{ "cells": [ { "cell_type": "code", "execution_count": 1, "id": "93e2cd0e", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T05:21:56.424114Z", "iopub.status.busy": "2025-03-25T05:21:56.423490Z", "iopub.status.idle": "2025-03-25T05:21:56.624005Z", "shell.execute_reply": "2025-03-25T05:21:56.623554Z" } }, "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 = \"Glioblastoma\"\n", "cohort = \"GSE175700\"\n", "\n", "# Input paths\n", "in_trait_dir = \"../../input/GEO/Glioblastoma\"\n", "in_cohort_dir = \"../../input/GEO/Glioblastoma/GSE175700\"\n", "\n", "# Output paths\n", "out_data_file = \"../../output/preprocess/Glioblastoma/GSE175700.csv\"\n", "out_gene_data_file = \"../../output/preprocess/Glioblastoma/gene_data/GSE175700.csv\"\n", "out_clinical_data_file = \"../../output/preprocess/Glioblastoma/clinical_data/GSE175700.csv\"\n", "json_path = \"../../output/preprocess/Glioblastoma/cohort_info.json\"\n" ] }, { "cell_type": "markdown", "id": "d7e6a5c0", "metadata": {}, "source": [ "### Step 1: Initial Data Loading" ] }, { "cell_type": "code", "execution_count": 2, "id": "745a18a1", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T05:21:56.625505Z", "iopub.status.busy": "2025-03-25T05:21:56.625350Z", "iopub.status.idle": "2025-03-25T05:21:56.924441Z", "shell.execute_reply": "2025-03-25T05:21:56.924023Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Background Information:\n", "!Series_title\t\"Identification of indoleamine 2, 3-dioxygenase 1 (IDO1) regulated genes in human glioblastoma cell line U87\"\n", "!Series_summary\t\"Transcriptome analysis of U87 cells under different treatments to identify IDO1-regulated genes\"\n", "!Series_summary\t\"Indoleamine 2, 3-dioxygenase 1 (IDO1) is a tryptophan (Trp) catabolic enzyme that converts Trp into downstream kynurinine (Kyn). Many studies have indicated that IDO1 is a critical suppressive immune checkpoint molecule invovled in various types of cancer. Canonically, the underlying mechanism of IDO1 immunosuppressive role is related with its enzyme activity, that is the depletion of Trp and accumulation of Kyn lead to increased tumor infiltrating suppressive regulatory T cells. Recent studies, however, challenged this hypothesis and imply that tumor cell-derived IDO1 can mediate immunosuppression independent of its enzyme activity. In this study, we aim to identify genes that are regulated by IDO1 in human glioblastoma cells, a gene expression regulatory function of IDO1 that is indepent of its enzyme activity.\"\n", "!Series_overall_design\t\"U87 cells were either non-treated or treated with 20 nM human IDO1-specific siRNA for 16-18 hours, followed by human IFN-g (100 ng/ml) treatment for another 24 hours. Human IDO1 overexpressing U87 (O/E) cells were either non-treated or treated with 20 nM human IDO1-siRNA for 24 hours. At the end of experiment, total RNAs were extracted from the following 6 groups: 1) U87 NT; 2) U87 + IFNg; 3) U87 + siRNA; 4) U87 + siRNA + IFNg; 5) IDO1-O/E U87 NT; 6) IDO1-O/E U87 + siRNA and subject to microarray analysis. Each treatment group has two replicates. Experiment was repeated 3 times. Totally 36 samples were analyzed.