{ "cells": [ { "cell_type": "code", "execution_count": 1, "id": "b2f24f8b", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T04:11:08.334565Z", "iopub.status.busy": "2025-03-25T04:11:08.334455Z", "iopub.status.idle": "2025-03-25T04:11:08.499788Z", "shell.execute_reply": "2025-03-25T04:11:08.499407Z" } }, "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 = \"Thyroid_Cancer\"\n", "cohort = \"GSE76039\"\n", "\n", "# Input paths\n", "in_trait_dir = \"../../input/GEO/Thyroid_Cancer\"\n", "in_cohort_dir = \"../../input/GEO/Thyroid_Cancer/GSE76039\"\n", "\n", "# Output paths\n", "out_data_file = \"../../output/preprocess/Thyroid_Cancer/GSE76039.csv\"\n", "out_gene_data_file = \"../../output/preprocess/Thyroid_Cancer/gene_data/GSE76039.csv\"\n", "out_clinical_data_file = \"../../output/preprocess/Thyroid_Cancer/clinical_data/GSE76039.csv\"\n", "json_path = \"../../output/preprocess/Thyroid_Cancer/cohort_info.json\"\n" ] }, { "cell_type": "markdown", "id": "b3ea4e7a", "metadata": {}, "source": [ "### Step 1: Initial Data Loading" ] }, { "cell_type": "code", "execution_count": 2, "id": "bf40a4f1", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T04:11:08.501342Z", "iopub.status.busy": "2025-03-25T04:11:08.501195Z", "iopub.status.idle": "2025-03-25T04:11:08.649467Z", "shell.execute_reply": "2025-03-25T04:11:08.649100Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Background Information:\n", "!Series_title\t\"Genomic and Transcriptomic Hallmarks of Poorly-Differentiated and Anaplastic Thyroid Cancers\"\n", "!Series_summary\t\"BACKGROUND. Poorly-differentiated (PDTC) and anaplastic (ATC) thyroid cancers are rare and frequently lethal tumors, which so far have not been subjected to comprehensive genetic characterization. METHODS. We performed next generation sequencing of 341 cancer genes in 117 PDTCs and ATCs, and a transcriptomic analysis of a representative subset of 37 tumors. Results were analyzed in the context of The Cancer Genome Atlas (TCGA) study of papillary thyroid cancers (PTC). RESULTS. ATCs have a greater mutation burden than PDTCs, and higher mutation frequency of TP53, TERT promoter, PI3K/AKT/mTOR pathway effectors, SWI/SNF subunits and histone methyltransferases. BRAF and RAS are the predominant drivers, and dictate remarkably distinct tropism for nodal vs. distant metastases in PDTC. RAS and BRAF sharply distinguish between PDTCs defined by the Turin (PDTC-Turin) vs. MSKCC (PDTC-MSK) criteria, respectively. Mutations of EIF1AX, a component of the translational preinitiation complex, are markedly enriched in PDTCs and ATCs, and have a striking pattern of co-occurrence with RAS. TERT promoter mutations are rare and subclonal in PTCs, whereas they are clonal and highly prevalent in advanced cancers. Application of the TCGA-derived BRAF-RAS score (a measure of MAPK transcriptional output) shows a preserved relationship with BRAF/RAS mutation in PDTCs, whereas ATCs are BRAF-like irrespective of driver mutation. CONCLUSIONS. These data support a model of tumorigenesis whereby PDTCs and ATCs arise from well-differentiated tumors through the accumulation of key additional genetic abnormalities, many of which have prognostic and possible therapeutic relevance. The widespread genomic disruptions in ATC compared to PDTC underscore their greater virulence and higher mortality.\"\n", "!Series_overall_design\t\"37 tumor specimens, including 17 poorly-differentiated and 20 anaplastic thyroid cancers were expression-profiled with Affymetrix U133 plus 2.0 array\"\n", "Sample Characteristics Dictionary:\n", "{0: ['gender: female', 'gender: male'], 1: ['tissue: Thyroid'], 2: ['tumor type: Primary', 'tumor type: Recurrent tumor in neck', 'tumor type: Recurrent/persistent metastasic tumor to lymph node', 'tumor type: Metastasis', 'tumor type: Recurrent tumor', 'tumor type: Primary (residual)']}\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": "631d6168", "metadata": {}, "source": [ "### Step 2: Dataset Analysis and Clinical Feature Extraction" ] }, { "cell_type": "code", "execution_count": 3, "id": "3afd7132", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T04:11:08.650769Z", "iopub.status.busy": "2025-03-25T04:11:08.650654Z", "iopub.status.idle": "2025-03-25T04:11:08.655273Z", "shell.execute_reply": "2025-03-25T04:11:08.654924Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Clinical data extraction will be performed in a subsequent step when the data is available.\n" ] } ], "source": [ "import pandas as pd\n", "import os\n", "import json\n", "from typing import Callable, Optional, Dict, Any\n", "\n", "# 1. Gene Expression Data Availability\n", "# Based on the background information, this is an Affymetrix U133 plus 2.0 array study\n", "# which is a gene expression microarray, so gene expression data is available\n", "is_gene_available = True\n", "\n", "# 2. Variable Availability and Data Type Conversion\n", "# 2.1 Trait Data\n", "# From the sample characteristics, we can identify the tumor type row (2) as containing the trait information\n", "trait_row = 2\n", "\n", "def convert_trait(value_str):\n", " # Extract the value after the colon and strip whitespace\n", " if ':' in value_str:\n", " value = value_str.split(':', 1)[1].strip()\n", " \n", " # Binary categorization: primary tumor (0) vs any other tumor type (1)\n", " if value.lower() == 'primary' or value.lower() == 'primary (residual)':\n", " return 0 # Primary tumor\n", " else:\n", " return 1 # Other tumor types (metastasis, recurrent, etc.)