{ "cells": [ { "cell_type": "code", "execution_count": 1, "id": "05879549", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T08:13:22.644035Z", "iopub.status.busy": "2025-03-25T08:13:22.643924Z", "iopub.status.idle": "2025-03-25T08:13:22.804045Z", "shell.execute_reply": "2025-03-25T08:13:22.803698Z" } }, "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 = \"Cervical_Cancer\"\n", "cohort = \"GSE131027\"\n", "\n", "# Input paths\n", "in_trait_dir = \"../../input/GEO/Cervical_Cancer\"\n", "in_cohort_dir = \"../../input/GEO/Cervical_Cancer/GSE131027\"\n", "\n", "# Output paths\n", "out_data_file = \"../../output/preprocess/Cervical_Cancer/GSE131027.csv\"\n", "out_gene_data_file = \"../../output/preprocess/Cervical_Cancer/gene_data/GSE131027.csv\"\n", "out_clinical_data_file = \"../../output/preprocess/Cervical_Cancer/clinical_data/GSE131027.csv\"\n", "json_path = \"../../output/preprocess/Cervical_Cancer/cohort_info.json\"\n" ] }, { "cell_type": "markdown", "id": "dd20341c", "metadata": {}, "source": [ "### Step 1: Initial Data Loading" ] }, { "cell_type": "code", "execution_count": 2, "id": "62d67ef4", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T08:13:22.805448Z", "iopub.status.busy": "2025-03-25T08:13:22.805307Z", "iopub.status.idle": "2025-03-25T08:13:23.127385Z", "shell.execute_reply": "2025-03-25T08:13:23.127015Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Background Information:\n", "!Series_title\t\"High frequency of pathogenic germline variants in genes associated with homologous recombination repair in patients with advanced solid cancers\"\n", "!Series_summary\t\"We identified pathogenic and likely pathogenic variants in 17.8% of the patients within a wide range of cancer types. In particular, mesothelioma, ovarian cancer, cervical cancer, urothelial cancer, and cancer of unknown primary origin displayed high frequencies of pathogenic variants. In total, 22 BRCA1 and BRCA2 germline variant were identified in 12 different cancer types, of which 10 (45%) variants were not previously identified in these patients. Pathogenic germline variants were predominantly found in DNA repair pathways; approximately half of the variants were within genes involved in homologous recombination repair. Loss of heterozygosity and somatic second hits were identified in several of these genes, supporting possible causality for cancer development. A potential treatment target based on pathogenic germline variant could be suggested in 25 patients (4%).\"\n", "!Series_overall_design\t\"investigation of expression features related to Class 4 and 5 germline mutations in cancer patients\"\n", "Sample Characteristics Dictionary:\n", "{0: ['tissue: tumor biopsy'], 1: ['cancer: Breast cancer', 'cancer: Colorectal cancer', 'cancer: Bile duct cancer', 'cancer: Mesothelioma', 'cancer: Urothelial cancer', 'cancer: Pancreatic cancer', 'cancer: Melanoma', 'cancer: Hepatocellular carcinoma', 'cancer: Ovarian cancer', 'cancer: Cervical cancer', 'cancer: Head and Neck cancer', 'cancer: Sarcoma', 'cancer: Prostate cancer', 'cancer: Adenoid cystic carcinoma', 'cancer: NSCLC', 'cancer: Oesophageal cancer', 'cancer: Thymoma', 'cancer: Others', 'cancer: CUP', 'cancer: Renal cell carcinoma', 'cancer: Gastric cancer', 'cancer: Neuroendocrine cancer', 'cancer: vulvovaginal'], 2: ['mutated gene: ATR', 'mutated gene: FAN1', 'mutated gene: ERCC3', 'mutated gene: FANCD2', 'mutated gene: BAP1', 'mutated gene: DDB2', 'mutated gene: TP53', 'mutated gene: ATM', 'mutated gene: CHEK1', 'mutated gene: BRCA1', 'mutated gene: WRN', 'mutated gene: CHEK2', 'mutated gene: BRCA2', 'mutated gene: XPC', 'mutated gene: PALB2', 'mutated gene: ABRAXAS1', 'mutated gene: NBN', 'mutated gene: BLM', 'mutated gene: FAM111B', 'mutated gene: FANCA', 'mutated gene: MLH1', 'mutated gene: BRIP1', 'mutated gene: IPMK', 'mutated gene: RECQL', 'mutated gene: RAD50', 'mutated gene: FANCM', 'mutated gene: GALNT12', 'mutated gene: SMAD9', 'mutated gene: ERCC2', 'mutated gene: FANCC'], 3: ['predicted: HRDEXP: HRD', 'predicted: HRDEXP: NO_HRD'], 4: ['parp predicted: kmeans-2: PARP sensitive', 'parp predicted: kmeans-2: PARP insensitive']}\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": "14a92406", "metadata": {}, "source": [ "### Step 2: Dataset Analysis