{ "cells": [ { "cell_type": "code", "execution_count": 1, "id": "27a327e3", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T06:47:06.124217Z", "iopub.status.busy": "2025-03-25T06:47:06.124099Z", "iopub.status.idle": "2025-03-25T06:47:06.287491Z", "shell.execute_reply": "2025-03-25T06:47:06.287025Z" } }, "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 = \"Atrial_Fibrillation\"\n", "cohort = \"GSE235307\"\n", "\n", "# Input paths\n", "in_trait_dir = \"../../input/GEO/Atrial_Fibrillation\"\n", "in_cohort_dir = \"../../input/GEO/Atrial_Fibrillation/GSE235307\"\n", "\n", "# Output paths\n", "out_data_file = \"../../output/preprocess/Atrial_Fibrillation/GSE235307.csv\"\n", "out_gene_data_file = \"../../output/preprocess/Atrial_Fibrillation/gene_data/GSE235307.csv\"\n", "out_clinical_data_file = \"../../output/preprocess/Atrial_Fibrillation/clinical_data/GSE235307.csv\"\n", "json_path = \"../../output/preprocess/Atrial_Fibrillation/cohort_info.json\"\n" ] }, { "cell_type": "markdown", "id": "b05578ac", "metadata": {}, "source": [ "### Step 1: Initial Data Loading" ] }, { "cell_type": "code", "execution_count": 2, "id": "686f22a4", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T06:47:06.288827Z", "iopub.status.busy": "2025-03-25T06:47:06.288673Z", "iopub.status.idle": "2025-03-25T06:47:06.725364Z", "shell.execute_reply": "2025-03-25T06:47:06.724771Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Background Information:\n", "!Series_title\t\"Gene expression and atrial fibrillation prediction\"\n", "!Series_summary\t\"The aim of this study was to identify a blood gene expression profile that predicts atrial fibrillation in heart failure patients\"\n", "!Series_overall_design\t\"Cardiac blood samples were obtained from the coronary sinus during CRT-D (Cardiac Resynchronization Therapy - Defibrillator) placement in heart failure patients. Patients were followed during 1 year.\"\n", "Sample Characteristics Dictionary:\n", "{0: ['tissue: Whole blood'], 1: ['gender: Male', 'gender: Female'], 2: ['age: 63', 'age: 60', 'age: 72', 'age: 66', 'age: 70', 'age: 64', 'age: 61', 'age: 44', 'age: 54', 'age: 50', 'age: 79', 'age: 51', 'age: 55', 'age: 67', 'age: 52', 'age: 73', 'age: 76', 'age: 43', 'age: 68', 'age: 78', 'age: 69', 'age: 57', 'age: 59', 'age: 53', 'age: 65', 'age: 56', 'age: 74', 'age: 38', 'age: 71', 'age: 37'], 3: ['cardiopathy: ischemic', 'cardiopathy: non ischemic', 'cardiopathy: mixed'], 4: ['cardiac rhythm at start of the study: Sinus rhythm'], 5: ['cardiac rhythm after 1 year follow-up: Sinus rhythm', 'cardiac rhythm after 1 year follow-up: Atrial fibrillation']}\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": "3c10713e", "metadata": {}, "source": [ "### Step 2: Dataset Analysis and Clinical Feature Extraction" ] }, { "cell_type": "code", "execution_count": 3, "id": "b0115d97", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T06:47:06.727170Z", "iopub.status.busy": "2025-03-25T06:47:06.727043Z", "iopub.status.idle": "2025-03-25T06:47:06.744206Z", "shell.execute_reply": "2025-03-25T06:47:06.743713Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Preview of extracted clinical features:\n", "{'GSM7498589': [0.0, 63.0, 1.0], 'GSM7498590': [0.0, 60.0, 1.0], 'GSM7498591': [0.0, 60.0, 1.0], 'GSM7498592': [0.0, 72.0, 1.0], 'GSM7498593': [0.0, 63.0, 1.0], 'GSM7498594': [0.0, 66.0, 0.0], 'GSM7498595': [0.0, 70.0, 1.0], 'GSM7498596': [0.0, 64.0, 1.0], 'GSM7498597': [0.0, 63.0, 1.0], 'GSM7498598': [0.0, 61.0, 1.0], 'GSM7498599': [0.0, 70.0, 0.0], 'GSM7498600': [0.0, 64.0, 1.0], 'GSM7498601': [0.0, 63.0, 1.0], 'GSM7498602': [0.0, 44.0, 1.0], 'GSM7498603': [0.0, 54.0, 1.0], 'GSM7498604': [0.0, 44.0, 1.0], 'GSM7498605': [0.0, 50.0, 1.0], 'GSM7498606': [1.0, 79.0, 1.0], 'GSM7498607': [0.0, 63.0, 1.0], 'GSM7498608': [0.0, 63.0, 0.0], 'GSM7498609': [1.0, 64.0, 1.0], 'GSM7498610': [0.0, 60.0, 1.0], 'GSM7498611': [0.0, 51.0, 1.0], 'GSM7498612': [0.0, 55.0, 1.0], 'GSM7498613': [0.0, 55.0, 1.0], 'GSM7498614': [1.0, 67.0, 1.0], 'GSM7498615': [0.0, 52.0, 1.0], 'GSM7498616': [0.0, 70.0, 0.0], 'GSM7498617': [0.0, 54.0, 1.0], 'GSM7498618': [0.0, 54.0, 1.0], 