize_gene_symbols_in_index(gene_data)\n", "print(f\"Gene expression data shape after normalization: {gene_data.shape}\")\n", "print(\"Gene expression data preview after normalization (first 5 genes and 5 samples):\")\n", "print(preview_df(gene_data.iloc[:5, :5], n=5))\n", "\n", "# Save the 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\"Gene expression data saved to {out_gene_data_file}\")\n" ] }, { "cell_type": "markdown", "id": "d719e3ca", "metadata": {}, "source": [ "### Step 7: Data Normalization and Linking" ] }, { "cell_type": "code", "execution_count": 8, "id": "d88d30e8", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T08:40:16.869803Z", "iopub.status.busy": "2025-03-25T08:40:16.869682Z", "iopub.status.idle": "2025-03-25T08:40:17.989109Z", "shell.execute_reply": "2025-03-25T08:40:17.988714Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Checking if clinical data extraction is needed...\n", "Clinical data file already exists at: ../../output/preprocess/Eczema/clinical_data/GSE120899.csv\n", "\n", "Normalizing gene symbols...\n", "Gene data shape after normalization: (3714, 58)\n", "Sample of normalized gene symbols: ['A2M', 'A2ML1', 'AAGAB', 'ABCA13', 'ABCB1', 'ABCB5', 'ABCB9', 'ABCC11', 'ABCC12', 'ABCC13']\n", "Normalized gene data saved to ../../output/preprocess/Eczema/gene_data/GSE120899.csv\n", "\n", "Linking clinical and genetic data...\n", "Linked data shape: (58, 3715)\n", "Linked data preview (first 5 rows, 5 columns):\n", " Eczema A2M A2ML1 AAGAB ABCA13\n", "GSM3418016 1.0 2.092183 7.971652 3.546681 6.299075\n", "GSM3418017 0.0 2.092183 8.218330 2.567064 6.808457\n", "GSM3418018 0.0 2.092183 6.388610 2.555676 7.662458\n", "GSM3418019 0.0 2.092183 6.603909 2.620776 12.454929\n", "GSM3418020 1.0 2.092183 6.978084 3.032865 15.321492\n", "\n", "Handling missing values...\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Linked data shape after handling missing values: (58, 3715)\n", "\n", "Checking for bias in dataset features...\n", "For the feature 'Eczema', the least common label is '0.0' with 23 occurrences. This represents 39.66% of the dataset.\n", "The distribution of the feature 'Eczema' in this dataset is fine.\n", "\n", "A new JSON file was created at: ../../output/preprocess/Eczema/cohort_info.json\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Linked data saved to ../../output/preprocess/Eczema/GSE120899.csv\n" ] } ], "source": [ "# 1. Check first if we need to complete the clinical feature extraction from Step 2\n", "print(\"Checking if clinical data extraction is needed...\")\n", "if not os.path.exists(out_clinical_data_file):\n", " print(\"Clinical data file not found. Extracting clinical features from original data...\")\n", " # Get the matrix file path\n", " _, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)\n", " \n", " # Get the clinical data from the matrix file\n", " background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']\n", " clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']\n", " _, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)\n", " \n", " # Define conversion functions from Step 2\n", " def convert_trait(value: str) -> Optional[int]:\n", " if value is None:\n", " return None\n", " if ':' in value:\n", " value = value.split(':', 1)[1].strip()\n", " \n", " if 'eczema' in value.lower():\n", " return 1 # Case\n", " elif 'control' in value.lower() or 'non-involved' in value.lower():\n", " return 0 # Control\n", " else:\n", " return None # Other conditions like psoriasis\n", "\n", " def convert_age(value: str) -> Optional[float]:\n", " if value is None:\n", " return None\n", " if ':' in value:\n", " value = value.split(':', 1)[1].strip()\n", " \n", " age_match = re.search(r'(\\d+)', value)\n", " if age_match:\n", " return float(age_match.group(1))\n", " return None\n", "\n", " def convert_gender(value: str) -> Optional[int]:\n", " if value is None:\n", " return None\n", " if ':' in value:\n", " value = value.split(':', 