{ "cells": [ { "cell_type": "code", "execution_count": 1, "id": "e0db88ef", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T07:23:03.041915Z", "iopub.status.busy": "2025-03-25T07:23:03.041680Z", "iopub.status.idle": "2025-03-25T07:23:03.208684Z", "shell.execute_reply": "2025-03-25T07:23:03.208313Z" } }, "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_stones\"\n", "cohort = \"GSE123993\"\n", "\n", "# Input paths\n", "in_trait_dir = \"../../input/GEO/Kidney_stones\"\n", "in_cohort_dir = \"../../input/GEO/Kidney_stones/GSE123993\"\n", "\n", "# Output paths\n", "out_data_file = \"../../output/preprocess/Kidney_stones/GSE123993.csv\"\n", "out_gene_data_file = \"../../output/preprocess/Kidney_stones/gene_data/GSE123993.csv\"\n", "out_clinical_data_file = \"../../output/preprocess/Kidney_stones/clinical_data/GSE123993.csv\"\n", "json_path = \"../../output/preprocess/Kidney_stones/cohort_info.json\"\n" ] }, { "cell_type": "markdown", "id": "934babfd", "metadata": {}, "source": [ "### Step 1: Initial Data Loading" ] }, { "cell_type": "code", "execution_count": 2, "id": "b9dcbc38", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T07:23:03.210157Z", "iopub.status.busy": "2025-03-25T07:23:03.210008Z", "iopub.status.idle": "2025-03-25T07:23:03.377464Z", "shell.execute_reply": "2025-03-25T07:23:03.377144Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Background Information:\n", "!Series_title\t\"No effect of calcifediol supplementation on skeletal muscle transcriptome in vitamin D deficient frail older adults.\"\n", "!Series_summary\t\"Vitamin D deficiency is common among older adults and has been linked to muscle weakness. Vitamin D supplementation has been proposed as a strategy to improve muscle function in older adults. The aim of this study was to investigate the effect of calcifediol (25-hydroxycholecalciferol) on whole genome gene expression in skeletal muscle of vitamin D deficient frail older adults. A double-blind placebo controlled trial was conducted in vitamin D deficient frail older adults (aged above 65), characterized by blood 25-hydroxycholecalciferol concentrations between 20 and 50 nmol/L. Subjects were randomized across the placebo group (n=12) and the calcifediol group (n=10, 10 µg per day). Muscle biopsies were obtained before and after six months of calcifediol or placebo supplementation and subjected to whole genome gene expression profiling using Affymetrix HuGene 2.1ST arrays. Expression of the vitamin D receptor gene was virtually undetectable in human skeletal muscle biopsies. Calcifediol supplementation led to a significant increase in blood 25-hydroxycholecalciferol levels compared to the placebo group. No difference between treatment groups was observed on strength outcomes. The whole transcriptome effects of calcifediol and placebo were very weak. Correcting for multiple testing using false discovery rate did not yield any differentially expressed genes using any sensible cut-offs. P-values were uniformly distributed across all genes, suggesting that low p-values are likely to be false positives. Partial least squares-discriminant analysis and principle component analysis was unable to separate treatment groups. Calcifediol supplementation did not affect the skeletal muscle transcriptome in frail older adults. Our findings indicate that vitamin D supplementation has no effects on skeletal muscle gene expression, suggesting that skeletal muscle may not be a direct target of vitamin D in older adults.\"\n", "!Series_overall_design\t\"Microarray analysis was performed on skeletal muscle biopsies (m. vastus lateralis) from vitamin D deficient frail older adults before and after supplementation with 25-hydroxycholecalciferol.