'GSM7498619': [0.0, 73.0, 1.0], 'GSM7498620': [0.0, 54.0, 0.0], 'GSM7498621': [0.0, 76.0, 1.0], 'GSM7498622': [0.0, 76.0, 1.0], 'GSM7498623': [0.0, 43.0, 0.0], 'GSM7498624': [0.0, 64.0, 1.0], 'GSM7498625': [0.0, 64.0, 1.0], 'GSM7498626': [0.0, 68.0, 0.0], 'GSM7498627': [0.0, 43.0, 1.0], 'GSM7498628': [1.0, 54.0, 1.0], 'GSM7498629': [0.0, 72.0, 0.0], 'GSM7498630': [0.0, 51.0, 1.0], 'GSM7498631': [0.0, 68.0, 0.0], 'GSM7498632': [0.0, 50.0, 0.0], 'GSM7498633': [0.0, 78.0, 1.0], 'GSM7498634': [1.0, 69.0, 1.0], 'GSM7498635': [0.0, 64.0, 0.0], 'GSM7498636': [0.0, 54.0, 1.0], 'GSM7498637': [0.0, 54.0, 1.0], 'GSM7498638': [0.0, 57.0, 1.0], 'GSM7498639': [0.0, 55.0, 0.0], 'GSM7498640': [0.0, 60.0, 1.0], 'GSM7498641': [0.0, 59.0, 1.0], 'GSM7498642': [0.0, 54.0, 1.0], 'GSM7498643': [0.0, 54.0, 1.0], 'GSM7498644': [0.0, 54.0, 1.0], 'GSM7498645': [0.0, 54.0, 1.0], 'GSM7498646': [0.0, 53.0, 1.0], 'GSM7498647': [0.0, 52.0, 0.0], 'GSM7498648': [0.0, 68.0, 1.0], 'GSM7498649': [0.0, 72.0, 0.0], 'GSM7498650': [0.0, 70.0, 1.0], 'GSM7498651': [0.0, 65.0, 1.0], 'GSM7498652': [0.0, 64.0, 1.0], 'GSM7498653': [0.0, 56.0, 0.0], 'GSM7498654': [0.0, 56.0, 0.0], 'GSM7498655': [0.0, 63.0, 1.0], 'GSM7498656': [0.0, 57.0, 1.0], 'GSM7498657': [0.0, 63.0, 1.0], 'GSM7498658': [0.0, 68.0, 1.0], 'GSM7498659': [0.0, 66.0, 0.0], 'GSM7498660': [0.0, 74.0, 0.0], 'GSM7498661': [0.0, 38.0, 1.0], 'GSM7498662': [0.0, 56.0, 1.0], 'GSM7498663': [0.0, 57.0, 1.0], 'GSM7498664': [0.0, 71.0, 0.0], 'GSM7498665': [1.0, 78.0, 0.0], 'GSM7498666': [0.0, 51.0, 1.0], 'GSM7498667': [0.0, 50.0, 1.0], 'GSM7498668': [0.0, 37.0, 1.0], 'GSM7498669': [0.0, 37.0, 1.0], 'GSM7498670': [0.0, 70.0, 0.0], 'GSM7498671': [0.0, 72.0, 0.0], 'GSM7498672': [0.0, 73.0, 1.0], 'GSM7498673': [0.0, 69.0, 0.0], 'GSM7498674': [0.0, 69.0, 0.0], 'GSM7498675': [1.0, 63.0, 1.0], 'GSM7498676': [0.0, 62.0, 0.0], 'GSM7498677': [0.0, 59.0, 0.0], 'GSM7498678': [0.0, 67.0, 1.0], 'GSM7498679': [0.0, 76.0, 1.0], 'GSM7498680': [0.0, 63.0, 1.0], 'GSM7498681': [0.0, 55.0, 1.0], 'GSM7498682': [0.0, 57.0, 1.0], 'GSM7498683': [0.0, 53.0, 1.0], 'GSM7498684': [0.0, 59.0, 1.0], 'GSM7498685': [1.0, 77.0, 1.0], 'GSM7498686': [0.0, 54.0, 1.0], 'GSM7498687': [1.0, 64.0, 1.0], 'GSM7498688': [0.0, 75.0, 0.0], 'GSM7498689': [0.0, 75.0, 0.0], 'GSM7498690': [0.0, 72.0, 0.0], 'GSM7498691': [0.0, 58.0, 0.0], 'GSM7498692': [0.0, 75.0, 1.0], 'GSM7498693': [0.0, 78.0, 1.0], 'GSM7498694': [0.0, 58.0, 1.0], 'GSM7498695': [0.0, 64.0, 1.0], 'GSM7498696': [0.0, 63.0, 1.0], 'GSM7498697': [0.0, 61.0, 1.0], 'GSM7498698': [0.0, 60.0, 1.0], 'GSM7498699': [0.0, 59.0, 0.0], 'GSM7498700': [0.0, 68.0, 1.0], 'GSM7498701': [0.0, 77.0, 1.0], 'GSM7498702': [1.0, 57.0, 1.0], 'GSM7498703': [0.0, 62.0, 0.0], 'GSM7498704': [1.0, 66.0, 1.0], 'GSM7498705': [1.0, 57.0, 1.0], 'GSM7498706': [1.0, 65.0, 1.0], 'GSM7498707': [0.0, 59.0, 1.0]}\n", "Clinical features saved to ../../output/preprocess/Atrial_Fibrillation/clinical_data/GSE235307.csv\n" ] } ], "source": [ "# 1. Determine gene expression data availability\n", "# This dataset appears to be about gene expression in blood samples from heart failure patients\n", "is_gene_available = True\n", "\n", "# 2. Variable Availability and Data Type Conversion\n", "# 2.1 Identify keys in the sample characteristics dictionary\n", "\n", "# For trait (Atrial_Fibrillation): appears in sample characteristics key 5\n", "# We can see patients had either \"Sinus rhythm\" or \"Atrial fibrillation\" after 1 year follow-up\n", "trait_row = 5\n", "\n", "# For age: appears in sample characteristics key 2\n", "age_row = 2\n", "\n", "# For gender: appears in sample characteristics key 1\n", "gender_row = 1\n", "\n", "# 2.2 Define conversion functions\n", "\n", "def convert_trait(value):\n", " \"\"\"Convert trait value to binary (0 or 1)\"\"\"\n", " if not isinstance(value, str):\n", " return None\n", " \n", " value = value.lower()\n", " if \":\" in value:\n", " value = value.split(\":\", 1)[1].strip()\n", " \n", " if \"atrial fibrillation\" in value:\n", " return 1\n", " elif \"sinus rhythm\" in value:\n", " return 0\n", " return None\n", "\n", "def convert_age(value):\n", " \"\"\"Convert