\n", " return None\n", "\n", "# 2.2 Age Data\n", "# Age data is not available in the sample characteristics\n", "age_row = None\n", "\n", "def convert_age(value_str):\n", " # This function won't be used since age_row is None\n", " return None\n", "\n", "# 2.3 Gender Data\n", "# Gender information is available in row 0\n", "gender_row = 0\n", "\n", "def convert_gender(value_str):\n", " # Extract the value after the colon and strip whitespace\n", " if ':' in value_str:\n", " value = value_str.split(':', 1)[1].strip().lower()\n", " \n", " # Convert gender to binary (female=0, male=1)\n", " if value == 'female':\n", " return 0\n", " elif value == 'male':\n", " return 1\n", " return None\n", "\n", "# 3. Save Metadata\n", "is_trait_available = trait_row is not None\n", "\n", "# Validate and save cohort info\n", "validate_and_save_cohort_info(\n", " is_final=False,\n", " cohort=cohort,\n", " info_path=json_path,\n", " is_gene_available=is_gene_available,\n", " is_trait_available=is_trait_available\n", ")\n", "\n", "# 4. Clinical Feature Extraction\n", "# Since trait_row is not None, we proceed with feature extraction\n", "if trait_row is not None:\n", " # We'll skip trying to load the clinical_data.csv file as it doesn't exist\n", " # Instead, we'll wait for a subsequent step where the clinical data will be available\n", " print(\"Clinical data extraction will be performed in a subsequent step when the data is available.\")\n", " \n", " # Note: We're not running the geo_select_clinical_features function here\n", " # as we don't have the clinical_data DataFrame yet\n", " \n", " # Create directory for future clinical data file\n", " os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)\n" ] }, { "cell_type": "markdown", "id": "cbdfdb4b", "metadata": {}, "source": [ "### Step 3: Gene Data Extraction" ] }, { "cell_type": "code", "execution_count": 4, "id": "aeea3018", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T04:11:08.656425Z", "iopub.status.busy": "2025-03-25T04:11:08.656318Z", "iopub.status.idle": "2025-03-25T04:11:08.868494Z", "shell.execute_reply": "2025-03-25T04:11:08.868121Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "SOFT file: ../../input/GEO/Thyroid_Cancer/GSE76039/GSE76039_family.soft.gz\n", "Matrix file: ../../input/GEO/Thyroid_Cancer/GSE76039/GSE76039_series_matrix.txt.gz\n", "Found the matrix table marker in the file.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Gene data shape: (54675, 37)\n", "First 20 gene/probe identifiers:\n", "['1007_s_at', '1053_at', '117_at', '121_at', '1255_g_at', '1294_at', '1316_at', '1320_at', '1405_i_at', '1431_at', '1438_at', '1487_at', '1494_f_at', '1552256_a_at', '1552257_a_at', '1552258_at', '1552261_at', '1552263_at', '1552264_a_at', '1552266_at']\n" ] } ], "source": [ "# 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", "print(f\"SOFT file: {soft_file}\")\n", "print(f\"Matrix file: {matrix_file}\")\n", "\n", "# Set gene availability flag\n", "is_gene_available = True # Initially assume gene data is available\n", "\n", "# First check if the matrix file contains the expected marker\n", "found_marker = False\n", "try:\n", " with gzip.open(matrix_file, 'rt') as file:\n", " for line in file:\n", " if \"!series_matrix_table_begin\" in line:\n", " found_marker = True\n", " break\n", " \n", " if found_marker:\n", " print(\"Found the matrix table marker in the file.\")\n", " else:\n", " print(\"Warning: Could not find '!series_matrix_table_begin' marker in the file.\")\n", " \n", " # Try to extract gene data from the matrix file\n", " gene_data = get_genetic_data(matrix_file)\n", " \n", " if gene_data.shape[0] == 0:\n", " print(\"Warning: Extracted gene data has 0 rows.\")\n", " is_gene_available = False\n", " else:\n", " print(f\"Gene data shape: {gene_data.shape}\")\n", " # Print the first 20 gene/probe identifiers\n", " print(\"First 20 gene/probe identifiers:\")\n", " print(gene_data.index[:20].tolist())\n", " \n", "except Exception as e:\n", " print(f\"Error extracting gene data: {e}\")\n", " is_gene_available = False\n", " \n", " # Try to diagnose the file format\n", " print(\"Examining file content to diagnose the issue:\")\n", " try:\n", " with gzip.open(matrix_file, 'rt') as file:\n", " for i, line in enumerate(file):\n", " if i < 10: # Print first 10 lines to diagnose\n", " print(f\"Line {i}: {line.strip()[:100]}...\") # Print first 100 chars of each line\n", " else:\n", " break\n", " except Exception as e2:\n", " print(f\"Error examining file: {e2}\")\n", "\n", "if not is_gene_available:\n", " print(\"Gene expression data could not be successfully extracted from this dataset.\")\n" ] }, { "cell_type": "markdown", "id": "61338b66", "metadata": {}, "source": [ "### Step 4: Gene Identifier Review" ] }, { "cell_type": "code", "execution_count": 5, "id": "4bcf2159", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T04:11:08.869740Z", "iopub.status.busy": "2025-03-25T04:11:08.869628Z", "iopub.status.idle": "2025-03-25T04:11:08.871664Z", "shell.execute_reply": "2025-03-25T04:11:08.871348Z" } }, "outputs": [], "source": [ "# These identifiers (like '1007_s_at', '1053_at', etc.) appear to be Affymetrix probe IDs,\n", "# not standard human gene symbols. Affymetrix probe IDs need to be mapped to official gene symbols\n", "# for proper biological interpretation.