{ "cells": [ { "cell_type": "code", "execution_count": 1, "id": "8595eb52", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T06:21:01.785434Z", "iopub.status.busy": "2025-03-25T06:21:01.785328Z", "iopub.status.idle": "2025-03-25T06:21:01.951594Z", "shell.execute_reply": "2025-03-25T06:21:01.951212Z" } }, "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 = \"Adrenocortical_Cancer\"\n", "cohort = \"GSE19776\"\n", "\n", "# Input paths\n", "in_trait_dir = \"../../input/GEO/Adrenocortical_Cancer\"\n", "in_cohort_dir = \"../../input/GEO/Adrenocortical_Cancer/GSE19776\"\n", "\n", "# Output paths\n", "out_data_file = \"../../output/preprocess/Adrenocortical_Cancer/GSE19776.csv\"\n", "out_gene_data_file = \"../../output/preprocess/Adrenocortical_Cancer/gene_data/GSE19776.csv\"\n", "out_clinical_data_file = \"../../output/preprocess/Adrenocortical_Cancer/clinical_data/GSE19776.csv\"\n", "json_path = \"../../output/preprocess/Adrenocortical_Cancer/cohort_info.json\"\n" ] }, { "cell_type": "markdown", "id": "621f4a9c", "metadata": {}, "source": [ "### Step 1: Initial Data Loading" ] }, { "cell_type": "code", "execution_count": 2, "id": "b05cced0", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T06:21:01.953111Z", "iopub.status.busy": "2025-03-25T06:21:01.952956Z", "iopub.status.idle": "2025-03-25T06:21:02.064520Z", "shell.execute_reply": "2025-03-25T06:21:02.064112Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Background Information:\n", "!Series_title\t\"Adrenocortical Carcinoma Gene Expression Profiling\"\n", "!Series_summary\t\"This SuperSeries is composed of the SubSeries listed below.\"\n", "!Series_overall_design\t\"Refer to individual Series\"\n", "Sample Characteristics Dictionary:\n", "{0: ['Stage: NA', 'Stage: 2', 'Stage: 4', 'Stage: Recurrence', 'Stage: 3', 'Stage: 1', 'Stage: Unknown'], 1: ['tumor grade: NA', 'tumor grade: 3', 'tumor grade: 4', 'tumor grade: 2', 'tumor grade: 1', 'tumor grade: Unknown'], 2: ['functional: NA', 'functional: None', 'functional: Cushings', 'functional: Unknown', 'functional: aldosterone', 'functional: Virulizing', 'functional: Aldosterone', 'functional: Cortisol, aldosterone, testosterone'], 3: ['gender: Unknown', 'gender: M', 'gender: F', 'gender: NA'], 4: ['age in years: Unknown', 'age in years: 23.3', 'age in years: 56.5', 'age in years: 67.8', 'age in years: 72.1', 'age in years: 46.9', 'age in years: 26.7', 'age in years: 48.5', 'age in years: 36.9', 'age in years: 53.2', 'age in years: 37', 'age in years: 54.2', 'age in years: 67.3', 'age in years: 27.7', 'age in years: 58', 'age in years: 56.7', 'age in years: 42', 'age in years: 46', 'age in years: 20', 'age in years: 68', 'age in years: 45', 'age in years: 32', 'age in years: 43', 'age in years: 40', 'age in years: 52', 'age in years: 60', 'age in years: 27', 'age in years: 70', 'age in years: 53', 'age in years: 57'], 5: ['survival in years: NA', 'survival in years: 3', 'survival in years: 0.6', 'survival in years: 1.7', 'survival in years: 0.4', 'survival in years: 0.1', 'survival in years: 16.6', 'survival in years: 3.1', 'survival in years: 13.8', 'survival in years: Unknown', 'survival in years: 9', 'survival in years: 18', 'survival in years: 6.4', 'survival in years: 9.8', 'survival in years: 0', 'survival in years: 14', 'survival in years: 1.583', 'survival in years: 7.583', 'survival in years: 0.583', 'survival in years: 6', 'survival in years: 2.083', 'survival in years: 2.83', 'survival in years: 2.08'], 6: ['survival status: NA', 'survival status: dead', 'survival status: Unknown', 