{ "cells": [ { "cell_type": "code", "execution_count": 1, "id": "222f601f", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T04:10:17.837446Z", "iopub.status.busy": "2025-03-25T04:10:17.837343Z", "iopub.status.idle": "2025-03-25T04:10:18.005251Z", "shell.execute_reply": "2025-03-25T04:10:18.004869Z" } }, "outputs": [], "source": [ "import sys\n", "import os\n", "sys.path.append(os.path.abspath(os.path.join(os.getcwd(), '../..')))\n", "\n", "# Path Configuration\n", "from tools.preprocess import *\n", "\n", "# Processing context\n", "trait = \"Thyroid_Cancer\"\n", "cohort = \"GSE138198\"\n", "\n", "# Input paths\n", "in_trait_dir = \"../../input/GEO/Thyroid_Cancer\"\n", "in_cohort_dir = \"../../input/GEO/Thyroid_Cancer/GSE138198\"\n", "\n", "# Output paths\n", "out_data_file = \"../../output/preprocess/Thyroid_Cancer/GSE138198.csv\"\n", "out_gene_data_file = \"../../output/preprocess/Thyroid_Cancer/gene_data/GSE138198.csv\"\n", "out_clinical_data_file = \"../../output/preprocess/Thyroid_Cancer/clinical_data/GSE138198.csv\"\n", "json_path = \"../../output/preprocess/Thyroid_Cancer/cohort_info.json\"\n" ] }, { "cell_type": "markdown", "id": "a1e4a946", "metadata": {}, "source": [ "### Step 1: Initial Data Loading" ] }, { "cell_type": "code", "execution_count": 2, "id": "89a21e3f", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T04:10:18.006707Z", "iopub.status.busy": "2025-03-25T04:10:18.006549Z", "iopub.status.idle": "2025-03-25T04:10:18.094584Z", "shell.execute_reply": "2025-03-25T04:10:18.094257Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Background Information:\n", "!Series_title\t\"Differentially expression profiling in Hashimoto's thyroiditis (HT), papillary thyroid carcinoma (PTC) with HT in background, PTC without HT in background, micro PTC (mPTC), and three normal thyroid samples (TN).\"\n", "!Series_summary\t\"Experiment: Establishment of expression profiles in HT, PTC with HT, PTC without HT, and mPTC in comparison to TN samples. TN samples were downloaded as CEL files from the repository of the microarray vendor. Biostatistical analysis focussed in first instance on identifying genes and biofunctions related to HT and PTC with HT.\"\n", "!Series_overall_design\t\"We extracted RNA from tumor specimens and RNA with an RIN > 5 (Bioanalyzer, Agilent) or, in exceptional cases, RNA assessed by gel electrophoresis, was processed using the recommended processing kits and hybridized to Affymetrix Human Gene 1.0 ST arrays. Three normal tissue samples were downloaded as CEL files from the data resource center of the vendor (https://www.thermofisher.com/us/en/home/life-science/microarray-analysis/microarray-data-analysis/microarray-analysis-sample-data/gene-st-array-data-set.html): TisMap_Thyroid_01_v1_WTGene1, TisMap_Thyroid_02_v1_WTGene1, TisMap_Thyroid_03_v1_WTGene1. All CEL files were RMA normalized and ANOVA was applied to generate differentially expressed probe sets. Differentially expressed genes that were significantly associated with one or more disease categories were subjected to biofunctional analysis.\"\n", "!Series_overall_design\t\"The renormalized cases from GSE54958 are included in the current study.\"\n", "Sample Characteristics Dictionary:\n", "{0: ['gender: F', 'gender: M', 'gender: not available'], 1: [\"sample type: Hashimoto's thyroiditis (HT), papillary thyroid carcinoma (PTC) with HT in background, PTC without HT in background, micro PTC (mPTC), and three normal thyroid samples (TN).\", 'sample type: papillary thyroid carcinoma (PTC) with HT in background', 'sample type: PTC without HT in background', 'sample type: micro PTC (mPTC)', 'sample type: normal thyroid samples (TN)']}\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": "98415271", "metadata": {}, "source": [ "### Step 2: Dataset Analysis and Clinical Feature Extraction" ] }, { "cell_type": "code", "execution_count": 3, "id": "d3480327", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T04:10:18.095722Z", "iopub.status.busy": "2025-03-25T04:10:18.095608Z", "iopub.status.idle": "2025-03-25T04:10:18.105836Z", "shell.execute_reply": "2025-03-25T04:10:18.105541Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Preview of clinical features:\n", "{'GSM4101749': [0.0, 0.0], 'GSM4101750': [0.0, 0.0], 'GSM4101751': [0.0, 0.0], 'GSM4101752': [0.0, 0.0], 'GSM4101753': [0.0, 0.0], 'GSM4101754': [0.0, 0.0], 'GSM4101755': [0.0, 0.0], 'GSM4101756': [0.0, 0.0], 'GSM4101757': [0.0, 0.0], 'GSM4101758': [0.0, 0.0], 'GSM4101759': [0.0, 0.0], 'GSM4101760': [0.0, 0.0], 'GSM4101761': [0.0, 1.0], 'GSM4101762': [1.0, 0.0], 'GSM4101763': [1.0, 1.0], 'GSM4101764': [1.0, 0.0], 'GSM4101765': [1.0, 0.0], 'GSM4101766': [1.0, 1.0], 'GSM4101767': [1.0, 0.0], 'GSM4101768': [1.0, 0.0], 'GSM4101769': [1.0, 0.0], 'GSM4101770': [1.0, 1.0], 'GSM4101771': [1.0, 0.0], 'GSM4101772': [1.0, 0.0], 'GSM4101773': [1.0, 0.0], 'GSM4101774': [1.0, 1.0], 'GSM4101775': [1.0, 1.0], 'GSM4101776': [1.0, 0.0], 'GSM4101777': [1.0, 0.0], 'GSM4101778': [1.0, 0.0], 'GSM4101779': [1.0, 1.0], 'GSM4101780': [1.0, 0.0], 'GSM4101781': [1.0, 0.0], 'GSM4101782': [0.0, nan], 'GSM4101783': [0.0, nan], 'GSM4101784': [0.0, nan]}\n", "Clinical features saved to ../../output/preprocess/Thyroid_Cancer/clinical_data/GSE138198.csv\n" ] } ], "source": [ "# 1. Gene Expression Data Availability\n", "# From the background information, this dataset appears to be microarray gene expression data\n", "# (Affymetrix Human Gene 1.0 ST arrays) so we can set is_gene_available to True\n", "is_gene_available = True \n", "\n", "# 2. Variable Availability and Data Type Conversion\n", "# 2.1 Data Availability\n", "\n", "# For trait: The information about thyroid cancer status is in row 1 (sample type)\n", "trait_row = 1\n", "\n", "# For age: No age information is available\n", "age_row = None\n", "\n", "# For gender: Gender information is available in row 0\n", "gender_row = 0\n", "\n", "# 2.2 Data Type Conversion\n", "def convert_trait(value):\n", " \"\"\"Convert trait value to binary (0: healthy, 1: thyroid cancer)\"\"\"\n", " if not value or 'not available' in value:\n", " return None\n", " \n", " # If the format contains a colon, extract the value after the colon\n", " if ':' in value:\n", " value = value.split(':', 1)[1].strip()\n", " \n", " if 'normal thyroid' in value.lower() or 'tn' in value.lower():\n", " return 0 # Normal/healthy thyroid\n", " elif any(cancer_term in value.lower() for cancer_term in ['ptc', 'carcinoma', 'mptc', 'micro ptc']):\n", " return 1 # Any type of thyroid cancer\n", " else:\n", " # For Hashimoto's thyroiditis without cancer, we'll return None as it's not clearly cancer or normal\n", " if \"hashimoto's thyroiditis\" in value.lower() and not \"ptc\" in value.lower():\n", " return None\n", " return None # For any other unclear values\n", "\n", "def convert_gender(value):\n", " \"\"\"Convert gender value to binary (0: female, 1: male)\"\"\"\n", " if not value or 'not available' in value:\n", " return None\n", " \n", " # If the format contains a colon, extract the value after the colon\n", " if ':' in value:\n", " value = value.split(':', 1)[1].strip()\n", " \n", " if value.upper() == 'F':\n", " return 0 # Female\n", " elif value.upper() == 'M':\n", " return 1 # Male\n", " else:\n", " return None # Unknown or not available\n", "\n", "# There's no age data, but we'll define the function anyway in case it's needed\n", "def convert_age(value):\n", " \"\"\"Convert age value to continuous\"\"\"\n", " if not value or 'not available' in value:\n", " return None\n", " \n", " # If the format contains a colon, extract the value after the colon\n", " if ':' in value:\n", " value = value.split(':', 1)[1].strip()\n", " \n", " try:\n", " return float(value)\n", " except:\n", " return None\n", "\n", "# 3. Save Metadata\n", "# Trait data availability depends on whether trait_row is None\n", "is_trait_available = trait_row is not None\n", "\n", "# Validate and save cohort info\n", "validate_and_save_cohort_info(\n", " is_final=False,\n", " cohort=cohort,\n", " info_path=json_path,\n", " is_gene_available=is_gene_available,\n", " is_trait_available=is_trait_available\n", ")\n", "\n", "# 4. Clinical Feature Extraction\n", "# Since trait_row is not None, we should extract clinical features\n", "if trait_row is not None:\n", " # Use the geo_select_clinical_features function to extract clinical features\n", " clinical_features_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 if age_row is not None else None,\n", " gender_row=gender_row,\n", " convert_gender=convert_gender if gender_row is not None else None\n", " )\n", " \n", " # Preview the extracted clinical features\n", " preview = preview_df(clinical_features_df)\n", " print(\"Preview of clinical features:\")\n", " print(preview)\n", " \n", " # Save the clinical features to CSV\n", " clinical_features_df.to_csv(out_clinical_data_file)\n", " print(f\"Clinical features saved to {out_clinical_data_file}\")\n" ] }, { "cell_type": "markdown", "id": "f7c8a59d", "metadata": {}, "source": [ "### Step 3: Gene Data Extraction" ] }, { "cell_type": "code", "execution_count": 4, "id": "54340913", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T04:10:18.106904Z", "iopub.status.busy": "2025-03-25T04:10:18.106795Z", "iopub.status.idle": "2025-03-25T04:10:18.212313Z", "shell.execute_reply": "2025-03-25T04:10:18.211937Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "SOFT file: ../../input/GEO/Thyroid_Cancer/GSE138198/GSE138198_family.soft.gz\n", "Matrix file: ../../input/GEO/Thyroid_Cancer/GSE138198/GSE138198_series_matrix.txt.gz\n", "Found the matrix table marker in the file.