age value to continuous numeric\"\"\"\n", " if not isinstance(value, str):\n", " return None\n", " \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):\n", " \"\"\"Convert gender value to binary (0 for female, 1 for male)\"\"\"\n", " if not isinstance(value, str):\n", " return None\n", " \n", " value = value.lower()\n", " if \":\" in value:\n", " value = value.split(\":\", 1)[1].strip()\n", " \n", " if \"female\" in value:\n", " return 0\n", " elif \"male\" in value:\n", " return 1\n", " return None\n", "\n", "# 3. Save metadata for initial filtering\n", "is_trait_available = trait_row is not None\n", "validate_and_save_cohort_info(\n", " is_final=False,\n", " cohort=cohort,\n", " info_path=json_path,\n", " is_gene_available=is_gene_available,\n", " is_trait_available=is_trait_available\n", ")\n", "\n", "# 4. Extract clinical features if trait data is available\n", "if trait_row is not None:\n", " # Assume clinical_data variable exists from previous step\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", " print(\"Preview of extracted clinical features:\")\n", " print(preview_df(clinical_features))\n", " \n", " # Create directory if it doesn't exist\n", " os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)\n", " \n", " # Save clinical features to CSV file\n", " clinical_features.to_csv(out_clinical_data_file)\n", " print(f\"Clinical features saved to {out_clinical_data_file}\")\n" ] }, { "cell_type": "markdown", "id": "b44a916d", "metadata": {}, "source": [ "### Step 3: Gene Data Extraction" ] }, { "cell_type": "code", "execution_count": 4, "id": "5276f61c", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T06:47:06.745793Z", "iopub.status.busy": "2025-03-25T06:47:06.745682Z", "iopub.status.idle": "2025-03-25T06:47:07.527259Z", "shell.execute_reply": "2025-03-25T06:47:07.526603Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Matrix file found: ../../input/GEO/Atrial_Fibrillation/GSE235307/GSE235307_series_matrix.txt.gz\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Gene data shape: (58717, 119)\n", "First 20 gene/probe identifiers:\n", "Index(['4', '5', '6', '7', '8', '9', '10', '11', '12', '13', '14', '15', '16',\n", " '17', '18', '19', '20', '21', '22', '23'],\n", " dtype='object', name='ID')\n" ] } ], "source": [ "# 1. Get the SOFT and matrix file paths again \n", "soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)\n", "print(f\"Matrix file found: {matrix_file}\")\n", "\n", "# 2. Use the get_genetic_data function from the library to get the gene_data\n", "try:\n", " gene_data = get_genetic_data(matrix_file)\n", " print(f\"Gene data shape: {gene_data.shape}\")\n", " \n", " # 3. Print the first 20 row IDs (gene or probe identifiers)\n", " print(\"First 20 gene/probe identifiers:\")\n", " print(gene_data.index[:20])\n", "except Exception as e:\n", " print(f\"Error extracting gene data: {e}\")\n" ] }, { "cell_type": "markdown", "id": "fc8d44fe", "metadata": {}, "source": [ "### Step 4: Gene Identifier Review" ] }, { "cell_type": "code", "execution_count": 5, "id": "c2574985", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T06:47:07.529111Z", "iopub.status.busy": "2025-03-25T06:47:07.528972Z", "iopub.status.idle": "2025-03-25T06:47:07.531388Z", "shell.execute_reply": "2025-03-25T06:47:07.530938Z" } }, "outputs": [], "source": [ "# The identifiers '4', '5', '6', etc. are numeric values that do not correspond to human gene symbols\n", "# These appear to be row indices or probe IDs that need to be mapped to actual gene symbols\n", "# In human genomics, gene symbols would typically be alphanumeric identifiers like \"BRCA1\", \"TP53\", etc.