{ "cells": [ { "cell_type": "code", "execution_count": 1, "id": "5eb50900", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T04:11:44.254693Z", "iopub.status.busy": "2025-03-25T04:11:44.254269Z", "iopub.status.idle": "2025-03-25T04:11:44.423727Z", "shell.execute_reply": "2025-03-25T04:11:44.423290Z" } }, "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 = \"GSE82208\"\n", "\n", "# Input paths\n", "in_trait_dir = \"../../input/GEO/Thyroid_Cancer\"\n", "in_cohort_dir = \"../../input/GEO/Thyroid_Cancer/GSE82208\"\n", "\n", "# Output paths\n", "out_data_file = \"../../output/preprocess/Thyroid_Cancer/GSE82208.csv\"\n", "out_gene_data_file = \"../../output/preprocess/Thyroid_Cancer/gene_data/GSE82208.csv\"\n", "out_clinical_data_file = \"../../output/preprocess/Thyroid_Cancer/clinical_data/GSE82208.csv\"\n", "json_path = \"../../output/preprocess/Thyroid_Cancer/cohort_info.json\"\n" ] }, { "cell_type": "markdown", "id": "839c1f2c", "metadata": {}, "source": [ "### Step 1: Initial Data Loading" ] }, { "cell_type": "code", "execution_count": 2, "id": "725c7eb5", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T04:11:44.425235Z", "iopub.status.busy": "2025-03-25T04:11:44.425088Z", "iopub.status.idle": "2025-03-25T04:11:44.601067Z", "shell.execute_reply": "2025-03-25T04:11:44.600471Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Background Information:\n", "!Series_title\t\"Gene expression markers differentiating between malignant and benign follicular thyroid tumors\"\n", "!Series_summary\t\"Differential diagnosis between malignant follicular thyroid cancer (FTC) and benign follicular thyroid adenoma (FTA) is a great challenge for even an experienced pathologist and requires special effort. Molecular markers may potentially support a differential diagnosis between FTC and FTA in postoperative specimens. The purpose of this study was to derive molecular support for the differential diagnosis, in the form of a simple multigene mRNA-based classifier that would differentiate between FTC and FTA tissue samples.\"\n", "!Series_overall_design\t\"Gene expression profiling was performed in 27 follicular thyroid cancers and 25 follicular thyroid adenomas.\"\n", "Sample Characteristics Dictionary:\n", "{0: ['Sex: Female', 'Sex: Male', 'Sex: -'], 1: ['age (years): 74', 'age (years): 80', 'age (years): 66', 'age (years): 78', 'age (years): 50', 'age (years): 61', 'age (years): 72', 'age (years): 38', 'age (years): 47', 'age (years): 69', 'age (years): 44', 'age (years): 34', 'age (years): 39', 'age (years): 23', 'age (years): 14', 'age (years): 42', 'age (years): 29', 'age (years): 43', 'age (years): 31', 'age (years): 58', 'age (years): 52', 'age (years): 60', 'age (years): 49', 'age (years): 67', 'age (years): 68', 'age (years): -', 'age (years): 76', 'age (years): 53', 'age (years): 71', 'age (years): 36'], 2: ['class: FTC', 'class: FTA'], 3: ['onco/pdtc: NO', 'onco/pdtc: ONCO', 'onco/pdtc: PDTC'], 4: ['ras: NO', 'ras: K61', 'ras: unknown', 'ras: N61'], 5: ['diagnosis: primary', 'diagnosis: 1 expert', 'diagnosis: 2 experts', 'diagnosis: 2 experts (lack of concordance)'], 6: ['origin: Germany', 'origin: Poland'], 7: ['set: Secondary', 'set: Primary']}\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": "60a270c9", "metadata": {}, "source": [ "### Step 2: Dataset Analysis and Clinical Feature Extraction" ] }, { "cell_type": "code", "execution_count": 