{ "cells": [ { "cell_type": "code", "execution_count": 1, "id": "5a801763", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T07:18:07.326394Z", "iopub.status.busy": "2025-03-25T07:18:07.326164Z", "iopub.status.idle": "2025-03-25T07:18:07.489527Z", "shell.execute_reply": "2025-03-25T07:18:07.489098Z" } }, "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 = \"Kidney_Clear_Cell_Carcinoma\"\n", "cohort = \"GSE150404\"\n", "\n", "# Input paths\n", "in_trait_dir = \"../../input/GEO/Kidney_Clear_Cell_Carcinoma\"\n", "in_cohort_dir = \"../../input/GEO/Kidney_Clear_Cell_Carcinoma/GSE150404\"\n", "\n", "# Output paths\n", "out_data_file = \"../../output/preprocess/Kidney_Clear_Cell_Carcinoma/GSE150404.csv\"\n", "out_gene_data_file = \"../../output/preprocess/Kidney_Clear_Cell_Carcinoma/gene_data/GSE150404.csv\"\n", "out_clinical_data_file = \"../../output/preprocess/Kidney_Clear_Cell_Carcinoma/clinical_data/GSE150404.csv\"\n", "json_path = \"../../output/preprocess/Kidney_Clear_Cell_Carcinoma/cohort_info.json\"\n" ] }, { "cell_type": "markdown", "id": "41aaa70f", "metadata": {}, "source": [ "### Step 1: Initial Data Loading" ] }, { "cell_type": "code", "execution_count": 2, "id": "00c8d784", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T07:18:07.490944Z", "iopub.status.busy": "2025-03-25T07:18:07.490809Z", "iopub.status.idle": "2025-03-25T07:18:07.655624Z", "shell.execute_reply": "2025-03-25T07:18:07.655253Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Background Information:\n", "!Series_title\t\"Expression data from patients with different stages of clear cell renal cell carcinoma\"\n", "!Series_summary\t\"Clear cell renal cell carcinoma is the most common type among kidney cancers, accounting for 80% of all kidney cancer cases and over 30% of patients have metastases at the time disease is diagnosed. The process od epithelial-mesenchymal transition is crucial for the development and progression of cancer, and here we wanted to check the expression of genes on different stages of tumor progression, and find which of them changes significantly with increased grade.\"\n", "!Series_overall_design\t\"Tumor samples from patients were divided into four grades by hospital pathologists. I- grade refers to the first stage of disease, II- grade to second, III-grade to third and IV-grade to fourth. Each patients received unrepetable number. RNA extraction were performed using Eurx kit, and then microarrays were performed using Affymetrix GeneChip WT PLUS Reagent Kit.\"\n", "Sample Characteristics Dictionary:\n", "{0: ['tissue: clear cell renal cell carcinoma'], 1: ['Stage: first', 'Stage: second', 'Stage: third', 'Stage: fourth'], 2: ['gender: female', 'gender: male', 'gender: no data'], 3: ['age: 55', 'age: 77', 'age: 63', 'age: 79', 'age: 65', 'age: 75', 'age: 70', 'age: 60', 'age: 56', 'age: 74', 'age: 61', 'age: 83', 'age: 81', 'age: 62', 'age: 73', 'age: 64', 'age: 66', 'age: 72', 'age: 52', 'age: 67', 'age: 71', 'age: 46', 'age: 59', 'age: no data', 'age: 50', 'age: 69', 'age: 54', 'age: 53', 'age: 51', 'age: 68']}\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": "f3ea1b31", "metadata": {}, "source": [ "### Step 2: Dataset Analysis and Clinical Feature Extraction" ] }, { "cell_type": "code", "execution_count": 3, "id": "0d3c2e28", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T07:18:07.656880Z", "iopub.status.busy": "2025-03-25T07:18:07.656765Z", "iopub.status.idle": "2025-03-25T07:18:07.670595Z", "shell.execute_reply": "2025-03-25T07:18:07.670294Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Clinical data preview:\n", "{'GSM4548605': [0.0, 55.0, 0.0], 'GSM4548606': [0.0, 77.0, 1.0], 'GSM4548607': [0.0, 63.0, 0.0], 'GSM4548608': [0.0, 79.0, 0.0], 'GSM4548609': [0.0, 65.0, 0.0], 'GSM4548610': [0.0, 75.0, 0.0], 'GSM4548611': [0.0, 70.0, 0.0], 'GSM4548612': [0.0, 60.0, 0.0], 'GSM4548613': [0.0, 70.0, 1.0], 'GSM4548614': [0.0, 65.0, 0.0], 'GSM4548615': [0.0, 56.0, 0.0], 'GSM4548616': [0.0, 74.0, 0.0], 'GSM4548617': [0.0, 60.0, 1.0], 'GSM4548618': [0.0, 61.0, 1.0], 'GSM4548619': [0.0, 83.0, 0.0], 'GSM4548620': [0.0, 81.0, 0.0], 'GSM4548621': [0.0, 62.0, 0.0], 'GSM4548622': [0.0, 63.0, 1.0], 'GSM4548623': [0.0, 65.0, 1.0], 'GSM4548624': [0.0, 61.0, 1.0], 'GSM4548625': [0.0, 73.0, 1.0], 'GSM4548626': [0.0, 63.0, 0.0], 'GSM4548627': [0.0, 64.0, 0.0], 'GSM4548628': [0.0, 66.0, 1.0], 'GSM4548629': [0.0, 64.0, 0.0], 'GSM4548630': [0.0, 72.0, 0.0], 'GSM4548631': [0.0, 52.0, 1.0], 'GSM4548632': [0.0, 61.0, 0.0], 'GSM4548633': [0.0, 67.0, 1.0], 'GSM4548634': [0.0, 71.0, 1.0], 'GSM4548635': [1.0, 65.0, 1.0], 'GSM4548636': [1.0, 79.0, 0.0], 'GSM4548637': [1.0, 79.0, 0.0], 'GSM4548638': [1.0, 46.0, 1.0], 'GSM4548639': [1.0, 59.0, 0.0], 'GSM4548640': [1.0, nan, nan], 'GSM4548641': [1.0, 59.0, 0.0], 'GSM4548642': [1.0, 64.0, 1.0], 'GSM4548643': [1.0, 50.0, 0.0], 'GSM4548644': [1.0, 69.0, 1.0], 'GSM4548645': [1.0, 79.0, 0.0], 'GSM4548646': [1.0, nan, 0.0], 'GSM4548647': [1.0, 69.0, 1.0], 'GSM4548648': [1.0, 67.0, 1.0], 'GSM4548649': [1.0, 54.0, 1.0], 'GSM4548650': [1.0, 53.0, 1.0], 'GSM4548651': [1.0, 63.0, 0.0], 'GSM4548652': [1.0, 51.0, 0.0], 'GSM4548653': [1.0, 68.0, 0.0], 'GSM4548654': [1.0, 60.0, 0.0], 'GSM4548655': [1.0, 60.0, 1.0], 'GSM4548656': [1.0, 64.0, 0.0], 'GSM4548657': [1.0, 52.0, 1.0], 'GSM4548658': [1.0, 58.0, 1.0], 'GSM4548659': [1.0, 58.0, 1.0], 'GSM4548660': [1.0, 56.0, 0.0], 'GSM4548661': [1.0, nan, nan], 'GSM4548662': [1.0, 57.0, 1.0], 'GSM4548663': [1.0, 56.0, 1.0], 'GSM4548664': [1.0, 45.0, 0.0]}\n", "Clinical data saved to ../../output/preprocess/Kidney_Clear_Cell_Carcinoma/clinical_data/GSE150404.csv\n" ] } ], "source": [ "# 1. Assessment of Gene Expression Data Availability\n", "# Based on the background information, this dataset contains gene expression data from clear cell renal cell carcinoma patients\n", "# The Series_overall_design mentions microarrays using Affymetrix GeneChip WT PLUS Reagent Kit, which indicates gene expression data\n", "is_gene_available = True\n", "\n", "# 2. Variable Availability and Type Conversion\n", "\n", "# 2.1 Trait data (clear cell renal cell carcinoma stage)\n", "# The trait row (Stage) is found at index 1 in the sample characteristics dictionary\n", "trait_row = 1\n", "\n", "# Function to convert trait values (Stage of cancer)\n", "def convert_trait(value):\n", " # The value format is 'Stage: value'\n", " if pd.isna(value) or not isinstance(value, str):\n", " return None\n", " \n", " # Extract the value after the colon\n", " if ':' in value:\n", " value = value.split(':', 1)[1].strip().lower()\n", " \n", " # Convert stages to binary (early vs late stage)\n", " if value in ['first', 'second']:\n", " return 0 # Early stage (I-II)\n", " elif value in ['third', 'fourth']:\n", " return 1 # Late stage (III-IV)\n", " else:\n", " return None\n", "\n", "# 2.2 Age data\n", "# Age data is at index 3\n", "age_row = 3\n", "\n", "# Function to convert age values\n", "def convert_age(value):\n", " # The value format is 'age: value'\n", " if pd.isna(value) or not isinstance(value, str):\n", " return None\n", " \n", " # Extract the value after the colon\n", " if ':' in value:\n", " value = value.split(':', 1)[1].strip().lower()\n", " \n", " if value == 'no data':\n", " return None\n", " \n", " try:\n", " return float(value) # Convert to numeric (continuous)\n", " except:\n", " return None\n", "\n", "# 2.3 Gender data\n", "# Gender data is at index 2\n", "gender_row = 2\n", "\n", "# Function to convert gender values\n", "def convert_gender(value):\n", " # The value format is 'gender: value'\n", " if pd.isna(value) or not isinstance(value, str):\n", " return None\n", " \n", " # Extract the value after the colon\n", " if ':' in value:\n", " value = value.split(':', 1)[1].strip().lower()\n", " \n", " if value == 'female':\n", " return 0\n", " elif value == 'male':\n", " return 1\n", " elif value == 'no data':\n", " return None\n", " else:\n", " return None\n", "\n", "# 3. Save Metadata - 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. Clinical Feature Extraction\n", "# Since trait_row is not None, we need to extract clinical features\n", "if trait_row is not None:\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", " # Preview the dataframe\n", " preview = preview_df(selected_clinical_df)\n", " print(\"Clinical data preview:\")\n", " print(preview)\n", " \n", " # Save the clinical data to CSV\n", " # First ensure the directory exists\n", " os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)\n", " selected_clinical_df.to_csv(out_clinical_data_file, index=True)\n", " print(f\"Clinical data saved to {out_clinical_data_file}\")\n" ] }, { "cell_type": "markdown", "id": "742a365b", "metadata": {}, "source": [ "### Step 3: Gene Data Extraction" ] }, { "cell_type": "code", "execution_count": 4, "id": "1c09d9a7", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T07:18:07.671766Z", "iopub.status.busy": "2025-03-25T07:18:07.671657Z", "iopub.status.idle": "2025-03-25T07:18:07.946501Z", "shell.execute_reply": "2025-03-25T07:18:07.946107Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Extracting gene data from matrix file:\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Successfully extracted gene data with 53617 rows\n", "First 20 gene IDs:\n", "Index(['16650001', '16650003', '16650005', '16650007', '16650009', '16650011',\n", " '16650013', '16650015', '16650017', '16650019', '16650021', '16650023',\n", " '16650025', '16650027', '16650029', '16650031', '16650033', '16650035',\n", " '16650037', '16650041'],\n", " dtype='object', name='ID')\n", "\n", "Gene expression data available: True\n" ] } ], "source": [ "# 1. Get the file paths for the SOFT file and matrix file\n", "soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)\n", "\n", "# 2. Extract gene expression data from the matrix file\n", "try:\n", " print(\"Extracting gene data from matrix file:\")\n", " gene_data = get_genetic_data(matrix_file)\n", " if gene_data.empty:\n", " print(\"Extracted gene expression data is empty\")\n", " is_gene_available = False\n", " else:\n", " print(f\"Successfully extracted gene data with {len(gene_data.index)} rows\")\n", " print(\"First 20 gene IDs:\")\n", " print(gene_data.index[:20])\n", " is_gene_available = True\n", "except Exception as e:\n", " print(f\"Error extracting gene data: {e}\")\n", " print(\"This dataset appears to have an empty or malformed gene expression matrix\")\n", " is_gene_available = False\n", "\n", "print(f\"\\nGene expression data available: {is_gene_available}\")\n" ] }, { "cell_type": "markdown", "id": "5b0d4768", "metadata": {}, "source": [ "### Step 4: Gene Identifier Review" ] }, { "cell_type": "code", "execution_count": 5, "id": "8f081e76", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T07:18:07.947877Z", "iopub.status.busy": "2025-03-25T07:18:07.947751Z", "iopub.status.idle": "2025-03-25T07:18:07.949962Z", "shell.execute_reply": "2025-03-25T07:18:07.949617Z" } }, "outputs": [], "source": [ "# The gene identifiers appear to be numeric IDs rather than standard human gene symbols\n", "# These appear to be probe IDs from a microarray platform which need to be mapped\n", "# to standard human gene symbols for biological interpretation\n", "\n", "requires_gene_mapping = True\n" ] }, { "cell_type": "markdown", "id": "6742ba51", "metadata": {}, "source": [ "### Step 5: Gene Annotation" ] }, { "cell_type": "code", "execution_count": 6, "id": "50779b22", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T07:18:07.951368Z", "iopub.status.busy": "2025-03-25T07:18:07.951261Z", "iopub.status.idle": "2025-03-25T07:18:17.648729Z", "shell.execute_reply": "2025-03-25T07:18:17.648332Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Extracting gene annotation data from SOFT file...\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Successfully extracted gene annotation data with 3270697 rows\n", "\n", "Gene annotation preview (first few rows):\n", "{'ID': ['16657436', '16657440', '16657445', '16657447', '16657450'], 'probeset_id': ['16657436', '16657440', '16657445', '16657447', '16657450'], 'seqname': ['chr1', 'chr1', 'chr1', 'chr1', 'chr1'], 'strand': ['+', '+', '+', '+', '+'], 'start': ['12190', '29554', '69091', '160446', '317811'], 'stop': ['13639', '31109', '70008', '161525', '328581'], 'total_probes': [25.0, 28.0, 8.0, 13.0, 36.0], 'gene_assignment': ['NR_046018 // DDX11L1 // DEAD/H (Asp-Glu-Ala-Asp/His) box helicase 11 like 1 // 1p36.33 // 100287102 /// NR_034090 // DDX11L9 // DEAD/H (Asp-Glu-Ala-Asp/His) box helicase 11 like 9 // 15q26.3 // 100288486 /// NR_051985 // DDX11L9 // DEAD/H (Asp-Glu-Ala-Asp/His) box helicase 11 like 9 // 15q26.3 // 100288486 /// NR_045117 // DDX11L10 // DEAD/H (Asp-Glu-Ala-Asp/His) box helicase 11 like 10 // 16p13.3 // 100287029 /// NR_024004 // DDX11L2 // DEAD/H (Asp-Glu-Ala-Asp/His) box helicase 11 like 2 // 2q13 // 84771 /// NR_024005 // DDX11L2 // DEAD/H (Asp-Glu-Ala-Asp/His) box helicase 11 like 2 // 2q13 // 84771 /// NR_051986 // DDX11L5 // DEAD/H (Asp-Glu-Ala-Asp/His) box helicase 11 like 5 // 9p24.3 // 100287596 /// ENST00000456328 // DDX11L1 // DEAD/H (Asp-Glu-Ala-Asp/His) box helicase 11 like 1 // 1p36.33 // 100287102 /// ENST00000559159 // DDX11L9 // DEAD/H (Asp-Glu-Ala-Asp/His) box helicase 11 like 9 // 15q26.3 // 100288486 /// ENST00000562189 // DDX11L9 // DEAD/H (Asp-Glu-Ala-Asp/His) box helicase 11 like 9 // 15q26.3 // 100288486 /// ENST00000513886 // DDX11L10 // DEAD/H (Asp-Glu-Ala-Asp/His) box helicase 11 like 10 // 16p13.3 // 100287029 /// ENST00000515242 // DDX11L1 // DEAD/H (Asp-Glu-Ala-Asp/His) box helicase 11 like 1 // 1p36.33 // 100287102 /// ENST00000518655 // DDX11L1 // DEAD/H (Asp-Glu-Ala-Asp/His) box helicase 11 like 1 // 1p36.33 // 100287102 /// ENST00000515173 // DDX11L9 // DEAD/H (Asp-Glu-Ala-Asp/His) box helicase 11 like 9 // 15q26.3 // 100288486 /// ENST00000545636 // DDX11L10 // DEAD/H (Asp-Glu-Ala-Asp/His) box helicase 11 like 10 // 16p13.3 // 100287029 /// ENST00000450305 // DDX11L1 // DEAD/H (Asp-Glu-Ala-Asp/His) box helicase 11 like 1 // 1p36.33 // 100287102 /// ENST00000560040 // DDX11L9 // DEAD/H (Asp-Glu-Ala-Asp/His) box helicase 11 like 9 // 15q26.3 // 100288486 /// ENST00000430178 // DDX11L10 // DEAD/H (Asp-Glu-Ala-Asp/His) box helicase 11 like 10 // 16p13.3 // 100287029 /// ENST00000538648 // DDX11L9 // DEAD/H (Asp-Glu-Ala-Asp/His) box helicase 11 like 9 // 15q26.3 // 100288486 /// ENST00000535848 // DDX11L2 // DEAD/H (Asp-Glu-Ala-Asp/His) box helicase 11 like 2 // --- // --- /// ENST00000457993 // DDX11L2 // DEAD/H (Asp-Glu-Ala-Asp/His) box helicase 11 like 2 // --- // --- /// ENST00000437401 // DDX11L2 // DEAD/H (Asp-Glu-Ala-Asp/His) box helicase 11 like 2 // --- // --- /// ENST00000426146 // DDX11L5 // DEAD/H (Asp-Glu-Ala-Asp/His) box helicase 11 like 5 // --- // --- /// ENST00000445777 // DDX11L16 // DEAD/H (Asp-Glu-Ala-Asp/His) box helicase 11 like 16 // --- // --- /// ENST00000507418 // DDX11L16 // DEAD/H (Asp-Glu-Ala-Asp/His) box helicase 11 like 16 // --- // --- /// ENST00000507418 // DDX11L16 // DEAD/H (Asp-Glu-Ala-Asp/His) box helicase 11 like 16 // --- // --- /// ENST00000507418 // DDX11L16 // DEAD/H (Asp-Glu-Ala-Asp/His) box helicase 11 like 16 // --- // --- /// ENST00000507418 // DDX11L16 // DEAD/H (Asp-Glu-Ala-Asp/His) box helicase 11 like 16 // --- // --- /// ENST00000421620 // DDX11L5 // DEAD/H (Asp-Glu-Ala-Asp/His) box helicase 11 like 5 // --- // ---', 'ENST00000473358 // MIR1302-11 // microRNA 1302-11 // --- // 100422919 /// ENST00000473358 // MIR1302-10 // microRNA 1302-10 // --- // 100422834 /// ENST00000473358 // MIR1302-9 // microRNA 1302-9 // --- // 100422831 /// ENST00000473358 // MIR1302-2 // microRNA 1302-2 // --- // 100302278', 'NM_001005484 // OR4F5 // olfactory receptor, family 4, subfamily F, member 5 // 1p36.33 // 79501 /// ENST00000335137 // OR4F5 // olfactory receptor, family 4, subfamily F, member 5 // 1p36.33 // 79501', '---', 'AK302511 // LOC100132062 // uncharacterized LOC100132062 // 5q35.3 // 100132062 /// AK294489 // LOC729737 // uncharacterized LOC729737 // 1p36.33 // 729737 /// AK303380 // LOC100132062 // uncharacterized LOC100132062 // 5q35.3 // 100132062 /// AK316554 // LOC100132062 // uncharacterized LOC100132062 // 5q35.3 // 100132062 /// AK316556 // LOC100132062 // uncharacterized LOC100132062 // 5q35.3 // 100132062 /// AK302573 // LOC729737 // uncharacterized LOC729737 // 1p36.33 // 729737 /// AK123446 // LOC441124 // uncharacterized LOC441124 // 1q42.11 // 441124 /// ENST00000425496 // LOC100506479 // uncharacterized LOC100506479 // --- // 100506479 /// ENST00000425496 // LOC100289306 // uncharacterized LOC100289306 // 7p11.2 // 100289306 /// ENST00000425496 // LOC100287894 // uncharacterized LOC100287894 // 7q11.21 // 100287894 /// ENST00000425496 // FLJ45445 // uncharacterized LOC399844 // 19p13.3 // 399844 /// ENST00000456623 // LOC100506479 // uncharacterized LOC100506479 // --- // 100506479 /// ENST00000456623 // LOC100289306 // uncharacterized LOC100289306 // 7p11.2 // 100289306 /// ENST00000456623 // LOC100287894 // uncharacterized LOC100287894 // 7q11.21 // 100287894 /// ENST00000456623 // FLJ45445 // uncharacterized LOC399844 // 19p13.3 // 399844 /// ENST00000418377 // LOC100506479 // uncharacterized LOC100506479 // --- // 100506479 /// ENST00000418377 // LOC100288102 // uncharacterized LOC100288102 // 1q42.11 // 100288102 /// ENST00000418377 // LOC731275 // uncharacterized LOC731275 // 1q43 // 731275 /// ENST00000534867 // LOC100506479 // uncharacterized LOC100506479 // --- // 100506479 /// ENST00000534867 // LOC100289306 // uncharacterized LOC100289306 // 7p11.2 // 100289306 /// ENST00000534867 // LOC100287894 // uncharacterized LOC100287894 // 7q11.21 // 100287894 /// ENST00000534867 // FLJ45445 // uncharacterized LOC399844 // 19p13.3 // 399844 /// ENST00000544678 // LOC100653346 // uncharacterized LOC100653346 // --- // 100653346 /// ENST00000544678 // LOC100653241 // uncharacterized LOC100653241 // --- // 100653241 /// ENST00000544678 // LOC100652945 // uncharacterized LOC100652945 // --- // 100652945 /// ENST00000544678 // LOC100508632 // uncharacterized LOC100508632 // --- // 100508632 /// ENST00000544678 // LOC100132050 // uncharacterized LOC100132050 // 7p11.2 // 100132050 /// ENST00000544678 // LOC100128326 // putative uncharacterized protein FLJ44672-like // 7p11.2 // 100128326 /// ENST00000419160 // LOC100506479 // uncharacterized LOC100506479 // --- // 100506479 /// ENST00000419160 // LOC100289306 // uncharacterized LOC100289306 // 7p11.2 // 100289306 /// ENST00000419160 // LOC100287894 // uncharacterized LOC100287894 // 7q11.21 // 100287894 /// ENST00000419160 // FLJ45445 // uncharacterized LOC399844 // 19p13.3 // 399844 /// ENST00000432964 // LOC100506479 // uncharacterized LOC100506479 // --- // 100506479 /// ENST00000432964 // LOC100289306 // uncharacterized LOC100289306 // 7p11.2 // 100289306 /// ENST00000432964 // LOC100287894 // uncharacterized LOC100287894 // 7q11.21 // 100287894 /// ENST00000432964 // FLJ45445 // uncharacterized LOC399844 // 19p13.3 // 399844 /// ENST00000423728 // LOC100506479 // uncharacterized LOC100506479 // --- // 100506479 /// ENST00000423728 // LOC100289306 // uncharacterized LOC100289306 // 7p11.2 // 100289306 /// ENST00000423728 // LOC100287894 // uncharacterized LOC100287894 // 7q11.21 // 100287894 /// ENST00000423728 // FLJ45445 // uncharacterized LOC399844 // 19p13.3 // 399844 /// ENST00000457364 // LOC100653346 // uncharacterized LOC100653346 // --- // 100653346 /// ENST00000457364 // LOC100653241 // uncharacterized LOC100653241 // --- // 100653241 /// ENST00000457364 // LOC100652945 // uncharacterized LOC100652945 // --- // 100652945 /// ENST00000457364 // LOC100508632 // uncharacterized LOC100508632 // --- // 100508632 /// ENST00000457364 // LOC100132050 // uncharacterized LOC100132050 // 7p11.2 // 100132050 /// ENST00000457364 // LOC100128326 // putative uncharacterized protein FLJ44672-like // 7p11.2 // 100128326 /// ENST00000438516 // LOC100653346 // uncharacterized LOC100653346 // --- // 100653346 /// ENST00000438516 // LOC100653241 // uncharacterized LOC100653241 // --- // 100653241 /// ENST00000438516 // LOC100652945 // uncharacterized LOC100652945 // --- // 100652945 /// ENST00000438516 // LOC100508632 // uncharacterized LOC100508632 // --- // 100508632 /// ENST00000438516 // LOC100132050 // uncharacterized LOC100132050 // 7p11.2 // 100132050 /// ENST00000438516 // LOC100128326 // putative uncharacterized protein FLJ44672-like // 7p11.2 // 100128326'], 'mrna_assignment': ['NR_046018 // RefSeq // Homo sapiens DEAD/H (Asp-Glu-Ala-Asp/His) box helicase 11 like 1 (DDX11L1), non-coding RNA. // chr1 // 100 // 100 // 25 // 25 // 0 /// NR_034090 // RefSeq // Homo sapiens DEAD/H (Asp-Glu-Ala-Asp/His) box helicase 11 like 9 (DDX11L9), transcript variant 1, non-coding RNA. // chr1 // 96 // 100 // 24 // 25 // 0 /// NR_051985 // RefSeq // Homo sapiens DEAD/H (Asp-Glu-Ala-Asp/His) box helicase 11 like 9 (DDX11L9), transcript variant 2, non-coding RNA. // chr1 // 96 // 100 // 24 // 25 // 0 /// NR_045117 // RefSeq // Homo sapiens DEAD/H (Asp-Glu-Ala-Asp/His) box helicase 11 like 10 (DDX11L10), non-coding RNA. // chr1 // 92 // 96 // 22 // 24 // 0 /// NR_024004 // RefSeq // Homo sapiens DEAD/H (Asp-Glu-Ala-Asp/His) box helicase 11 like 2 (DDX11L2), transcript variant 1, non-coding RNA. // chr1 // 83 // 96 // 20 // 24 // 0 /// NR_024005 // RefSeq // Homo sapiens DEAD/H (Asp-Glu-Ala-Asp/His) box helicase 11 like 2 (DDX11L2), transcript variant 2, non-coding RNA. // chr1 // 83 // 96 // 20 // 24 // 0 /// NR_051986 // RefSeq // Homo sapiens DEAD/H (Asp-Glu-Ala-Asp/His) box helicase 11 like 5 (DDX11L5), non-coding RNA. // chr1 // 50 // 96 // 12 // 24 // 0 /// TCONS_l2_00010384-XLOC_l2_005087 // Broad TUCP // linc-SNRNP25-2 chr16:+:61554-64041 // chr1 // 92 // 96 // 22 // 24 // 0 /// TCONS_l2_00010385-XLOC_l2_005087 // Broad TUCP // linc-SNRNP25-2 chr16:+:61554-64090 // chr1 // 92 // 96 // 22 // 24 // 0 /// TCONS_l2_00030644-XLOC_l2_015857 // Broad TUCP // linc-TMLHE chrX:-:155255810-155257756 // chr1 // 50 // 96 // 12 // 24 // 0 /// TCONS_l2_00028588-XLOC_l2_014685 // Broad TUCP // linc-DOCK8-2 chr9:+:11235-13811 // chr1 // 50 // 64 // 8 // 16 // 0 /// TCONS_l2_00030643-XLOC_l2_015857 // Broad TUCP // linc-TMLHE chrX:-:155255810-155257756 // chr1 // 50 // 64 // 8 // 16 // 0 /// ENST00000456328 // ENSEMBL // cdna:known chromosome:GRCh37:1:11869:14409:1 gene:ENSG00000223972 gene_biotype:pseudogene transcript_biotype:processed_transcript // chr1 // 100 // 100 // 25 // 25 // 0 /// ENST00000559159 // ENSEMBL // cdna:known chromosome:GRCh37:15:102516761:102519296:-1 gene:ENSG00000248472 gene_biotype:pseudogene transcript_biotype:processed_transcript // chr1 // 96 // 100 // 24 // 25 // 0 /// ENST00000562189 // ENSEMBL // cdna:known chromosome:GRCh37:15:102516761:102519296:-1 gene:ENSG00000248472 gene_biotype:pseudogene transcript_biotype:processed_transcript // chr1 // 96 // 100 // 24 // 25 // 0 /// ENST00000513886 // ENSEMBL // cdna:known chromosome:GRCh37:16:61555:64090:1 gene:ENSG00000233614 gene_biotype:pseudogene transcript_biotype:processed_transcript // chr1 // 92 // 96 // 22 // 24 // 0 /// AK125998 // GenBank // Homo sapiens cDNA FLJ44010 fis, clone TESTI4024344. // chr1 // 50 // 96 // 12 // 24 // 0 /// BC070227 // GenBank // Homo sapiens similar to DEAD/H (Asp-Glu-Ala-Asp/His) box polypeptide 11 isoform 