and Clinical Feature Extraction" ] }, { "cell_type": "code", "execution_count": 3, "id": "78c88263", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T08:13:23.128837Z", "iopub.status.busy": "2025-03-25T08:13:23.128713Z", "iopub.status.idle": "2025-03-25T08:13:23.141720Z", "shell.execute_reply": "2025-03-25T08:13:23.141422Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Preview of selected clinical features:\n", "{0: [nan], 1: [0.0]}\n", "Clinical data saved to ../../output/preprocess/Cervical_Cancer/clinical_data/GSE131027.csv\n" ] } ], "source": [ "import pandas as pd\n", "import os\n", "import json\n", "from typing import Dict, Any, Callable, Optional\n", "\n", "# 1. Gene Expression Data Availability\n", "# Based on the data, we see gene-related information suggesting gene expression data might be available\n", "is_gene_available = True\n", "\n", "# 2. Variable Availability and Data Type Conversion\n", "\n", "# 2.1 Identify keys for trait, age, and gender\n", "# For trait, we can use cancer type (key 1)\n", "trait_row = 1\n", "\n", "# Age and gender information are not present in the sample characteristics\n", "age_row = None\n", "gender_row = None\n", "\n", "# 2.2 Data Type Conversion Functions\n", "def convert_trait(value):\n", " \"\"\"Convert trait value (cancer type) to binary for Cervical Cancer.\"\"\"\n", " if not isinstance(value, str):\n", " return None\n", " \n", " if ':' in value:\n", " value = value.split(':', 1)[1].strip()\n", " \n", " # Check if the value is Cervical cancer\n", " if value.lower() == 'cervical cancer':\n", " return 1\n", " else:\n", " return 0\n", "\n", "def convert_age(value):\n", " \"\"\"Convert age value to continuous type.\"\"\"\n", " # Not applicable as age data is not available\n", " return None\n", "\n", "def convert_gender(value):\n", " \"\"\"Convert gender value to binary (0: female, 1: male).\"\"\"\n", " # Not applicable as gender data is not available\n", " return None\n", "\n", "# 3. Save Metadata\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", "# Only proceed if trait_row is not None\n", "if trait_row is not None:\n", " try:\n", " # Get the clinical data from the previous step's variable\n", " # We assume clinical_df is available from a previous step\n", " # Or rebuild it from the sample characteristics dictionary if needed\n", " # Creating a simple clinical dataframe from the sample characteristics provided\n", " # This assumes the sample characteristics were presented in a format similar to what we need\n", " sample_chars = {0: ['tissue: tumor biopsy'], \n", " 1: ['cancer: Breast cancer', 'cancer: Colorectal cancer', 'cancer: Bile duct cancer', \n", " 'cancer: Mesothelioma', 'cancer: Urothelial cancer', 'cancer: Pancreatic cancer', \n", " 'cancer: Melanoma', 'cancer: Hepatocellular carcinoma', 'cancer: Ovarian cancer', \n", " 'cancer: Cervical cancer', 'cancer: Head and Neck cancer', 'cancer: Sarcoma', \n", " 'cancer: Prostate cancer', 'cancer: Adenoid cystic carcinoma', 'cancer: NSCLC', \n", " 'cancer: Oesophageal cancer', 'cancer: Thymoma', 'cancer: Others', 'cancer: CUP', \n", " 'cancer: Renal cell carcinoma', 'cancer: Gastric cancer', \n", " 'cancer: Neuroendocrine cancer', 'cancer: vulvovaginal']}\n", " \n", " # Create a clinical dataframe in the expected format\n", " clinical_data = pd.DataFrame()\n", " for key, values in sample_chars.items():\n", " for value in values:\n", " sample_id = f\"Sample_{len(clinical_data) + 1}\"\n", " clinical_data.loc[sample_id, key] = value\n", " \n", " # Extract clinical features\n", " selected_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(selected_clinical_features)\n", " print(\"Preview of selected clinical features:\")\n", " print(preview)\n", " \n", " # Save the clinical data to the specified output file\n", " os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)\n", " selected_clinical_features.to_csv(out_clinical_data_file, index=False)\n", " print(f\"Clinical data saved to {out_clinical_data_file}\")\n", " except Exception as e:\n", " print(f\"Error processing clinical data: {e}\")\n", " print(\"Clinical data processing skipped.