\n", "\n", "requires_gene_mapping = True\n" ] }, { "cell_type": "markdown", "id": "1b7fd471", "metadata": {}, "source": [ "### Step 5: Gene Annotation" ] }, { "cell_type": "code", "execution_count": 6, "id": "592732a0", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T04:11:08.872732Z", "iopub.status.busy": "2025-03-25T04:11:08.872626Z", "iopub.status.idle": "2025-03-25T04:11:12.755472Z", "shell.execute_reply": "2025-03-25T04:11:12.755021Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Gene annotation preview:\n", "Columns in gene annotation: ['ID', 'GB_ACC', 'SPOT_ID', 'Species Scientific Name', 'Annotation Date', 'Sequence Type', 'Sequence Source', 'Target Description', 'Representative Public ID', 'Gene Title', 'Gene Symbol', 'ENTREZ_GENE_ID', 'RefSeq Transcript ID', 'Gene Ontology Biological Process', 'Gene Ontology Cellular Component', 'Gene Ontology Molecular Function']\n", "{'ID': ['1007_s_at', '1053_at', '117_at', '121_at', '1255_g_at'], 'GB_ACC': ['U48705', 'M87338', 'X51757', 'X69699', 'L36861'], 'SPOT_ID': [nan, nan, nan, nan, nan], 'Species Scientific Name': ['Homo sapiens', 'Homo sapiens', 'Homo sapiens', 'Homo sapiens', 'Homo sapiens'], 'Annotation Date': ['Oct 6, 2014', 'Oct 6, 2014', 'Oct 6, 2014', 'Oct 6, 2014', 'Oct 6, 2014'], 'Sequence Type': ['Exemplar sequence', 'Exemplar sequence', 'Exemplar sequence', 'Exemplar sequence', 'Exemplar sequence'], 'Sequence Source': ['Affymetrix Proprietary Database', 'GenBank', 'Affymetrix Proprietary Database', 'GenBank', 'Affymetrix Proprietary Database'], 'Target Description': ['U48705 /FEATURE=mRNA /DEFINITION=HSU48705 Human receptor tyrosine kinase DDR gene, complete cds', 'M87338 /FEATURE= /DEFINITION=HUMA1SBU Human replication factor C, 40-kDa subunit (A1) mRNA, complete cds', \"X51757 /FEATURE=cds /DEFINITION=HSP70B Human heat-shock protein HSP70B' gene\", 'X69699 /FEATURE= /DEFINITION=HSPAX8A H.sapiens Pax8 mRNA', 'L36861 /FEATURE=expanded_cds /DEFINITION=HUMGCAPB Homo sapiens guanylate cyclase activating protein (GCAP) gene exons 1-4, complete cds'], 'Representative Public ID': ['U48705', 'M87338', 'X51757', 'X69699', 'L36861'], 'Gene Title': ['discoidin domain receptor tyrosine kinase 1 /// microRNA 4640', 'replication factor C (activator 1) 2, 40kDa', \"heat shock 70kDa protein 6 (HSP70B')\", 'paired box 8', 'guanylate cyclase activator 1A (retina)'], 'Gene Symbol': ['DDR1 /// MIR4640', 'RFC2', 'HSPA6', 'PAX8', 'GUCA1A'], 'ENTREZ_GENE_ID': ['780 /// 100616237', '5982', '3310', '7849', '2978'], 'RefSeq Transcript ID': ['NM_001202521 /// NM_001202522 /// NM_001202523 /// NM_001954 /// NM_013993 /// NM_013994 /// NR_039783 /// XM_005249385 /// XM_005249386 /// XM_005249387 /// XM_005249389 /// XM_005272873 /// XM_005272874 /// XM_005272875 /// XM_005272877 /// XM_005275027 /// XM_005275028 /// XM_005275030 /// XM_005275031 /// XM_005275162 /// XM_005275163 /// XM_005275164 /// XM_005275166 /// XM_005275457 /// XM_005275458 /// XM_005275459 /// XM_005275461 /// XM_006715185 /// XM_006715186 /// XM_006715187 /// XM_006715188 /// XM_006715189 /// XM_006715190 /// XM_006725501 /// XM_006725502 /// XM_006725503 /// XM_006725504 /// XM_006725505 /// XM_006725506 /// XM_006725714 /// XM_006725715 /// XM_006725716 /// XM_006725717 /// XM_006725718 /// XM_006725719 /// XM_006725720 /// XM_006725721 /// XM_006725722 /// XM_006725827 /// XM_006725828 /// XM_006725829 /// XM_006725830 /// XM_006725831 /// XM_006725832 /// XM_006726017 /// XM_006726018 /// XM_006726019 /// XM_006726020 /// XM_006726021 /// XM_006726022 /// XR_427836 /// XR_430858 /// XR_430938 /// XR_430974 /// XR_431015', 'NM_001278791 /// NM_001278792 /// NM_001278793 /// NM_002914 /// NM_181471 /// XM_006716080', 'NM_002155', 'NM_003466 /// NM_013951 /// NM_013952 /// NM_013953 /// NM_013992', 'NM_000409 /// XM_006715073'], 'Gene Ontology Biological Process': ['0001558 // regulation of cell growth // inferred from electronic annotation /// 0001952 // regulation of cell-matrix adhesion // inferred from electronic annotation /// 0006468 // protein phosphorylation // inferred from electronic annotation /// 0007155 // cell adhesion // traceable author statement /// 0007169 // transmembrane receptor protein tyrosine kinase signaling pathway // inferred from electronic annotation /// 0007565 // female pregnancy // inferred from electronic annotation /// 0007566 // embryo implantation // inferred from electronic annotation /// 0007595 // lactation // inferred from electronic annotation /// 0008285 // negative regulation of cell proliferation // inferred from electronic annotation /// 0010715 // regulation of extracellular matrix disassembly // inferred from mutant phenotype /// 0014909 // smooth muscle cell migration // inferred from mutant phenotype /// 0016310 // phosphorylation // inferred from electronic annotation /// 0018108 // peptidyl-tyrosine phosphorylation // inferred from electronic annotation /// 0030198 // extracellular matrix organization // traceable author statement /// 0038063 // collagen-activated tyrosine kinase receptor signaling pathway // inferred from direct assay /// 0038063 // collagen-activated tyrosine kinase receptor signaling pathway // inferred from mutant phenotype /// 0038083 // peptidyl-tyrosine autophosphorylation // inferred from direct assay /// 0043583 // ear development // inferred from electronic annotation /// 0044319 // wound healing, spreading of cells // inferred from mutant phenotype /// 0046777 // protein autophosphorylation // inferred from direct assay /// 0060444 // branching involved in mammary gland duct morphogenesis // inferred from electronic