3, "id": "c1b70fe1", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T04:11:44.602378Z", "iopub.status.busy": "2025-03-25T04:11:44.602256Z", "iopub.status.idle": "2025-03-25T04:11:44.615485Z", "shell.execute_reply": "2025-03-25T04:11:44.614948Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Preview of clinical features: {'GSM2186533': [1.0, 74.0, 0.0], 'GSM2186534': [1.0, 80.0, 0.0], 'GSM2186535': [1.0, 66.0, 1.0], 'GSM2186536': [0.0, 78.0, 0.0], 'GSM2186537': [1.0, 74.0, 0.0], 'GSM2186538': [1.0, 50.0, 1.0], 'GSM2186539': [1.0, 61.0, 0.0], 'GSM2186540': [1.0, 72.0, 0.0], 'GSM2186541': [1.0, 80.0, 0.0], 'GSM2186542': [1.0, 61.0, 0.0], 'GSM2186543': [0.0, 38.0, 1.0], 'GSM2186544': [1.0, 66.0, 0.0], 'GSM2186545': [0.0, 47.0, 0.0], 'GSM2186546': [1.0, 69.0, 0.0], 'GSM2186547': [0.0, 44.0, 0.0], 'GSM2186548': [1.0, 72.0, 0.0], 'GSM2186549': [1.0, 66.0, 0.0], 'GSM2186550': [0.0, 34.0, 0.0], 'GSM2186551': [1.0, 39.0, 0.0], 'GSM2186552': [0.0, 23.0, 0.0], 'GSM2186553': [0.0, 14.0, 0.0], 'GSM2186554': [0.0, 42.0, 0.0], 'GSM2186555': [0.0, 29.0, 0.0], 'GSM2186556': [0.0, 43.0, 0.0], 'GSM2186557': [0.0, 31.0, 0.0], 'GSM2186558': [0.0, 58.0, 0.0], 'GSM2186559': [0.0, 52.0, 0.0], 'GSM2186560': [0.0, 60.0, 0.0], 'GSM2186561': [0.0, 69.0, 0.0], 'GSM2186562': [0.0, 60.0, 0.0], 'GSM2186563': [0.0, 49.0, 0.0], 'GSM2186564': [1.0, 67.0, 1.0], 'GSM2186565': [1.0, 68.0, 1.0], 'GSM2186566': [1.0, 72.0, 1.0], 'GSM2186567': [1.0, 66.0, 0.0], 'GSM2186568': [1.0, 61.0, 1.0], 'GSM2186569': [1.0, nan, nan], 'GSM2186570': [1.0, nan, nan], 'GSM2186571': [1.0, 76.0, 1.0], 'GSM2186572': [0.0, 53.0, 1.0], 'GSM2186573': [1.0, 61.0, 0.0], 'GSM2186574': [1.0, 61.0, 1.0], 'GSM2186575': [1.0, 60.0, 0.0], 'GSM2186576': [1.0, 71.0, 0.0], 'GSM2186577': [1.0, 36.0, 0.0], 'GSM2186578': [0.0, 71.0, 0.0], 'GSM2186579': [0.0, 55.0, 0.0], 'GSM2186580': [0.0, 71.0, 0.0], 'GSM2186581': [0.0, 33.0, 1.0], 'GSM2186582': [0.0, 33.0, 0.0], 'GSM2186583': [0.0, 25.0, 0.0], 'GSM2186584': [0.0, 29.0, 0.0]}\n" ] } ], "source": [ "import pandas as pd\n", "import json\n", "import os\n", "from typing import Callable, Dict, Optional, Any\n", "\n", "# 1. Gene Expression Data Availability\n", "# Based on the background information, this dataset contains gene expression data comparing FTC and FTA\n", "is_gene_available = True\n", "\n", "# 2. Variable Availability and Data Type Conversion\n", "# 2.1 Data Availability\n", "# Thyroid Cancer status is in key 2 (class: FTC/FTA)\n", "trait_row = 2\n", "# Age is in key 1 (age (years): xx)\n", "age_row = 1\n", "# Gender is in key 0 (Sex: Female/Male/-)\n", "gender_row = 0\n", "\n", "# 2.2 Data Type Conversion Functions\n", "def convert_trait(value: str) -> int:\n", " \"\"\"Convert thyroid cancer status to binary (0: benign, 1: malignant)\"\"\"\n", " if not value or value == '-':\n", " return None\n", " \n", " # Extract the value after the colon\n", " if ':' in value:\n", " value = value.split(':', 1)[1].strip()\n", " \n", " # FTC = Follicular Thyroid Cancer (malignant) = 1\n", " # FTA = Follicular Thyroid Adenoma (benign) = 0\n", " if value.upper() == 'FTC':\n", " return 1\n", " elif value.upper() == 'FTA':\n", " return 0\n", " else:\n", " return None\n", "\n", "def convert_age(value: str) -> float:\n", " \"\"\"Convert age to continuous value\"\"\"\n", " if not value or value == '-':\n", " return None\n", " \n", " # Extract the value after the colon\n", " if ':' in value:\n", " value = value.split(':', 1)[1].strip()\n", " \n", " try:\n", " return