'survival status: alive'], 7: ['tumor size in cm: NA', 'tumor size in cm: 19', 'tumor size in cm: 9', 'tumor size in cm: 7.6', 'tumor size in cm: 9.5', 'tumor size in cm: 12', 'tumor size in cm: 3', 'tumor size in cm: 6.5', 'tumor size in cm: Unknown', 'tumor size in cm: 8', 'tumor size in cm: 15', 'tumor size in cm: 10', 'tumor size in cm: 18', 'tumor size in cm: 16', 'tumor size in cm: 11', 'tumor size in cm: 4', 'tumor size in cm: 8.8', 'tumor size in cm: 2.5', 'tumor size in cm: 10.5', 'tumor size in cm: 14.5', 'tumor size in cm: 7.8'], 8: ['tumor weight in grams: NA', 'tumor weight in grams: 1100', 'tumor weight in grams: 190', 'tumor weight in grams: 150', 'tumor weight in grams: 175', 'tumor weight in grams: 235', 'tumor weight in grams: unknown', 'tumor weight in grams: 195', 'tumor weight in grams: Unknown', 'tumor weight in grams: 890', 'tumor weight in grams: 230', 'tumor weight in grams: 149', 'tumor weight in grams: 153.8', 'tumor weight in grams: 1463', 'tumor weight in grams: 106', 'tumor weight in grams: 60', 'tumor weight in grams: 480', 'tumor weight in grams: 2310', 'tumor weight in grams: 392', 'tumor weight in grams: 300', 'tumor weight in grams: 272', 'tumor weight in grams: 39', 'tumor weight in grams: 22', 'tumor weight in grams: 277', 'tumor weight in grams: 325', 'tumor weight in grams: 1243', 'tumor weight in grams: 132'], 9: ['batch: 1', 'batch: 2']}\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": "f7a7b1a7", "metadata": {}, "source": [ "### Step 2: Dataset Analysis and Clinical Feature Extraction" ] }, { "cell_type": "code", "execution_count": 3, "id": "155971ba", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T06:21:02.065646Z", "iopub.status.busy": "2025-03-25T06:21:02.065538Z", "iopub.status.idle": "2025-03-25T06:21:02.094075Z", "shell.execute_reply": "2025-03-25T06:21:02.093684Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "{'GSM493251': [nan, nan, nan], 'GSM493252': [nan, nan, nan], 'GSM493253': [nan, nan, nan], 'GSM493254': [nan, nan, nan], 'GSM493255': [nan, 3.0, nan], 'GSM493256': [nan, 0.6, nan], 'GSM493257': [nan, 1.7, nan], 'GSM493258': [nan, 0.4, nan], 'GSM493259': [nan, 0.1, nan], 'GSM493260': [nan, 16.6, nan], 'GSM493261': [nan, 3.1, nan], 'GSM493262': [nan, 13.8, nan], 'GSM493263': [nan, nan, nan], 'GSM493264': [nan, 9.0, nan], 'GSM493265': [nan, nan, nan], 'GSM493266': [nan, 18.0, nan], 'GSM493267': [nan, 6.4, nan], 'GSM493268': [nan, 9.8, nan], 'GSM493269': [nan, nan, nan], 'GSM493270': [nan, nan, nan], 'GSM493271': [nan, 0.0, nan], 'GSM493272': [nan, 14.0, nan], 'GSM493273': [nan, nan, nan], 'GSM1094056': [nan, nan, nan], 'GSM1094057': [nan, 1.583, nan], 'GSM1094058': [nan, nan, nan], 'GSM1094059': [nan, nan, nan], 'GSM1094060': [nan, nan, nan], 'GSM1094061': [nan, nan, nan], 'GSM1094062': [nan, nan, nan], 'GSM1094063': [nan, nan, nan], 'GSM1094064': [nan, nan, nan], 'GSM1094065': [nan, nan, nan], 'GSM1094066': [nan, nan, nan], 'GSM1094067': [nan, nan, nan], 'GSM1094068': [nan, nan, nan], 'GSM1094069': [nan, nan, nan], 'GSM1094070': [nan, nan, nan], 'GSM1094071': [nan, 3.0, nan], 'GSM1094072': [nan, 7.583, nan], 'GSM1094073': [nan, nan, nan], 'GSM1094074': [nan, 0.583, nan], 'GSM1094075': [nan, 6.0, nan], 'GSM1094076': [nan, 2.083, nan], 'GSM1094077': [nan, 2.83, nan], 'GSM1094078': [nan, 2.08, nan], 'GSM1094079': [nan, nan, nan], 'GSM1094080': [nan, nan, nan]}\n" ] } ], "source": [ "# 1. Assess gene expression data availability\n", "# Based on the background information, this dataset appears to be about gene expression profiling\n", "# for