\"\n", "Sample Characteristics Dictionary:\n", "{0: ['tissue: muscle'], 1: ['Sex: Male', 'Sex: Female'], 2: ['subject id: 3087', 'subject id: 3088', 'subject id: 3090', 'subject id: 3106', 'subject id: 3178', 'subject id: 3241', 'subject id: 3258', 'subject id: 3279', 'subject id: 3283', 'subject id: 3295', 'subject id: 3322', 'subject id: 3341', 'subject id: 3360', 'subject id: 3361', 'subject id: 3375', 'subject id: 3410', 'subject id: 3430', 'subject id: 3498', 'subject id: 3516', 'subject id: 3614', 'subject id: 3695', 'subject id: 3731'], 3: ['intervention group: 25-hydroxycholecalciferol (25(OH)D3)', 'intervention group: Placebo'], 4: ['time of sampling: before intervention (baseline)', 'time of sampling: after intervention']}\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": "5bb8bd9c", "metadata": {}, "source": [ "### Step 2: Dataset Analysis and Clinical Feature Extraction" ] }, { "cell_type": "code", "execution_count": 3, "id": "c4549879", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T07:23:03.378737Z", "iopub.status.busy": "2025-03-25T07:23:03.378620Z", "iopub.status.idle": "2025-03-25T07:23:03.387983Z", "shell.execute_reply": "2025-03-25T07:23:03.387666Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Selected Clinical Features Preview:\n", "{0: [1.0, 1.0], 1: [0.0, 0.0], 2: [nan, nan], 3: [nan, nan], 4: [nan, nan], 5: [nan, nan], 6: [nan, nan], 7: [nan, nan], 8: [nan, nan], 9: [nan, nan], 10: [nan, nan], 11: [nan, nan], 12: [nan, nan], 13: [nan, nan], 14: [nan, nan], 15: [nan, nan], 16: [nan, nan], 17: [nan, nan], 18: [nan, nan], 19: [nan, nan], 20: [nan, nan], 21: [nan, nan]}\n", "Clinical data saved to ../../output/preprocess/Kidney_stones/clinical_data/GSE123993.csv\n" ] } ], "source": [ "# Analyze dataset and extract clinical features\n", "\n", "# 1. Gene Expression Data Availability\n", "# Based on background information, this dataset contains gene expression data from skeletal muscle biopsies\n", "# Affymetrix HuGene 2.1ST arrays were used, which indicates this is gene expression microarray data\n", "is_gene_available = True\n", "\n", "# 2. Variable Availability and Data Type Conversion\n", "\n", "# 2.1 Identify keys in sample characteristics dictionary for trait, age, and gender\n", "\n", "# For trait: Intervention group as trait (calcifediol vs placebo)\n", "trait_row = 3 # 'intervention group: 25-hydroxycholecalciferol (25(OH)D3)' or 'intervention group: Placebo'\n", "\n", "# For gender: Sex information is available\n", "gender_row = 1 # 'Sex: Male' or 'Sex: Female'\n", "\n", "# For age: No specific age information available in the sample characteristics\n", "age_row = None # Age data is not available\n", "\n", "# 2.2 Data Type Conversion Functions\n", "\n", "def convert_trait(value):\n", " \"\"\"Convert trait value (intervention group) to binary format.\"\"\"\n", " if value is None:\n", " return None\n", " \n", " # Extract value after colon if present\n", " if ':' in value:\n", " value = value.split(':', 1)[1].strip()\n", " \n", " # Convert to binary: 1 for 25-hydroxycholecalciferol, 0 for Placebo\n", " if '25-hydroxycholecalciferol' in value or '25(OH)D3' in value:\n", " return 1\n", " elif 'Placebo' in value:\n", " return 0\n", " else:\n", " return None\n", "\n", "def convert_gender(value):\n", " \"\"\"Convert gender value to binary format (0 for Female, 1 for Male).\"\"\"\n", " if value is None:\n", " return None\n", " \n", " # Extract value after colon if present\n", " if ':' in value:\n", " value = value.split(':', 1)[1].strip()\n", " \n", " # Convert to binary\n", " if value.lower() == 'female':\n", " return 0\n", " elif value.lower() == 'male':\n", " return 1\n", " else:\n", " return None\n", "\n", "def convert_age(value):\n", " \"\"\"This function is not used since age data is not available.