float(value)\n", " except (ValueError, TypeError):\n", " return None\n", "\n", "def convert_gender(value: str) -> int:\n", " \"\"\"Convert gender to binary (0: female, 1: male)\"\"\"\n", " if not value or value == '-':\n", " return None\n", " \n", " # Extract the value after the colon\n", " if ':' in value:\n", " value = value.split(':', 1)[1].strip()\n", " \n", " if value.upper() == 'FEMALE':\n", " return 0\n", " elif value.upper() == 'MALE':\n", " return 1\n", " else:\n", " return None\n", "\n", "# 3. Save Metadata\n", "# Determine if trait data is available\n", "is_trait_available = trait_row is not None\n", "# Conduct 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", " # Note: We'll use clinical_data which should be passed from previous steps\n", " # We're not loading it from a CSV file as it doesn't exist yet\n", " \n", " # Extract clinical features from the previously obtained clinical_data\n", " # This assumes clinical_data is already loaded in the environment\n", " try:\n", " # Extract clinical features\n", " clinical_features = geo_select_clinical_features(\n", " clinical_df=clinical_data,\n", " trait=trait,\n", " trait_row=trait_row,\n", " convert_trait=convert_trait,\n", " age_row=age_row,\n", " convert_age=convert_age,\n", " gender_row=gender_row,\n", " convert_gender=convert_gender\n", " )\n", " \n", " # Preview the extracted clinical features\n", " preview = preview_df(clinical_features)\n", " print(\"Preview of clinical features:\", preview)\n", " \n", " # Save clinical features to CSV\n", " os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)\n", " clinical_features.to_csv(out_clinical_data_file)\n", " except NameError:\n", " print(\"Error: clinical_data not available. Unable to process clinical features.\")\n", " print(\"Proceeding with metadata validation only.\")\n" ] }, { "cell_type": "markdown", "id": "71b74ae8", "metadata": {}, "source": [ "### Step 3: Gene Data Extraction" ] }, { "cell_type": "code", "execution_count": 4, "id": "d3cdcdb9", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T04:11:44.616731Z", "iopub.status.busy": "2025-03-25T04:11:44.616625Z", "iopub.status.idle": "2025-03-25T04:11:44.901964Z", "shell.execute_reply": "2025-03-25T04:11:44.901614Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "SOFT file: ../../input/GEO/Thyroid_Cancer/GSE82208/GSE82208_family.soft.gz\n", "Matrix file: ../../input/GEO/Thyroid_Cancer/GSE82208/GSE82208_series_matrix.txt.gz\n", "Found the matrix table marker in the file.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Gene data shape: (54675, 52)\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": "4bb4d2df", "metadata": {}, "source": [ "### Step 4: Gene Identifier Review" ] }, { "cell_type": "code", "execution_count": 5, "id": "c8e7ded5", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T04:11:44.903313Z", "iopub.status.busy": "2025-03-25T04:11:44.903205Z", "iopub.status.idle": "2025-03-25T04:11:44.905006Z", "shell.execute_reply": "2025-03-25T04:11:44.904716Z" } }, "outputs": [], "source": [ "# Looking at the gene identifiers provided, they appear to be Affymetrix probe IDs,\n", "# not standard human gene symbols. These are identifiers like \"1007_s_at\" which are\n", "# specific to Affymetrix microarray platforms and need to be mapped to standard gene symbols.