annotation /// 0060749 // mammary gland alveolus development // inferred from electronic annotation /// 0061302 // smooth muscle cell-matrix adhesion // inferred from mutant phenotype', '0000278 // mitotic cell cycle // traceable author statement /// 0000722 // telomere maintenance via recombination // traceable author statement /// 0000723 // telomere maintenance // traceable author statement /// 0006260 // DNA replication // traceable author statement /// 0006271 // DNA strand elongation involved in DNA replication // traceable author statement /// 0006281 // DNA repair // traceable author statement /// 0006283 // transcription-coupled nucleotide-excision repair // traceable author statement /// 0006289 // nucleotide-excision repair // traceable author statement /// 0006297 // nucleotide-excision repair, DNA gap filling // traceable author statement /// 0015979 // photosynthesis // inferred from electronic annotation /// 0015995 // chlorophyll biosynthetic process // inferred from electronic annotation /// 0032201 // telomere maintenance via semi-conservative replication // traceable author statement', '0000902 // cell morphogenesis // inferred from electronic annotation /// 0006200 // ATP catabolic process // inferred from direct assay /// 0006950 // response to stress // inferred from electronic annotation /// 0006986 // response to unfolded protein // traceable author statement /// 0034605 // cellular response to heat // inferred from direct assay /// 0042026 // protein refolding // inferred from direct assay /// 0070370 // cellular heat acclimation // inferred from mutant phenotype', '0001655 // urogenital system development // inferred from sequence or structural similarity /// 0001656 // metanephros development // inferred from electronic annotation /// 0001658 // branching involved in ureteric bud morphogenesis // inferred from expression pattern /// 0001822 // kidney development // inferred from expression pattern /// 0001823 // mesonephros development // inferred from sequence or structural similarity /// 0003337 // mesenchymal to epithelial transition involved in metanephros morphogenesis // inferred from expression pattern /// 0006351 // transcription, DNA-templated // inferred from direct assay /// 0006355 // regulation of transcription, DNA-templated // inferred from electronic annotation /// 0007275 // multicellular organismal development // inferred from electronic annotation /// 0007417 // central nervous system development // inferred from expression pattern /// 0009653 // anatomical structure morphogenesis // traceable author statement /// 0030154 // cell differentiation // inferred from electronic annotation /// 0030878 // thyroid gland development // inferred from expression pattern /// 0030878 // thyroid gland development // inferred from mutant phenotype /// 0038194 // thyroid-stimulating hormone signaling pathway // traceable author statement /// 0039003 // pronephric field specification // inferred from sequence or structural similarity /// 0042472 // inner ear morphogenesis // inferred from sequence or structural similarity /// 0042981 // regulation of apoptotic process // inferred from sequence or structural similarity /// 0045893 // positive regulation of transcription, DNA-templated // inferred from direct assay /// 0045893 // positive regulation of transcription, DNA-templated // inferred from sequence or structural similarity /// 0045944 // positive regulation of transcription from RNA polymerase II promoter // inferred from direct assay /// 0048793 // pronephros development // inferred from sequence or structural similarity /// 0071371 // cellular response to gonadotropin stimulus // inferred from direct assay /// 0071599 // otic vesicle development // inferred from expression pattern /// 0072050 // S-shaped body morphogenesis // inferred from electronic annotation /// 0072073 // kidney epithelium development // inferred from electronic annotation /// 0072108 // positive regulation of mesenchymal to epithelial transition involved in metanephros morphogenesis // inferred from sequence or structural similarity /// 0072164 // mesonephric tubule development // inferred from electronic annotation /// 0072207 // metanephric epithelium development // inferred from expression pattern /// 0072221 // metanephric distal convoluted tubule development // inferred from sequence or structural similarity /// 0072278 // metanephric comma-shaped body morphogenesis // inferred from expression pattern /// 0072284 // metanephric S-shaped body morphogenesis // inferred from expression pattern /// 0072289 // metanephric nephron tubule formation // inferred from sequence or structural similarity /// 0072305 // negative regulation of mesenchymal cell apoptotic process involved in metanephric nephron morphogenesis // inferred from sequence or structural similarity /// 0072307 // regulation of metanephric nephron tubule epithelial cell differentiation // inferred from sequence or structural similarity /// 0090190 // positive regulation of branching involved in ureteric bud morphogenesis // inferred from sequence or structural similarity /// 1900212 // negative regulation of mesenchymal cell apoptotic process involved in metanephros development // inferred from sequence or structural similarity /// 1900215 // negative regulation of apoptotic process involved in metanephric collecting duct development // inferred from sequence or structural similarity /// 1900218 // negative regulation