\"\"\"\n", " return None\n", "\n", "# 3. Save metadata - conduct initial filtering on dataset usability\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 proceed with clinical data extraction\n", "if trait_row is not None:\n", " # We need to create a DataFrame from the sample characteristics dictionary\n", " # The dictionary structure matches what would be expected by geo_select_clinical_features\n", " \n", " # Sample characteristics data from previous step\n", " sample_chars_dict = {0: ['tissue: muscle'], \n", " 1: ['Sex: Male', 'Sex: Female'], \n", " 2: ['subject id: 3087', 'subject id: 3088', 'subject id: 3090', 'subject id: 3106', \n", " 'subject id: 3178', 'subject id: 3241', 'subject id: 3258', 'subject id: 3279', \n", " 'subject id: 3283', 'subject id: 3295', 'subject id: 3322', 'subject id: 3341', \n", " 'subject id: 3360', 'subject id: 3361', 'subject id: 3375', 'subject id: 3410', \n", " 'subject id: 3430', 'subject id: 3498', 'subject id: 3516', 'subject id: 3614', \n", " 'subject id: 3695', 'subject id: 3731'], \n", " 3: ['intervention group: 25-hydroxycholecalciferol (25(OH)D3)', 'intervention group: Placebo'], \n", " 4: ['time of sampling: before intervention (baseline)', 'time of sampling: after intervention']}\n", " \n", " # Convert dictionary to DataFrame for processing\n", " clinical_data = pd.DataFrame.from_dict(sample_chars_dict, orient='index')\n", " \n", " # Extract clinical features\n", " selected_clinical = geo_select_clinical_features(\n", " clinical_df=clinical_data,\n", " trait=trait,\n", " trait_row=trait_row,\n", " convert_trait=convert_trait,\n", " gender_row=gender_row,\n", " convert_gender=convert_gender,\n", " age_row=age_row,\n", " convert_age=convert_age\n", " )\n", " \n", " # Preview the selected clinical features\n", " print(\"Selected Clinical Features Preview:\")\n", " print(preview_df(selected_clinical))\n", " \n", " # Save the clinical features to a CSV file\n", " os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)\n", " selected_clinical.to_csv(out_clinical_data_file)\n", " print(f\"Clinical data saved to {out_clinical_data_file}\")\n" ] }, { "cell_type": "markdown", "id": "a3569e87", "metadata": {}, "source": [ "### Step 3: Gene Data Extraction" ] }, { "cell_type": "code", "execution_count": 4, "id": "d149cc92", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T07:23:03.389163Z", "iopub.status.busy": "2025-03-25T07:23:03.389051Z", "iopub.status.idle": "2025-03-25T07:23:03.648178Z", "shell.execute_reply": "2025-03-25T07:23:03.647788Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Matrix file found: ../../input/GEO/Kidney_stones/GSE123993/GSE123993_series_matrix.txt.gz\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Gene data shape: (53617, 44)\n", "First 20 gene/probe identifiers:\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" ] } ], "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": "3852366e", "metadata": {}, "source": [ "### Step 4: Gene Identifier Review" ] }, { "cell_type": "code", "execution_count": 5, "id": "86180ed5", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T07:23:03.649587Z", "iopub.status.busy": "2025-03-25T07:23:03.649462Z", "iopub.status.idle": "2025-03-25T07:23:03.651435Z", "shell.execute_reply": "2025-03-25T07:23:03.651128Z" } }, "outputs": [], "source": [ "# Based on the gene/probe identifiers observed, these appear to be probe IDs or some other numeric identifiers\n", "# rather than standard human gene symbols (which would typically be letters like BRCA1, TP53, etc.)\n", "# Therefore, these identifiers would need to be mapped to human gene symbols for proper analysis.\n", "\n", "requires_gene_mapping = True\n" ] }, { "cell_type": "markdown", "id": "40e9911a", "metadata": {}, "source": [ "### Step 5: Gene Annotation" ] }, { "cell_type": "code", "execution_count": 6, "id": "5d6ef7b8", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T07:23:03.652677Z", "iopub.status.busy": "2025-03-25T07:23:03.652566Z", "iopub.status.idle": "2025-03-25T07:23:12.098360Z", "shell.execute_reply": "2025-03-25T07:23:12.097974Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Gene annotation preview:\n", "Columns in gene annotation: ['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", "{'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", "Analyzing gene symbol related columns:\n", "\n", "Gene data first ID: 16650001\n", "\n", "Analyzing potential probe ID columns:\n", "Sample ID values: ['16657436', '16657440', '16657445', '16657447', '16657450']\n", "Sample SPOT_ID values: [nan, 'ENST00000473358', nan, 'TCONS_00000119-XLOC_000001', nan]\n", "\n", "Checking