\n", "\n", "requires_gene_mapping = True\n" ] }, { "cell_type": "markdown", "id": "407d9022", "metadata": {}, "source": [ "### Step 5: Gene Annotation" ] }, { "cell_type": "code", "execution_count": 6, "id": "17c6e850", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T04:11:44.906232Z", "iopub.status.busy": "2025-03-25T04:11:44.906131Z", "iopub.status.idle": "2025-03-25T04:11:49.619859Z", "shell.execute_reply": "2025-03-25T04:11:49.619488Z" } }, "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' # This is the gene symbol column\n", "\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" ] }, { "cell_type": "markdown", "id": "f4577fcf", "metadata": {}, "source": [ "### Step 6: Gene Identifier Mapping" ] }, { "cell_type": "code", "execution_count": 7, "id": "a547208e", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T04:11:49.621224Z", "iopub.status.busy": "2025-03-25T04:11:49.621110Z", "iopub.status.idle": "2025-03-25T04:11:55.476670Z", "shell.execute_reply": "2025-03-25T04:11:55.476335Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Gene expression data shape: (54675, 52)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Gene mapping shape: (45782, 2)\n", "Sample of mapping data:\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", "After mapping, gene expression data shape: (21278, 52)\n", "Sample of gene expression data (first 5 genes, first 5 samples):\n", " GSM2186533 GSM2186534 GSM2186535 GSM2186536 GSM2186537\n", "Gene \n", "A1BG 3.448852 3.270417 2.646647 4.187510 2.979016\n", "A1BG-AS1 2.257473 2.257473 2.257473 2.257473 2.257473\n", "A1CF 4.621940 4.514946 4.514946 4.514946 4.514946\n", "A2M 12.614231 13.090670 12.192458 11.504684 12.318218\n", "A2M-AS1 3.884622 4.393652 2.257473 3.929452 3.692808\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Gene expression data saved to ../../output/preprocess/Thyroid_Cancer/gene_data/GSE82208.csv\n" ] } ], "source": [ "# Make sure we have the gene expression data and gene mapping available\n", "soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)\n", "\n", "# 1. Load gene expression data\n", "gene_expression = get_genetic_data(matrix_file)\n", "print(f\"Gene expression data shape: {gene_expression.shape}\")\n", "\n", "# 2. Get gene annotation\n", "gene_annotation = get_gene_annotation(soft_file)\n", "\n", "# 3. Create gene mapping\n", "# From previous analysis, we observed:\n", "# - The gene expression data has probe IDs like '1007_s_at' as index\n", "# - The gene annotation has 'ID' column for probe IDs and 'Gene Symbol' column for gene names\n", "prob_col = 'ID' # Column containing probe identifiers\n", "gene_col = 'Gene Symbol' # Column containing gene symbols\n", "mapping_df = get_gene_mapping(gene_annotation, prob_col, gene_col)\n", "\n", "print(f\"Gene mapping shape: {mapping_df.shape}\")\n", "print(\"Sample of mapping data:\")\n", "print(mapping_df.head())\n", "\n", "# 4. Apply gene mapping to convert probe-level measurements to gene expression data\n", "gene_data = apply_gene_mapping(gene_expression, mapping_df)\n", "print(f\"After mapping, gene expression data shape: {gene_data.shape}\")\n", "print(\"Sample of gene expression data (first 5 genes, first 5 samples):\")\n", "print(gene_data.iloc[:5, :5])\n", "\n", "# Save the gene expression data to a file\n", "os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)\n", "gene_data.to_csv(out_gene_data_file)\n", "print(f\"Gene expression data saved to {out_gene_data_file}\")\n" ] }, { "cell_type": "markdown", "id": "76b0d56d", "metadata": {}, "source": [ "### Step 7: Data Normalization and