1)[1].strip()\n", " \n", " if 'female' in value.lower():\n", " return 0\n", " elif 'male' in value.lower():\n", " return 1\n", " return None\n", " \n", " # Extract clinical features with identified rows from Step 2\n", " trait_row = 1\n", " age_row = 4\n", " gender_row = 3\n", " \n", " clinical_features = geo_select_clinical_features(\n", " clinical_data,\n", " trait=trait,\n", " trait_row=trait_row,\n", " convert_trait=convert_trait,\n", " age_row=age_row,\n", " convert_age=convert_age,\n", " gender_row=gender_row,\n", " convert_gender=convert_gender\n", " )\n", " \n", " # Save clinical features\n", " os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)\n", " clinical_features.to_csv(out_clinical_data_file)\n", " print(f\"Clinical features extracted and saved to: {out_clinical_data_file}\")\n", "else:\n", " print(f\"Clinical data file already exists at: {out_clinical_data_file}\")\n", " clinical_features = pd.read_csv(out_clinical_data_file, index_col=0)\n", "\n", "# Now proceed with Step 7 as originally planned\n", "# 1. Normalize gene symbols using NCBI Gene database information\n", "print(\"\\nNormalizing gene symbols...\")\n", "try:\n", " # Load the gene data if needed\n", " if 'gene_data' not in locals() or gene_data is None:\n", " gene_data = pd.read_csv(out_gene_data_file, index_col=0)\n", " \n", " # Normalize 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\"Sample of normalized gene symbols: {normalized_gene_data.index[:10].tolist()}\")\n", " \n", " # Save the normalized gene data\n", " normalized_gene_data.to_csv(out_gene_data_file)\n", " print(f\"Normalized gene data saved to {out_gene_data_file}\")\n", "except Exception as e:\n", " print(f\"Error normalizing gene symbols: {e}\")\n", "\n", "# 2. Link clinical and genetic data\n", "print(\"\\nLinking clinical and genetic data...\")\n", "try:\n", " # 3. Link clinical and genetic data\n", " linked_data = geo_link_clinical_genetic_data(clinical_features, normalized_gene_data)\n", " print(f\"Linked data shape: {linked_data.shape}\")\n", " print(\"Linked data preview (first 5 rows, 5 columns):\")\n", " if linked_data.shape[0] > 0 and linked_data.shape[1] > 5:\n", " print(linked_data.iloc[:5, :5])\n", " else:\n", " print(linked_data)\n", " \n", " # 4. Handle missing values\n", " print(\"\\nHandling missing values...\")\n", " linked_data_clean = handle_missing_values(linked_data, trait)\n", " print(f\"Linked data shape after handling missing values: {linked_data_clean.shape}\")\n", " \n", " # 5. Check for bias in the dataset\n", " print(\"\\nChecking for bias in dataset features...\")\n", " is_biased, linked_data_clean = judge_and_remove_biased_features(linked_data_clean, trait)\n", " \n", " # 6. Conduct final quality validation\n", " note = \"Dataset contains gene expression data from skin biopsies comparing different skin conditions including eczema (atopic dermatitis and contact eczema) against other conditions like psoriasis and healthy controls.\"\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_biased,\n", " df=linked_data_clean,\n", " note=note\n", " )\n", " \n", " # 7. Save the linked data if it's usable\n", " if is_usable:\n", " os.makedirs(os.path.dirname(out_data_file), exist_ok=True)\n", " linked_data_clean.to_csv(out_data_file, index=True)\n", " print(f\"Linked data saved to {out_data_file}\")\n", " else:\n", " print(\"Dataset deemed not usable for associative studies. Linked data not saved.\")\n", " \n", "except Exception as e:\n", " print(f\"Error processing data: {e}\")\n", " # If processing fails, we should still validate the dataset status\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, # We know trait data is available from step 2\n", " is_biased=True, # Set to True to ensure it's not marked usable\n", " df=pd.DataFrame(), # Empty dataframe since processing failed\n", " note=f\"Failed to process data: {e}\"\n", " )\n", " print(\"Dataset validation completed with error status.\")" ] } ], "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 }