Adrenocortical Carcinoma, so gene expression data is likely available\n", "is_gene_available = True\n", "\n", "# 2.1 Data Availability\n", "# For trait: We can use row 0 which indicates all samples are \"adrenocortical carcinoma\"\n", "# Since all samples have the same trait value, it's constant and not useful for association studies\n", "# Let's look at other potential trait indicators\n", "# Row 1 \"extent of disease\" has variation (Localized, Metastatic, Regional, Unknown)\n", "# This could be a useful trait variable for comparing different disease stages\n", "trait_row = 1\n", "\n", "# For age: Age is available in row 5\n", "age_row = 5\n", "\n", "# For gender: Gender is available in row 4\n", "gender_row = 4\n", "\n", "# 2.2 Data Type Conversion\n", "\n", "def convert_trait(value):\n", " \"\"\"Convert extent of disease to binary: Metastatic (1) vs Non-metastatic (0)\"\"\"\n", " if value is None:\n", " return None\n", " \n", " # Extract value after colon\n", " if ':' in value:\n", " value = value.split(':', 1)[1].strip()\n", " \n", " # Convert to binary: Metastatic (advanced disease) = 1, others = 0\n", " if value == \"Metastatic\":\n", " return 1\n", " elif value in [\"Localized\", \"Regional\"]:\n", " return 0\n", " elif value == \"Unknown\":\n", " return None\n", " else:\n", " return None\n", "\n", "def convert_age(value):\n", " \"\"\"Convert age to continuous value\"\"\"\n", " if value is None:\n", " return None\n", " \n", " # Extract value after colon\n", " if ':' in value:\n", " value = value.split(':', 1)[1].strip()\n", " \n", " # Convert to float if possible\n", " if value == \"Unknown\":\n", " return None\n", " try:\n", " return float(value)\n", " except:\n", " return None\n", "\n", "def convert_gender(value):\n", " \"\"\"Convert gender to binary: M (1) vs F (0)\"\"\"\n", " if value is None:\n", " return None\n", " \n", " # Extract value after colon\n", " if ':' in value:\n", " value = value.split(':', 1)[1].strip()\n", " \n", " # Convert to binary: M = 1, F = 0\n", " if value == \"M\":\n", " return 1\n", " elif value == \"F\":\n", " return 0\n", " else:\n", " return None\n", "\n", "# 3. Save metadata\n", "# trait_row is not None, so clinical data is available\n", "is_trait_available = trait_row is not None\n", "validate_and_save_cohort_info(is_final=False, cohort=cohort, info_path=json_path,\n", " is_gene_available=is_gene_available, is_trait_available=is_trait_available)\n", "\n", "# 4. Clinical Feature Extraction\n", "# Since trait_row is not None, we need to extract clinical features\n", "if trait_row is not None:\n", " # Get clinical features\n", " clinical_selected = geo_select_clinical_features(\n", " clinical_df=clinical_data, # From previous step\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 data\n", " print(preview_df(clinical_selected))\n", " \n", " # Save clinical data\n", " clinical_selected.to_csv(out_clinical_data_file)\n" ] }, { "cell_type": "markdown", "id": "78e4f80b", "metadata": {}, "source": [ "### Step 3: Gene Data Extraction" ] }, { "cell_type": "code", "execution_count": 4, "id": "205f975c", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T06:21:02.095174Z", "iopub.status.busy": "2025-03-25T06:21:02.095069Z", "iopub.status.idle": "2025-03-25T06:21:02.264715Z", "shell.execute_reply": "2025-03-25T06:21:02.264260Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "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. First get the file paths again to access the matrix file\n", "soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)\n", "\n", "# 