of apoptotic process involved in metanephric nephron tubule development // inferred from sequence or structural similarity /// 2000594 // positive regulation of metanephric DCT cell differentiation // inferred from sequence or structural similarity /// 2000611 // positive regulation of thyroid hormone generation // inferred from mutant phenotype /// 2000612 // regulation of thyroid-stimulating hormone secretion // inferred from mutant phenotype', '0007165 // signal transduction // non-traceable author statement /// 0007601 // visual perception // inferred from electronic annotation /// 0007602 // phototransduction // inferred from electronic annotation /// 0007603 // phototransduction, visible light // traceable author statement /// 0016056 // rhodopsin mediated signaling pathway // traceable author statement /// 0022400 // regulation of rhodopsin mediated signaling pathway // traceable author statement /// 0030828 // positive regulation of cGMP biosynthetic process // inferred from electronic annotation /// 0031282 // regulation of guanylate cyclase activity // inferred from electronic annotation /// 0031284 // positive regulation of guanylate cyclase activity // inferred from electronic annotation /// 0050896 // response to stimulus // inferred from electronic annotation'], 'Gene Ontology Cellular Component': ['0005576 // extracellular region // inferred from electronic annotation /// 0005615 // extracellular space // inferred from direct assay /// 0005886 // plasma membrane // traceable author statement /// 0005887 // integral component of plasma membrane // traceable author statement /// 0016020 // membrane // inferred from electronic annotation /// 0016021 // integral component of membrane // inferred from electronic annotation /// 0043235 // receptor complex // inferred from direct assay /// 0070062 // extracellular vesicular exosome // inferred from direct assay', '0005634 // nucleus // inferred from electronic annotation /// 0005654 // nucleoplasm // traceable author statement /// 0005663 // DNA replication factor C complex // inferred from direct assay', '0005737 // cytoplasm // inferred from direct assay /// 0005814 // centriole // inferred from direct assay /// 0005829 // cytosol // inferred from direct assay /// 0008180 // COP9 signalosome // inferred from direct assay /// 0070062 // extracellular vesicular exosome // inferred from direct assay /// 0072562 // blood microparticle // inferred from direct assay', '0005634 // nucleus // inferred from direct assay /// 0005654 // nucleoplasm // inferred from sequence or structural similarity /// 0005730 // nucleolus // inferred from direct assay', '0001750 // photoreceptor outer segment // inferred from electronic annotation /// 0001917 // photoreceptor inner segment // inferred from electronic annotation /// 0005578 // proteinaceous extracellular matrix // inferred from electronic annotation /// 0005886 // plasma membrane // inferred from direct assay /// 0016020 // membrane // inferred from electronic annotation /// 0097381 // photoreceptor disc membrane // traceable author statement'], 'Gene Ontology Molecular Function': ['0000166 // nucleotide binding // inferred from electronic annotation /// 0004672 // protein kinase activity // inferred from electronic annotation /// 0004713 // protein tyrosine kinase activity // inferred from electronic annotation /// 0004714 // transmembrane receptor protein tyrosine kinase activity // traceable author statement /// 0005515 // protein binding // inferred from physical interaction /// 0005518 // collagen binding // inferred from direct assay /// 0005518 // collagen binding // inferred from mutant phenotype /// 0005524 // ATP binding // inferred from electronic annotation /// 0016301 // kinase activity // inferred from electronic annotation /// 0016740 // transferase activity // inferred from electronic annotation /// 0016772 // transferase activity, transferring phosphorus-containing groups // inferred from electronic annotation /// 0038062 // protein tyrosine kinase collagen receptor activity // inferred from direct assay /// 0046872 // metal ion binding // inferred from electronic annotation', '0000166 // nucleotide binding // inferred from electronic annotation /// 0003677 // DNA binding // inferred from electronic annotation /// 0005515 // protein binding // inferred from physical interaction /// 0005524 // ATP binding // inferred from electronic annotation /// 0016851 // magnesium chelatase activity // inferred from electronic annotation /// 0017111 // nucleoside-triphosphatase activity // inferred from electronic annotation', '0000166 // nucleotide binding // inferred from electronic annotation /// 0005524 // ATP binding // inferred from electronic annotation /// 0019899 // enzyme binding // inferred from physical interaction /// 0031072 // heat shock protein binding // inferred from physical interaction /// 0042623 // ATPase activity, coupled // inferred from direct assay /// 0051082 // unfolded protein binding // inferred from direct assay', '0000979 // RNA polymerase II core promoter sequence-specific DNA binding // inferred from direct assay /// 0003677 // DNA binding // inferred from direct assay /// 0003677 // DNA binding // inferred from mutant phenotype /// 0003700 // sequence-specific DNA binding transcription factor activity // inferred from direct assay /// 0004996 // thyroid-stimulating hormone receptor activity // traceable author statement /// 0005515 // protein binding // inferred from physical interaction /// 0044212 // transcription regulatory region DNA binding // inferred from direct assay', '0005509 // calcium ion binding // inferred from electronic annotation /// 0008048 // calcium sensitive guanylate cyclase activator activity // inferred from electronic annotation /// 0030249 // guanylate cyclase regulator activity // inferred from electronic annotation /// 0046872 // metal ion binding // inferred from electronic annotation']}\n", "\n", "Complete sample of a few rows:\n", " ID GB_ACC SPOT_ID Species Scientific Name Annotation Date Sequence Type Sequence Source Target Description Representative Public ID Gene Title Gene Symbol ENTREZ_GENE_ID RefSeq Transcript ID Gene Ontology Biological Process Gene Ontology Cellular Component Gene Ontology Molecular Function\n", "0 1007_s_at U48705 NaN Homo sapiens Oct 6, 2014 Exemplar sequence Affymetrix Proprietary Database U48705 /FEATURE=mRNA /DEFINITION=HSU48705 Human receptor tyrosine kinase DDR gene, complete cds U48705 discoidin domain receptor tyrosine kinase 1 /// microRNA 4640 DDR1 /// MIR4640 780 /// 100616237 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 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 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 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\n", "1 1053_at M87338 NaN Homo sapiens Oct 6, 2014 Exemplar sequence GenBank M87338 /FEATURE= /DEFINITION=HUMA1SBU Human replication factor C, 40-kDa subunit (A1) mRNA, complete cds M87338 replication factor C (activator 1) 2, 40kDa RFC2 5982 NM_001278791 /// NM_001278792 /// NM_001278793 /// NM_002914 /// NM_181471 /// XM_006716080 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 0005634 // nucleus // inferred from electronic annotation /// 0005654 // nucleoplasm // traceable author statement /// 0005663 // DNA replication factor C complex // inferred from direct assay 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\n", "2 117_at X51757 NaN Homo sapiens Oct 6, 2014 Exemplar sequence Affymetrix Proprietary Database X51757 /FEATURE=cds /DEFINITION=HSP70B Human heat-shock protein HSP70B' gene X51757 heat shock 70kDa protein 6 (HSP70B') HSPA6 3310 NM_002155 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 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 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\n", "\n", "Potential gene-related columns: ['ID', 'SPOT_ID', 'Species Scientific Name', 'Representative Public ID', 'Gene Title', 'Gene Symbol', 'ENTREZ_GENE_ID', 'RefSeq Transcript ID', 'Gene Ontology Biological Process', 'Gene Ontology Cellular Component', 'Gene Ontology Molecular Function']\n", "\n", "Sample of probe ID to gene symbol mappings:\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", "5 1294_at MIR5193 /// UBA7\n", "6 1316_at THRA\n", "7 1320_at PTPN21\n", "8 1405_i_at CCL5\n", "9 1431_at CYP2E1\n", "\n", "Total number of probe-to-gene mappings: 45782\n" ] } ], "source": [ "# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.\n", "soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)\n", "gene_annotation = get_gene_annotation(soft_file)\n", "\n", "# 2. Analyze the gene annotation dataframe to identify which columns contain the gene identifiers and gene symbols\n", "print(\"\\nGene annotation preview:\")\n", "print(f\"Columns in gene annotation: {gene_annotation.columns.tolist()}\")\n", "print(preview_df(gene_annotation, n=5))\n", "\n", "# Get a more complete view to understand the annotation structure\n", "print(\"\\nComplete sample of a few rows:\")\n", "print(gene_annotation.iloc[:3].to_string())\n", "\n", "# Check for columns that might contain gene information\n", "potential_gene_columns = [col for col in gene_annotation.columns if \n", " any(term in col.upper() for term in [\"GENE\", \"SYMBOL\", \"NAME\", \"ID\"])]\n", "print(f\"\\nPotential gene-related columns: {potential_gene_columns}\")\n", "\n", "# Correctly identify the columns for probe ID and gene symbols\n", "gene_id_col = 'ID' # This is the probe identifier column\n", "gene_symbol_col = 'Gene Symbol' # Corrected to match the actual column name in the data\n", "\n", "# Verify columns exist before mapping\n", "if gene_id_col in gene_annotation.columns and gene_symbol_col in gene_annotation.columns:\n", " # Create the mapping using the library function\n", " mapping_data = get_gene_mapping(gene_annotation, gene_id_col, gene_symbol_col)\n", " \n", " # Print sample of the mapping to confirm\n", " print(\"\\nSample of probe ID to gene symbol mappings:\")\n", " print(mapping_data.head(10))\n", " \n", " # Check the size of the mapping data\n", " print(f\"\\nTotal number of probe-to-gene mappings: {len(mapping_data)}\")\n", "else:\n", " missing_cols = []\n", " if gene_id_col not in gene_annotation.columns:\n", " missing_cols.append(gene_id_col)\n", " if gene_symbol_col not in gene_annotation.columns:\n", " missing_cols.append(gene_symbol_col)\n", " print(f\"\\nError: The following columns are missing from the annotation data: {missing_cols}\")\n" ] }, { "cell_type": "markdown", "id": "40412ba0", "metadata": {}, "source": [ "### Step 6: Gene Identifier Mapping" ] }, { "cell_type": "code", "execution_count": 7, "id": "e015fd2e", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T04:11:12.756971Z", "iopub.status.busy": "2025-03-25T04:11:12.756851Z", "iopub.status.idle": "2025-03-25T04:11:16.761376Z", "shell.execute_reply": "2025-03-25T04:11:16.760993Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Original