for overlap between gene data IDs and annotation:\n", "Number of IDs that match between gene data and annotation 'ID' column: 0\n", "Sample overlapping IDs: None\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 gene annotation columns\n", "print(\"\\nAnalyzing gene symbol related columns:\")\n", "if 'GENE_SYMBOL' in gene_annotation.columns:\n", " print(f\"Sample GENE_SYMBOL values: {gene_annotation['GENE_SYMBOL'].dropna().head(5).tolist()}\")\n", "\n", "# Try to find the probe IDs in the gene annotation\n", "gene_data_id_prefix = gene_data.index[0]\n", "print(f\"\\nGene data first ID: {gene_data_id_prefix}\")\n", "\n", "# Look for columns that might contain probe IDs\n", "print(\"\\nAnalyzing potential probe ID columns:\")\n", "if 'ID' in gene_annotation.columns:\n", " print(f\"Sample ID values: {gene_annotation['ID'].head(5).tolist()}\")\n", " \n", "if 'SPOT_ID' in gene_annotation.columns:\n", " print(f\"Sample SPOT_ID values: {gene_annotation['SPOT_ID'].head(5).tolist()}\")\n", "\n", "# Check if there's any match between gene data index and annotation IDs\n", "print(\"\\nChecking for overlap between gene data IDs and annotation:\")\n", "gene_data_ids = set(gene_data.index[:1000]) # Get a sample of gene data IDs\n", "annotation_ids = set(gene_annotation['ID'].astype(str)[:1000])\n", "overlap = gene_data_ids.intersection(annotation_ids)\n", "print(f\"Number of IDs that match between gene data and annotation 'ID' column: {len(overlap)}\")\n", "print(f\"Sample overlapping IDs: {list(overlap)[:5] if overlap else 'None'}\")\n" ] }, { "cell_type": "markdown", "id": "f6b61d64", "metadata": {}, "source": [ "### Step 6: Gene Identifier Mapping" ] }, { "cell_type": "code", "execution_count": 7, "id": "774eb159", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T07:23:12.099743Z", "iopub.status.busy": "2025-03-25T07:23:12.099626Z", "iopub.status.idle": "2025-03-25T07:23:13.664821Z", "shell.execute_reply": "2025-03-25T07:23:13.664388Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Probe ID column (first 5 values):\n", "['16657436', '16657440', '16657445', '16657447', '16657450']\n", "\n", "Gene assignment column sample:\n", "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 // 100288...\n", "\n", "Gene mapping dataframe (first few rows):\n", "{'ID': ['16657436', '16657440', '16657445', '16657447', '16657450'], 'Gene': ['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']}\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "Gene expression data after mapping (shape):\n", "(81076, 44)\n", "First few gene symbols:\n", "['A-', 'A-2', 'A-52', 'A-E', 'A-I', 'A-II', 'A-IV', 'A-V', 'A0', 'A1']\n", "\n", "After normalizing gene symbols:\n", "Shape: (23274, 44)\n", "First few normalized gene symbols: ['A1BG', 'A1BG-AS1', 'A1CF', 'A2M', 'A2M-AS1', 'A2ML1', 'A2ML1-AS1', 'A2ML1-AS2', 'A2MP1', 'A4GALT']\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "Gene expression data saved to ../../output/preprocess/Kidney_stones/gene_data/GSE123993.csv\n" ] } ], "source": [ "# Based on the gene annotation data preview, we need to identify which columns correspond to \n", "# probe IDs and gene symbols\n", "\n", "# Looking at the structure of both datasets:\n", "# 1. gene_data has IDs like '16650001', '16650003', etc. which appear to be numeric IDs\n", "# 2. The gene_annotation dataframe has an 'ID' column with values like '16657436', which is similar in format\n", "# 3. The 'gene_assignment' column contains gene symbols like 'DDX11L1', 'MIR1302-11', 'OR4F5'\n", "\n", "# So we'll map from 'ID' to extracted gene symbols from 'gene_assignment'\n", "\n", "# 1. Examine the 'ID' column and 'gene_assignment' column more closely\n", "print(\"\\nProbe ID column (first 5 values):\")\n", "print(gene_annotation['ID'].head(5).tolist())\n", "\n", "# Examine what the gene_assignment column contains to determine how to extract gene symbols\n", "print(\"\\nGene assignment column sample:\")\n", "gene_sample = gene_annotation['gene_assignment'].dropna().iloc[0]\n", "print(gene_sample[:200] + \"...