\n", "Gene data shape: (33297, 36)\n", "First 20 gene/probe identifiers:\n", "['7892501', '7892502', '7892503', '7892504', '7892505', '7892506', '7892507', '7892508', '7892509', '7892510', '7892511', '7892512', '7892513', '7892514', '7892515', '7892516', '7892517', '7892518', '7892519', '7892520']\n" ] } ], "source": [ "# 1. Identify the paths to the SOFT file and the matrix file\n", "soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)\n", "print(f\"SOFT file: {soft_file}\")\n", "print(f\"Matrix file: {matrix_file}\")\n", "\n", "# Set gene availability flag\n", "is_gene_available = True # Initially assume gene data is available\n", "\n", "# First check if the matrix file contains the expected marker\n", "found_marker = False\n", "try:\n", " with gzip.open(matrix_file, 'rt') as file:\n", " for line in file:\n", " if \"!series_matrix_table_begin\" in line:\n", " found_marker = True\n", " break\n", " \n", " if found_marker:\n", " print(\"Found the matrix table marker in the file.\")\n", " else:\n", " print(\"Warning: Could not find '!series_matrix_table_begin' marker in the file.\")\n", " \n", " # Try to extract gene data from the matrix file\n", " gene_data = get_genetic_data(matrix_file)\n", " \n", " if gene_data.shape[0] == 0:\n", " print(\"Warning: Extracted gene data has 0 rows.\")\n", " is_gene_available = False\n", " else:\n", " print(f\"Gene data shape: {gene_data.shape}\")\n", " # Print the first 20 gene/probe identifiers\n", " print(\"First 20 gene/probe identifiers:\")\n", " print(gene_data.index[:20].tolist())\n", " \n", "except Exception as e:\n", " print(f\"Error extracting gene data: {e}\")\n", " is_gene_available = False\n", " \n", " # Try to diagnose the file format\n", " print(\"Examining file content to diagnose the issue:\")\n", " try:\n", " with gzip.open(matrix_file, 'rt') as file:\n", " for i, line in enumerate(file):\n", " if i < 10: # Print first 10 lines to diagnose\n", " print(f\"Line {i}: {line.strip()[:100]}...\") # Print first 100 chars of each line\n", " else:\n", " break\n", " except Exception as e2:\n", " print(f\"Error examining file: {e2}\")\n", "\n", "if not is_gene_available:\n", " print(\"Gene expression data could not be successfully extracted from this dataset.\")\n" ] }, { "cell_type": "markdown", "id": "7d2d5e82", "metadata": {}, "source": [ "### Step 4: Gene Identifier Review" ] }, { "cell_type": "code", "execution_count": 5, "id": "c300617e", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T04:10:18.213468Z", "iopub.status.busy": "2025-03-25T04:10:18.213358Z", "iopub.status.idle": "2025-03-25T04:10:18.215214Z", "shell.execute_reply": "2025-03-25T04:10:18.214929Z" } }, "outputs": [], "source": [ "# These identifiers appear to be probe IDs rather than human gene symbols.\n", "# They look like Illumina or Affymetrix probe IDs (numeric IDs).\n", "# We need to map these probe IDs to human gene symbols.\n", "\n", "requires_gene_mapping = True\n" ] }, { "cell_type": "markdown", "id": "d73b581d", "metadata": {}, "source": [ "### Step 5: Gene Annotation" ] }, { "cell_type": "code", "execution_count": 6, "id": "dcd6b69f", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T04:10:18.216165Z", "iopub.status.busy": "2025-03-25T04:10:18.216067Z", "iopub.status.idle": "2025-03-25T04:10:22.648515Z", "shell.execute_reply": "2025-03-25T04:10:22.648026Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Gene annotation preview:\n", "Columns in gene annotation: ['ID', 'GB_LIST', 'SPOT_ID', 'seqname', 'RANGE_GB', 'RANGE_STRAND', 'RANGE_START', 'RANGE_STOP', 'total_probes', 'gene_assignment', 'mrna_assignment', 'category']\n", "{'ID': ['7896736', '7896738', '7896740', '7896742', '7896744'], 'GB_LIST': [nan, nan, 'NM_001004195,NM_001005240,NM_001005484,BC136848,BC136867,BC136907,BC136908', 'NR_024437,XM_006711854,XM_006726377,XR_430662,AK298283,AL137655,BC032332,BC118988,BC122537,BC131690,NM_207366,AK301928,BC071667', 'NM_001005221,NM_001005224,NM_001005277,NM_001005504,BC137547,BC137568'], 'SPOT_ID': ['chr1:53049-54936', 'chr1:63015-63887', 'chr1:69091-70008', 'chr1:334129-334296', 'chr1:367659-368597'], 'seqname': ['chr1', 'chr1', 'chr1', 'chr1', 'chr1'], 'RANGE_GB': ['NC_000001.10', 'NC_000001.10', 'NC_000001.10', 'NC_000001.10', 'NC_000001.10'], 'RANGE_STRAND': ['+', '+', '+', '+', '+'], 'RANGE_START': ['53049', '63015', '69091', '334129', '367659'], 'RANGE_STOP': ['54936', '63887', '70008', '334296', '368597'], 'total_probes': [7.0, 31.0, 24.0, 6.0, 36.0], 'gene_assignment': ['---', 'ENST00000328113 // OR4G2P // olfactory receptor, family 4, subfamily G, member 2 pseudogene // --- // --- /// ENST00000492842 // OR4G11P // olfactory receptor, family 4, subfamily G, member 11 pseudogene // --- // --- /// ENST00000588632 // OR4G1P // olfactory receptor, family 4, subfamily G, member 1 pseudogene // --- // ---', 'NM_001004195 // OR4F4 // olfactory receptor, family 4, subfamily F, member 4 // 15q26.3 // 26682 /// NM_001005240 // OR4F17 // olfactory receptor, family 4, subfamily F, member 17 // 19p13.3 // 81099 /// NM_001005484 // OR4F5 // olfactory receptor, family 4, subfamily F, member 5 // 1p36.33 // 79501 /// ENST00000318050 // OR4F17 // olfactory receptor, family 4, subfamily F, member 17 // 19p13.3 // 81099 /// ENST00000326183 // OR4F4 // olfactory receptor, family 4, subfamily F, member 4 // 15q26.3 // 26682 /// ENST00000335137 // OR4F5 // olfactory receptor, family 4, subfamily F, member 5 // 1p36.33 // 79501 /// ENST00000585993 // OR4F17 // olfactory receptor, family 4, subfamily F, member 17 // 19p13.3 // 81099 /// BC136848 // OR4F17 // olfactory receptor, family 4, subfamily F, member 17 // 19p13.3 // 81099 /// BC136867 // OR4F17 // olfactory receptor, family 4, subfamily F, member 17 // 19p13.3 // 81099 /// BC136907 // OR4F4 // olfactory receptor, family 4, subfamily F, member 4 // 15q26.3 // 26682 /// BC136908 // OR4F4 // olfactory receptor, family 4, subfamily F, member 4 // 15q26.3 // 26682', 'NR_024437 // LOC728323 // uncharacterized LOC728323 // 2q37.3 // 728323 /// XM_006711854 // LOC101060626 // F-box only protein 25-like // --- // 101060626 /// XM_006726377 // LOC101060626 // F-box only protein 25-like // --- // 101060626 /// XR_430662 // LOC101927097 // uncharacterized LOC101927097 // --- // 101927097 /// ENST00000279067 // LINC00266-1 // long intergenic non-protein coding RNA 266-1 // 20q13.33 // 140849 /// ENST00000431812 // LOC101928706 // uncharacterized LOC101928706 // --- // 101928706 /// ENST00000431812 // LOC101929823 // uncharacterized LOC101929823 // --- // 101929823 /// ENST00000433444 // LOC728323 // uncharacterized LOC728323 // 2q37.3 // 728323 /// ENST00000436899 // LINC00266-3 // long intergenic non-protein coding RNA 266-3 // --- // --- /// ENST00000445252 // LINC00266-1 // long intergenic non-protein coding RNA 266-1 // 20q13.33 // 140849 /// ENST00000455207 // LOC101928706 // uncharacterized LOC101928706 // --- // 101928706 /// ENST00000455207 // LOC101929823 // uncharacterized LOC101929823 // --- // 101929823 /// ENST00000455464 // LOC101928706 // uncharacterized LOC101928706 // --- // 101928706 /// ENST00000455464 // LOC101929823 // uncharacterized LOC101929823 // --- // 101929823 /// ENST00000456398 // LOC728323 // uncharacterized LOC728323 // 2q37.3 // 728323 /// ENST00000601814 // LOC101928706 // uncharacterized LOC101928706 // --- // 101928706 /// ENST00000601814 // LOC101929823 // uncharacterized LOC101929823 // --- // 101929823 /// AK298283 // LOC728323 // uncharacterized LOC728323 // 2q37.3 // 728323 /// AL137655 // LOC100134822 // uncharacterized LOC100134822 // --- // 100134822 /// BC032332 // PCMTD2 // protein-L-isoaspartate (D-aspartate) O-methyltransferase domain containing 2 // 20q13.33 // 55251 /// BC118988 // LINC00266-1 // long intergenic non-protein coding RNA 266-1 // 20q13.33 // 140849 /// BC122537 // LINC00266-1 // long intergenic non-protein coding RNA 266-1 // 20q13.33 // 140849 /// BC131690 // LOC728323 // uncharacterized LOC728323 // 2q37.3 // 728323 /// NM_207366 // SEPT14 // septin 14 // 7p11.2 // 346288 /// ENST00000388975 // SEPT14 // septin 14 // 7p11.2 // 346288 /// ENST00000427373 // LINC00266-4P // long intergenic non-protein coding RNA 266-4, pseudogene // --- // --- /// ENST00000431796 // LOC728323 // uncharacterized LOC728323 // 2q37.3 // 728323 /// ENST00000509776 // LINC00266-2P // long intergenic non-protein coding RNA 266-2, pseudogene // --- // --- /// ENST00000570230 // LOC101929008 // uncharacterized LOC101929008 // --- // 101929008 /// ENST00000570230 // LOC101929038 // uncharacterized LOC101929038 // --- // 101929038 /// ENST00000570230 // LOC101930130 // uncharacterized LOC101930130 // --- // 101930130 /// ENST00000570230 // LOC101930567 // uncharacterized LOC101930567 // --- // 101930567 /// AK301928 // SEPT14 // septin 14 // 7p11.2 // 346288', 'NM_001005221 // OR4F29 // olfactory receptor, family 4, subfamily F, member 29 // 1p36.33 // 729759 /// NM_001005224 // OR4F3 // olfactory receptor, family 4, subfamily F, member 3 // 5q35.3 // 26683 /// NM_001005277 // OR4F16 // olfactory receptor, family 4, subfamily F, member 16 // 1p36.33 // 81399 /// NM_001005504 // OR4F21 // olfactory receptor, family 4, subfamily F, member 21 // 8p23.3 // 441308 /// ENST00000320901 // OR4F21 // olfactory receptor, family 4, subfamily F, member 21 // 8p23.3 // 441308 /// ENST00000332831 // OR4F3 // olfactory receptor, family 4, subfamily F, member 3 // 5q35.3 // 26683 /// ENST00000332831 // OR4F16 // olfactory receptor, family 4, subfamily F, member 16 // 1p36.33 // 81399 /// ENST00000332831 // OR4F29 // olfactory receptor, family 4, subfamily F, member 29 // 1p36.33 // 729759 /// ENST00000402444 // OR4F7P // olfactory receptor, family 4, subfamily F, member 7 pseudogene // --- // --- /// ENST00000405102 // OR4F1P // olfactory receptor, family 4, subfamily F, member 1 pseudogene // --- // --- /// ENST00000424047 // OR4F2P // olfactory receptor, family 4, subfamily F, member 2 pseudogene // --- // --- /// ENST00000426406 // OR4F3 // olfactory receptor, family 4, subfamily F, member 3 // 5q35.3 // 26683 /// ENST00000426406 // OR4F16 // olfactory receptor, family 4, subfamily F, member 16 // 1p36.33 // 81399 /// ENST00000426406 // OR4F29 // olfactory receptor, family 4, subfamily F, member 29 // 1p36.33 // 729759 /// ENST00000456475 // OR4F3 // olfactory receptor, family 4, subfamily F, member 3 // 5q35.3 // 26683 /// ENST00000456475 // OR4F16 // olfactory receptor, family 4, subfamily F, member 16 // 1p36.33 // 81399 /// ENST00000456475 // OR4F29 // olfactory receptor, family 4, subfamily F, member 29 // 1p36.33 // 729759 /// ENST00000559128 // OR4F28P // olfactory receptor, family 4, subfamily F, member 28 pseudogene // --- // --- /// BC137547 // OR4F3 // olfactory receptor, family 4, subfamily F, member 3 // 5q35.3 // 26683 /// BC137547 // OR4F16 // olfactory receptor, family 4, subfamily F, member 16 // 1p36.33 // 81399 /// BC137547 // OR4F29 // olfactory receptor, family 4, subfamily F, member 29 // 1p36.33 // 729759 /// BC137568 // OR4F3 // olfactory receptor, family 4, subfamily F, member 3 // 5q35.3 // 26683 /// BC137568 // OR4F16 // olfactory receptor, family 4, subfamily F, member 16 // 1p36.33 // 81399 /// BC137568 // OR4F29 // olfactory receptor, family 4, subfamily F, member 29 // 1p36.33 // 729759 /// ENST00000589943 // OR4F8P // olfactory receptor, family 4, subfamily F, member 8 pseudogene // --- // ---'], 'mrna_assignment': ['NONHSAT060105 // NONCODE // Non-coding transcript identified by NONCODE // chr1 // 100 // 100 // 7 // 7 // 0', 'ENST00000328113 // ENSEMBL // havana:known chromosome:GRCh38:15:101926805:101927707:-1 gene:ENSG00000183909 gene_biotype:unprocessed_pseudogene transcript_biotype:unprocessed_pseudogene // chr1 // 100 // 100 // 31 // 31 // 0 /// ENST00000492842 // ENSEMBL // havana:known chromosome:GRCh38:1:62948:63887:1 gene:ENSG00000240361 gene_biotype:unprocessed_pseudogene transcript_biotype:unprocessed_pseudogene // chr1 // 100 // 100 // 31 // 31 // 0 /// ENST00000588632 // ENSEMBL // havana:known chromosome:GRCh38:19:104535:105471:1 gene:ENSG00000267310 gene_biotype:unprocessed_pseudogene transcript_biotype:unprocessed_pseudogene // chr1 // 100 // 100 // 31 // 31 // 0 /// NONHSAT000016 // NONCODE // Non-coding transcript identified by NONCODE: Linc // chr1 // 100 // 100 // 31 // 31 // 0 /// NONHSAT051704 // NONCODE // Non-coding transcript identified by NONCODE: Linc // chr1 // 100 // 100 // 31 // 31 // 0 /// NONHSAT060106 // NONCODE // Non-coding transcript identified by NONCODE: Linc // chr1 // 100 // 100 // 31 // 31 // 0', 'NM_001004195 // RefSeq // Homo sapiens olfactory receptor, family 4, subfamily F, member 4 (OR4F4), mRNA. // chr1 // 100 // 100 // 24 // 24 // 0 /// NM_001005240 // RefSeq // Homo sapiens olfactory receptor, family 4, subfamily F, member 17 (OR4F17), mRNA. // chr1 // 100 // 100 // 24 // 24 // 0 /// NM_001005484 // RefSeq // Homo sapiens olfactory receptor, family 4, subfamily F, member 5 (OR4F5), mRNA. // chr1 // 100 // 100 // 24 // 24 // 0 /// ENST00000318050 // ENSEMBL // ensembl:known chromosome:GRCh38:19:110643:111696:1 gene:ENSG00000176695 gene_biotype:protein_coding transcript_biotype:protein_coding // chr1 // 100 // 100 // 24 // 24 // 0 /// ENST00000326183 // ENSEMBL // ensembl_havana_transcript:known chromosome:GRCh38:15:101922042:101923095:-1 gene:ENSG00000177693 gene_biotype:protein_coding transcript_biotype:protein_coding // chr1 // 100 // 100 // 24 // 24 // 0 /// ENST00000335137 // ENSEMBL // ensembl_havana_transcript:known chromosome:GRCh38:1:69091:70008:1 gene:ENSG00000186092 gene_biotype:protein_coding transcript_biotype:protein_coding // chr1 // 100 // 100 // 24 // 24 // 0 /// ENST00000585993 // ENSEMBL // havana:known chromosome:GRCh38:19:107461:111696:1 gene:ENSG00000176695 gene_biotype:protein_coding transcript_biotype:protein_coding // chr1 // 100 // 100 // 24 // 24 // 0 /// BC136848 // GenBank // Homo sapiens olfactory receptor, family 4, subfamily F, member 17, mRNA (cDNA clone MGC:168462 IMAGE:9020839), complete cds. // chr1 // 100 // 100 // 24 // 24 // 0 /// BC136867 // GenBank // Homo sapiens olfactory receptor, family 4, subfamily F, member 17, mRNA (cDNA clone MGC:168481 IMAGE:9020858), complete cds. // chr1 // 100 // 100 // 24 // 24 // 0 /// BC136907 // GenBank // Homo sapiens olfactory receptor, family 4, subfamily F, member 4, mRNA (cDNA clone MGC:168521 IMAGE:9020898), complete cds. // chr1 // 100 // 100 // 24 // 24 // 0 /// BC136908 // GenBank // Homo sapiens olfactory receptor, family 4, subfamily F, member 4, mRNA (cDNA clone MGC:168522 IMAGE:9020899), complete cds. // chr1 // 100 // 100 // 24 // 24 // 0 /// ENST00000618231 // ENSEMBL // havana:known chromosome:GRCh38:19:110613:111417:1 gene:ENSG00000176695 gene_biotype:protein_coding transcript_biotype:retained_intron // chr1 // 100 // 88 // 21 // 21 // 0', 'NR_024437 // RefSeq // Homo sapiens uncharacterized LOC728323 (LOC728323), long non-coding RNA. // chr1 // 100 // 100 // 6 // 6 // 0 /// XM_006711854 // RefSeq // PREDICTED: Homo sapiens F-box only protein 25-like (LOC101060626), partial mRNA. // chr1 // 100 // 100 // 6 // 6 // 0 /// XM_006726377 // RefSeq // PREDICTED: Homo sapiens F-box only protein 25-like (LOC101060626), partial mRNA. // chr1 // 100 // 100 // 6 // 6 // 0 /// XR_430662 // RefSeq // PREDICTED: Homo sapiens uncharacterized LOC101927097 (LOC101927097), misc_RNA. // chr1 // 100 // 100 // 6 // 6 // 0 /// ENST00000279067 // ENSEMBL // ensembl_havana_transcript:known chromosome:GRCh38:20:64290385:64303559:1 gene:ENSG00000149656 gene_biotype:processed_transcript transcript_biotype:processed_transcript // chr1 // 100 // 100 // 6 // 6 // 0 /// ENST00000431812 // ENSEMBL // havana:known chromosome:GRCh38:1:485066:489553:-1 gene:ENSG00000237094 gene_biotype:lincRNA transcript_biotype:lincRNA // chr1 // 83 // 100 // 5 // 6 // 0 /// ENST00000433444 // ENSEMBL // havana:putative chromosome:GRCh38:2:242122293:242138888:1 gene:ENSG00000220804 gene_biotype:transcribed_unprocessed_pseudogene transcript_biotype:processed_transcript // chr1 // 100 // 100 // 6 // 6 // 0 /// ENST00000436899 // ENSEMBL // havana:known chromosome:GRCh38:6:131910:144885:-1 gene:ENSG00000170590 gene_biotype:lincRNA transcript_biotype:lincRNA // chr1 // 100 // 100 // 6 // 6 // 0 /// ENST00000445252 // ENSEMBL // havana:known chromosome:GRCh38:20:64294897:64311371:1 gene:ENSG00000149656 gene_biotype:processed_transcript transcript_biotype:processed_transcript // chr1 // 100 // 100 // 6 // 6 // 0 /// ENST00000455207 // ENSEMBL // havana:known chromosome:GRCh38:1:373182:485208:-1 gene:ENSG00000237094 gene_biotype:lincRNA transcript_biotype:lincRNA // chr1 // 100 // 100 // 6 // 6 // 0 /// ENST00000455464 // ENSEMBL // havana:known chromosome:GRCh38:1:476531:497259:-1 gene:ENSG00000237094 gene_biotype:lincRNA transcript_biotype:lincRNA // chr1 // 100 // 100 // 6 // 6 // 0 /// ENST00000456398 // ENSEMBL // havana:known chromosome:GRCh38:2:242088633:242140638:1 gene:ENSG00000220804 gene_biotype:transcribed_unprocessed_pseudogene transcript_biotype:processed_transcript // chr1 // 100 // 100 // 6 // 6 // 0 /// ENST00000601814 // ENSEMBL // havana:known chromosome:GRCh38:1:484832:495476:-1 gene:ENSG00000237094 gene_biotype:lincRNA transcript_biotype:lincRNA // chr1 // 100 // 100 // 6 // 6 // 0 /// AK298283 // GenBank // Homo sapiens cDNA FLJ60027 complete cds, moderately similar to F-box only protein 25. // chr1 // 100 // 100 // 6 // 6 // 0 /// AL137655 // GenBank // Homo sapiens mRNA; cDNA DKFZp434B2016 (from clone DKFZp434B2016). // chr1 // 100 // 100 // 6 // 6 // 0 /// BC032332 // GenBank // Homo sapiens protein-L-isoaspartate (D-aspartate) O-methyltransferase domain containing 2, mRNA (cDNA clone MGC:40288 IMAGE:5169056), complete cds. // chr1 // 100 // 100 // 6 // 6 // 0 /// BC118988 // GenBank // Homo sapiens chromosome 20 open reading frame 69, mRNA (cDNA clone MGC:141807 IMAGE:40035995), complete cds. // chr1 // 100 // 100 // 6 // 6 // 0 /// BC122537 // GenBank // Homo sapiens chromosome 20 open reading frame 69, mRNA (cDNA clone MGC:141808 IMAGE:40035996), complete cds. // chr1 // 100 // 100 // 6 // 6 // 0 /// BC131690 // GenBank // Homo sapiens similar to bA476I15.3 (novel protein similar to septin), mRNA (cDNA clone IMAGE:40119684), partial cds. // chr1 // 100 // 100 // 6 // 6 // 0 /// NM_207366 // RefSeq // Homo sapiens septin 14 (SEPT14), mRNA. // chr1 // 50 // 100 // 3 // 6 // 0 /// ENST00000388975 // ENSEMBL // ensembl_havana_transcript:known chromosome:GRCh38:7:55793544:55862789:-1 gene:ENSG00000154997 gene_biotype:protein_coding transcript_biotype:protein_coding // chr1 // 50 // 100 // 3 // 6 // 0 /// ENST00000427373 // ENSEMBL // havana:known chromosome:GRCh38:Y:25378300:25394719:-1 gene:ENSG00000228786 gene_biotype:lincRNA transcript_biotype:lincRNA // chr1 // 67 // 100 // 4 // 6 // 0 /// ENST00000431796 // ENSEMBL // havana:known chromosome:GRCh38:2:242088693:242122405:1 gene:ENSG00000220804 gene_biotype:transcribed_unprocessed_pseudogene transcript_biotype:processed_transcript // chr1 // 60 // 83 // 3 // 5 // 0 /// ENST00000509776 // ENSEMBL // havana:known chromosome:GRCh38:Y:24278681:24291346:1 gene:ENSG00000248792 gene_biotype:unprocessed_pseudogene transcript_biotype:unprocessed_pseudogene // chr1 // 67 // 100 // 4 // 6 // 0 /// ENST00000570230 // ENSEMBL // havana:known chromosome:GRCh38:16:90157932:90178344:1 gene:ENSG00000260528 gene_biotype:processed_transcript transcript_biotype:processed_transcript // chr1 // 67 // 100 // 4 // 6 // 0 /// AK301928 // GenBank // Homo sapiens cDNA FLJ59065 complete cds, moderately similar to Septin-10. // chr1 // 50 // 100 // 3 // 6 // 0 /// ENST00000413839 // ENSEMBL // havana:known chromosome:GRCh38:7:45816557:45821064:1 gene:ENSG00000226838 gene_biotype:unprocessed_pseudogene transcript_biotype:unprocessed_pseudogene // chr1 // 83 // 100 // 5 // 6 // 0 /// ENST00000414688 // ENSEMBL // havana:known chromosome:GRCh38:1:711342:720200:-1 gene:ENSG00000230021 gene_biotype:lincRNA transcript_biotype:lincRNA // chr1 // 100 // 100 // 6 // 6 // 0 /// ENST00000419394 // ENSEMBL // havana:known chromosome:GRCh38:1:703685:720194:-1 gene:ENSG00000230021 gene_biotype:lincRNA transcript_biotype:lincRNA // chr1 // 100 // 100 // 6 // 6 // 0 /// ENST00000420830 // ENSEMBL // havana:known chromosome:GRCh38:1:243031272:243047869:-1 gene:ENSG00000231512 gene_biotype:lincRNA transcript_biotype:lincRNA // chr1 // 83 // 100 // 5 // 6 // 0 /// ENST00000428915 // ENSEMBL // havana:known chromosome:GRCh38:10:38453181:38466176:1 gene:ENSG00000203496 gene_biotype:lincRNA transcript_biotype:lincRNA // chr1 // 100 // 100 // 6 // 6 // 0 /// ENST00000439401 // ENSEMBL // havana:known chromosome:GRCh38:3:198228194:198228376:1 gene:ENSG00000226008 gene_biotype:unprocessed_pseudogene transcript_biotype:unprocessed_pseudogene // chr1 // 100 // 100 // 6 // 6 // 0 /// ENST00000440200 // ENSEMBL // havana:known chromosome:GRCh38:1:601436:720200:-1 gene:ENSG00000230021 gene_biotype:lincRNA transcript_biotype:lincRNA // chr1 // 100 // 100 // 6 // 6 // 0 /// ENST00000441245 // ENSEMBL // havana:known chromosome:GRCh38:1:701936:720150:-1 gene:ENSG00000230021 gene_biotype:lincRNA transcript_biotype:lincRNA // chr1 // 100 // 67 // 4 // 4 // 0 /// ENST00000445840 // ENSEMBL // havana:known chromosome:GRCh38:1:485032:485211:-1 gene:ENSG00000224813 gene_biotype:transcribed_unprocessed_pseudogene transcript_biotype:transcribed_unprocessed_pseudogene // chr1 // 100 // 100 // 6 // 6 // 0 /// ENST00000447954 // ENSEMBL // havana:known chromosome:GRCh38:1:720058:724550:-1 gene:ENSG00000230021 gene_biotype:lincRNA transcript_biotype:lincRNA // chr1 // 83 // 100 // 5 // 6 // 0 /// ENST00000450226 // ENSEMBL // havana:known chromosome:GRCh38:1:243038914:243047875:-1 gene:ENSG00000231512 gene_biotype:lincRNA transcript_biotype:lincRNA // chr1 // 83 // 100 // 5 // 6 // 0 /// ENST00000453405 // ENSEMBL // havana:known chromosome:GRCh38:2:242122287:242122469:1 gene:ENSG00000244528 gene_biotype:processed_pseudogene transcript_biotype:processed_pseudogene // chr1 // 100 // 100 // 6 // 6 // 0 /// ENST00000477740 // ENSEMBL // havana:known chromosome:GRCh38:1:92230:129217:-1 gene:ENSG00000238009 gene_biotype:lincRNA transcript_biotype:lincRNA // chr1 // 83 // 100 // 5 // 6 // 0 /// ENST00000508026 // ENSEMBL // havana:known chromosome:GRCh38:8:200385:200562:-1 gene:ENSG00000255464 gene_biotype:processed_pseudogene transcript_biotype:processed_pseudogene // chr1 // 100 // 100 // 6 // 6 // 0 /// ENST00000509192 // ENSEMBL // havana:known chromosome:GRCh38:5:181329241:181342213:1 gene:ENSG00000250765 gene_biotype:lincRNA transcript_biotype:lincRNA // chr1 // 100 // 100 // 6 // 6 // 0 /// ENST00000513445 // ENSEMBL // havana:known chromosome:GRCh38:4:118640673:118640858:1 gene:ENSG00000251155 gene_biotype:processed_pseudogene transcript_biotype:processed_pseudogene // chr1 // 83 // 100 // 5 // 6 // 0 /// ENST00000523795 // ENSEMBL // havana:known chromosome:GRCh38:8:192091:200563:-1 gene:ENSG00000250210 gene_biotype:protein_coding transcript_biotype:protein_coding // chr1 // 100 // 100 // 6 // 6 // 0 /// ENST00000529266 // ENSEMBL // havana:known chromosome:GRCh38:11:121279:125784:-1 gene:ENSG00000254468 gene_biotype:lincRNA transcript_biotype:lincRNA // chr1 // 100 // 100 // 6 // 6 // 0 /// ENST00000587432 // ENSEMBL // havana:known chromosome:GRCh38:19:191212:195696:-1 gene:ENSG00000267237 gene_biotype:transcribed_unprocessed_pseudogene transcript_biotype:processed_transcript // chr1 // 83 // 100 // 5 // 6 // 0 /// ENST00000610542 // ENSEMBL // ensembl:known chromosome:GRCh38:1:120725:133723:-1 gene:ENSG00000238009 gene_biotype:lincRNA transcript_biotype:lincRNA // chr1 // 83 // 100 // 5 // 6 // 0 /// ENST00000612088 // ENSEMBL // ensembl:known chromosome:GRCh38:10:38453181:38466176:1 gene:ENSG00000203496 gene_biotype:lincRNA transcript_biotype:lincRNA // chr1 // 100 // 100 // 6 // 6 // 0 /// ENST00000612214 // ENSEMBL // havana:known chromosome:GRCh38:19:186371:191429:-1 gene:ENSG00000267237 gene_biotype:transcribed_unprocessed_pseudogene transcript_biotype:processed_transcript // chr1 // 100 // 100 // 6 // 