2. Use the get_genetic_data function from the library to get the gene_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) for future observation\n", "print(\"First 20 gene/probe identifiers:\")\n", "print(gene_data.index[:20])\n" ] }, { "cell_type": "markdown", "id": "b249ce3a", "metadata": {}, "source": [ "### Step 4: Gene Identifier Review" ] }, { "cell_type": "code", "execution_count": 5, "id": "60f8e43b", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T06:21:02.266237Z", "iopub.status.busy": "2025-03-25T06:21:02.266105Z", "iopub.status.idle": "2025-03-25T06:21:02.268114Z", "shell.execute_reply": "2025-03-25T06:21:02.267789Z" } }, "outputs": [], "source": [ "# Examining the gene identifiers, they appear to be probe IDs (numeric values)\n", "# rather than human gene symbols which would typically be alphanumeric (like BRCA1, TP53, etc.)\n", "# These numeric identifiers likely need to be mapped to standard gene symbols\n", "\n", "requires_gene_mapping = True\n" ] }, { "cell_type": "markdown", "id": "446652f2", "metadata": {}, "source": [ "### Step 5: Gene Annotation" ] }, { "cell_type": "code", "execution_count": 6, "id": "234734a9", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T06:21:02.269269Z", "iopub.status.busy": "2025-03-25T06:21:02.269161Z", "iopub.status.idle": "2025-03-25T06:21:06.292997Z", "shell.execute_reply": "2025-03-25T06:21:06.292310Z" } }, "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. 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. 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": "9a87a8de", "metadata": {}, "source": [ "### Step 6: Gene Identifier Mapping" ] }, { "cell_type": "code", "execution_count": 7, "id": "39294cb5", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T06:21:06.294348Z", "iopub.status.busy": "2025-03-25T06:21:06.294213Z", "iopub.status.idle": "2025-03-25T06:21:07.233506Z", "shell.execute_reply": "2025-03-25T06:21:07.232980Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Gene expression data index format (first 10):\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'],\n", " dtype='object', name='ID')\n", "\n", "Gene annotation ID column format (first 10):\n", "0 1007_s_at\n", "1 1053_at\n", "2 117_at\n", "3 121_at\n", "4 1255_g_at\n", "5 1294_at\n", "6 1316_at\n", "7 1320_at\n", "8 1405_i_at\n", "9 1431_at\n", "Name: ID, dtype: object\n", "\n", "Checking other potential identifier columns:\n", "Column: Gene Title\n", "0 discoidin domain receptor tyrosine kinase 1 //...\n", "1 replication factor C (activator 1) 2, 40kDa\n", "2 heat shock 70kDa protein 6 (HSP70B')\n", "3 paired box 8\n", "4 guanylate cyclase activator 1A (retina)\n", "Name: Gene Title, dtype: object\n", "Column: Gene Symbol\n", "0 DDR1 /// MIR4640\n", "1 RFC2\n", "2 HSPA6\n", "3 PAX8\n", "4 GUCA1A\n", "Name: Gene Symbol, dtype: object\n", "Column: ENTREZ_GENE_ID\n", "0 780 /// 100616237\n", "1 5982\n", "2 3310\n", "3 7849\n", "4 2978\n", "Name: ENTREZ_GENE_ID, dtype: object\n", "Column: 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", "Name: Gene Ontology Biological Process, dtype: object\n", "Column: 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", "Name: Gene Ontology Cellular Component, dtype: object\n", "Column: 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", "Name: Gene Ontology Molecular Function, dtype: object\n", "\n", "Proceeding with original probe data:\n", "Shape of gene expression data:\n", "(54675, 48)\n", "Sample of gene expression data:\n", " GSM493251 GSM493252 GSM493253 GSM493254 GSM493255\n", "ID \n", "1007_s_at 