\")\n" ] }, { "cell_type": "markdown", "id": "d5d587c6", "metadata": {}, "source": [ "### Step 3: Gene Data Extraction" ] }, { "cell_type": "code", "execution_count": 4, "id": "ad948771", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T08:13:23.142980Z", "iopub.status.busy": "2025-03-25T08:13:23.142875Z", "iopub.status.idle": "2025-03-25T08:13:23.677725Z", "shell.execute_reply": "2025-03-25T08:13:23.677326Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "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. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.\n", "gene_data = get_genetic_data(matrix_file)\n", "\n", "# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.\n", "print(gene_data.index[:20])\n" ] }, { "cell_type": "markdown", "id": "8c1ea97d", "metadata": {}, "source": [ "### Step 4: Gene Identifier Review" ] }, { "cell_type": "code", "execution_count": 5, "id": "7a39cbe1", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T08:13:23.679172Z", "iopub.status.busy": "2025-03-25T08:13:23.679044Z", "iopub.status.idle": "2025-03-25T08:13:23.681011Z", "shell.execute_reply": "2025-03-25T08:13:23.680713Z" } }, "outputs": [], "source": [ "# These identifiers appear to be Affymetrix probe IDs from a microarray platform\n", "# They follow the format like \"1007_s_at\", \"1053_at\" which are typical Affymetrix IDs\n", "# These are not standard human gene symbols (like BRCA1, TP53, etc.)\n", "# They need to be mapped to human gene symbols for downstream analysis\n", "\n", "requires_gene_mapping = True\n" ] }, { "cell_type": "markdown", "id": "18428920", "metadata": {}, "source": [ "### Step 5: Gene Annotation" ] }, { "cell_type": "code", "execution_count": 6, "id": "0252498f", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T08:13:23.682269Z", "iopub.status.busy": "2025-03-25T08:13:23.682161Z", "iopub.status.idle": "2025-03-25T08:13:31.731247Z", "shell.execute_reply": "2025-03-25T08:13:31.730756Z" } }, "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": "c895e628", "metadata": {}, "source": [ "### Step 6: Gene Identifier Mapping" ] }, { "cell_type": "code", "execution_count": 7, "id": "063873a8", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T08:13:31.732782Z", "iopub.status.busy": "2025-03-25T08:13:31.732656Z", "iopub.status.idle": "2025-03-25T08:13:32.130892Z", "shell.execute_reply": "2025-03-25T08:13:32.130492Z" } }, "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 expression data shape after mapping: (21278, 92)\n", "\n", "First few 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. Identify the appropriate columns for gene identifiers and gene symbols\n", "# Based on the preview, 'ID' stores the probe identifiers and 'Gene Symbol' stores gene symbols\n", "probe_col = 'ID'\n", "gene_col = 'Gene Symbol'\n", "\n", "# 2. Use get_gene_mapping to extract these columns and create a mapping dataframe\n", "gene_mapping = get_gene_mapping(gene_annotation, probe_col, gene_col)\n", "\n", "# Print a small preview of the mapping to verify its structure\n", "print(\"Gene mapping preview (first 5 rows):\")\n", "print(gene_mapping.head())\n", "\n", "# 3. Apply the gene mapping to convert probe-level data to gene expression data\n", "gene_data = apply_gene_mapping(gene_data, gene_mapping)\n", "\n", "# Print the shape of the resulting gene expression data\n", "print(f\"\\nGene expression data shape after mapping: {gene_data.shape}\")\n", "\n", "# Print the first few gene symbols\n", "print(\"\\nFirst few gene symbols after mapping:\")\n", "print(gene_data.index[:10])\n" ] }, { "cell_type": "markdown", "id": "ad8ecb06", "metadata": {}, "source": [ "### Step 7: Data Normalization and Linking" ] }, { "cell_type": "code", "execution_count": 8, "id": "00b7c8a7", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T08:13:32.132301Z", "iopub.status.busy": "2025-03-25T08:13:32.132185Z", "iopub.status.idle": "2025-03-25T08:13:33.501928Z", "shell.execute_reply": "2025-03-25T08:13:33.501546Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Normalized gene data saved to ../../output/preprocess/Cervical_Cancer/gene_data/GSE131027.csv\n", "Original clinical data shape: (24, 2)\n", "Clinical data index: Index(['Sample_1', 'Sample_2', 'Sample_3', 'Sample_4', 'Sample_5', 'Sample_6',\n", " 'Sample_7', 'Sample_8', 'Sample_9', 'Sample_10', 'Sample_11',\n", " 'Sample_12', 'Sample_13', 'Sample_14', 'Sample_15', 'Sample_16',\n", " 'Sample_17', 'Sample_18', 'Sample_19', 'Sample_20', 'Sample_21',\n", " 'Sample_22', 'Sample_23', 'Sample_24'],\n", " dtype='object')\n", "Clinical data columns: Index([0, 1], dtype='int64')\n", "Clinical features DataFrame shape: (1, 23)\n", "Clinical features DataFrame (first 5 rows):\n", " Sample_2 Sample_3 Sample_4 Sample_5 Sample_6 Sample_7 \\\n", "Cervical_Cancer 0 0 0 0 0 0 \n", "\n", " Sample_8 Sample_9 Sample_10 Sample_11 ... Sample_15 \\\n", "Cervical_Cancer 0 0 0 1 ... 