{ "cells": [ { "cell_type": "code", "execution_count": 1, "id": "57843a86", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T06:22:18.040721Z", "iopub.status.busy": "2025-03-25T06:22:18.040500Z", "iopub.status.idle": "2025-03-25T06:22:18.203340Z", "shell.execute_reply": "2025-03-25T06:22:18.203031Z" } }, "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 = \"GSE75415\"\n", "\n", "# Input paths\n", "in_trait_dir = \"../../input/GEO/Adrenocortical_Cancer\"\n", "in_cohort_dir = \"../../input/GEO/Adrenocortical_Cancer/GSE75415\"\n", "\n", "# Output paths\n", "out_data_file = \"../../output/preprocess/Adrenocortical_Cancer/GSE75415.csv\"\n", "out_gene_data_file = \"../../output/preprocess/Adrenocortical_Cancer/gene_data/GSE75415.csv\"\n", "out_clinical_data_file = \"../../output/preprocess/Adrenocortical_Cancer/clinical_data/GSE75415.csv\"\n", "json_path = \"../../output/preprocess/Adrenocortical_Cancer/cohort_info.json\"\n" ] }, { "cell_type": "markdown", "id": "5abefb80", "metadata": {}, "source": [ "### Step 1: Initial Data Loading" ] }, { "cell_type": "code", "execution_count": 2, "id": "3f25084c", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T06:22:18.204746Z", "iopub.status.busy": "2025-03-25T06:22:18.204615Z", "iopub.status.idle": "2025-03-25T06:22:18.259175Z", "shell.execute_reply": "2025-03-25T06:22:18.258873Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Background Information:\n", "!Series_title\t\"Gene exrpression profiling of childhood adrenocortical tumors\"\n", "!Series_summary\t\"Pediatric adrenocortical tumors (ACT) are rare and often fatal malignancies; little is known regarding their etiology and biology. To provide additional insight into the nature of ACT, we determined the gene expression profiles of 24 pediatric tumors (five adenomas, 18 carcinomas, and one undetermined) and seven normal adrenal glands. Distinct patterns of gene expression, validated by quantitative real-time PCR and Western blot analysis, were identified that distinguish normal adrenal cortex from tumor. Differences in gene expression were also identified between adrenocortical adenomas and carcinomas. In addition, pediatric adrenocortical carcinomas were found to share similar patterns of gene expression when compared with those published for adult ACT. This study represents the first microarray analysis of childhood ACT. Our findings lay the groundwork for establishing gene expression profiles that may aid in the diagnosis and prognosis of pediatric ACT, and in the identification of signaling pathways that contribute to this disease.\"\n", "!Series_overall_design\t\"We used microarrays to explore the expression profiles differentially expressed in childhood adrenocortical tumors and in normal adrenal gland tissues. Pediatric adrenocortical adenoma and carcinoma patients were enrolled on the International Pediatric Adrenocortical Tumor Registry (IPACTR) and Bank protocol. Tumor specimens were harvested during surgery and snap frozen in liquid nitrogen to preserve tissue integrity. Data have been compiled for eight males and 15 females between 0 and 16 years of age. Table 1 (West et al, Cancer Research 67:601-608, 2007) summarizes the primary clinical information for each subject (excluding sample Unk1 with ACT of undetermined histology), including stage of the disease, tumor class, sex, age, relapse-free survival, and overall survival.