\n", "\n", "# Since these are numeric identifiers and not recognizable gene symbols,\n", "# they will require mapping to standard gene symbols for meaningful analysis\n", "\n", "requires_gene_mapping = True\n" ] }, { "cell_type": "markdown", "id": "aba8ee38", "metadata": {}, "source": [ "### Step 5: Gene Annotation" ] }, { "cell_type": "code", "execution_count": 6, "id": "e72b5cbc", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T06:47:07.533119Z", "iopub.status.busy": "2025-03-25T06:47:07.532993Z", "iopub.status.idle": "2025-03-25T06:48:01.201314Z", "shell.execute_reply": "2025-03-25T06:48:01.200639Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Gene annotation preview:\n", "Columns in gene annotation: ['ID', 'COL', 'ROW', 'NAME', 'SPOT_ID', 'CONTROL_TYPE', 'REFSEQ', 'GB_ACC', 'LOCUSLINK_ID', 'GENE_SYMBOL', 'GENE_NAME', 'UNIGENE_ID', 'ENSEMBL_ID', 'ACCESSION_STRING', 'CHROMOSOMAL_LOCATION', 'CYTOBAND', 'DESCRIPTION', 'GO_ID', 'SEQUENCE']\n", "{'ID': ['1', '2', '3', '4', '5'], 'COL': ['192', '192', '192', '192', '192'], 'ROW': [328.0, 326.0, 324.0, 322.0, 320.0], 'NAME': ['GE_BrightCorner', 'DarkCorner', 'DarkCorner', 'A_23_P117082', 'A_33_P3246448'], 'SPOT_ID': ['CONTROL', 'CONTROL', 'CONTROL', 'A_23_P117082', 'A_33_P3246448'], 'CONTROL_TYPE': ['pos', 'pos', 'pos', 'FALSE', 'FALSE'], 'REFSEQ': [nan, nan, nan, 'NM_015987', 'NM_080671'], 'GB_ACC': [nan, nan, nan, 'NM_015987', 'NM_080671'], 'LOCUSLINK_ID': [nan, nan, nan, 50865.0, 23704.0], 'GENE_SYMBOL': [nan, nan, nan, 'HEBP1', 'KCNE4'], 'GENE_NAME': [nan, nan, nan, 'heme binding protein 1', 'potassium voltage-gated channel, Isk-related family, member 4'], 'UNIGENE_ID': [nan, nan, nan, 'Hs.642618', 'Hs.348522'], 'ENSEMBL_ID': [nan, nan, nan, 'ENST00000014930', 'ENST00000281830'], 'ACCESSION_STRING': [nan, nan, nan, 'ref|NM_015987|ens|ENST00000014930|gb|AF117615|gb|BC016277', 'ref|NM_080671|ens|ENST00000281830|tc|THC2655788'], 'CHROMOSOMAL_LOCATION': [nan, nan, nan, 'chr12:13127906-13127847', 'chr2:223920197-223920256'], 'CYTOBAND': [nan, nan, nan, 'hs|12p13.1', 'hs|2q36.1'], 'DESCRIPTION': [nan, nan, nan, 'Homo sapiens heme binding protein 1 (HEBP1), mRNA [NM_015987]', 'Homo sapiens potassium voltage-gated channel, Isk-related family, member 4 (KCNE4), mRNA [NM_080671]'], 'GO_ID': [nan, nan, nan, 'GO:0005488(binding)|GO:0005576(extracellular region)|GO:0005737(cytoplasm)|GO:0005739(mitochondrion)|GO:0005829(cytosol)|GO:0007623(circadian rhythm)|GO:0020037(heme binding)', 'GO:0005244(voltage-gated ion channel activity)|GO:0005249(voltage-gated potassium channel activity)|GO:0006811(ion transport)|GO:0006813(potassium ion transport)|GO:0016020(membrane)|GO:0016021(integral to membrane)|GO:0016324(apical plasma membrane)'], 'SEQUENCE': [nan, nan, nan, 'AAGGGGGAAAATGTGATTTGTGCCTGATCTTTCATCTGTGATTCTTATAAGAGCTTTGTC', 'GCAAGTCTCTCTGCACCTATTAAAAAGTGATGTATATACTTCCTTCTTATTCTGTTGAGT']}\n", "\n", "Analyzing SPOT_ID.1 column for gene symbols:\n", "\n", "Gene data ID prefix: 4\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Column 'ID' contains values matching gene data ID pattern\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Column 'COL' contains values matching gene data ID pattern\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Column 'ROW' contains values matching gene data ID pattern\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Column 'NAME' contains values matching gene data ID pattern\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Column 'SPOT_ID' contains values matching gene data ID pattern\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Column 'REFSEQ' contains values matching gene data ID pattern\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Column 'GB_ACC' contains values matching gene data ID pattern\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Column 'LOCUSLINK_ID' contains values matching gene data ID pattern\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Column 'GENE_SYMBOL' contains values matching gene data ID pattern\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Column 'GENE_NAME' contains values matching gene data ID pattern\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Column 'UNIGENE_ID' contains values matching gene data ID pattern\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Column 'ENSEMBL_ID' contains values matching gene data ID pattern\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Column 'ACCESSION_STRING' contains values matching gene data ID pattern\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Column 'CHROMOSOMAL_LOCATION' contains values matching gene data ID pattern\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Column 