6 // 0 /// ENST00000613471 // ENSEMBL // ensembl:known chromosome:GRCh38:1:476738:489710:-1 gene:ENSG00000237094 gene_biotype:lincRNA transcript_biotype:lincRNA // chr1 // 100 // 100 // 6 // 6 // 0 /// ENST00000615295 // ENSEMBL // ensembl:known chromosome:GRCh38:5:181329241:181342213:1 gene:ENSG00000250765 gene_biotype:lincRNA transcript_biotype:lincRNA // chr1 // 100 // 100 // 6 // 6 // 0 /// ENST00000616585 // ENSEMBL // ensembl:known chromosome:GRCh38:1:711715:724707:-1 gene:ENSG00000230021 gene_biotype:lincRNA transcript_biotype:lincRNA // chr1 // 100 // 100 // 6 // 6 // 0 /// ENST00000618096 // ENSEMBL // havana:known chromosome:GRCh38:19:191178:191354:-1 gene:ENSG00000267237 gene_biotype:transcribed_unprocessed_pseudogene transcript_biotype:transcribed_unprocessed_pseudogene // chr1 // 100 // 100 // 6 // 6 // 0 /// ENST00000618222 // ENSEMBL // ensembl:known chromosome:GRCh38:6:131910:144885:-1 gene:ENSG00000170590 gene_biotype:lincRNA transcript_biotype:lincRNA // chr1 // 100 // 100 // 6 // 6 // 0 /// ENST00000622435 // ENSEMBL // havana:known chromosome:GRCh38:2:242088684:242159382:1 gene:ENSG00000220804 gene_biotype:transcribed_unprocessed_pseudogene transcript_biotype:processed_transcript // chr1 // 100 // 100 // 6 // 6 // 0 /// ENST00000622626 // ENSEMBL // ensembl:known chromosome:GRCh38:11:112967:125927:-1 gene:ENSG00000254468 gene_biotype:lincRNA transcript_biotype:lincRNA // chr1 // 100 // 100 // 6 // 6 // 0 /// GENSCAN00000007486 // ENSEMBL // cdna:genscan chromosome:GRCh38:2:242089132:242175655:1 transcript_biotype:protein_coding // chr1 // 100 // 100 // 6 // 6 // 0 /// GENSCAN00000023775 // ENSEMBL // cdna:genscan chromosome:GRCh38:7:45812479:45856081:1 transcript_biotype:protein_coding // chr1 // 100 // 100 // 6 // 6 // 0 /// GENSCAN00000045845 // ENSEMBL // cdna:genscan chromosome:GRCh38:8:166086:213433:-1 transcript_biotype:protein_coding // chr1 // 100 // 100 // 6 // 6 // 0 /// BC071667 // GenBank HTC // Homo sapiens cDNA clone IMAGE:4384656, **** WARNING: chimeric clone ****. // chr1 // 100 // 100 // 6 // 6 // 0 /// NONHSAT000053 // NONCODE // Non-coding transcript identified by NONCODE: Sense No Exonic // chr1 // 100 // 100 // 6 // 6 // 0 /// NONHSAT000055 // NONCODE // Non-coding transcript identified by NONCODE: Sense No Exonic // chr1 // 100 // 100 // 6 // 6 // 0 /// NONHSAT000063 // NONCODE // Non-coding transcript identified by NONCODE: Sense No Exonic // chr1 // 83 // 100 // 5 // 6 // 0 /// NONHSAT000064 // NONCODE // Non-coding transcript identified by NONCODE // chr1 // 100 // 100 // 6 // 6 // 0 /// NONHSAT000065 // NONCODE // Non-coding transcript identified by NONCODE: Sense No Exonic // chr1 // 100 // 100 // 6 // 6 // 0 /// NONHSAT000086 // NONCODE // Non-coding transcript identified by NONCODE: Sense No Exonic // chr1 // 100 // 100 // 6 // 6 // 0 /// NONHSAT000097 // NONCODE // Non-coding transcript identified by NONCODE: Sense No Exonic // chr1 // 100 // 67 // 4 // 4 // 0 /// NONHSAT000098 // NONCODE // Non-coding transcript identified by NONCODE: Sense No Exonic // chr1 // 83 // 100 // 5 // 6 // 0 /// NONHSAT010578 // NONCODE // Non-coding transcript identified by NONCODE: Linc // chr1 // 83 // 100 // 5 // 6 // 0 /// NONHSAT012829 // NONCODE // Non-coding transcript identified by NONCODE: Linc // chr1 // 100 // 100 // 6 // 6 // 0 /// NONHSAT017180 // NONCODE // Non-coding transcript identified by NONCODE: Linc // chr1 // 100 // 100 // 6 // 6 // 0 /// NONHSAT060112 // NONCODE // Non-coding transcript identified by NONCODE: Linc // chr1 // 100 // 100 // 6 // 6 // 0 /// NONHSAT078034 // NONCODE // Non-coding transcript identified by NONCODE: Linc // chr1 // 100 // 100 // 6 // 6 // 0 /// NONHSAT078035 // NONCODE // Non-coding transcript identified by NONCODE: Linc // chr1 // 100 // 100 // 6 // 6 // 0 /// NONHSAT078039 // NONCODE // Non-coding transcript identified by NONCODE // chr1 // 100 // 100 // 6 // 6 // 0 /// NONHSAT078040 // NONCODE // Non-coding transcript identified by NONCODE: Linc // chr1 // 100 // 100 // 6 // 6 // 0 /// NONHSAT078041 // NONCODE // Non-coding transcript identified by NONCODE: Linc // chr1 // 100 // 100 // 6 // 6 // 0 /// NONHSAT081035 // NONCODE // Non-coding transcript identified by NONCODE: Linc // chr1 // 100 // 100 // 6 // 6 // 0 /// NONHSAT081036 // NONCODE // Non-coding transcript identified by NONCODE: Linc // chr1 // 100 // 100 // 6 // 6 // 0 /// NONHSAT094494 // NONCODE // Non-coding transcript identified by NONCODE: Linc // chr1 // 100 // 100 // 6 // 6 // 0 /// NONHSAT094497 // NONCODE // Non-coding transcript identified by NONCODE // chr1 // 100 // 100 // 6 // 6 // 0 /// NONHSAT098010 // NONCODE // Non-coding transcript identified by NONCODE // chr1 // 83 // 100 // 5 // 6 // 0 /// NONHSAT105956 // NONCODE // Non-coding transcript identified by NONCODE: Linc // chr1 // 100 // 100 // 6 // 6 // 0 /// NONHSAT105968 // NONCODE // Non-coding transcript identified by NONCODE: Linc // chr1 // 100 // 100 // 6 // 6 // 0 /// NONHSAT120472 // NONCODE // Non-coding transcript identified by NONCODE: Linc // chr1 // 83 // 100 // 5 // 6 // 0 /// NONHSAT124571 // NONCODE // Non-coding transcript identified by NONCODE // chr1 // 100 // 100 // 6 // 6 // 0 /// TCONS_l2_00001800-XLOC_l2_001331 // Broad TUCP // linc-TP53BP2-4 chr1:-:224133091-224222680 // chr1 // 100 // 100 // 6 // 6 // 0 /// TCONS_l2_00001926-XLOC_l2_000004 // Broad TUCP // linc-OR4F16-1 chr1:+:329783-334271 // chr1 // 83 // 100 // 5 // 6 // 0 /// TCONS_l2_00001927-XLOC_l2_000004 // Broad TUCP // linc-OR4F16-1 chr1:+:334139-342806 // chr1 // 100 // 100 // 6 // 6 // 0 /// TCONS_l2_00002370-XLOC_l2_000720 // Broad TUCP // linc-ZNF692-5 chr1:-:92229-129217 // chr1 // 83 // 100 // 5 // 6 // 0 /// TCONS_l2_00002386-XLOC_l2_000726 // Broad TUCP // linc-OR4F29-1 chr1:-:637315-655530 // chr1 // 100 // 67 // 4 // 4 // 0 /// TCONS_l2_00002387-XLOC_l2_000726 // Broad TUCP // linc-OR4F29-1 chr1:-:639064-655574 // chr1 // 100 // 100 // 6 // 6 // 0 /// TCONS_l2_00002388-XLOC_l2_000726 // Broad TUCP // linc-OR4F29-1 chr1:-:646721-655580 // chr1 // 100 // 100 // 6 // 6 // 0 /// TCONS_l2_00002389-XLOC_l2_000726 // Broad TUCP // linc-OR4F29-1 chr1:-:655437-659930 // chr1 // 83 // 100 // 5 // 6 // 0 /// TCONS_l2_00002812-XLOC_l2_001398 // Broad TUCP // linc-PLD5-4 chr1:-:243194573-243211171 // chr1 // 83 // 100 // 5 // 6 // 0 /// TCONS_l2_00003949-XLOC_l2_001561 // Broad TUCP // linc-BMS1-9 chr10:+:38742108-38755311 // chr1 // 100 // 100 // 6 // 6 // 0 /// TCONS_l2_00003950-XLOC_l2_001561 // Broad TUCP // linc-BMS1-9 chr10:+:38742265-38764837 // chr1 // 100 // 100 // 6 // 6 // 0 /// TCONS_l2_00014349-XLOC_l2_007835 // Broad TUCP // linc-ORC6-14 chr2:+:243030831-243101574 // chr1 // 100 // 100 // 6 // 6 // 0 /// TCONS_l2_00014350-XLOC_l2_007835 // Broad TUCP // linc-ORC6-14 chr2:+:243030855-243102147 // chr1 // 100 // 100 // 6 // 6 // 0 /// TCONS_l2_00014351-XLOC_l2_007835 // Broad TUCP // linc-ORC6-14 chr2:+:243030868-243101569 // chr1 // 100 // 100 // 6 // 6 // 0 /// TCONS_l2_00014352-XLOC_l2_007835 // Broad TUCP // linc-ORC6-14 chr2:+:243030886-243064759 // chr1 // 100 // 100 // 6 // 6 // 0 /// TCONS_l2_00014354-XLOC_l2_007835 // Broad TUCP // linc-ORC6-14 chr2:+:243030931-243067562 // chr1 // 100 // 100 // 6 // 6 // 0 /// TCONS_l2_00014355-XLOC_l2_007835 // Broad TUCP // linc-ORC6-14 chr2:+:243030941-243102157 // chr1 // 100 // 100 // 6 // 6 // 0 /// TCONS_l2_00014357-XLOC_l2_007835 // Broad TUCP // linc-ORC6-14 chr2:+:243037045-243101538 // chr1 // 100 // 100 // 6 // 6 // 0 /// TCONS_l2_00014358-XLOC_l2_007835 // Broad TUCP // linc-ORC6-14 chr2:+:243058329-243064628 // chr1 // 100 // 100 // 6 // 6 // 0 /// TCONS_l2_00015637-XLOC_l2_007835 // Broad TUCP // linc-ORC6-14 chr2:+:243030783-243082789 // chr1 // 100 // 100 // 6 // 6 // 0 /// TCONS_l2_00015638-XLOC_l2_007835 // Broad TUCP // linc-ORC6-14 chr2:+:243030843-243065243 // chr1 // 100 // 100 // 6 // 6 // 0 /// TCONS_l2_00015639-XLOC_l2_007835 // Broad TUCP // linc-ORC6-14 chr2:+:243030843-243102469 // chr1 // 100 // 100 // 6 // 6 // 0 /// TCONS_l2_00015640-XLOC_l2_007835 // Broad TUCP // linc-ORC6-14 chr2:+:243030843-243102469 // chr1 // 100 // 100 // 6 // 6 // 0 /// TCONS_l2_00015641-XLOC_l2_007835 // Broad TUCP // linc-ORC6-14 chr2:+:243030843-243102469 // chr1 // 100 // 100 // 6 // 6 // 0 /// TCONS_l2_00015643-XLOC_l2_007835 // Broad TUCP // linc-ORC6-14 chr2:+:243064443-243081039 // chr1 // 100 // 100 // 6 // 6 // 0 /// TCONS_l2_00016828-XLOC_l2_008724 // Broad TUCP // linc-HNF1B-4 chr20:+:62921737-62934707 // chr1 // 100 // 100 // 6 // 6 // 0 /// TCONS_l2_00020055-XLOC_l2_010084 // Broad TUCP // linc-MCMBP-2 chr3:+:197937115-197955676 // chr1 // 100 // 100 // 6 // 6 // 0 /// TCONS_l2_00025304-XLOC_l2_012836 // Broad TUCP // linc-PDCD2-1 chr6:-:131909-144885 // chr1 // 100 // 100 // 6 // 6 // 0 /// TCONS_l2_00025849-XLOC_l2_013383 // Broad TUCP // linc-IGFBP1-1 chr7:+:45831387-45863181 // chr1 // 100 // 100 // 6 // 6 // 0 /// TCONS_l2_00025850-XLOC_l2_013383 // Broad TUCP // linc-IGFBP1-1 chr7:+:45836951-45863174 // chr1 // 100 // 100 // 6 // 6 // 0 /// ENST00000437691 // ENSEMBL // havana:known chromosome:GRCh38:1:243047737:243052252:-1 gene:ENSG00000231512 gene_biotype:lincRNA transcript_biotype:lincRNA // chr1 // 67 // 100 // 4 // 6 // 0 /// ENST00000447236 // ENSEMBL // havana:known chromosome:GRCh38:7:56360362:56360541:-1 gene:ENSG00000231299 gene_biotype:unprocessed_pseudogene transcript_biotype:unprocessed_pseudogene // chr1 // 50 // 100 // 3 // 6 // 0 /// ENST00000453576 // ENSEMBL // havana:known chromosome:GRCh38:1:129081:133566:-1 gene:ENSG00000238009 gene_biotype:lincRNA transcript_biotype:lincRNA // chr1 // 67 // 100 // 4 // 6 // 0 /// ENST00000611754 // ENSEMBL // ensembl:known chromosome:GRCh38:Y:25378671:25391610:-1 gene:ENSG00000228786 gene_biotype:lincRNA transcript_biotype:lincRNA // chr1 // 67 // 100 // 4 // 6 // 0 /// ENST00000617978 // ENSEMBL // havana:known chromosome:GRCh38:1:227980051:227980227:1 gene:ENSG00000274886 gene_biotype:unprocessed_pseudogene transcript_biotype:unprocessed_pseudogene // chr1 // 67 // 100 // 4 // 6 // 0 /// ENST00000621799 // ENSEMBL // ensembl:known chromosome:GRCh38:16:90173217:90186204:1 gene:ENSG00000260528 gene_biotype:processed_transcript transcript_biotype:processed_transcript // chr1 // 67 // 100 // 4 // 6 // 0 /// NONHSAT000022 // NONCODE // Non-coding transcript identified by NONCODE: Linc // chr1 // 67 // 100 // 4 // 6 // 0 /// NONHSAT010579 // NONCODE // Non-coding transcript identified by NONCODE: Linc // chr1 // 67 // 100 // 4 // 6 // 0 /// NONHSAT010580 // NONCODE // Non-coding transcript identified by NONCODE: Linc // chr1 // 67 // 100 // 4 // 6 // 0 /// NONHSAT120743 // NONCODE // Non-coding transcript identified by NONCODE // chr1 // 50 // 100 // 3 // 6 // 0 /// NONHSAT139746 // NONCODE // Non-coding transcript identified by NONCODE: Linc // chr1 // 67 // 100 // 4 // 6 // 0 /// NONHSAT144650 // NONCODE // Non-coding transcript identified by NONCODE: Linc // chr1 // 67 // 100 // 4 // 6 // 0 /// NONHSAT144655 // NONCODE // Non-coding transcript identified by NONCODE: Linc // chr1 // 67 // 100 // 4 // 6 // 0 /// TCONS_l2_00002372-XLOC_l2_000720 // Broad TUCP // linc-ZNF692-5 chr1:-:129080-133566 // chr1 // 67 // 100 // 4 // 6 // 0 /// TCONS_l2_00002813-XLOC_l2_001398 // Broad TUCP // linc-PLD5-4 chr1:-:243202215-243211826 // chr1 // 67 // 100 // 4 // 6 // 0 /// TCONS_l2_00002814-XLOC_l2_001398 // Broad TUCP // linc-PLD5-4 chr1:-:243211038-243215554 // chr1 // 67 // 100 // 4 // 6 // 0 /// TCONS_l2_00010440-XLOC_l2_005352 // Broad TUCP // linc-RBM11-5 chr16:+:90244124-90289080 // chr1 // 67 // 100 // 4 // 6 // 0 /// TCONS_l2_00031062-XLOC_l2_015962 // Broad TUCP // linc-BPY2B-4 chrY:-:27524446-27540866 // chr1 // 67 // 100 // 4 // 6 // 0', 'NM_001005221 // RefSeq // Homo sapiens olfactory receptor, family 4, subfamily F, member 29 (OR4F29), mRNA. // chr1 // 100 // 100 // 36 // 36 // 0 /// NM_001005224 // RefSeq // Homo sapiens olfactory receptor, family 4, subfamily F, member 3 (OR4F3), mRNA. // chr1 // 100 // 100 // 36 // 36 // 0 /// NM_001005277 // RefSeq // Homo sapiens olfactory receptor, family 4, subfamily F, member 16 (OR4F16), mRNA. // chr1 // 100 // 100 // 36 // 36 // 0 /// NM_001005504 // RefSeq // Homo sapiens olfactory receptor, family 4, subfamily F, member 21 (OR4F21), mRNA. // chr1 // 89 // 100 // 32 // 36 // 0 /// ENST00000320901 // ENSEMBL // ensembl_havana_transcript:known chromosome:GRCh38:8:166049:167043:-1 gene:ENSG00000176269 gene_biotype:protein_coding transcript_biotype:protein_coding // chr1 // 89 // 100 // 32 // 36 // 0 /// ENST00000332831 // ENSEMBL // ensembl_havana_transcript:known chromosome:GRCh38:1:685716:686654:-1 gene:ENSG00000273547 gene_biotype:protein_coding transcript_biotype:protein_coding gene:ENSG00000185097 // chr1 // 100 // 100 // 36 // 36 // 0 /// ENST00000402444 // ENSEMBL // havana:known chromosome:GRCh38:6:170639606:170640536:1 gene:ENSG00000217874 gene_biotype:unprocessed_pseudogene transcript_biotype:unprocessed_pseudogene // chr1 // 78 // 100 // 28 // 36 // 0 /// ENST00000405102 // ENSEMBL // havana:known chromosome:GRCh38:6:105919:106856:-1 gene:ENSG00000220212 gene_biotype:unprocessed_pseudogene transcript_biotype:unprocessed_pseudogene // chr1 // 81 // 100 // 29 // 36 // 0 /// ENST00000424047 // ENSEMBL // havana:known chromosome:GRCh38:11:86649:87586:-1 gene:ENSG00000224777 gene_biotype:unprocessed_pseudogene transcript_biotype:unprocessed_pseudogene // chr1 // 78 // 100 // 28 // 36 // 0 /// ENST00000426406 // ENSEMBL // ensembl_havana_transcript:known chromosome:GRCh38:1:450740:451678:-1 gene:ENSG00000278566 gene_biotype:protein_coding transcript_biotype:protein_coding gene:ENSG00000235249 // chr1 // 100 // 100 // 36 // 36 // 0 /// ENST00000456475 // ENSEMBL // ensembl_havana_transcript:known chromosome:GRCh38:5:181367268:181368262:1 gene:ENSG00000230178 gene_biotype:protein_coding transcript_biotype:protein_coding // chr1 // 100 // 100 // 36 // 36 // 0 /// ENST00000559128 // ENSEMBL // havana:known chromosome:GRCh38:15:101875964:101876901:1 gene:ENSG00000257109 gene_biotype:unprocessed_pseudogene transcript_biotype:unprocessed_pseudogene // chr1 // 83 // 100 // 30 // 36 // 0 /// BC137547 // GenBank // Homo sapiens olfactory receptor, family 4, subfamily F, member 3, mRNA (cDNA clone MGC:169170 IMAGE:9021547), complete cds. // chr1 // 100 // 100 // 36 // 36 // 0 /// BC137568 // GenBank // Homo sapiens olfactory receptor, family 4, subfamily F, member 3, mRNA (cDNA clone MGC:169191 IMAGE:9021568), complete cds. // chr1 // 100 // 100 // 36 // 36 // 0 /// ENST00000589943 // ENSEMBL // havana:known chromosome:GRCh38:19:156279:157215:-1 gene:ENSG00000266971 gene_biotype:unprocessed_pseudogene transcript_biotype:unprocessed_pseudogene // chr1 // 72 // 100 // 26 // 36 // 0 /// GENSCAN00000011446 // ENSEMBL // cdna:genscan chromosome:GRCh38:5:181367527:181368225:1 transcript_biotype:protein_coding // chr1 // 100 // 64 // 23 // 23 // 0 /// GENSCAN00000017675 // ENSEMBL // cdna:genscan chromosome:GRCh38:1:685716:686414:-1 transcript_biotype:protein_coding // chr1 // 100 // 64 // 23 // 23 // 0 /// GENSCAN00000017679 // ENSEMBL // cdna:genscan chromosome:GRCh38:1:450740:451438:-1 transcript_biotype:protein_coding // chr1 // 100 // 64 // 23 // 23 // 0 /// GENSCAN00000045845 // ENSEMBL // cdna:genscan chromosome:GRCh38:8:166086:213433:-1 transcript_biotype:protein_coding // chr1 // 87 // 83 // 26 // 30 // 0 /// NONHSAT051700 // NONCODE // Non-coding transcript identified by NONCODE: Linc // chr1 // 83 // 100 // 30 // 36 // 0 /// NONHSAT051701 // NONCODE // Non-coding transcript identified by NONCODE: Linc // chr1 // 83 // 100 // 30 // 36 // 0 /// NONHSAT105966 // NONCODE // Non-coding transcript identified by NONCODE: Linc // chr1 // 81 // 100 // 29 // 36 // 0 /// NONHSAT060109 // NONCODE // Non-coding transcript identified by NONCODE: Linc // chr1 // 72 // 100 // 26 // 36 // 0'], 'category': ['main', 'main', 'main', 'main', 'main']}\n", "\n", "Complete sample of a few rows:\n", " ID GB_LIST SPOT_ID seqname\n", "0 7896736 NaN chr1:53049-54936 chr1\n", "1 7896738 NaN chr1:63015-63887 chr1\n", "2 7896740 NM_001004195,NM_001005240,NM_001005484,BC136848,BC136867,BC136907,BC136908 chr1:69091-70008 chr1\n", "\n", "Checking for gene information in gene_assignment column:\n", "Sample gene_assignment text: ---...\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "Sample of extracted gene symbols:\n", " ID Gene\n", "0 7896736 []\n", "1 7896738 [OR4G2P, OR4G11P, OR4G1P]\n", "2 7896740 [OR4F4, OR4F17, OR4F5, BC136848, BC136867, BC1...\n", "3 7896742 [F-, AK298283, AL137655, BC032332, PCMTD2, L-,...\n", "4 7896744 [OR4F29, OR4F3, OR4F16, OR4F21, OR4F7P, OR4F1P...