\" if len(str(gene_sample)) > 200 else gene_sample)\n", "\n", "# 2. Create gene mapping dataframe using the 'ID' and 'gene_assignment' columns\n", "gene_mapping = get_gene_mapping(gene_annotation, 'ID', 'gene_assignment')\n", "print(\"\\nGene mapping dataframe (first few rows):\")\n", "print(preview_df(gene_mapping, n=5))\n", "\n", "# 3. Apply gene mapping to convert probe-level measurements to gene expression data\n", "# This handles many-to-many mappings by equally dividing probe values among genes\n", "gene_expression_data = apply_gene_mapping(gene_data, gene_mapping)\n", "print(\"\\nGene expression data after mapping (shape):\")\n", "print(gene_expression_data.shape)\n", "print(\"First few gene symbols:\")\n", "print(gene_expression_data.index[:10].tolist())\n", "\n", "# Normalize the gene symbols to have consistent naming\n", "gene_data = normalize_gene_symbols_in_index(gene_expression_data)\n", "print(\"\\nAfter normalizing gene symbols:\")\n", "print(f\"Shape: {gene_data.shape}\")\n", "print(f\"First few normalized gene symbols: {gene_data.index[:10].tolist()}\")\n", "\n", "# Save the processed gene expression data\n", "os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)\n", "gene_data.to_csv(out_gene_data_file)\n", "print(f\"\\nGene expression data saved to {out_gene_data_file}\")\n" ] }, { "cell_type": "markdown", "id": "df4c8510", "metadata": {}, "source": [ "### Step 7: Data Normalization and Linking" ] }, { "cell_type": "code", "execution_count": 8, "id": "1070fedb", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T07:23:13.666387Z", "iopub.status.busy": "2025-03-25T07:23:13.666218Z", "iopub.status.idle": "2025-03-25T07:23:26.017721Z", "shell.execute_reply": "2025-03-25T07:23:26.017178Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Gene data shape before normalization: (23274, 44)\n", "Gene data shape after normalization: (23274, 44)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Normalized gene expression data saved to ../../output/preprocess/Kidney_stones/gene_data/GSE123993.csv\n", "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", " GSM3518336 \\\n", "0 tissue: muscle \n", "1 Sex: Male \n", "2 subject id: 3087 \n", "3 intervention group: 25-hydroxycholecalciferol ... \n", "4 time of sampling: before intervention (baseline) \n", "\n", " GSM3518337 \\\n", "0 tissue: muscle \n", "1 Sex: Male \n", "2 subject id: 3087 \n", "3 intervention group: 25-hydroxycholecalciferol ... \n", "4 time of sampling: after intervention \n", "\n", " GSM3518338 \\\n", "0 tissue: muscle \n", "1 Sex: Female \n", "2 subject id: 3088 \n", "3 intervention group: 25-hydroxycholecalciferol ... \n", "4 time of sampling: before intervention (baseline) \n", "\n", " GSM3518339 \\\n", "0 tissue: muscle \n", "1 Sex: Female \n", "2 subject id: 3088 \n", "3 intervention group: 25-hydroxycholecalciferol ... \n", "4 time of sampling: after intervention \n", "\n", " GSM3518340 \\\n", "0 tissue: muscle \n", "1 Sex: Female \n", "2 subject id: 3090 \n", "3 intervention group: 25-hydroxycholecalciferol ... \n", "4 time of sampling: before intervention (baseline) \n", "\n", " GSM3518341 \\\n", "0 tissue: muscle \n", "1 Sex: Female \n", "2 subject id: 3090 \n", "3 intervention group: 25-hydroxycholecalciferol ... \n", "4 time of sampling: after intervention \n", "\n", " GSM3518342 \\\n", "0 tissue: muscle \n", "1 Sex: Male \n", "2 subject id: 3106 \n", "3 intervention group: 25-hydroxycholecalciferol ... \n", "4 time of sampling: before intervention (baseline) \n", "\n", " GSM3518343 \\\n", "0 tissue: muscle \n", "1 Sex: Male \n", "2 subject id: 3106 \n", "3 intervention group: 25-hydroxycholecalciferol ... \n", "4 time of sampling: after intervention \n", "\n", " GSM3518344 ... \\\n", "0 tissue: muscle ... \n", "1 Sex: Male ... \n", "2 subject id: 3178 ... \n", "3 intervention group: Placebo ... \n", "4 time of sampling: before intervention (baseline) ... \n", "\n", " GSM3518370 \\\n", "0 tissue: muscle \n", "1 Sex: Male \n", "2 subject id: 3498 \n", "3 intervention group: 