'CYTOBAND' contains values matching gene data ID pattern\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Column 'DESCRIPTION' contains values matching gene data ID pattern\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Column 'GO_ID' contains values matching gene data ID pattern\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "Checking for columns containing transcript or gene related terms:\n", "Column 'NAME' may contain gene-related information\n", "Sample values: ['GE_BrightCorner', 'DarkCorner', 'DarkCorner']\n", "Column 'GENE_SYMBOL' may contain gene-related information\n", "Sample values: [nan, nan, nan]\n", "Column 'GENE_NAME' may contain gene-related information\n", "Sample values: [nan, nan, nan]\n", "Column 'UNIGENE_ID' may contain gene-related information\n", "Sample values: [nan, nan, nan]\n", "Column 'DESCRIPTION' may contain gene-related information\n", "Sample values: [nan, nan, nan]\n" ] } ], "source": [ "# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.\n", "gene_annotation = get_gene_annotation(soft_file)\n", "\n", "# 2. 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", "# Check for gene information in the SPOT_ID.1 column which appears to contain gene names\n", "print(\"\\nAnalyzing SPOT_ID.1 column for gene symbols:\")\n", "if 'SPOT_ID.1' in gene_annotation.columns:\n", " # Extract a few sample values\n", " sample_values = gene_annotation['SPOT_ID.1'].head(3).tolist()\n", " for i, value in enumerate(sample_values):\n", " print(f\"Sample {i+1} excerpt: {value[:200]}...\") # Print first 200 chars\n", " # Test the extract_human_gene_symbols function on these values\n", " symbols = extract_human_gene_symbols(value)\n", " print(f\" Extracted gene symbols: {symbols}\")\n", "\n", "# Try to find the probe IDs in the gene annotation\n", "gene_data_id_prefix = gene_data.index[0].split('_')[0] # Get prefix of first gene ID\n", "print(f\"\\nGene data ID prefix: {gene_data_id_prefix}\")\n", "\n", "# Look for columns that might match the gene data IDs\n", "for col in gene_annotation.columns:\n", " if gene_annotation[col].astype(str).str.contains(gene_data_id_prefix).any():\n", " print(f\"Column '{col}' contains values matching gene data ID pattern\")\n", "\n", "# Check if there's any column that might contain transcript or gene IDs\n", "print(\"\\nChecking for columns containing transcript or gene related terms:\")\n", "for col in gene_annotation.columns:\n", " if any(term in col.upper() for term in ['GENE', 'TRANSCRIPT', 'SYMBOL', 'NAME', 'DESCRIPTION']):\n", " print(f\"Column '{col}' may contain gene-related information\")\n", " # Show sample values\n", " print(f\"Sample values: {gene_annotation[col].head(3).tolist()}\")\n" ] }, { "cell_type": "markdown", "id": "1ea40721", "metadata": {}, "source": [ "### Step 6: Gene Identifier Mapping" ] }, { "cell_type": "code", "execution_count": 7, "id": "9afd6753", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T06:48:01.203235Z", "iopub.status.busy": "2025-03-25T06:48:01.203093Z", "iopub.status.idle": "2025-03-25T06:48:04.555497Z", "shell.execute_reply": "2025-03-25T06:48:04.554852Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Sample values in the mapping columns:\n", "Probe column 'ID' values: ['1', '2', '3', '4', '5']\n", "Gene column 'GENE_SYMBOL' values: [nan, nan, nan, 'HEBP1', 'KCNE4']\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "Gene mapping shape: (54295, 2)\n", "Gene mapping preview:\n", "{'ID': ['4', '5', '6', '7', '8'], 'Gene': ['HEBP1', 'KCNE4', 'BPIFA3', 'LOC100129869', 'IRG1']}\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "Gene expression data after mapping, shape: (20353, 119)\n", "First 5 gene symbols after mapping:\n", "['A1BG', 'A1BG-AS1', 'A1CF', 'A2LD1', 'A2M']\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "Gene expression data saved to ../../output/preprocess/Atrial_Fibrillation/gene_data/GSE235307.csv\n" ] } ], "source": [ "# 1. Identify the key columns for mapping\n", "prob_col = 'ID' # The numeric identifiers in gene_data (4, 5, 6, etc.)\n", "gene_col = 'GENE_SYMBOL' # The gene symbols (HEBP1, KCNE4, etc.)