\n", "5 7896746 [MT-TM]\n", "6 7896748 [MT-TW]\n", "7 7896750 [MT-TD]\n", "8 7896752 [MT-TK]\n", "9 7896754 [AK026836, AK290103]\n", "\n", "Number of probes with no gene symbols extracted: 1206898 out of 1232025\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "Sample of probe ID to gene symbol mappings:\n", " ID Gene\n", "1 7896738 [OR4G2P, OR4G11P, OR4G1P]\n", "2 7896740 [OR4F4, OR4F17, OR4F5, BC136848, BC136867, BC1...\n", "3 7896742 [F-, AK298283, AL137655, BC032332, PCMTD2, L-,...\n", "4 7896744 [OR4F29, OR4F3, OR4F16, OR4F21, OR4F7P, OR4F1P...\n", "5 7896746 [MT-TM]\n", "6 7896748 [MT-TW]\n", "7 7896750 [MT-TD]\n", "8 7896752 [MT-TK]\n", "9 7896754 [AK026836, AK290103]\n", "10 7896756 [FAM87B, FAM87A, BC037297, BC118644]\n", "\n", "Total number of probe-to-gene mappings: 25127\n" ] } ], "source": [ "# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.\n", "soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)\n", "gene_annotation = get_gene_annotation(soft_file)\n", "\n", "# 2. Analyze the gene annotation dataframe to identify which columns contain the gene identifiers and gene symbols\n", "print(\"\\nGene annotation preview:\")\n", "print(f\"Columns in gene annotation: {gene_annotation.columns.tolist()}\")\n", "print(preview_df(gene_annotation, n=5))\n", "\n", "# Get a more complete view to understand the annotation structure\n", "print(\"\\nComplete sample of a few rows:\")\n", "print(gene_annotation.iloc[:3, :4].to_string()) # Show only first few columns for readability\n", "\n", "# Examine the gene_assignment column which contains gene information\n", "print(\"\\nChecking for gene information in gene_assignment column:\")\n", "sample_text = gene_annotation.loc[0, 'gene_assignment'] if 'gene_assignment' in gene_annotation.columns else \"Column not found\"\n", "print(f\"Sample gene_assignment text: {sample_text[:200]}...\") # Print first 200 chars\n", "\n", "# Extract gene symbols from gene_assignment column\n", "if 'gene_assignment' in gene_annotation.columns:\n", " # Create a new column with extracted gene symbols\n", " gene_annotation['Gene'] = gene_annotation['gene_assignment'].apply(extract_human_gene_symbols)\n", " \n", " # Display a sample of the extracted gene symbols\n", " print(\"\\nSample of extracted gene symbols:\")\n", " sample_df = gene_annotation[['ID', 'Gene']].head(10)\n", " print(sample_df)\n", " \n", " # Check if gene symbols were successfully extracted\n", " empty_genes = gene_annotation['Gene'].apply(lambda x: len(x) == 0).sum()\n", " print(f\"\\nNumber of probes with no gene symbols extracted: {empty_genes} out of {len(gene_annotation)}\")\n", " \n", " # Create the mapping using our new Gene column\n", " mapping_data = gene_annotation[['ID', 'Gene']].copy()\n", " mapping_data = mapping_data[mapping_data['Gene'].apply(len) > 0] # Filter out empty gene lists\n", " \n", " # Print sample of the mapping to confirm\n", " print(\"\\nSample of probe ID to gene symbol mappings:\")\n", " print(mapping_data.head(10))\n", " \n", " # Check the size of the mapping data\n", " print(f\"\\nTotal number of probe-to-gene mappings: {len(mapping_data)}\")\n", "else:\n", " print(\"\\nError: Could not find 'gene_assignment' column in the annotation data\")\n" ] }, { "cell_type": "markdown", "id": "2071ae29", "metadata": {}, "source": [ "### Step 6: Gene Identifier Mapping" ] }, { "cell_type": "code", "execution_count": 7, "id": "e1fecae4", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T04:10:22.650028Z", "iopub.status.busy": "2025-03-25T04:10:22.649895Z", "iopub.status.idle": "2025-03-25T04:10:27.142691Z", "shell.execute_reply": "2025-03-25T04:10:27.142150Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Gene expression data shape: (33297, 36)\n", "Sample of gene_data index (probe IDs): ['7892501', '7892502', '7892503', '7892504', '7892505']\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Total gene symbols extracted: 155269\n", "Probes with at least one gene symbol: 25127\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Gene data index type: \n", "Mapping ID column type: \n", "Number of matching IDs between gene data and mapping: 25127\n", "After mapping, gene expression data shape: (0, 36)\n", "First 10 gene symbols after mapping:\n", "[]\n", "\n", "Sample of gene expression data:\n", "No gene expression data available after mapping.\n", "Warning: No gene data was saved because mapping produced empty results.\n" ] } ], "source": [ "# Based on our analysis, we need to map the probe IDs from the gene expression matrix to gene symbols\n", "# using the gene_assignment column from the annotation data\n", "\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. Extract gene data from matrix file\n", "gene_data = get_genetic_data(matrix_file)\n", "print(f\"Gene expression data shape: {gene_data.shape}\")\n", "print(f\"Sample of gene_data index (probe IDs): {gene_data.index[:5].tolist()}\")\n", "\n", "# 3. Get gene annotation data\n", "gene_annotation = get_gene_annotation(soft_file)\n", "\n", "if 'gene_assignment' in gene_annotation.columns:\n", " # Extract gene symbols from gene_assignment column\n", " gene_annotation['Gene'] = gene_annotation['gene_assignment'].apply(extract_human_gene_symbols)\n", " \n", " # Print detailed mapping info\n", " symbol_counts = gene_annotation['Gene'].apply(len).sum()\n", " print(f\"Total gene symbols extracted: {symbol_counts}\")\n", " print(f\"Probes with at least one gene symbol: {(gene_annotation['Gene'].apply(len) > 0).sum()}\")\n", " \n", " # Create mapping dataframe\n", " mapping_df = gene_annotation[['ID', 'Gene']].copy()\n", " # Filter out empty gene lists while preserving ID as string type\n", " mapping_df = mapping_df[mapping_df['Gene'].apply(len) > 0]\n", " mapping_df['ID'] = mapping_df['ID'].astype(str)\n", " \n", " # Check that gene_data index and mapping_df ID column are compatible\n", " print(f\"Gene data index type: {type(gene_data.index[0])}\")\n", " print(f\"Mapping ID column type: {type(mapping_df['ID'].iloc[0])}\")\n", " \n", " # Check if there are matching IDs between the two dataframes\n", " gene_ids_set = set(gene_data.index.astype(str))\n", " mapping_ids_set = set(mapping_df['ID'])\n", " intersection = gene_ids_set.intersection(mapping_ids_set)\n", " print(f\"Number of matching IDs between gene data and mapping: {len(intersection)}\")\n", " \n", " if len(intersection) > 0:\n", " # Apply gene mapping to convert probe-level data to gene expression data\n", " gene_data = apply_gene_mapping(gene_data, mapping_df)\n", " \n", " print(f\"After mapping, gene expression data shape: {gene_data.shape}\")\n", " print(\"First 10 gene symbols after mapping:\")\n", " print(gene_data.index[:10].tolist() if len(gene_data.index) >= 10 else gene_data.index.tolist())\n", " \n", " # Preview a small portion of the gene expression data\n", " print(\"\\nSample of gene expression data:\")\n", " display_rows = min(5, len(gene_data))\n", " display_cols = min(3, len(gene_data.columns))\n", " if display_rows > 0:\n", " print(gene_data.iloc[:display_rows, :display_cols])\n", " else:\n", " print(\"No gene expression data available after mapping.\")\n", " \n", " # If we successfully mapped genes, normalize gene symbols\n", " if len(gene_data) > 0:\n", " # Optional: normalize gene symbols to handle different versions of the same gene\n", " try:\n", " gene_data = normalize_gene_symbols_in_index(gene_data)\n", " print(f\"\\nAfter normalizing gene symbols, shape: {gene_data.shape}\")\n", " print(f\"Sample of normalized gene symbols: {gene_data.index[:10].tolist() if len(gene_data.index) >= 10 else gene_data.index.tolist()}\")\n", " except Exception as e:\n", " print(f\"Warning: Could not normalize gene symbols: {str(e)}\")\n", " else:\n", " print(\"Error: No matching IDs between gene data and mapping data.\")\n", " is_gene_available = False\n", "else:\n", " print(\"Error: Could not find gene_assignment column for mapping\")\n", " is_gene_available = False\n", "\n", "# Save gene data if we have a valid result\n", "if 'gene_data' in locals() and len(gene_data) > 0:\n", " # Create directory if it doesn't exist\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\"Saved gene expression data to {out_gene_data_file}\")\n", "else:\n", " print(\"Warning: No gene data was saved because mapping produced empty results.