\"\n", "Sample Characteristics Dictionary:\n", "{0: ['gender: female', 'gender: male', 'gender: unknown'], 1: ['histologic type: adrenocortical adenoma', 'histologic type: adrenocortical carcinoma', 'histologic type: unknown', 'histologic type: normal'], 2: ['tumor stage: not staged', 'tumor stage: 4', 'tumor stage: 2', 'tumor stage: 3', 'tumor stage: 1', 'tumor stage: unknown', 'tumor stage: not applicable']}\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": "4eb7f258", "metadata": {}, "source": [ "### Step 2: Dataset Analysis and Clinical Feature Extraction" ] }, { "cell_type": "code", "execution_count": 3, "id": "c90c80ca", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T06:22:18.260179Z", "iopub.status.busy": "2025-03-25T06:22:18.260076Z", "iopub.status.idle": "2025-03-25T06:22:18.268753Z", "shell.execute_reply": "2025-03-25T06:22:18.268470Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Clinical data preview:\n", "{'GSM1954726': [0.0, 0.0], 'GSM1954727': [0.0, 0.0], 'GSM1954728': [0.0, 0.0], 'GSM1954729': [0.0, 0.0], 'GSM1954730': [0.0, 0.0], 'GSM1954731': [1.0, 1.0], 'GSM1954732': [1.0, 0.0], 'GSM1954733': [1.0, 1.0], 'GSM1954734': [1.0, 0.0], 'GSM1954735': [1.0, 1.0], 'GSM1954736': [1.0, 0.0], 'GSM1954737': [1.0, 0.0], 'GSM1954738': [1.0, 1.0], 'GSM1954739': [1.0, 1.0], 'GSM1954740': [1.0, 0.0], 'GSM1954741': [1.0, 1.0], 'GSM1954742': [1.0, 1.0], 'GSM1954743': [1.0, 1.0], 'GSM1954744': [1.0, 0.0], 'GSM1954745': [1.0, 0.0], 'GSM1954746': [1.0, 0.0], 'GSM1954747': [1.0, 0.0], 'GSM1954748': [1.0, 0.0], 'GSM1954749': [nan, nan], 'GSM1954750': [0.0, nan], 'GSM1954751': [0.0, nan], 'GSM1954752': [0.0, nan], 'GSM1954753': [0.0, nan], 'GSM1954754': [0.0, nan], 'GSM1954755': [0.0, nan], 'GSM1954756': [0.0, nan]}\n", "Clinical data saved to ../../output/preprocess/Adrenocortical_Cancer/clinical_data/GSE75415.csv\n" ] } ], "source": [ "# 1. Gene Expression Data Availability\n", "# Based on the background information, this dataset contains gene expression profiling data for adrenocortical tumors\n", "is_gene_available = True\n", "\n", "# 2. Variable Availability and Data Type Conversion\n", "# 2.1 Data Availability\n", "# For trait (adrenocortical cancer), we can use histologic type (row 1)\n", "trait_row = 1\n", "\n", "# Age is not available in the sample characteristics\n", "age_row = None\n", "\n", "# Gender is available (row 0)\n", "gender_row = 0\n", "\n", "# 2.2 Data Type Conversion\n", "def convert_trait(value):\n", " \"\"\"Convert trait value to binary: 1 for adrenocortical carcinoma, 0 for adenoma/normal\"\"\"\n", " if value is None:\n", " return None\n", " \n", " # Extract value after colon if present\n", " if ':' in value:\n", " value = value.split(':', 1)[1].strip().lower()\n", " else:\n", " value = value.strip().lower()\n", " \n", " if 'carcinoma' in value:\n", " return 1 # Positive for cancer\n", " elif 'adenoma' in value or 'normal' in value:\n", " return 0 # Negative for cancer\n", " else:\n", " return None # Unknown or undetermined\n", "\n", "def convert_age(value):\n", " \"\"\"Age conversion function (not used since age is not available)\"\"\"\n", " return None\n", "\n", "def convert_gender(value):\n", " \"\"\"Convert gender value to binary: 0 for female, 1 for male\"\"\"\n", " if value is None:\n", " return None\n", " \n", " # Extract value after colon if present\n", " if ':' in value:\n", " value = value.split(':', 1)[1].strip().lower()\n", " else:\n", " value = value.strip().lower()\n", " \n", " if 'female' in value:\n", " return 0\n", " elif 'male' in value:\n", " return 1\n", " else:\n", " return None # Unknown gender\n", "\n", "# 3. Save Metadata\n", "# Trait data is available since trait_row is not None\n", "is_trait_available = trait_row is not None\n", "\n", "# Initial filtering\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", " # Extract clinical features\n", " clinical_df = geo_select_clinical_features(\n", " clinical_df=clinical_data,\n", " trait=trait,\n", " trait_row=trait_row,\n", " convert_trait=convert_trait,\n", " age_row=age_row,\n", " convert_age=convert_age,\n", " gender_row=gender_row,\n", " convert_gender=convert_gender\n", " )\n", " \n", " # Preview the clinical data\n", " preview = preview_df(clinical_df)\n", " print(\"Clinical data preview:\")\n", " print(preview)\n", " \n", " # Save clinical data to CSV file\n", " os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)\n", " clinical_df.to_csv(out_clinical_data_file, index=True)\n", " print(f\"Clinical data saved to {out_clinical_data_file}\")\n" ] }, { "cell_type": "markdown", "id": "85c63c03", "metadata": {}, "source": [ "### Step 3: Gene Data Extraction" ] }, { "cell_type": "code", "execution_count": 4, "id": "ae3ba146", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T06:22:18.269713Z", "iopub.status.busy": "2025-03-25T06:22:18.269613Z", "iopub.status.idle": "2025-03-25T06:22:18.327786Z", "shell.execute_reply": "2025-03-25T06:22:18.327468Z" } }, "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', '1598_g_at', '160020_at', '1729_at', '1773_at', '177_at',\n", " '179_at', '1861_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": "158af261", "metadata": {}, "source": [ "### Step 4: Gene Identifier Review" ] }, { "cell_type": "code", "execution_count": 5, "id": "3a721c14", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T06:22:18.329022Z", "iopub.status.busy": "2025-03-25T06:22:18.328913Z", "iopub.status.idle": "2025-03-25T06:22:18.330699Z", "shell.execute_reply": "2025-03-25T06:22:18.330427Z" } }, "outputs": [], "source": [ "# Examining the gene identifiers shown in the previous step.\n", "# These identifiers appear to be microarray probe IDs (like '1007_s_at', '1053_at')\n", "# rather than standard human gene symbols (like 'TP53', 'BRCA1', etc.).\n", "# These are likely Affymetrix probe IDs, which need to be mapped to human gene symbols.\n", "\n", "requires_gene_mapping = True\n" ] }, { "cell_type": "markdown", "id": "f8500984", "metadata": {}, "source": [ "### Step 5: Gene Annotation" ] }, { "cell_type": "code", "execution_count": 6, "id": "ac359391", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T06:22:18.331770Z", "iopub.status.busy": "2025-03-25T06:22:18.331674Z", "iopub.status.idle": "2025-03-25T06:22:19.640977Z", "shell.execute_reply": "2025-03-25T06:22:19.640485Z" } }, "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": "14152343", "metadata": {}, "source": [ "### Step 6: Gene Identifier Mapping" ] }, { "cell_type": "code", "execution_count": 7, "id": "eed20dd8", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T06:22:19.642391Z", "iopub.status.busy": "2025-03-25T06:22:19.642272Z", "iopub.status.idle": "2025-03-25T06:22:19.739819Z", "shell.execute_reply": "2025-03-25T06:22:19.739383Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "First few rows of gene expression data after mapping:\n", " GSM1954726 GSM1954727 GSM1954728 GSM1954729 GSM1954730 \\\n", "Gene \n", "A1CF 435.0 344.6 540.8 268.1 435.4 \n", "A2M 4469.0 7595.8 5954.9 6257.1 6142.2 \n", "A4GALT 42.6 61.3 101.8 140.7 142.6 \n", "A4GNT 352.8 220.6 201.6 110.0 181.6 \n", "AAAS 299.7 268.3 481.9 409.7 488.8 \n", "\n", " GSM1954731 GSM1954732 GSM1954733 GSM1954734 GSM1954735 ... \\\n", "Gene ... \n", "A1CF 410.7 514.8 279.2 275.3 418.3 ... \n", "A2M 4218.6 2760.3 3700.1 4408.8 3862.6 ... \n", "A4GALT 29.3 279.3 77.1 55.4 63.3 ... \n", "A4GNT 136.6 380.4 194.5 151.6 198.0 ... \n", "AAAS 330.6 542.7 715.2 638.3 1012.2 ... \n", "\n", " GSM1954747 GSM1954748 GSM1954749 GSM1954750 GSM1954751 \\\n", "Gene \n", "A1CF 441.3 689.4 401.6 381.4 323.1 \n", "A2M 2561.2 2880.8 3540.0 7180.5 11459.7 \n", "A4GALT 