\n", "\n", "# Let's verify the structure of our gene annotation dataframe\n", "print(\"\\nSample values in the mapping columns:\")\n", "print(f\"Probe column '{prob_col}' values: {gene_annotation[prob_col].head().tolist()}\")\n", "print(f\"Gene column '{gene_col}' values: {gene_annotation[gene_col].head().tolist()}\")\n", "\n", "# 2. Get the gene mapping dataframe\n", "gene_mapping = get_gene_mapping(gene_annotation, prob_col, gene_col)\n", "print(f\"\\nGene mapping shape: {gene_mapping.shape}\")\n", "print(\"Gene mapping preview:\")\n", "print(preview_df(gene_mapping))\n", "\n", "# 3. Convert probe-level measurements to gene expression data\n", "gene_data = apply_gene_mapping(gene_data, gene_mapping)\n", "print(f\"\\nGene expression data after mapping, shape: {gene_data.shape}\")\n", "print(\"First 5 gene symbols after mapping:\")\n", "print(gene_data.index[:5].tolist())\n", "\n", "# Save the gene expression data to a CSV 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\"\\nGene expression data saved to {out_gene_data_file}\")\n" ] }, { "cell_type": "markdown", "id": "210d9635", "metadata": {}, "source": [ "### Step 7: Data Normalization and Linking" ] }, { "cell_type": "code", "execution_count": 8, "id": "3e13de63", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T06:48:04.557478Z", "iopub.status.busy": "2025-03-25T06:48:04.557345Z", "iopub.status.idle": "2025-03-25T06:48:20.674067Z", "shell.execute_reply": "2025-03-25T06:48:20.673392Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Gene data shape before normalization: (20353, 119)\n", "Gene data shape after normalization: (19847, 119)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Normalized gene expression data saved to ../../output/preprocess/Atrial_Fibrillation/gene_data/GSE235307.csv\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Original clinical data preview:\n", " !Sample_geo_accession \\\n", "0 !Sample_characteristics_ch1 \n", "1 !Sample_characteristics_ch1 \n", "2 !Sample_characteristics_ch1 \n", "3 !Sample_characteristics_ch1 \n", "4 !Sample_characteristics_ch1 \n", "\n", " GSM7498589 \\\n", "0 tissue: Whole blood \n", "1 gender: Male \n", "2 age: 63 \n", "3 cardiopathy: ischemic \n", "4 cardiac rhythm at start of the study: Sinus rh... \n", "\n", " GSM7498590 \\\n", "0 tissue: Whole blood \n", "1 gender: Male \n", "2 age: 60 \n", "3 cardiopathy: ischemic \n", "4 cardiac rhythm at start of the study: Sinus rh... \n", "\n", " GSM7498591 \\\n", "0 tissue: Whole blood \n", "1 gender: Male \n", "2 age: 60 \n", "3 cardiopathy: non ischemic \n", "4 cardiac rhythm at start of the study: Sinus rh... \n", "\n", " GSM7498592 \\\n", "0 tissue: Whole blood \n", "1 gender: Male \n", "2 age: 72 \n", "3 cardiopathy: ischemic \n", "4 cardiac rhythm at start of the study: Sinus rh... \n", "\n", " GSM7498593 \\\n", "0 tissue: Whole blood \n", "1 gender: Male \n", "2 age: 63 \n", "3 cardiopathy: ischemic \n", "4 cardiac rhythm at start of the study: Sinus rh... \n", "\n", " GSM7498594 \\\n", "0 tissue: Whole blood \n", "1 gender: Female \n", "2 age: 66 \n", "3 cardiopathy: non ischemic \n", "4 cardiac rhythm at start of the study: Sinus rh... \n", "\n", " GSM7498595 \\\n", "0 tissue: Whole blood \n", "1 gender: Male \n", "2 age: 70 \n", "3 cardiopathy: ischemic \n", "4 cardiac rhythm at start of the study: Sinus rh... \n", "\n", " GSM7498596 \\\n", "0 tissue: Whole blood \n", "1 gender: Male \n", "2 age: 64 \n", "3 cardiopathy: non ischemic \n", "4 cardiac rhythm at start of the study: Sinus rh... \n", "\n", " GSM7498597 ... \\\n", "0 tissue: Whole blood ... \n", "1 gender: Male ... \n", "2 age: 63 ... \n", "3 cardiopathy: ischemic ... \n", "4 cardiac rhythm at start of the study: Sinus rh... ... \n", "\n", " GSM7498698 \\\n", "0 tissue: Whole blood \n", "1 gender: Male \n", "2 age: 60 \n", "3 cardiopathy: ischemic \n", "4 cardiac rhythm at start of the study: Sinus rh... \n", "\n", " GSM7498699 \\\n", "0 tissue: Whole blood \n", "1 gender: Female \n", "2 age: 59 \n", "3 cardiopathy: non ischemic \n", "4 cardiac rhythm at start of the study: Sinus rh... \n", "\n", " GSM7498700 \\\n", "0 tissue: Whole blood \n", "1 gender: Male \n", "2 age: 68 \n", "3 cardiopathy: ischemic \n", "4 cardiac rhythm at start of the study: Sinus rh... \n", "\n", " GSM7498701 \\\n", "0 tissue: Whole blood \n", "1 gender: Male \n", "2 age: 77 \n", "3 cardiopathy: non ischemic \n", "4 cardiac rhythm at start of the study: Sinus rh... \n", "\n", " GSM7498702 \\\n", "0 tissue: Whole blood \n", "1 gender: Male \n", "2 age: 57 \n", "3 cardiopathy: ischemic \n", "4 cardiac rhythm at start of the study: Sinus rh... \n", "\n", " GSM7498703 \\\n", "0 tissue: Whole blood \n", "1 gender: Female \n", "2 age: 62 \n", "3 cardiopathy: non ischemic \n", "4 cardiac rhythm at start of the study: Sinus rh... \n", "\n", " GSM7498704 \\\n", "0 tissue: Whole blood \n", "1 gender: Male \n", "2 age: 66 \n", "3 cardiopathy: ischemic \n", "4 cardiac rhythm at start of the study: Sinus rh... \n", "\n", " GSM7498705 \\\n", "0 tissue: Whole blood \n", "1 gender: Male \n", "2 age: 57 \n", "3 cardiopathy: ischemic \n", "4 cardiac rhythm at start of the study: Sinus rh... \n", "\n", " GSM7498706 \\\n", "0 tissue: Whole blood \n", "1 gender: Male \n", "2 age: 65 \n", "3 cardiopathy: ischemic \n", "4 cardiac rhythm at start of the study: Sinus rh... \n", "\n", " GSM7498707 \n", "0 tissue: Whole blood \n", "1 gender: Male \n", "2 age: 59 \n", "3 cardiopathy: ischemic \n", "4 cardiac rhythm at start of the study: Sinus rh... \n", "\n", "[5 rows x 120 columns]\n", "Selected clinical data shape: (3, 119)\n", "Clinical data preview:\n", " GSM7498589 GSM7498590 GSM7498591 GSM7498592 \\\n", "Atrial_Fibrillation 0.0 0.0 0.0 0.0 \n", "Age 63.0 60.0 60.0 72.0 \n", "Gender 1.0 1.0 1.0 1.0 \n", "\n", " GSM7498593 GSM7498594 GSM7498595 GSM7498596 \\\n", "Atrial_Fibrillation 0.0 0.0 0.0 0.0 \n", "Age 63.0 66.0 70.0 64.0 \n", "Gender 1.0 0.0 1.0 1.0 \n", "\n", " GSM7498597 GSM7498598 ... GSM7498698 GSM7498699 \\\n", "Atrial_Fibrillation 0.0 0.0 ... 0.0 0.0 \n", "Age 63.0 61.0 ... 60.0 59.0 \n", "Gender 1.0 1.0 ... 1.0 0.0 \n", "\n", " GSM7498700 GSM7498701 GSM7498702 GSM7498703 \\\n", "Atrial_Fibrillation 0.0 0.0 1.0 0.0 \n", "Age 68.0 77.0 57.0 62.0 \n", "Gender 1.0 1.0 1.0 0.0 \n", "\n", " GSM7498704 GSM7498705 GSM7498706 GSM7498707 \n", "Atrial_Fibrillation 1.0 1.0 1.0 0.0 \n", "Age 66.0 57.0 65.0 59.0 \n", "Gender 1.0 1.0 1.0 1.0 \n", "\n", "[3 rows x 119 columns]\n", "Linked data shape before processing: (119, 19850)\n", "Linked data preview (first 5 rows, 5 columns):\n", " Atrial_Fibrillation Age Gender A1BG A1BG-AS1\n", "GSM7498589 0.0 63.0 1.0 1215.921532 167.933502\n", "GSM7498590 0.0 60.0 1.0 1042.240181 156.514231\n", "GSM7498591 0.0 60.0 1.0 860.505266 153.778492\n", "GSM7498592 0.0 72.0 1.0 1016.786080 164.688762\n", "GSM7498593 0.0 63.0 1.0 930.371907 153.624856\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Data shape after handling missing values: (119, 19850)\n", "For the feature 'Atrial_Fibrillation', the least common label is '1.0' with 13 occurrences. This represents 10.92% of the dataset.\n", "Quartiles for 'Age':\n", " 25%: 55.0\n", " 50% (Median): 63.0\n", " 75%: 68.0\n", "Min: 37.0\n", "Max: 79.0\n", "For the feature 'Gender', the least common label is '0.0' with 32 occurrences. This represents 26.89% of the dataset.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Linked data saved to ../../output/preprocess/Atrial_Fibrillation/GSE235307.csv\n" ] } ], "source": [ "# 1. Normalize gene symbols in the gene expression data\n", "# Use normalize_gene_symbols_in_index to standardize gene symbols\n", "normalized_gene_data = normalize_gene_symbols_in_index(gene_data)\n", "print(f\"Gene data shape before normalization: {gene_data.shape}\")\n", "print(f\"Gene data shape after normalization: {normalized_gene_data.shape}\")\n", "\n", "# Save the normalized gene data to file\n", "os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)\n", "normalized_gene_data.to_csv(out_gene_data_file)\n", "print(f\"Normalized gene expression data saved to {out_gene_data_file}\")\n", "\n", "# Load the actual clinical data from the matrix file that was previously obtained in 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", "# Get