1, mRNA (cDNA clone IMAGE:6103207). // chr1 // 100 // 44 // 11 // 11 // 0 /// ENST00000515242 // ENSEMBL // cdna:pseudogene chromosome:GRCh37:1:11872:14412:1 gene:ENSG00000223972 gene_biotype:pseudogene transcript_biotype:transcribed_unprocessed_pseudogene // chr1 // 100 // 100 // 25 // 25 // 0 /// ENST00000518655 // ENSEMBL // cdna:pseudogene chromosome:GRCh37:1:11874:14409:1 gene:ENSG00000223972 gene_biotype:pseudogene transcript_biotype:transcribed_unprocessed_pseudogene // chr1 // 100 // 100 // 25 // 25 // 0 /// ENST00000515173 // ENSEMBL // cdna:pseudogene chromosome:GRCh37:15:102516758:102519298:-1 gene:ENSG00000248472 gene_biotype:pseudogene transcript_biotype:transcribed_unprocessed_pseudogene // chr1 // 96 // 100 // 24 // 25 // 0 /// ENST00000545636 // ENSEMBL // cdna:pseudogene chromosome:GRCh37:16:61553:64093:1 gene:ENSG00000233614 gene_biotype:pseudogene transcript_biotype:transcribed_unprocessed_pseudogene // chr1 // 92 // 96 // 22 // 24 // 0 /// ENST00000450305 // ENSEMBL // cdna:pseudogene chromosome:GRCh37:1:12010:13670:1 gene:ENSG00000223972 gene_biotype:pseudogene transcript_biotype:transcribed_unprocessed_pseudogene // chr1 // 100 // 68 // 17 // 17 // 0 /// ENST00000560040 // ENSEMBL // cdna:pseudogene chromosome:GRCh37:15:102517497:102518994:-1 gene:ENSG00000248472 gene_biotype:pseudogene transcript_biotype:transcribed_unprocessed_pseudogene // chr1 // 94 // 68 // 16 // 17 // 0 /// ENST00000430178 // ENSEMBL // cdna:pseudogene chromosome:GRCh37:16:61861:63351:1 gene:ENSG00000233614 gene_biotype:pseudogene transcript_biotype:transcribed_unprocessed_pseudogene // chr1 // 88 // 64 // 14 // 16 // 0 /// ENST00000538648 // ENSEMBL // cdna:pseudogene chromosome:GRCh37:15:102517351:102517622:-1 gene:ENSG00000248472 gene_biotype:pseudogene transcript_biotype:pseudogene // chr1 // 100 // 16 // 4 // 4 // 0 /// ENST00000535848 // ENSEMBL // cdna:pseudogene chromosome:GRCh37:2:114356606:114359144:-1 gene:ENSG00000236397 gene_biotype:pseudogene transcript_biotype:unprocessed_pseudogene // chr1 // 83 // 96 // 20 // 24 // 0 /// ENST00000457993 // ENSEMBL // cdna:pseudogene chromosome:GRCh37:2:114356613:114358838:-1 gene:ENSG00000236397 gene_biotype:pseudogene transcript_biotype:unprocessed_pseudogene // chr1 // 85 // 80 // 17 // 20 // 0 /// ENST00000437401 // ENSEMBL // cdna:pseudogene chromosome:GRCh37:2:114356613:114358838:-1 gene:ENSG00000236397 gene_biotype:pseudogene transcript_biotype:unprocessed_pseudogene // chr1 // 80 // 80 // 16 // 20 // 0 /// ENST00000426146 // ENSEMBL // cdna:pseudogene chromosome:GRCh37:9:11987:14522:1 gene:ENSG00000236875 gene_biotype:pseudogene transcript_biotype:unprocessed_pseudogene // chr1 // 50 // 96 // 12 // 24 // 0 /// ENST00000445777 // ENSEMBL // cdna:pseudogene chromosome:GRCh37:X:155255323:155257848:-1 gene:ENSG00000227159 gene_biotype:pseudogene transcript_biotype:unprocessed_pseudogene // chr1 // 50 // 96 // 12 // 24 // 0 /// ENST00000507418 // ENSEMBL // cdna:pseudogene chromosome:GRCh37:X:155255329:155257542:-1 gene:ENSG00000227159 gene_biotype:pseudogene transcript_biotype:unprocessed_pseudogene // chr1 // 50 // 64 // 8 // 16 // 0 /// ENST00000421620 // ENSEMBL // cdna:pseudogene chromosome:GRCh37:9:12134:13439:1 gene:ENSG00000236875 gene_biotype:pseudogene transcript_biotype:unprocessed_pseudogene // chr1 // 100 // 12 // 3 // 3 // 0 /// GENSCAN00000003613 // ENSEMBL // cdna:genscan chromosome:GRCh37:15:102517021:102518980:-1 transcript_biotype:protein_coding // chr1 // 100 // 52 // 13 // 13 // 0 /// GENSCAN00000026650 // ENSEMBL // cdna:genscan chromosome:GRCh37:1:12190:14149:1 transcript_biotype:protein_coding // chr1 // 100 // 52 // 13 // 13 // 0 /// GENSCAN00000029586 // ENSEMBL // cdna:genscan chromosome:GRCh37:16:61871:63830:1 transcript_biotype:protein_coding // chr1 // 100 // 48 // 12 // 12 // 0 /// ENST00000535849 // ENSEMBL // cdna:pseudogene chromosome:GRCh37:12:92239:93430:-1 gene:ENSG00000256263 gene_biotype:pseudogene transcript_biotype:unprocessed_pseudogene // chr1 // 38 // 32 // 3 // 8 // 1 /// ENST00000575871 // ENSEMBL // cdna:pseudogene chromosome:GRCh37:HG858_PATCH:62310:63501:1 gene:ENSG00000262195 gene_biotype:pseudogene transcript_biotype:unprocessed_pseudogene // chr1 // 38 // 32 // 3 // 8 // 1 /// ENST00000572276 // ENSEMBL // cdna:pseudogene chromosome:GRCh37:HSCHR12_1_CTG1:62310:63501:1 gene:ENSG00000263289 gene_biotype:pseudogene transcript_biotype:unprocessed_pseudogene // chr1 // 38 // 32 // 3 // 8 // 1 /// GENSCAN00000048516 // ENSEMBL // cdna:genscan chromosome:GRCh37:HG858_PATCH:62740:64276:1 transcript_biotype:protein_coding // chr1 // 25 // 48 // 3 // 12 // 1 /// GENSCAN00000048612 // ENSEMBL // cdna:genscan chromosome:GRCh37:HSCHR12_1_CTG1:62740:64276:1 transcript_biotype:protein_coding // chr1 // 25 // 48 // 3 // 12 // 1', 'ENST00000473358 // ENSEMBL // cdna:known chromosome:GRCh37:1:29554:31097:1 gene:ENSG00000243485 gene_biotype:antisense transcript_biotype:antisense // chr1 // 100 // 71 // 20 // 20 // 0', 'NM_001005484 // RefSeq // Homo sapiens olfactory receptor, family 4, subfamily F, member 5 (OR4F5), mRNA. // chr1 // 100 // 100 // 8 // 8 // 0 /// ENST00000335137 // ENSEMBL // cdna:known chromosome:GRCh37:1:69091:70008:1 gene:ENSG00000186092 gene_biotype:protein_coding transcript_biotype:protein_coding // chr1 // 100 // 100 // 8 // 8 // 0', 'TCONS_00000119-XLOC_000001 // Rinn lincRNA // linc-OR4F16-10 chr1:+:160445-161525 // chr1 // 100 // 100 // 13 // 13 // 0', 'AK302511 // GenBank // Homo sapiens cDNA FLJ61476 complete cds. // chr1 // 92 // 33 // 11 // 12 // 0 /// AK294489 // GenBank // Homo sapiens cDNA FLJ52615 complete cds. // chr1 // 77 // 36 // 10 // 13 // 0 /// AK303380 // GenBank // Homo sapiens cDNA FLJ53527 complete cds. // chr1 // 100 // 14 // 5 // 5 // 0 /// AK316554 // GenBank // Homo sapiens cDNA, FLJ79453 complete cds. // chr1 // 100 // 11 // 4 // 4 // 0 /// AK316556 // GenBank // Homo sapiens cDNA, FLJ79455 complete cds. // chr1 // 100 // 11 // 4 // 4 // 0 /// AK302573 // GenBank // Homo sapiens cDNA FLJ52612 complete cds. // chr1 // 80 // 14 // 4 // 5 // 0 /// TCONS_l2_00002815-XLOC_l2_001399 // Broad TUCP // linc-PLD5-5 chr1:-:243219130-243221165 // chr1 // 92 // 33 // 11 // 12 // 0 /// TCONS_l2_00001802-XLOC_l2_001332 // Broad TUCP // linc-TP53BP2-3 chr1:-:224139117-224140327 // chr1 // 100 // 14 // 5 // 5 // 0 /// TCONS_l2_00001804-XLOC_l2_001332 // Broad TUCP // linc-TP53BP2-3 chr1:-:224139117-224142371 // chr1 // 100 // 14 // 5 // 5 // 0 /// TCONS_00000120-XLOC_000002 // Rinn lincRNA // linc-OR4F16-9 chr1:+:320161-321056 // chr1 // 100 // 11 // 4 // 4 // 0 /// TCONS_l2_00002817-XLOC_l2_001399 // Broad TUCP // linc-PLD5-5 chr1:-:243220177-243221150 // chr1 // 100 // 6 // 2 // 2 // 0 /// TCONS_00000437-XLOC_000658 // Rinn lincRNA // linc-ZNF692-6 chr1:-:139789-140339 // chr1 // 100 // 6 // 2 // 2 // 0 /// AK299469 // GenBank // Homo sapiens cDNA FLJ52610 complete cds. // chr1 // 100 // 33 // 12 // 12 // 0 /// AK302889 // GenBank // Homo sapiens cDNA FLJ54896 complete cds. // chr1 // 100 // 22 // 8 // 8 // 0 /// AK123446 // GenBank // Homo sapiens cDNA FLJ41452 fis, clone BRSTN2010363. // chr1 // 100 // 19 // 7 // 7 // 0 /// ENST00000425496 // ENSEMBL // cdna:known chromosome:GRCh37:1:324756:328453:1 gene:ENSG00000237094 gene_biotype:processed_transcript transcript_biotype:processed_transcript // chr1 // 100 // 33 // 13 // 12 // 0 /// ENST00000456623 // ENSEMBL // cdna:known chromosome:GRCh37:1:324515:326852:1 gene:ENSG00000237094 gene_biotype:processed_transcript transcript_biotype:processed_transcript // chr1 // 100 // 33 // 12 // 12 // 0 /// ENST00000418377 // ENSEMBL // cdna:known chromosome:GRCh37:1:243219131:243221165:-1 gene:ENSG00000214837 gene_biotype:processed_transcript transcript_biotype:processed_transcript // chr1 // 92 // 33 // 11 // 12 // 0 /// ENST00000534867 // ENSEMBL // cdna:known chromosome:GRCh37:1:324438:325896:1 gene:ENSG00000237094 gene_biotype:processed_transcript transcript_biotype:processed_transcript // chr1 // 100 // 28 // 10 // 10 // 0 /// ENST00000544678 // ENSEMBL // cdna:known chromosome:GRCh37:5:180751053:180752511:1 gene:ENSG00000238035 gene_biotype:protein_coding transcript_biotype:protein_coding // chr1 // 100 // 22 // 8 // 8 // 0 /// ENST00000419160 // ENSEMBL // cdna:known chromosome:GRCh37:1:322732:324955:1 gene:ENSG00000237094 gene_biotype:processed_transcript transcript_biotype:processed_transcript // chr1 // 100 // 17 // 6 // 6 // 0 /// ENST00000432964 // ENSEMBL // cdna:known chromosome:GRCh37:1:320162:321056:1 gene:ENSG00000237094 gene_biotype:processed_transcript transcript_biotype:processed_transcript // chr1 // 100 // 11 // 4 // 4 // 0 /// ENST00000423728 // ENSEMBL // cdna:known chromosome:GRCh37:1:320162:324461:1 gene:ENSG00000237094 gene_biotype:processed_transcript transcript_biotype:processed_transcript // chr1 // 100 // 11 // 4 // 4 // 0 /// BC092421 // GenBank // Homo sapiens cDNA clone IMAGE:30378758. // chr1 // 100 // 33 // 12 // 12 // 0 /// ENST00000426316 // ENSEMBL // cdna:known chromosome:GRCh37:1:317811:328455:1 gene:ENSG00000240876 gene_biotype:processed_transcript transcript_biotype:processed_transcript // chr1 // 100 // 8 // 3 // 3 // 0 /// ENST00000465971 // ENSEMBL // cdna:pseudogene chromosome:GRCh37:7:128291239:128292388:1 gene:ENSG00000243302 gene_biotype:pseudogene transcript_biotype:processed_pseudogene // chr1 // 100 // 31 // 11 // 11 // 0 /// ENST00000535314 // ENSEMBL // cdna:pseudogene chromosome:GRCh37:7:128291243:128292355:1 gene:ENSG00000243302 gene_biotype:pseudogene transcript_biotype:processed_pseudogene // chr1 // 100 // 31 // 11 // 11 // 0 /// ENST00000423372 // ENSEMBL // cdna:pseudogene chromosome:GRCh37:1:134901:139379:-1 gene:ENSG00000237683 gene_biotype:pseudogene transcript_biotype:processed_pseudogene // chr1 // 90 // 28 // 9 // 10 // 0 /// ENST00000435839 // ENSEMBL // cdna:pseudogene chromosome:GRCh37:1:137283:139620:-1 gene:ENSG00000237683 gene_biotype:pseudogene transcript_biotype:processed_pseudogene // chr1 // 90 // 28 // 9 // 10 // 0 /// ENST00000537461 // ENSEMBL // cdna:pseudogene chromosome:GRCh37:1:138239:139697:-1 gene:ENSG00000237683 gene_biotype:pseudogene transcript_biotype:processed_pseudogene // chr1 // 100 // 19 // 7 // 7 // 0 /// ENST00000494149 // ENSEMBL // cdna:pseudogene chromosome:GRCh37:1:135247:138039:-1 gene:ENSG00000237683 gene_biotype:pseudogene transcript_biotype:processed_pseudogene // chr1 // 100 // 8 // 3 // 3 // 0 /// ENST00000514436 // ENSEMBL // cdna:pseudogene chromosome:GRCh37:1:326096:328112:1 gene:ENSG00000250575 gene_biotype:pseudogene transcript_biotype:unprocessed_pseudogene // chr1 // 100 // 8 // 3 // 3 // 0 /// ENST00000457364 // ENSEMBL // cdna:known chromosome:GRCh37:5:180751371:180755068:1 gene:ENSG00000238035 gene_biotype:protein_coding transcript_biotype:protein_coding // chr1 // 100 // 28 // 11 // 10 // 0 /// ENST00000438516 // ENSEMBL // cdna:known chromosome:GRCh37:5:180751130:180753467:1 gene:ENSG00000238035 gene_biotype:protein_coding transcript_biotype:protein_coding // chr1 // 100 // 28 // 10 // 10 // 0 /// ENST00000526704 // ENSEMBL // ensembl_havana_lincrna:lincRNA chromosome:GRCh37:11:129531:139099:-1 gene:ENSG00000230724 gene_biotype:lincRNA transcript_biotype:processed_transcript // chr1 // 93 // 42 // 14 // 15 // 0 /// ENST00000540375 // ENSEMBL // ensembl_havana_lincrna:lincRNA chromosome:GRCh37:11:127115:131056:-1 gene:ENSG00000230724 gene_biotype:lincRNA transcript_biotype:processed_transcript // chr1 // 100 // 28 // 11 // 10 // 0 /// ENST00000457006 // ENSEMBL // ensembl_havana_lincrna:lincRNA chromosome:GRCh37:11:128960:131297:-1 gene:ENSG00000230724 gene_biotype:lincRNA transcript_biotype:processed_transcript // chr1 // 90 // 28 // 9 // 10 // 0 /// ENST00000427071 // ENSEMBL // ensembl_havana_lincrna:lincRNA chromosome:GRCh37:11:130207:131297:-1 gene:ENSG00000230724 gene_biotype:lincRNA transcript_biotype:processed_transcript // chr1 // 100 // 25 // 9 // 9 // 0 /// ENST00000542435 // ENSEMBL // ensembl_havana_lincrna:lincRNA chromosome:GRCh37:11:129916:131374:-1 gene:ENSG00000230724 gene_biotype:lincRNA transcript_biotype:processed_transcript // chr1 // 100 // 22 // 8 // 8 // 0'], 'swissprot': ['NR_046018 // B7ZGW9 /// NR_046018 // B7ZGX0 /// NR_046018 // B7ZGX2 /// NR_046018 // B7ZGX3 /// NR_046018 // B7ZGX5 /// NR_046018 // B7ZGX6 /// NR_046018 // B7ZGX7 /// NR_046018 // B7ZGX8 /// NR_046018 // B7ZGX9 /// NR_046018 // B7ZGY0 /// NR_034090 // B7ZGW9 /// NR_034090 // B7ZGX0 /// NR_034090 // B7ZGX2 /// NR_034090 // B7ZGX3 /// NR_034090 // B7ZGX5 /// NR_034090 // B7ZGX6 /// NR_034090 // B7ZGX7 /// NR_034090 // B7ZGX8 /// NR_034090 // B7ZGX9 /// NR_034090 // B7ZGY0 /// NR_051985 // B7ZGW9 /// NR_051985 // B7ZGX0 /// NR_051985 // B7ZGX2 /// NR_051985 // B7ZGX3 /// NR_051985 // B7ZGX5 /// NR_051985 // B7ZGX6 /// NR_051985 // B7ZGX7 /// NR_051985 // B7ZGX8 /// NR_051985 // B7ZGX9 /// NR_051985 // B7ZGY0 /// NR_045117 // B7ZGW9 /// NR_045117 // B7ZGX0 /// NR_045117 // B7ZGX2 /// NR_045117 // B7ZGX3 /// NR_045117 // B7ZGX5 /// NR_045117 // B7ZGX6 /// NR_045117 // B7ZGX7 /// NR_045117 // B7ZGX8 /// NR_045117 // B7ZGX9 /// NR_045117 // B7ZGY0 /// NR_024005 // B7ZGW9 /// NR_024005 // B7ZGX0 /// NR_024005 // B7ZGX2 /// NR_024005 // B7ZGX3 /// NR_024005 // B7ZGX5 /// NR_024005 // B7ZGX6 /// NR_024005 // B7ZGX7 /// NR_024005 // B7ZGX8 /// NR_024005 // B7ZGX9 /// NR_024005 // B7ZGY0 /// NR_051986 // B7ZGW9 /// NR_051986 // B7ZGX0 /// NR_051986 // B7ZGX2 /// NR_051986 // B7ZGX3 /// NR_051986 // B7ZGX5 /// NR_051986 // B7ZGX6 /// NR_051986 // B7ZGX7 /// NR_051986 // B7ZGX8 /// NR_051986 // B7ZGX9 /// NR_051986 // B7ZGY0 /// AK125998 // Q6ZU42 /// AK125998 // B7ZGW9 /// AK125998 // B7ZGX0 /// AK125998 // B7ZGX2 /// AK125998 // B7ZGX3 /// AK125998 // B7ZGX5 /// AK125998 // B7ZGX6 /// AK125998 // B7ZGX7 /// AK125998 // B7ZGX8 /// AK125998 // B7ZGX9 /// AK125998 // B7ZGY0', '---', '---', '---', 'AK302511 // B4DYM5 /// AK294489 // B4DGA0 /// AK294489 // Q6ZSN7 /// AK303380 // B4E0H4 /// AK303380 // Q6ZQS4 /// AK303380 // A8E4K2 /// AK316554 // B4E3X0 /// AK316554 // Q6ZSN7 /// AK316556 // B4E3X2 /// AK316556 // Q6ZSN7 /// AK302573 // B7Z7W4 /// AK302573 // Q6ZQS4 /// AK302573 // A8E4K2 /// AK299469 // B7Z5V7 /// AK299469 // Q6ZSN7 /// AK302889 // B7Z846 /// AK302889 // Q6ZSN7 /// AK123446 // B3KVU4'], 'unigene': ['NR_046018 // Hs.714157 // testis| normal| adult /// NR_034090 // Hs.644359 // blood| normal| adult /// NR_051985 // Hs.644359 // blood| normal| adult /// NR_045117 // Hs.592089 // brain| glioma /// NR_024004 // Hs.712940 // bladder| bone marrow| brain| embryonic tissue| intestine| mammary gland| muscle| pharynx| placenta| prostate| skin| spleen| stomach| testis| thymus| breast (mammary gland) tumor| gastrointestinal tumor| glioma| non-neoplasia| normal| prostate cancer| skin tumor| soft tissue/muscle tissue tumor|embryoid body| adult /// NR_024005 // Hs.712940 // bladder| bone marrow| brain| embryonic tissue| intestine| mammary gland| muscle| pharynx| placenta| prostate| skin| spleen| stomach| testis| thymus| breast (mammary gland) tumor| gastrointestinal tumor| glioma| non-neoplasia| normal| prostate cancer| skin tumor| soft tissue/muscle tissue tumor|embryoid body| adult /// NR_051986 // Hs.719844 // brain| normal /// ENST00000456328 // Hs.714157 // testis| normal| adult /// ENST00000559159 // Hs.644359 // blood| normal| adult /// ENST00000562189 // Hs.644359 // blood| normal| adult /// ENST00000513886 // Hs.592089 // brain| glioma /// ENST00000515242 // Hs.714157 // testis| normal| adult /// ENST00000518655 // Hs.714157 // testis| normal| adult /// ENST00000515173 // Hs.644359 // blood| normal| adult /// ENST00000545636 // Hs.592089 // brain| glioma /// ENST00000450305 // Hs.714157 // testis| normal| adult /// ENST00000560040 // Hs.644359 // blood| normal| adult /// ENST00000430178 // Hs.592089 // brain| glioma /// ENST00000538648 // Hs.644359 // blood| normal| adult', '---', 'NM_001005484 // Hs.554500 // --- /// ENST00000335137 // Hs.554500 // ---', '---', 'AK302511 // Hs.732199 // ascites| blood| brain| connective tissue| embryonic tissue| eye| intestine| kidney| larynx| lung| ovary| placenta| prostate| stomach| testis| thymus| uterus| chondrosarcoma| colorectal tumor| gastrointestinal tumor| head and neck tumor| leukemia| lung tumor| normal| ovarian tumor| fetus| adult /// AK294489 // Hs.534942 // blood| brain| embryonic tissue| intestine| lung| mammary gland| mouth| ovary| pancreas| pharynx| placenta| spleen| stomach| testis| thymus| trachea| breast (mammary gland) tumor| colorectal tumor| head and neck tumor| leukemia| lung tumor| normal| ovarian tumor|embryoid body| blastocyst| fetus| adult /// AK294489 // Hs.734488 // blood| brain| esophagus| intestine| kidney| lung| mammary gland| mouth| placenta| prostate| testis| thymus| thyroid| uterus| breast (mammary gland) tumor| colorectal tumor| esophageal tumor| head and neck tumor| kidney tumor| leukemia| lung tumor| normal| adult /// AK303380 // Hs.732199 // ascites| blood| brain| connective tissue| embryonic tissue| eye| intestine| kidney| larynx| lung| ovary| placenta| prostate| stomach| testis| thymus| uterus| chondrosarcoma| colorectal tumor| gastrointestinal tumor| head and neck tumor| leukemia| lung tumor| normal| ovarian tumor| fetus| adult /// AK316554 // Hs.732199 // ascites| blood| brain| connective tissue| embryonic tissue| eye| intestine| kidney| larynx| lung| ovary| placenta| prostate| stomach| testis| thymus| uterus| chondrosarcoma| colorectal tumor| gastrointestinal tumor| head and neck tumor| leukemia| lung tumor| normal| ovarian tumor| fetus| adult /// AK316556 // Hs.732199 // ascites| blood| brain| connective tissue| embryonic tissue| eye| intestine| kidney| larynx| lung| ovary| placenta| prostate| stomach| testis| thymus| uterus| chondrosarcoma| colorectal tumor| gastrointestinal tumor| head and neck tumor| leukemia| lung tumor| normal| ovarian tumor| fetus| adult /// AK302573 // Hs.534942 // blood| brain| embryonic tissue| intestine| lung| mammary gland| mouth| ovary| pancreas| pharynx| placenta| spleen| stomach| testis| thymus| trachea| breast (mammary gland) tumor| colorectal tumor| head and neck tumor| leukemia| lung tumor| normal| ovarian tumor|embryoid body| blastocyst| fetus| adult /// AK302573 // Hs.734488 // blood| brain| esophagus| intestine| kidney| lung| mammary gland| mouth| placenta| prostate| testis| thymus| thyroid| uterus| breast (mammary gland) tumor| colorectal tumor| esophageal tumor| head and neck tumor| kidney tumor| leukemia| lung tumor| normal| adult /// AK123446 // Hs.520589 // bladder| blood| bone| brain| embryonic tissue| intestine| kidney| liver| lung| lymph node| ovary| pancreas| parathyroid| placenta| testis| thyroid| uterus| colorectal tumor| glioma| head and neck tumor| kidney tumor| leukemia| liver tumor| normal| ovarian tumor| uterine tumor|embryoid body| fetus| adult /// ENST00000425496 // Hs.356758 // blood| bone| brain| cervix| connective tissue| embryonic tissue| intestine| kidney| lung| mammary gland| mouth| pancreas| pharynx| placenta| prostate| spleen| stomach| testis| trachea| uterus| vascular| breast (mammary gland) tumor| chondrosarcoma| colorectal tumor| gastrointestinal tumor| glioma| head and neck tumor| leukemia| lung tumor| normal| uterine tumor| adult /// ENST00000425496 // Hs.733048 // ascites| bladder| blood| brain| embryonic tissue| eye| intestine| kidney| larynx| liver| lung| mammary gland| mouth| pancreas| placenta| prostate| skin| stomach| testis| thymus| thyroid| trachea| uterus| bladder carcinoma| breast (mammary gland) tumor| colorectal tumor| gastrointestinal tumor| head and neck tumor| kidney tumor| leukemia| liver tumor| lung tumor| normal| pancreatic tumor| prostate cancer| retinoblastoma| skin tumor| soft tissue/muscle tissue tumor| uterine tumor|embryoid body| blastocyst| fetus| adult /// ENST00000456623 // Hs.356758 // blood| bone| brain| cervix| connective tissue| embryonic tissue| intestine| kidney| lung| mammary gland| mouth| pancreas| pharynx| placenta| prostate| spleen| stomach| testis| trachea| uterus| vascular| breast (mammary gland) tumor| chondrosarcoma| colorectal tumor| gastrointestinal tumor| glioma| head and neck tumor| leukemia| lung tumor| normal| uterine tumor| adult /// ENST00000456623 // Hs.733048 // ascites| bladder| blood| brain| embryonic tissue| eye| intestine| kidney| larynx| liver| lung| mammary gland| mouth| pancreas| placenta| prostate| skin| stomach| testis| thymus| thyroid| trachea| uterus| bladder carcinoma| breast (mammary gland) tumor| colorectal tumor| gastrointestinal tumor| head and neck tumor| kidney tumor| leukemia| liver tumor| lung tumor| normal| pancreatic tumor| prostate cancer| retinoblastoma| skin tumor| soft tissue/muscle tissue tumor| uterine tumor|embryoid body| blastocyst| fetus| adult /// ENST00000534867 // Hs.356758 // blood| bone| brain| cervix| connective tissue| embryonic tissue| intestine| kidney| lung| mammary gland| mouth| pancreas| pharynx| placenta| prostate| spleen| stomach| testis| trachea| uterus| vascular| breast (mammary gland) tumor| chondrosarcoma| colorectal tumor| gastrointestinal tumor| glioma| head and neck tumor| leukemia| lung tumor| normal| uterine tumor| adult /// ENST00000534867 // Hs.733048 // ascites| bladder| blood| brain| embryonic tissue| eye| intestine| kidney| larynx| liver| lung| mammary gland| mouth| pancreas| placenta| prostate| skin| stomach| testis| thymus| thyroid| trachea| uterus| bladder carcinoma| breast (mammary gland) tumor| colorectal tumor| gastrointestinal tumor| head and neck tumor| kidney tumor| leukemia| liver tumor| lung tumor| normal| pancreatic tumor| prostate cancer| retinoblastoma| skin tumor| soft tissue/muscle tissue tumor| uterine tumor|embryoid body| blastocyst| fetus| adult /// ENST00000419160 // Hs.356758 // blood| bone| brain| cervix| connective tissue| embryonic tissue| intestine| kidney| lung| mammary gland| mouth| pancreas| pharynx| placenta| prostate| spleen| stomach| testis| trachea| uterus| vascular| breast (mammary gland) tumor| chondrosarcoma| colorectal tumor| gastrointestinal tumor| glioma| head and neck tumor| leukemia| lung tumor| normal| uterine tumor| adult /// ENST00000419160 // Hs.733048 // ascites| bladder| blood| brain| embryonic tissue| eye| intestine| kidney| larynx| liver| lung| mammary gland| mouth| pancreas| placenta| prostate| skin| stomach| testis| thymus| thyroid| trachea| uterus| bladder carcinoma| breast (mammary gland) tumor| colorectal tumor| gastrointestinal tumor| head and neck tumor| kidney tumor| leukemia| liver tumor| lung tumor| normal| pancreatic tumor| prostate cancer| retinoblastoma| skin tumor| soft tissue/muscle tissue tumor| uterine tumor|embryoid body| blastocyst| fetus| adult /// ENST00000432964 // Hs.356758 // blood| bone| brain| cervix| connective tissue| embryonic tissue| intestine| kidney| lung| mammary gland| mouth| pancreas| pharynx| placenta| prostate| spleen| stomach| testis| trachea| uterus| vascular| breast (mammary gland) tumor| chondrosarcoma| colorectal tumor| gastrointestinal tumor| glioma| head and neck tumor| leukemia| lung tumor| normal| uterine tumor| adult /// ENST00000432964 // Hs.733048 // ascites| bladder| blood| brain| embryonic tissue| eye| intestine| kidney| larynx| liver| lung| mammary gland| mouth| pancreas| placenta| prostate| skin| stomach| testis| thymus| thyroid| trachea| uterus| bladder