76.0 190.0 208.3 37.4 97.8 \n", "A4GNT 177.8 363.6 175.6 154.0 441.1 \n", "AAAS 180.8 420.7 497.0 311.9 431.8 \n", "\n", " GSM1954752 GSM1954753 GSM1954754 GSM1954755 GSM1954756 \n", "Gene \n", "A1CF 560.4 651.2 1040.3 655.8 872.7 \n", "A2M 5347.7 4320.2 5798.3 5584.7 9061.2 \n", "A4GALT 220.8 88.7 84.7 28.0 92.7 \n", "A4GNT 442.7 406.3 368.1 446.9 342.7 \n", "AAAS 339.8 439.4 336.4 419.1 249.6 \n", "\n", "[5 rows x 31 columns]\n", "Shape of gene expression dataframe: (13830, 31)\n" ] } ], "source": [ "# 1. Identify the columns needed for mapping\n", "# Based on the annotation preview, 'ID' is the probe identifier and 'Gene Symbol' contains gene symbols\n", "probe_col = 'ID'\n", "gene_col = 'Gene Symbol'\n", "\n", "# 2. Get a mapping dataframe with the probe ID and gene symbol columns\n", "mapping_df = get_gene_mapping(gene_annotation, probe_col, gene_col)\n", "\n", "# 3. Apply gene mapping to convert from probe-level to gene-level expression\n", "gene_data = apply_gene_mapping(gene_data, mapping_df)\n", "\n", "# Print the first few rows of the resulting gene expression dataframe\n", "print(\"First few rows of gene expression data after mapping:\")\n", "print(gene_data.head())\n", "\n", "# Print the shape of the gene expression dataframe\n", "print(f\"Shape of gene expression dataframe: {gene_data.shape}\")\n" ] }, { "cell_type": "markdown", "id": "83bd1757", "metadata": {}, "source": [ "### Step 7: Data Normalization and Linking" ] }, { "cell_type": "code", "execution_count": 8, "id": "2b87847d", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T06:22:19.741327Z", "iopub.status.busy": "2025-03-25T06:22:19.741213Z", "iopub.status.idle": "2025-03-25T06:22:24.230739Z", "shell.execute_reply": "2025-03-25T06:22:24.230355Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Normalizing gene symbols using NCBI Gene database...\n", "After normalization, gene data shape: (13542, 31)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Normalized gene expression data saved to ../../output/preprocess/Adrenocortical_Cancer/gene_data/GSE75415.csv\n", "Linked data shape: (31, 13544)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "After handling missing values, linked data shape: (30, 13544)\n", "For the feature 'Adrenocortical_Cancer', the least common label is '0.0' with 12 occurrences. This represents 40.00% of the dataset.\n", "The distribution of the feature 'Adrenocortical_Cancer' in this dataset is fine.\n", "\n", "For the feature 'Gender', the least common label is '1.0' with 8 occurrences. This represents 26.67% of the dataset.\n", "The distribution of the feature 'Gender' in this dataset is fine.\n", "\n", "Is trait biased: False\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Linked data saved to ../../output/preprocess/Adrenocortical_Cancer/GSE75415.csv\n" ] } ], "source": [ "# 1. Normalize gene symbols in the gene expression data\n", "print(\"Normalizing gene symbols using NCBI Gene database...\")\n", "gene_data = normalize_gene_symbols_in_index(gene_data)\n", "print(f\"After normalization, gene data shape: {gene_data.shape}\")\n", "\n", "# Save the normalized gene 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\"Normalized gene expression data saved to {out_gene_data_file}\")\n", "\n", "# 2. Link the clinical and genetic data\n", "linked_data = geo_link_clinical_genetic_data(clinical_df, gene_data)\n", "print(f\"Linked data shape: {linked_data.shape}\")\n", "\n", "# 3. Handle missing values in the linked data systematically\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 childhood adrenocortical tumors including adenomas and carcinomas.\"\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 }