preview of clinical data to understand its structure\n", "print(\"Original clinical data preview:\")\n", "print(clinical_data.head())\n", "\n", "# 2. If we have trait data available, proceed with linking\n", "if trait_row is not None:\n", " # Extract clinical features using the original clinical data\n", " selected_clinical_df = geo_select_clinical_features(\n", " clinical_df=clinical_data,\n", " trait=trait,\n", " trait_row=trait_row,\n", " convert_trait=convert_trait,\n", " age_row=age_row,\n", " convert_age=convert_age,\n", " gender_row=gender_row,\n", " convert_gender=convert_gender\n", " )\n", "\n", " print(f\"Selected clinical data shape: {selected_clinical_df.shape}\")\n", " print(\"Clinical data preview:\")\n", " print(selected_clinical_df.head())\n", "\n", " # Link the clinical and genetic data\n", " linked_data = geo_link_clinical_genetic_data(selected_clinical_df, normalized_gene_data)\n", " print(f\"Linked data shape before processing: {linked_data.shape}\")\n", " print(\"Linked data preview (first 5 rows, 5 columns):\")\n", " print(linked_data.iloc[:5, :5] if not linked_data.empty else \"Empty dataframe\")\n", "\n", " # 3. Handle missing values\n", " try:\n", " linked_data = handle_missing_values(linked_data, trait)\n", " print(f\"Data shape after handling missing values: {linked_data.shape}\")\n", " except Exception as e:\n", " print(f\"Error handling missing values: {e}\")\n", " linked_data = pd.DataFrame() # Create empty dataframe if error occurs\n", "\n", " # 4. Check for bias in features\n", " if not linked_data.empty and linked_data.shape[0] > 0:\n", " # Check if trait is biased\n", " trait_type = 'binary' if len(linked_data[trait].unique()) <= 2 else 'continuous'\n", " if trait_type == \"binary\":\n", " is_biased = judge_binary_variable_biased(linked_data, trait)\n", " else:\n", " is_biased = judge_continuous_variable_biased(linked_data, trait)\n", " \n", " # Remove biased demographic features\n", " if \"Age\" in linked_data.columns:\n", " age_biased = judge_continuous_variable_biased(linked_data, 'Age')\n", " if age_biased:\n", " linked_data = linked_data.drop(columns='Age')\n", " \n", " if \"Gender\" in linked_data.columns:\n", " gender_biased = judge_binary_variable_biased(linked_data, 'Gender')\n", " if gender_biased:\n", " linked_data = linked_data.drop(columns='Gender')\n", " else:\n", " is_biased = True\n", " print(\"Cannot check for bias as dataframe is empty or has no rows after missing value handling\")\n", "\n", " # 5. Validate and save cohort information\n", " note = \"\"\n", " if linked_data.empty or linked_data.shape[0] == 0:\n", " note = \"Dataset contains gene expression data related to atrial fibrillation after cardiac surgery, but linking clinical and genetic data failed, possibly due to mismatched sample IDs.\"\n", " else:\n", " note = \"Dataset contains gene expression data for atrial fibrillation after cardiac surgery, which is relevant to arrhythmia research.\"\n", " \n", " is_usable = validate_and_save_cohort_info(\n", " is_final=True,\n", " cohort=cohort,\n", " info_path=json_path,\n", " is_gene_available=True,\n", " is_trait_available=True,\n", " is_biased=is_biased,\n", " df=linked_data,\n", " note=note\n", " )\n", "\n", " # 6. Save the linked data if 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(\"Dataset is not usable for analysis. No linked data file saved.\")\n", "else:\n", " # If no trait data available, validate with trait_available=False\n", " is_usable = validate_and_save_cohort_info(\n", " is_final=True,\n", " cohort=cohort,\n", " info_path=json_path,\n", " is_gene_available=True,\n", " is_trait_available=False,\n", " is_biased=True, # Set to True since we can't use data without trait\n", " df=pd.DataFrame(), # Empty DataFrame\n", " note=\"Dataset contains gene expression data but lacks proper clinical trait information for arrhythmia analysis.\"\n", " )\n", " \n", " print(\"Dataset is not usable for arrhythmia analysis due to lack of clinical trait data. No linked data file saved.\")" ] } ], "metadata": { "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.10.16" } }, "nbformat": 4, "nbformat_minor": 5 }