\")\n", " is_gene_available = False\n" ] }, { "cell_type": "markdown", "id": "91797091", "metadata": {}, "source": [ "### Step 7: Data Normalization and Linking" ] }, { "cell_type": "code", "execution_count": 8, "id": "cd31151c", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T04:10:27.144631Z", "iopub.status.busy": "2025-03-25T04:10:27.144507Z", "iopub.status.idle": "2025-03-25T04:10:27.290824Z", "shell.execute_reply": "2025-03-25T04:10:27.290211Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Gene data shape before normalization: (0, 36)\n", "Gene data shape after normalization: (0, 36)\n", "Normalized gene data saved to ../../output/preprocess/Thyroid_Cancer/gene_data/GSE138198.csv\n", "Sample conversion results:\n", "Sample trait value: 'gender: F' -> None\n", "Sample gender value: 'sample type: Hashimoto's thyroiditis (HT), papillary thyroid carcinoma (PTC) with HT in background, PTC without HT in background, micro PTC (mPTC), and three normal thyroid samples (TN).' -> None\n", "Extracted clinical data shape: (2, 36)\n", "Preview of clinical data (first 5 samples):\n", " GSM4101749 GSM4101750 GSM4101751 GSM4101752 GSM4101753\n", "Thyroid_Cancer NaN NaN NaN NaN NaN\n", "Gender NaN NaN NaN NaN NaN\n", "Clinical data saved to ../../output/preprocess/Thyroid_Cancer/clinical_data/GSE138198.csv\n", "Gene data columns (first 5): ['GSM4101749', 'GSM4101750', 'GSM4101751', 'GSM4101752', 'GSM4101753']\n", "Clinical data columns (first 5): ['GSM4101749', 'GSM4101750', 'GSM4101751', 'GSM4101752', 'GSM4101753']\n", "Found 36 common samples between gene and clinical data\n", "Initial linked data shape: (36, 2)\n", "Preview of linked data (first 5 rows, first 5 columns):\n", " Thyroid_Cancer Gender\n", "GSM4101749 NaN NaN\n", "GSM4101750 NaN NaN\n", "GSM4101751 NaN NaN\n", "GSM4101752 NaN NaN\n", "GSM4101753 NaN NaN\n", "Linked data shape after handling missing values: (0, 1)\n", "After handling missing values, no samples remain.\n", "Abnormality detected in the cohort: GSE138198. Preprocessing failed.\n" ] } ], "source": [ "# 1. Normalize gene symbols in the gene expression data\n", "try:\n", " # Make sure the directory exists\n", " os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)\n", " \n", " # Use the gene_data variable from the previous step (don't try to load it from file)\n", " print(f\"Gene data shape before normalization: {gene_data.shape}\")\n", " \n", " # Apply normalization to gene symbols\n", " normalized_gene_data = normalize_gene_symbols_in_index(gene_data)\n", " print(f\"Gene data shape after normalization: {normalized_gene_data.shape}\")\n", " \n", " # Save the normalized gene data\n", " normalized_gene_data.to_csv(out_gene_data_file)\n", " print(f\"Normalized gene data saved to {out_gene_data_file}\")\n", " \n", " # Use the normalized data for further processing\n", " gene_data = normalized_gene_data\n", " is_gene_available = True\n", "except Exception as e:\n", " print(f\"Error normalizing gene data: {e}\")\n", " is_gene_available = False\n", "\n", "# 2. Load clinical data with correct row indices\n", "try:\n", " # Load the clinical data from file\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", " # Debug the trait and gender conversion functions\n", " print(\"Sample conversion results:\")\n", " sample_trait = list(clinical_data.iloc[0])[1] # Get a sample from trait row\n", " sample_gender = list(clinical_data.iloc[1])[1] # Get a sample from gender row\n", " print(f\"Sample trait value: '{sample_trait}' -> {convert_trait(sample_trait)}\")\n", " print(f\"Sample gender value: '{sample_gender}' -> {convert_gender(sample_gender)}\")\n", " \n", " # Extract clinical features with CORRECT indices from Step 2\n", " clinical_features = geo_select_clinical_features(\n", " clinical_df=clinical_data,\n", " trait=trait,\n", " trait_row=0, # Correct trait row from Step 2\n", " convert_trait=convert_trait,\n", " gender_row=1, # Correct gender row from Step 2\n", " convert_gender=convert_gender,\n", " age_row=None, # Age row is None as per Step 2\n", " convert_age=None\n", " )\n", " \n", " print(f\"Extracted clinical data shape: {clinical_features.shape}\")\n", " print(\"Preview of clinical data (first 5 samples):\")\n", " print(clinical_features.iloc[:, :5])\n", " \n", " # Save the properly extracted clinical data\n", " os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)\n", " clinical_features.to_csv(out_clinical_data_file)\n", " print(f\"Clinical data saved to {out_clinical_data_file}\")\n", " \n", " is_trait_available = True\n", "except Exception as e:\n", " print(f\"Error extracting clinical data: {e}\")\n", " is_trait_available = False\n", "\n", "# 3. Link clinical and genetic data if both are available\n", "if is_trait_available and is_gene_available:\n", " try:\n", " # Debug the column names to ensure they match\n", " print(f\"Gene data columns (first 5): {gene_data.columns[:5].tolist()}\")\n", " print(f\"Clinical data columns (first 5): {clinical_features.columns[:5].tolist()}\")\n", " \n", " # Check for common sample IDs\n", " common_samples = set(gene_data.columns).intersection(clinical_features.columns)\n", " print(f\"Found {len(common_samples)} common samples between gene and clinical data\")\n", " \n", " if len(common_samples) > 0:\n", " # Link the clinical and genetic data\n", " linked_data = geo_link_clinical_genetic_data(clinical_features, gene_data)\n", " print(f\"Initial linked data shape: {linked_data.shape}\")\n", " \n", " # Debug the trait values before handling missing values\n", " print(\"Preview of linked data (first 5 rows, first 5 columns):\")\n", " print(linked_data.iloc[:5, :5])\n", " \n", " # Handle missing values\n", " linked_data = handle_missing_values(linked_data, trait)\n", " print(f\"Linked data shape after handling missing values: {linked_data.shape}\")\n", " \n", " if linked_data.shape[0] > 0:\n", " # Check for bias in trait and demographic features\n", " is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)\n", " \n", " # Validate the data quality and save cohort info\n", " note = \"Dataset contains gene expression data from thyroid cancer samples with tumor type information.\"\n", " is_usable = validate_and_save_cohort_info(\n", " is_final=True,\n", " cohort=cohort,\n", " info_path=json_path,\n", " is_gene_available=is_gene_available,\n", " is_trait_available=is_trait_available,\n", " is_biased=is_biased,\n", " df=linked_data,\n", " note=note\n", " )\n", " \n", " # Save the linked data if it's usable\n", " if is_usable:\n", " os.makedirs(os.path.dirname(out_data_file), exist_ok=True)\n", " linked_data.to_csv(out_data_file)\n", " print(f\"Linked data saved to {out_data_file}\")\n", " else:\n", " print(\"Data not usable for the trait study - not saving final linked data.\")\n", " else:\n", " print(\"After handling missing values, no samples remain.\")\n", " validate_and_save_cohort_info(\n", " is_final=True,\n", " cohort=cohort,\n", " info_path=json_path,\n", " is_gene_available=is_gene_available,\n", " is_trait_available=is_trait_available,\n", " is_biased=True,\n", " df=pd.DataFrame(),\n", " note=\"No valid samples after handling missing values.\"\n", " )\n", " else:\n", " print(\"No common samples found between gene expression and clinical data.\")\n", " validate_and_save_cohort_info(\n", " is_final=True,\n", " cohort=cohort,\n", " info_path=json_path,\n", " is_gene_available=is_gene_available,\n", " is_trait_available=is_trait_available,\n", " is_biased=True,\n", " df=pd.DataFrame(),\n", " note=\"No common samples between gene expression and clinical data.\"\n", " )\n", " except Exception as e:\n", " print(f\"Error linking or processing data: {e}\")\n", " validate_and_save_cohort_info(\n", " is_final=True,\n", " cohort=cohort,\n", " info_path=json_path,\n", " is_gene_available=is_gene_available,\n", " is_trait_available=is_trait_available,\n", " is_biased=True, # Assume biased if there's an error\n", " df=pd.DataFrame(), # Empty dataframe for metadata\n", " note=f\"Error in data processing: {str(e)}\"\n", " )\n", "else:\n", " # We can't proceed with linking if either trait or gene data is missing\n", " print(\"Cannot proceed with data linking due to missing trait or gene data.\")\n", " validate_and_save_cohort_info(\n", " is_final=True,\n", " cohort=cohort,\n", " info_path=json_path,\n", " is_gene_available=is_gene_available,\n", " is_trait_available=is_trait_available,\n", " is_biased=True, # Data is unusable if we're missing components\n", " df=pd.DataFrame(), # Empty dataframe for metadata\n", " note=\"Missing essential data components for linking (trait data or gene expression data).\"\n", " )" ] } ], "metadata": { "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.10.16" } }, "nbformat": 4, "nbformat_minor": 5 }