\"\n", "Sample Characteristics Dictionary:\n", "{0: ['tissue: brain'], 1: ['Sex: male']}\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": "d33bb0c5", "metadata": {}, "source": [ "### Step 2: Dataset Analysis and Clinical Feature Extraction" ] }, { "cell_type": "code", "execution_count": 3, "id": "c7109983", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T05:21:56.925835Z", "iopub.status.busy": "2025-03-25T05:21:56.925720Z", "iopub.status.idle": "2025-03-25T05:21:56.931200Z", "shell.execute_reply": "2025-03-25T05:21:56.930842Z" } }, "outputs": [], "source": [ "# 1. Gene Expression Data Availability\n", "# Based on the background information, this is a microarray study of gene expression\n", "# in U87 glioblastoma cells under different treatment conditions\n", "is_gene_available = True\n", "\n", "# 2. Variable Availability and Data Type Conversion\n", "# Looking at the sample characteristics dictionary\n", "\n", "# 2.1 Data Availability\n", "# From the sample characteristics dictionary and background information:\n", "# For trait (Glioblastoma): The dataset consists of U87 glioblastoma cells\n", "# Everyone in the dataset has glioblastoma (cell line), so trait is constant\n", "trait_row = None # Trait data is not useful for association study since it's constant\n", "\n", "# For age: No age information provided\n", "age_row = None\n", "\n", "# For gender: There's 'Sex: male' at index 1\n", "gender_row = 1\n", "\n", "# 2.2 Data Type Conversion functions\n", "def convert_trait(value):\n", " # Not used since trait_row is None, but defining for completeness\n", " if value and \":\" in value:\n", " trait_value = value.split(\":\", 1)[1].strip().lower()\n", " if \"glioblastoma\" in trait_value:\n", " return 1\n", " else:\n", " return 0\n", " return None\n", "\n", "def convert_age(value):\n", " # Not used since age_row is None, but defining for completeness\n", " if value and \":\" in value:\n", " age_value = value.split(\":\", 1)[1].strip()\n", " try:\n", " return float(age_value)\n", " except ValueError:\n", " return None\n", " return None\n", "\n", "def convert_gender(value):\n", " if value and \":\" in value:\n", " gender_value = value.split(\":\", 1)[1].strip().lower()\n", " if \"female\" in gender_value:\n", " return 0\n", " elif \"male\" in gender_value:\n", " return 1\n", " else:\n", " return None\n", " return None\n", "\n", "# 3. Save Metadata\n", "# Determine trait data availability (if trait_row is not None)\n", "is_trait_available = trait_row is not None\n", "\n", "# Use the validate_and_save_cohort_info function for initial filtering\n", "validate_and_save_cohort_info(\n", " is_final=False,\n", " cohort=cohort,\n", " info_path=json_path,\n", " is_gene_available=is_gene_available,\n", " is_trait_available=is_trait_available\n", ")\n", "\n", "# 4. Clinical Feature Extraction (if trait_row is not None)\n", "# In this case, trait_row is None, so we skip this step\n", "# But let's still include the code for completeness with a condition\n", "\n", "if trait_row is not None:\n", " # Define the sample characteristics dictionary\n", " sample_characteristics = {0: ['tissue: brain'], 1: ['Sex: male']}\n", " \n", " # Define the dataframe for clinical data\n", " clinical_data = pd.DataFrame(list(sample_characteristics.values()), \n", " index=sample_characteristics.keys(),\n", " columns=[\"characteristics\"])\n", " \n", " # Extract clinical features\n", " selected_clinical_df = geo_select_clinical_features(\n", " clinical_df=clinical_data,\n", " trait=trait,\n", " trait_row=trait_row,\n", " convert_trait=convert_trait,\n", " age_row=age_row,\n", " convert_age=convert_age,\n", " gender_row=gender_row,\n", " convert_gender=convert_gender\n", " )\n", " \n", " # Preview the output\n", " preview = preview_df(selected_clinical_df)\n", " print(\"Clinical data preview:\", preview)\n", " \n", " # Save the clinical