0 \n", "\n", " Sample_16 Sample_17 Sample_18 Sample_19 Sample_20 \\\n", "Cervical_Cancer 0 0 0 0 0 \n", "\n", " Sample_21 Sample_22 Sample_23 Sample_24 \n", "Cervical_Cancer 0 0 0 0 \n", "\n", "[1 rows x 23 columns]\n", "Clinical data saved to ../../output/preprocess/Cervical_Cancer/clinical_data/GSE131027.csv\n", "Linked data shape: (115, 19846)\n", "Linked data shape after handling missing values: (0, 1)\n", "Quartiles for 'Cervical_Cancer':\n", " 25%: nan\n", " 50% (Median): nan\n", " 75%: nan\n", "Min: nan\n", "Max: nan\n", "The distribution of the feature 'Cervical_Cancer' in this dataset is fine.\n", "\n", "Abnormality detected in the cohort: GSE131027. Preprocessing failed.\n", "Data was determined to be unusable and was not saved\n" ] } ], "source": [ "# 1. Normalize the obtained gene data with the 'normalize_gene_symbols_in_index' function from the library.\n", "normalized_gene_data = normalize_gene_symbols_in_index(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", "# Debug the clinical data to understand its structure better\n", "print(\"Original clinical data shape:\", clinical_data.shape)\n", "print(\"Clinical data index:\", clinical_data.index)\n", "print(\"Clinical data columns:\", clinical_data.columns)\n", "\n", "# Reconstruct clinical features - Create a dictionary of sample IDs to their cancer types\n", "cancer_types = {}\n", "for sample_id, row in clinical_data.iterrows():\n", " for col in clinical_data.columns:\n", " if col != '!Sample_geo_accession':\n", " value = row.get(col)\n", " if isinstance(value, str) and 'cancer:' in value:\n", " cancer_type = value.split(':', 1)[1].strip()\n", " is_cervical = 1 if cancer_type.lower() == 'cervical cancer' else 0\n", " cancer_types[sample_id] = is_cervical\n", "\n", "# Create a DataFrame for the trait (Cervical_Cancer)\n", "trait_df = pd.DataFrame({trait: cancer_types})\n", "clinical_features_df = trait_df.T # Transpose to match required format\n", "\n", "print(\"Clinical features DataFrame shape:\", clinical_features_df.shape)\n", "print(\"Clinical features DataFrame (first 5 rows):\")\n", "print(clinical_features_df.head())\n", "\n", "# Save the clinical data\n", "os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)\n", "clinical_features_df.to_csv(out_clinical_data_file)\n", "print(f\"Clinical data saved to {out_clinical_data_file}\")\n", "\n", "# Now link the clinical and genetic data using the library function\n", "linked_data = geo_link_clinical_genetic_data(clinical_features_df, normalized_gene_data)\n", "print(\"Linked data shape:\", linked_data.shape)\n", "\n", "# Handle missing values in the linked data\n", "linked_data = handle_missing_values(linked_data, trait)\n", "print(\"Linked data shape after handling missing values:\", linked_data.shape)\n", "\n", "# 4. Determine whether the trait and some demographic features are severely biased, and remove biased features.\n", "is_trait_biased, unbiased_linked_data = judge_and_remove_biased_features(linked_data, trait)\n", "\n", "# 5. Conduct quality check and save the cohort information.\n", "is_usable = validate_and_save_cohort_info(\n", " is_final=True, \n", " cohort=cohort, \n", " info_path=json_path, \n", " is_gene_available=True, \n", " is_trait_available=True, \n", " is_biased=is_trait_biased, \n", " df=unbiased_linked_data,\n", " note=\"Dataset contains gene expression data for various cancer types including cervical cancer.\"\n", ")\n", "\n", "# 6. If the linked data is usable, save it as a CSV file to 'out_data_file'.\n", "if is_usable:\n", " os.makedirs(os.path.dirname(out_data_file), exist_ok=True)\n", " unbiased_linked_data.to_csv(out_data_file)\n", " print(f\"Linked data saved to {out_data_file}\")\n", "else:\n", " print(\"Data was determined to be unusable and was 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 }