carcinoma| breast (mammary gland) tumor| colorectal tumor| gastrointestinal tumor| head and neck tumor| kidney tumor| leukemia| liver tumor| lung tumor| normal| pancreatic tumor| prostate cancer| retinoblastoma| skin tumor| soft tissue/muscle tissue tumor| uterine tumor|embryoid body| blastocyst| fetus| adult /// ENST00000423728 // Hs.356758 // blood| bone| brain| cervix| connective tissue| embryonic tissue| intestine| kidney| lung| mammary gland| mouth| pancreas| pharynx| placenta| prostate| spleen| stomach| testis| trachea| uterus| vascular| breast (mammary gland) tumor| chondrosarcoma| colorectal tumor| gastrointestinal tumor| glioma| head and neck tumor| leukemia| lung tumor| normal| uterine tumor| adult /// ENST00000423728 // Hs.733048 // ascites| bladder| blood| brain| embryonic tissue| eye| intestine| kidney| larynx| liver| lung| mammary gland| mouth| pancreas| placenta| prostate| skin| stomach| testis| thymus| thyroid| trachea| uterus| bladder carcinoma| breast (mammary gland) tumor| colorectal tumor| gastrointestinal tumor| head and neck tumor| kidney tumor| leukemia| liver tumor| lung tumor| normal| pancreatic tumor| prostate cancer| retinoblastoma| skin tumor| soft tissue/muscle tissue tumor| uterine tumor|embryoid body| blastocyst| fetus| adult'], 'GO_biological_process': ['---', '---', '---', '---', '---'], 'GO_cellular_component': ['---', '---', 'NM_001005484 // GO:0005886 // plasma membrane // traceable author statement /// NM_001005484 // GO:0016021 // integral to membrane // inferred from electronic annotation /// ENST00000335137 // GO:0005886 // plasma membrane // traceable author statement /// ENST00000335137 // GO:0016021 // integral to membrane // inferred from electronic annotation', '---', '---'], 'GO_molecular_function': ['---', '---', 'NM_001005484 // GO:0004930 // G-protein coupled receptor activity // inferred from electronic annotation /// NM_001005484 // GO:0004984 // olfactory receptor activity // inferred from electronic annotation /// ENST00000335137 // GO:0004930 // G-protein coupled receptor activity // inferred from electronic annotation /// ENST00000335137 // GO:0004984 // olfactory receptor activity // inferred from electronic annotation', '---', '---'], 'pathway': ['---', '---', '---', '---', '---'], 'protein_domains': ['---', '---', 'ENST00000335137 // Pfam // IPR000276 // GPCR, rhodopsin-like, 7TM /// ENST00000335137 // Pfam // IPR019424 // 7TM GPCR, olfactory receptor/chemoreceptor Srsx', '---', '---'], 'crosshyb_type': ['3', '3', '3', '3', '3'], 'category': ['main', 'main', 'main', 'main', 'main'], 'GB_ACC': ['NR_046018', nan, 'NM_001005484', nan, 'AK302511'], 'SPOT_ID': [nan, 'ENST00000473358', nan, 'TCONS_00000119-XLOC_000001', nan]}\n", "\n", "Column names in gene annotation data:\n", "['ID', 'probeset_id', 'seqname', 'strand', 'start', 'stop', 'total_probes', 'gene_assignment', 'mrna_assignment', 'swissprot', 'unigene', 'GO_biological_process', 'GO_cellular_component', 'GO_molecular_function', 'pathway', 'protein_domains', 'crosshyb_type', 'category', 'GB_ACC', 'SPOT_ID']\n", "\n", "The dataset contains GenBank accessions (GB_ACC) that could be used for gene mapping.\n", "Number of rows with GenBank accessions: 20942 out of 3270697\n", "\n", "The dataset contains genomic regions (SPOT_ID) that could be used for location-based gene mapping.\n", "Example SPOT_ID format: nan\n" ] } ], "source": [ "# 1. Extract gene annotation data from the SOFT file\n", "print(\"Extracting gene annotation data from SOFT file...\")\n", "try:\n", " # Use the library function to extract gene annotation\n", " gene_annotation = get_gene_annotation(soft_file)\n", " print(f\"Successfully extracted gene annotation data with {len(gene_annotation.index)} rows\")\n", " \n", " # Preview the annotation DataFrame\n", " print(\"\\nGene annotation preview (first few rows):\")\n", " print(preview_df(gene_annotation))\n", " \n", " # Show column names to help identify which columns we need for mapping\n", " print(\"\\nColumn names in gene annotation data:\")\n", " print(gene_annotation.columns.tolist())\n", " \n", " # Check for relevant mapping columns\n", " if 'GB_ACC' in gene_annotation.columns:\n", " print(\"\\nThe dataset contains GenBank accessions (GB_ACC) that could be used for gene mapping.\")\n", " # Count non-null values in GB_ACC column\n", " non_null_count = gene_annotation['GB_ACC'].count()\n", " print(f\"Number of rows with GenBank accessions: {non_null_count} out of {len(gene_annotation)}\")\n", " \n", " if 'SPOT_ID' in gene_annotation.columns:\n", " print(\"\\nThe dataset contains genomic regions (SPOT_ID) that could be used for location-based gene mapping.\")\n", " print(\"Example SPOT_ID format:\", gene_annotation['SPOT_ID'].iloc[0])\n", " \n", "except Exception as e:\n", " print(f\"Error processing gene annotation data: {e}\")\n", " is_gene_available = False\n" ] }, { "cell_type": "markdown", "id": "b10781db", "metadata": {}, "source": [ "### Step 6: Gene Identifier Mapping" ] }, { "cell_type": "code", "execution_count": 7, "id": "9ec91f39", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T07:18:17.650134Z", "iopub.status.busy": "2025-03-25T07:18:17.650016Z", "iopub.status.idle": "2025-03-25T07:18:21.737960Z", "shell.execute_reply": "2025-03-25T07:18:21.737562Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Step 1: Creating gene mapping from annotation data\n", "Successfully created gene mapping with 53617 rows\n", "\n", "First few rows of the gene mapping:\n", " ID Gene\n", "0 16657436 NR_046018 // DDX11L1 // DEAD/H (Asp-Glu-Ala-As...\n", "1 16657440 ENST00000473358 // MIR1302-11 // microRNA 1302...\n", "2 16657445 NM_001005484 // OR4F5 // olfactory receptor, f...\n", "3 16657447 ---\n", "4 16657450 AK302511 // LOC100132062 // uncharacterized LO...\n", "\n", "Step 2: Converting probe measurements to gene expression data\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Successfully mapped probes to genes. Gene expression data has 81076 genes and 60 samples\n", "\n", "First few gene symbols in the gene expression data:\n", "Index(['A-', 'A-2', 'A-52', 'A-E', 'A-I'], dtype='object', name='Gene')\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "Gene expression data saved to ../../output/preprocess/Kidney_Clear_Cell_Carcinoma/gene_data/GSE150404.csv\n" ] } ], "source": [ "# 1. Determine which columns in the gene annotation data contain the IDs and gene symbols\n", "# From the previous output, we can see the gene identifiers are numeric IDs like '16650001'\n", "# Looking at the gene_annotation preview, 'ID' is the column containing these probe IDs\n", "# For gene symbols, we need to extract from the 'gene_assignment' column\n", "\n", "print(\"Step 1: Creating gene mapping from annotation data\")\n", "# 2. Extract relevant columns for mapping\n", "try:\n", " # The 'ID' column in gene_annotation contains probe IDs that match gene_data index\n", " # The 'gene_assignment' column contains gene information with symbols\n", " mapping_df = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='gene_assignment')\n", " print(f\"Successfully created gene mapping with {len(mapping_df)} rows\")\n", " \n", " # Preview the mapping data\n", " print(\"\\nFirst few rows of the gene mapping:\")\n", " print(mapping_df.head())\n", " \n", " # 3. Apply the mapping to convert probe-level data to gene-level data\n", " print(\"\\nStep 2: Converting probe measurements to gene expression data\")\n", " gene_data = apply_gene_mapping(gene_data, mapping_df)\n", " print(f\"Successfully mapped probes to genes. Gene expression data has {len(gene_data)} genes and {gene_data.shape[1]} samples\")\n", " \n", " # Preview the gene expression data\n", " print(\"\\nFirst few gene symbols in the gene expression data:\")\n", " print(gene_data.index[:5])\n", " \n", " # Save the gene data to 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", " \n", "except Exception as e:\n", " print(f\"Error in gene mapping: {e}\")\n", " is_gene_available = False\n" ] }, { "cell_type": "markdown", "id": "14d2cd53", "metadata": {}, "source": [ "### Step 7: Data Normalization and Linking" ] }, { "cell_type": "code", "execution_count": 8, "id": "3d391bb4", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T07:18:21.739418Z", "iopub.status.busy": "2025-03-25T07:18:21.739292Z", "iopub.status.idle": "2025-03-25T07:18:35.855344Z", "shell.execute_reply": "2025-03-25T07:18:35.854937Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Normalizing gene symbols...