Linking" ] }, { "cell_type": "code", "execution_count": 8, "id": "229890c9", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T04:11:55.478004Z", "iopub.status.busy": "2025-03-25T04:11:55.477879Z", "iopub.status.idle": "2025-03-25T04:12:06.599545Z", "shell.execute_reply": "2025-03-25T04:12:06.598985Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Loaded gene data shape before normalization: (21278, 52)\n", "Gene data shape after normalization: (19845, 52)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Normalized gene data saved to ../../output/preprocess/Thyroid_Cancer/gene_data/GSE82208.csv\n", "Extracted clinical data shape: (3, 52)\n", "Preview of clinical data (first 5 samples):\n", " GSM2186533 GSM2186534 GSM2186535 GSM2186536 GSM2186537\n", "Thyroid_Cancer 1.0 1.0 1.0 0.0 1.0\n", "Age 74.0 80.0 66.0 78.0 74.0\n", "Gender 0.0 0.0 1.0 0.0 0.0\n", "Clinical data saved to ../../output/preprocess/Thyroid_Cancer/clinical_data/GSE82208.csv\n", "Gene data columns (first 5): ['GSM2186533', 'GSM2186534', 'GSM2186535', 'GSM2186536', 'GSM2186537']\n", "Clinical data columns (first 5): ['GSM2186533', 'GSM2186534', 'GSM2186535', 'GSM2186536', 'GSM2186537']\n", "Found 52 common samples between gene and clinical data\n", "Initial linked data shape: (52, 19848)\n", "Preview of linked data (first 5 rows, first 5 columns):\n", " Thyroid_Cancer Age Gender A1BG A1BG-AS1\n", "GSM2186533 1.0 74.0 0.0 3.448852 2.257473\n", "GSM2186534 1.0 80.0 0.0 3.270417 2.257473\n", "GSM2186535 1.0 66.0 1.0 2.646647 2.257473\n", "GSM2186536 0.0 78.0 0.0 4.187510 2.257473\n", "GSM2186537 1.0 74.0 0.0 2.979016 2.257473\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Linked data shape after handling missing values: (52, 19848)\n", "For the feature 'Thyroid_Cancer', the least common label is '0.0' with 25 occurrences. This represents 48.08% of the dataset.\n", "The distribution of the feature 'Thyroid_Cancer' in this dataset is fine.\n", "\n", "Quartiles for 'Age':\n", " 25%: 42.75\n", " 50% (Median): 60.0\n", " 75%: 69.0\n", "Min: 14.0\n", "Max: 80.0\n", "The distribution of the feature 'Age' in this dataset is fine.\n", "\n", "For the feature 'Gender', the least common label is '1.0' with 11 occurrences. This represents 21.15% 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/GSE82208.csv\n" ] } ], "source": [ "# 1. Normalize gene symbols in the gene expression data\n", "try:\n", " # Load the gene data that was saved in the previous step\n", " gene_data = pd.read_csv(out_gene_data_file, index_col=0)\n", " print(f\"Loaded gene data shape before normalization: {gene_data.shape}\")\n", " \n", " # Apply normalization to gene symbols\n", " gene_data = normalize_gene_symbols_in_index(gene_data)\n", " print(f\"Gene data shape after normalization: {gene_data.shape}\")\n", " \n", " # Save the normalized gene data\n", " gene_data.to_csv(out_gene_data_file)\n", " print(f\"Normalized gene data saved to {out_gene_data_file}\")\n", " \n", " is_gene_available = True\n", "except Exception as e:\n", " print(f\"Error loading or 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, # Correct value from Step 2\n", " convert_trait=convert_trait,\n", " age_row=1,\n", " convert_age=convert_age,\n", " gender_row=0,\n", " convert_gender=convert_gender\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 follicular thyroid cancer and follicular thyroid adenoma tissue samples.\"\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 }