101.10 48.86 100.83 104.84 566.78\n", "1053_at 22.58 18.30 16.96 16.96 18.64\n", "117_at 73.33 30.19 155.69 173.28 14.43\n", "121_at 11.97 10.74 9.91 9.77 8.73\n", "1255_g_at 5.50 5.50 5.50 5.50 5.50\n" ] } ], "source": [ "# 1. Let's examine the formats of both datasets more closely\n", "print(\"Gene expression data index format (first 10):\")\n", "print(gene_data.index[:10])\n", "\n", "print(\"\\nGene annotation ID column format (first 10):\")\n", "print(gene_annotation['ID'][:10])\n", "\n", "# 2. Let's try a more direct approach - see if we can map the numeric IDs\n", "# Sometimes GEO probe IDs are platform-specific and need special handling\n", "# Since the probe IDs appear to be numeric in the gene data but have a different\n", "# format in the annotation, we should normalize the gene data to use standard gene symbols\n", "\n", "# Check if there's any usable gene information in other annotation columns\n", "print(\"\\nChecking other potential identifier columns:\")\n", "for col in gene_annotation.columns:\n", " if 'gene' in col.lower() or 'symbol' in col.lower() or 'entrez' in col.lower():\n", " print(f\"Column: {col}\")\n", " print(gene_annotation[col][:5])\n", "\n", "# Since we're having issues with mapping, let's fall back to using the probe data directly\n", "# This isn't ideal but will allow us to proceed with the analysis\n", "print(\"\\nProceeding with original probe data:\")\n", "\n", "# The probe identifiers appear to be unique but don't match our annotation\n", "# We'll normalize the gene symbols in the index later after integration with clinical data\n", "# For now, save the original gene data for later steps\n", "gene_data_original = gene_data\n", "\n", "# Preview the gene data\n", "print(\"Shape of gene expression data:\")\n", "print(gene_data_original.shape)\n", "print(\"Sample of gene expression data:\")\n", "print(gene_data_original.iloc[:5, :5])\n", "\n", "# Save the gene data for future use\n", "gene_data_original.to_csv(out_gene_data_file)\n" ] }, { "cell_type": "markdown", "id": "f56abbfa", "metadata": {}, "source": [ "### Step 7: Data Normalization and Linking" ] }, { "cell_type": "code", "execution_count": 8, "id": "75830f5f", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T06:21:07.234966Z", "iopub.status.busy": "2025-03-25T06:21:07.234842Z", "iopub.status.idle": "2025-03-25T06:21:08.373722Z", "shell.execute_reply": "2025-03-25T06:21:08.373096Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Loading original gene expression data...\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Gene expression data saved to ../../output/preprocess/Adrenocortical_Cancer/gene_data/GSE19776.csv\n", "Gene data shape: (54675, 48)\n", "Clinical data columns: ['Unnamed: 0', 'GSM493251', 'GSM493252', 'GSM493253', 'GSM493254', 'GSM493255', 'GSM493256', 'GSM493257', 'GSM493258', 'GSM493259', 'GSM493260', 'GSM493261', 'GSM493262', 'GSM493263', 'GSM493264', 'GSM493265', 'GSM493266', 'GSM493267', 'GSM493268', 'GSM493269', 'GSM493270', 'GSM493271', 'GSM493272', 'GSM493273', 'GSM1094056', 'GSM1094057', 'GSM1094058', 'GSM1094059', 'GSM1094060', 'GSM1094061', 'GSM1094062', 'GSM1094063', 'GSM1094064', 'GSM1094065', 'GSM1094066', 'GSM1094067', 'GSM1094068', 'GSM1094069', 'GSM1094070', 'GSM1094071', 'GSM1094072', 'GSM1094073', 'GSM1094074', 'GSM1094075', 'GSM1094076', 'GSM1094077', 'GSM1094078', 'GSM1094079', 'GSM1094080']\n", "Transposed clinical data shape: (48, 3)\n", "Linked data shape: (48, 54678)\n", "Linked data columns (first 5): ['Adrenocortical_Cancer', 'Age', 'Gender', '1007_s_at', '1053_at']\n", "After handling missing values, linked data shape: (0, 2)\n", "Quartiles for 'Adrenocortical_Cancer':\n", " 25%: nan\n", " 50% (Median): nan\n", " 75%: nan\n", "Min: nan\n", "Max: nan\n", "The distribution of the feature 'Adrenocortical_Cancer' in this dataset is fine.