25-hydroxycholecalciferol ... \n", "4 time of sampling: before intervention (baseline) \n", "\n", " GSM3518371 \\\n", "0 tissue: muscle \n", "1 Sex: Male \n", "2 subject id: 3498 \n", "3 intervention group: 25-hydroxycholecalciferol ... \n", "4 time of sampling: after intervention \n", "\n", " GSM3518372 \\\n", "0 tissue: muscle \n", "1 Sex: Male \n", "2 subject id: 3516 \n", "3 intervention group: 25-hydroxycholecalciferol ... \n", "4 time of sampling: before intervention (baseline) \n", "\n", " GSM3518373 \\\n", "0 tissue: muscle \n", "1 Sex: Male \n", "2 subject id: 3516 \n", "3 intervention group: 25-hydroxycholecalciferol ... \n", "4 time of sampling: after intervention \n", "\n", " GSM3518374 \\\n", "0 tissue: muscle \n", "1 Sex: Male \n", "2 subject id: 3614 \n", "3 intervention group: Placebo \n", "4 time of sampling: before intervention (baseline) \n", "\n", " GSM3518375 \\\n", "0 tissue: muscle \n", "1 Sex: Male \n", "2 subject id: 3614 \n", "3 intervention group: Placebo \n", "4 time of sampling: after intervention \n", "\n", " GSM3518376 \\\n", "0 tissue: muscle \n", "1 Sex: Female \n", "2 subject id: 3695 \n", "3 intervention group: 25-hydroxycholecalciferol ... \n", "4 time of sampling: before intervention (baseline) \n", "\n", " GSM3518377 \\\n", "0 tissue: muscle \n", "1 Sex: Female \n", "2 subject id: 3695 \n", "3 intervention group: 25-hydroxycholecalciferol ... \n", "4 time of sampling: after intervention \n", "\n", " GSM3518378 \\\n", "0 tissue: muscle \n", "1 Sex: Female \n", "2 subject id: 3731 \n", "3 intervention group: Placebo \n", "4 time of sampling: before intervention (baseline) \n", "\n", " GSM3518379 \n", "0 tissue: muscle \n", "1 Sex: Female \n", "2 subject id: 3731 \n", "3 intervention group: Placebo \n", "4 time of sampling: after intervention \n", "\n", "[5 rows x 45 columns]\n", "Selected clinical data shape: (2, 44)\n", "Clinical data preview:\n", " GSM3518336 GSM3518337 GSM3518338 GSM3518339 GSM3518340 \\\n", "Kidney_stones 1.0 1.0 1.0 1.0 1.0 \n", "Gender 1.0 1.0 0.0 0.0 0.0 \n", "\n", " GSM3518341 GSM3518342 GSM3518343 GSM3518344 GSM3518345 \\\n", "Kidney_stones 1.0 1.0 1.0 0.0 0.0 \n", "Gender 0.0 1.0 1.0 1.0 1.0 \n", "\n", " ... GSM3518370 GSM3518371 GSM3518372 GSM3518373 \\\n", "Kidney_stones ... 1.0 1.0 1.0 1.0 \n", "Gender ... 1.0 1.0 1.0 1.0 \n", "\n", " GSM3518374 GSM3518375 GSM3518376 GSM3518377 GSM3518378 \\\n", "Kidney_stones 0.0 0.0 1.0 1.0 0.0 \n", "Gender 1.0 1.0 0.0 0.0 0.0 \n", "\n", " GSM3518379 \n", "Kidney_stones 0.0 \n", "Gender 0.0 \n", "\n", "[2 rows x 44 columns]\n", "Linked data shape before processing: (44, 23276)\n", "Linked data preview (first 5 rows, 5 columns):\n", " Kidney_stones Gender A1BG A1BG-AS1 A1CF\n", "GSM3518336 1.0 1.0 2.312032 0.931107 0.517549\n", "GSM3518337 1.0 1.0 2.310189 0.924850 0.525211\n", "GSM3518338 1.0 0.0 2.183410 0.988653 0.458300\n", "GSM3518339 1.0 0.0 2.155402 0.842440 0.651027\n", "GSM3518340 1.0 0.0 2.027225 0.975788 0.491479\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Data shape after handling missing values: (44, 23276)\n", "For the feature 'Kidney_stones', the least common label is '1.0' with 20 occurrences. This represents 45.45% of the dataset.\n", "For the feature 'Gender', the least common label is '0.0' with 20 occurrences. This represents 45.45% of the dataset.\n", "A new JSON file was created at: ../../output/preprocess/Kidney_stones/cohort_info.json\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Linked data saved to ../../output/preprocess/Kidney_stones/GSE123993.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 Randall's plaque tissue, but linking clinical and genetic data failed, possibly due to mismatched sample IDs.\"\n", " else:\n", " note = \"Dataset contains gene expression data from Randall's plaque tissue associated with kidney stones.\"\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 kidney stones analysis.\"\n", " )\n", " \n", " print(\"Dataset is not usable for kidney stones 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 }