\n", "After normalization: 23274 unique gene symbols\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Normalized gene expression data saved to ../../output/preprocess/Kidney_Clear_Cell_Carcinoma/gene_data/GSE150404.csv\n", "\n", "Loading clinical data...\n", "Loaded clinical data with shape: (3, 60)\n", "\n", "Linking clinical and genetic data...\n", "Clinical data index: ['Kidney_Clear_Cell_Carcinoma', 'Age', 'Gender']\n", "First few clinical sample columns: ['GSM4548605', 'GSM4548606', 'GSM4548607', 'GSM4548608', 'GSM4548609']\n", "First few genetic sample columns: ['GSM4548605', 'GSM4548606', 'GSM4548607', 'GSM4548608', 'GSM4548609']\n", "Linked data shape: (60, 23277)\n", "Linked data columns: ['Kidney_Clear_Cell_Carcinoma', 'Age', 'Gender', 'A1BG', 'A1BG-AS1', 'A1CF', 'A2M', 'A2M-AS1', 'A2ML1', 'A2ML1-AS1']\n", "Actual trait column name: Kidney_Clear_Cell_Carcinoma\n", "\n", "Handling missing values...\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "After handling missing values, data shape: (60, 23277)\n", "\n", "Checking for bias in features...\n", "For the feature 'Kidney_Clear_Cell_Carcinoma', the least common label is '0.0' with 30 occurrences. This represents 50.00% of the dataset.\n", "The distribution of the feature 'Kidney_Clear_Cell_Carcinoma' in this dataset is fine.\n", "\n", "Quartiles for 'Age':\n", " 25%: 58.75\n", " 50% (Median): 63.824561403508774\n", " 75%: 69.0\n", "Min: 45.0\n", "Max: 83.0\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 26 occurrences. This represents 43.33% of the dataset.\n", "The distribution of the feature 'Gender' in this dataset is fine.\n", "\n", "\n", "Performing final validation...\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Linked data saved to ../../output/preprocess/Kidney_Clear_Cell_Carcinoma/GSE150404.csv\n" ] } ], "source": [ "# 1. Normalize gene symbols in the gene expression data\n", "print(\"\\nNormalizing gene symbols...\")\n", "try:\n", " # Check if gene_data is empty before normalization\n", " if gene_data.empty:\n", " print(\"Gene data is empty. Skipping normalization.\")\n", " normalized_gene_data = gene_data\n", " else:\n", " normalized_gene_data = normalize_gene_symbols_in_index(gene_data)\n", " print(f\"After normalization: {len(normalized_gene_data.index)} unique gene symbols\")\n", " \n", " # Save the normalized gene data (even if empty, to maintain consistent workflow)\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", "except Exception as e:\n", " print(f\"Error normalizing gene symbols: {e}\")\n", " normalized_gene_data = gene_data # Use original data if normalization fails\n", "\n", "# Load the clinical data from the previously saved file\n", "print(\"\\nLoading clinical data...\")\n", "try:\n", " clinical_df = pd.read_csv(out_clinical_data_file, index_col=0)\n", " print(f\"Loaded clinical data with shape: {clinical_df.shape}\")\n", " is_trait_available = True\n", "except Exception as e:\n", " print(f\"Error loading clinical data: {e}\")\n", " clinical_df = pd.DataFrame()\n", " is_trait_available = False\n", "\n", "# 2. Link clinical and genetic data if available\n", "print(\"\\nLinking clinical and genetic data...\")\n", "try:\n", " if is_trait_available and not normalized_gene_data.empty:\n", " # Print sample IDs from both datasets for debugging\n", " print(\"Clinical data index:\", clinical_df.index.tolist())\n", " print(\"First few clinical sample columns:\", list(clinical_df.columns)[:5])\n", " print(\"First few genetic sample columns:\", list(normalized_gene_data.columns)[:5])\n", " \n", " # Link clinical and genetic data\n", " linked_data = geo_link_clinical_genetic_data(clinical_df, normalized_gene_data)\n", " print(f\"Linked data shape: {linked_data.shape}\")\n", " \n", " # After linking, the trait should be a column (not a row)\n", " # Check if the trait name is in the columns or if we need to use the first row of clinical data\n", " print(f\"Linked data columns: {linked_data.columns[:10].tolist()}\")\n", " \n", " # In the linked data, the trait is the first index from clinical_df\n", " actual_trait_name = clinical_df.index[0]\n", " print(f\"Actual trait column name: {actual_trait_name}\")\n", " \n", " if actual_trait_name in linked_data.columns:\n", " # 3. Handle missing values systematically\n", " print(\"\\nHandling missing values...\")\n", " linked_data = handle_missing_values(linked_data, actual_trait_name)\n", " print(f\"After handling missing values, data shape: {linked_data.shape}\")\n", " \n", " # Check if we still have samples after missing value handling\n", " if linked_data.shape[0] > 0:\n", " # 4. Determine whether the trait and demographic features are biased\n", " print(\"\\nChecking for bias in features...\")\n", " is_biased, linked_data = judge_and_remove_biased_features(linked_data, actual_trait_name)\n", " else:\n", " print(\"Error: All samples were removed during missing value handling.\")\n", " is_biased = True\n", " else:\n", " print(f\"Trait column '{actual_trait_name}' not found in linked data.\")\n", " is_biased = True\n", " else:\n", " print(\"Cannot link data: clinical or genetic data is missing.\")\n", " if not is_trait_available:\n", " print(\"Reason: Trait data is not available.\")\n", " if normalized_gene_data.empty:\n", " print(\"Reason: Gene expression data is empty.\")\n", " linked_data = pd.DataFrame()\n", " is_biased = True\n", " \n", "except Exception as e:\n", " print(f\"Error in linking clinical and genetic data: {e}\")\n", " linked_data = pd.DataFrame()\n", " is_biased = True\n", "\n", "# 5. Final quality validation\n", "print(\"\\nPerforming final validation...\")\n", "note = \"\"\n", "if 'linked_data' not in locals() or linked_data.empty:\n", " note = \"Dataset failed preprocessing: Unable to create usable linked data. \"\n", " if not is_trait_available:\n", " note += \"Clinical trait data was not available or could not be loaded.\"\n", " elif normalized_gene_data.empty:\n", " note += \"Gene expression data was empty after processing.\"\n", " else:\n", " note += \"The clinical data and gene data were available but could not be properly linked or processed.\"\n", "\n", "is_gene_available = not normalized_gene_data.empty if 'normalized_gene_data' in locals() else False\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=is_gene_available,\n", " is_trait_available=is_trait_available,\n", " is_biased=is_biased if 'is_biased' in locals() else True,\n", " df=linked_data if 'linked_data' in locals() and not linked_data.empty else pd.DataFrame(),\n", " note=note\n", ")\n", "\n", "# 6. Save linked data if usable\n", "if is_usable and 'linked_data' in locals() and not linked_data.empty:\n", " # Create directory if it doesn't exist\n", " os.makedirs(os.path.dirname(out_data_file), exist_ok=True)\n", " \n", " # Save linked data\n", " linked_data.to_csv(out_data_file)\n", " print(f\"Linked data saved to {out_data_file}\")\n", "else:\n", " print(f\"Dataset not usable for {trait} association studies. Data not saved.\")" ] } ], "metadata": { "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.10.16" } }, "nbformat": 4, "nbformat_minor": 5 }