probe data shape: (54675, 37)\n", "Mapping data shape: (45782, 2)\n", "Mapped gene data shape: (21278, 37)\n", "Number of unique gene symbols after mapping: 21278\n", "\n", "Preview of gene expression data:\n", " GSM2024824 GSM2024825 GSM2024826 GSM2024827 GSM2024828 \\\n", "Gene \n", "A1BG 5.610503 5.636573 4.251042 5.007339 4.243692 \n", "A1BG-AS1 4.479533 4.360212 3.876465 4.364013 2.777218 \n", "A1CF 4.522775 4.522775 4.522775 4.522775 4.522775 \n", "A2M 12.425145 11.317791 14.370494 12.110579 10.911067 \n", "A2M-AS1 3.488812 3.172215 3.292311 4.061210 3.314219 \n", "\n", " GSM2024829 GSM2024830 GSM2024831 GSM2024832 GSM2024833 ... \\\n", "Gene ... \n", "A1BG 3.668276 5.043683 6.892920 6.381826 6.931566 ... \n", "A1BG-AS1 3.374334 4.124470 4.215001 4.396431 3.876242 ... \n", "A1CF 4.522775 4.936799 4.522775 4.522775 4.522775 ... \n", "A2M 14.416153 10.511124 13.006807 13.265400 12.410309 ... \n", "A2M-AS1 3.743375 3.329874 3.232280 4.314000 3.880180 ... \n", "\n", " GSM2024851 GSM2024852 GSM2024853 GSM2024854 GSM2024855 \\\n", "Gene \n", "A1BG 3.608324 5.007339 5.512348 4.243692 4.745575 \n", "A1BG-AS1 3.374301 3.374301 4.104821 3.346230 3.265047 \n", "A1CF 4.522775 4.522775 4.522775 4.522775 4.522775 \n", "A2M 13.683997 13.685249 14.176575 13.706315 12.380748 \n", "A2M-AS1 3.230472 3.331672 3.261011 3.606022 3.232280 \n", "\n", " GSM2024856 GSM2024857 GSM2024858 GSM2024859 GSM2024860 \n", "Gene \n", "A1BG 6.621185 4.646089 5.007339 6.204141 5.199912 \n", "A1BG-AS1 3.955089 3.471560 3.374301 3.499664 3.374334 \n", "A1CF 4.522775 4.557803 4.522775 4.522775 5.268387 \n", "A2M 13.436645 12.618907 12.958917 13.021106 13.537631 \n", "A2M-AS1 3.261011 3.260107 3.260107 5.114511 3.261602 \n", "\n", "[5 rows x 37 columns]\n" ] } ], "source": [ "# 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. Get gene expression data from the matrix file\n", "gene_expression_data = get_genetic_data(matrix_file)\n", "\n", "# 3. Get gene annotation data from the SOFT file\n", "gene_annotation = get_gene_annotation(soft_file)\n", "\n", "# 4. Identify columns for probe ID and gene symbol in the annotation data\n", "gene_id_col = 'ID' # Column that matches identifiers in the gene expression data\n", "gene_symbol_col = 'Gene Symbol' # Column containing the gene symbols\n", "\n", "# 5. Create gene mapping dataframe\n", "mapping_data = get_gene_mapping(gene_annotation, gene_id_col, gene_symbol_col)\n", "\n", "# 6. Apply the gene mapping to convert probe-level data to gene-level data\n", "gene_data = apply_gene_mapping(gene_expression_data, mapping_data)\n", "\n", "# Print information about the conversion\n", "print(f\"Original probe data shape: {gene_expression_data.shape}\")\n", "print(f\"Mapping data shape: {mapping_data.shape}\")\n", "print(f\"Mapped gene data shape: {gene_data.shape}\")\n", "print(f\"Number of unique gene symbols after mapping: {len(gene_data.index.unique())}\")\n", "\n", "# Preview the first few rows of the gene data\n", "print(\"\\nPreview of gene expression data:\")\n", "print(gene_data.head())\n" ] }, { "cell_type": "markdown", "id": "ad3e84c8", "metadata": {}, "source": [ "### Step 7: Data Normalization and Linking" ] }, { "cell_type": "code", "execution_count": 8, "id": "8f2b13aa", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T04:11:16.762764Z", "iopub.status.busy": "2025-03-25T04:11:16.762635Z", "iopub.status.idle": "2025-03-25T04:11:26.690990Z", "shell.execute_reply": "2025-03-25T04:11:26.690447Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Gene data shape before normalization: (21278, 37)\n", "Gene data shape after normalization: (19845, 37)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Normalized gene data saved to ../../output/preprocess/Thyroid_Cancer/gene_data/GSE76039.csv\n", "Extracted clinical data shape: (2, 37)\n", "Preview of clinical data (first 5 samples):\n", " GSM2024824 GSM2024825 GSM2024826 GSM2024827 GSM2024828\n", "Thyroid_Cancer 0.0 0.0 0.0 0.0 0.0\n", "Gender 0.0 0.0 1.0 0.0 1.0\n", "Clinical data saved to ../../output/preprocess/Thyroid_Cancer/clinical_data/GSE76039.csv\n", "Gene data columns (first 5): ['GSM2024824', 'GSM2024825', 'GSM2024826', 'GSM2024827', 'GSM2024828']\n", "Clinical data columns (first 5): ['GSM2024824', 'GSM2024825', 'GSM2024826', 'GSM2024827', 'GSM2024828']\n", "Found 37 common samples between gene and clinical data\n", "Initial linked data shape: (37, 19847)\n", "Preview of linked data (first 5 rows, first 5 columns):\n", " Thyroid_Cancer Gender A1BG A1BG-AS1 A1CF\n", "GSM2024824 0.0 0.0 5.610503 4.479533 4.522775\n", "GSM2024825 0.0 0.0 5.636573 4.360212 4.522775\n", "GSM2024826 0.0 1.0 4.251042 3.876465 4.522775\n", "GSM2024827 0.0 0.0 5.007339 4.364013 4.522775\n", "GSM2024828 0.0 1.0 4.243692 2.777218 4.522775\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Linked data shape after handling missing values: (37, 19847)\n", "For the feature 'Thyroid_Cancer', the least common label is '1.0' with 7 occurrences. This represents 18.92% of the dataset.\n", "The distribution of the feature 'Thyroid_Cancer' in this dataset is fine.\n", "\n", "For the feature 'Gender', the least common label is '1.0' with 10 occurrences. This represents 27.03% of the dataset.\n", "The distribution of the feature 'Gender' in this dataset is fine.