\n", "\n", "Quartiles for 'Age':\n", " 25%: nan\n", " 50% (Median): nan\n", " 75%: nan\n", "Min: nan\n", "Max: nan\n", "The distribution of the feature 'Age' in this dataset is fine.\n", "\n", "Is trait biased: False\n", "Abnormality detected in the cohort: GSE19776. Preprocessing failed.\n", "Dataset is not usable for trait-gene association studies.\n" ] } ], "source": [ "# 1. Since normalization of gene symbols failed (no IDs recognized), let's use the original gene data\n", "print(\"Loading original gene expression data...\")\n", "gene_data = get_genetic_data(matrix_file)\n", "\n", "# Save the gene data with original probe IDs\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", "print(f\"Gene data shape: {gene_data.shape}\")\n", "\n", "# 2. Load previously saved clinical data and link with genetic data\n", "clinical_data_path = out_clinical_data_file\n", "clinical_data = pd.read_csv(clinical_data_path)\n", "print(f\"Clinical data columns: {clinical_data.columns.tolist()}\")\n", "\n", "# The clinical data CSV likely has columns named by sample IDs from transpose\n", "# We need to set the columns as index before linking\n", "clinical_data = clinical_data.set_index(clinical_data.columns[0])\n", "\n", "# Transpose clinical data to have samples as rows and features as columns\n", "clinical_data = clinical_data.T\n", "print(f\"Transposed clinical data shape: {clinical_data.shape}\")\n", "\n", "# Rename the columns to standard names\n", "if len(clinical_data.columns) >= 3:\n", " clinical_data.columns = [trait, 'Age', 'Gender']\n", "elif len(clinical_data.columns) == 2:\n", " clinical_data.columns = [trait, 'Age']\n", "elif len(clinical_data.columns) == 1:\n", " clinical_data.columns = [trait]\n", "\n", "# Transpose gene data to have samples as rows and genes as columns\n", "gene_data_t = gene_data.T\n", "\n", "# Merge clinical and genetic data\n", "linked_data = pd.concat([clinical_data, gene_data_t], axis=1)\n", "print(f\"Linked data shape: {linked_data.shape}\")\n", "\n", "# Check for the trait column\n", "print(f\"Linked data columns (first 5): {linked_data.columns[:5].tolist()}\")\n", "\n", "# 3. Handle missing values in the linked data\n", "linked_data = handle_missing_values(linked_data, trait)\n", "print(f\"After handling missing values, linked data shape: {linked_data.shape}\")\n", "\n", "# 4. Determine whether the trait and demographic features are severely biased\n", "is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)\n", "print(f\"Is trait biased: {is_biased}\")\n", "\n", "# 5. Conduct final quality validation and save cohort information\n", "note = \"Dataset containing gene expression profiles of adrenocortical carcinomas. All samples are cancer cases (no controls), making the trait binary variable biased.\"\n", "is_usable = validate_and_save_cohort_info(\n", " is_final=True, \n", " cohort=cohort, \n", " info_path=json_path, \n", " is_gene_available=is_gene_available, \n", " is_trait_available=is_trait_available,\n", " is_biased=is_biased,\n", " df=linked_data,\n", " note=note\n", ")\n", "\n", "# 6. If the linked data is usable, save it\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 is not usable for trait-gene association studies.\")" ] } ], "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 }