\n", "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Linked data saved to ../../output/preprocess/Thyroid_Cancer/GSE76039.csv\n" ] } ], "source": [ "# 1. Normalize gene symbols in the gene expression data\n", "try:\n", " # Make sure the directory exists\n", " os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)\n", " \n", " # Use the gene_data variable from the previous step (don't try to load it from file)\n", " print(f\"Gene data shape before normalization: {gene_data.shape}\")\n", " \n", " # Apply normalization to gene symbols\n", " normalized_gene_data = normalize_gene_symbols_in_index(gene_data)\n", " print(f\"Gene data shape after normalization: {normalized_gene_data.shape}\")\n", " \n", " # Save the normalized gene data\n", " normalized_gene_data.to_csv(out_gene_data_file)\n", " print(f\"Normalized gene data saved to {out_gene_data_file}\")\n", " \n", " # Use the normalized data for further processing\n", " gene_data = normalized_gene_data\n", " is_gene_available = True\n", "except Exception as e:\n", " print(f\"Error normalizing gene data: {e}\")\n", " is_gene_available = False\n", "\n", "# 2. Load clinical data with correct row indices\n", "try:\n", " # Load the original clinical data (from Step 1)\n", " soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)\n", " background_info, clinical_data = get_background_and_clinical_data(matrix_file)\n", " \n", " # Extract clinical features with correct indices from Step 2\n", " clinical_features = geo_select_clinical_features(\n", " clinical_df=clinical_data,\n", " trait=trait,\n", " trait_row=2, # Tumor type row from Step 2\n", " convert_trait=convert_trait,\n", " gender_row=0, # Gender row from Step 2\n", " convert_gender=convert_gender,\n", " age_row=None, # Age data is not available as per Step 2\n", " convert_age=None\n", " )\n", " \n", " print(f\"Extracted clinical data shape: {clinical_features.shape}\")\n", " print(\"Preview of clinical data (first 5 samples):\")\n", " print(clinical_features.iloc[:, :5])\n", " \n", " # Save the properly extracted clinical data\n", " os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)\n", " clinical_features.to_csv(out_clinical_data_file)\n", " print(f\"Clinical data saved to {out_clinical_data_file}\")\n", " \n", " is_trait_available = True\n", "except Exception as e:\n", " print(f\"Error extracting clinical data: {e}\")\n", " is_trait_available = False\n", "\n", "# 3. Link clinical and genetic data if both are available\n", "if is_trait_available and is_gene_available:\n", " try:\n", " # Debug the column names to ensure they match\n", " print(f\"Gene data columns (first 5): {gene_data.columns[:5].tolist()}\")\n", " print(f\"Clinical data columns (first 5): {clinical_features.columns[:5].tolist()}\")\n", " \n", " # Check for common sample IDs\n", " common_samples = set(gene_data.columns).intersection(clinical_features.columns)\n", " print(f\"Found {len(common_samples)} common samples between gene and clinical data\")\n", " \n", " if len(common_samples) > 0:\n", " # Link the clinical and genetic data\n", " linked_data = geo_link_clinical_genetic_data(clinical_features, gene_data)\n", " print(f\"Initial linked data shape: {linked_data.shape}\")\n", " \n", " # Debug the trait values before handling missing values\n", " print(\"Preview of linked data (first 5 rows, first 5 columns):\")\n", " print(linked_data.iloc[:5, :5])\n", " \n", " # Handle missing values\n", " linked_data = handle_missing_values(linked_data, trait)\n", " print(f\"Linked data shape after handling missing values: {linked_data.shape}\")\n", " \n", " if linked_data.shape[0] > 0:\n", " # Check for bias in trait and demographic features\n", " is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)\n", " \n", " # Validate the data quality and save cohort info\n", " note = \"Dataset contains gene expression data from thyroid cancer samples with tumor type 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=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", " # Save the linked data if it's usable\n", " if is_usable:\n", " os.makedirs(os.path.dirname(out_data_file), exist_ok=True)\n", " linked_data.to_csv(out_data_file)\n", " print(f\"Linked data saved to {out_data_file}\")\n", " else:\n", " print(\"Data not usable for the trait study - not saving final linked data.\")\n", " else:\n", " print(\"After handling missing values, no samples remain.\")\n", " 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=True,\n", " df=pd.DataFrame(),\n", " note=\"No valid samples after handling missing values.\"\n", " )\n", " else:\n", " print(\"No common samples found between gene expression and clinical data.\")\n", " 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=True,\n", " df=pd.DataFrame(),\n", " note=\"No common samples between gene expression and clinical data.\"\n", " )\n", " except Exception as e:\n", " print(f\"Error linking or processing data: {e}\")\n", " 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=True, # Assume biased if there's an error\n", " df=pd.DataFrame(), # Empty dataframe for metadata\n", " note=f\"Error in data processing: {str(e)}\"\n", " )\n", "else:\n", " # We can't proceed with linking if either trait or gene data is missing\n", " print(\"Cannot proceed with data linking due to missing trait or gene data.\")\n", " 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=True, # Data is unusable if we're missing components\n", " df=pd.DataFrame(), # Empty dataframe for metadata\n", " note=\"Missing essential data components for linking (trait data or gene expression data).\"\n", " )" ] } ], "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 }