data\n", " os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)\n", " selected_clinical_df.to_csv(out_clinical_data_file)\n" ] }, { "cell_type": "markdown", "id": "954c15db", "metadata": {}, "source": [ "### Step 3: Gene Data Extraction" ] }, { "cell_type": "code", "execution_count": 4, "id": "64efdc12", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T05:21:56.932328Z", "iopub.status.busy": "2025-03-25T05:21:56.932220Z", "iopub.status.idle": "2025-03-25T05:21:57.344446Z", "shell.execute_reply": "2025-03-25T05:21:57.343972Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Found data marker at line 69\n", "Header line: \"ID_REF\"\t\"GSM5344433\"\t\"GSM5344434\"\t\"GSM5344435\"\t\"GSM5344436\"\t\"GSM5344437\"\t\"GSM5344438\"\t\"GSM5344439\"\t\"GSM5344440\"\t\"GSM5344441\"\t\"GSM5344442\"\t\"GSM5344443\"\t\"GSM5344444\"\t\"GSM5344445\"\t\"GSM5344446\"\t\"GSM5344447\"\t\"GSM5344448\"\t\"GSM5344449\"\t\"GSM5344450\"\t\"GSM5344451\"\t\"GSM5344452\"\t\"GSM5344453\"\t\"GSM5344454\"\t\"GSM5344455\"\t\"GSM5344456\"\t\"GSM5344457\"\t\"GSM5344458\"\t\"GSM5344459\"\t\"GSM5344460\"\t\"GSM5344461\"\t\"GSM5344462\"\t\"GSM5344463\"\t\"GSM5344464\"\t\"GSM5344465\"\t\"GSM5344466\"\t\"GSM5344467\"\t\"GSM5344468\"\n", "First data line: \"AFFX-BkGr-GC03_st\"\t8.7173\t9.07515\t8.58114\t7.23554\t8.72152\t8.81188\t9.45325\t8.28566\t10.5923\t8.7657\t8.08258\t8.94281\t9.22912\t9.4206\t10.2476\t9.18288\t8.11761\t8.5086\t7.88719\t9.10813\t8.64127\t9.05306\t8.84052\t7.82312\t9.88867\t9.80206\t10.9257\t9.94282\t9.51898\t8.61312\t9.44908\t9.06246\t8.82998\t9.3153\t9.54165\t9.37893\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Index(['AFFX-BkGr-GC03_st', 'AFFX-BkGr-GC04_st', 'AFFX-BkGr-GC05_st',\n", " 'AFFX-BkGr-GC06_st', 'AFFX-BkGr-GC07_st', 'AFFX-BkGr-GC08_st',\n", " 'AFFX-BkGr-GC09_st', 'AFFX-BkGr-GC10_st', 'AFFX-BkGr-GC11_st',\n", " 'AFFX-BkGr-GC12_st', 'AFFX-BkGr-GC13_st', 'AFFX-BkGr-GC14_st',\n", " 'AFFX-BkGr-GC15_st', 'AFFX-BkGr-GC16_st', 'AFFX-BkGr-GC17_st',\n", " 'AFFX-BkGr-GC18_st', 'AFFX-BkGr-GC19_st', 'AFFX-BkGr-GC20_st',\n", " 'AFFX-BkGr-GC21_st', 'AFFX-BkGr-GC22_st'],\n", " dtype='object', name='ID')\n" ] } ], "source": [ "# 1. Get the file paths for the SOFT file and matrix file\n", "soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)\n", "\n", "# 2. First, let's examine the structure of the matrix file to understand its format\n", "import gzip\n", "\n", "# Peek at the first few lines of the file to understand its structure\n", "with gzip.open(matrix_file, 'rt') as file:\n", " # Read first 100 lines to find the header structure\n", " for i, line in enumerate(file):\n", " if '!series_matrix_table_begin' in line:\n", " print(f\"Found data marker at line {i}\")\n", " # Read the next line which should be the header\n", " header_line = next(file)\n", " print(f\"Header line: {header_line.strip()}\")\n", " # And the first data line\n", " first_data_line = next(file)\n", " print(f\"First data line: {first_data_line.strip()}\")\n", " break\n", " if i > 100: # Limit search to first 100 lines\n", " print(\"Matrix table marker not found in first 100 lines\")\n", " break\n", "\n", "# 3. Now try to get the genetic data with better error handling\n", "try:\n", " gene_data = get_genetic_data(matrix_file)\n", " print(gene_data.index[:20])\n", "except KeyError as e:\n", " print(f\"KeyError: {e}\")\n", " \n", " # Alternative approach: manually extract the data\n", " print(\"\\nTrying alternative approach to read the gene data:\")\n", " with gzip.open(matrix_file, 'rt') as file:\n", " # Find the start of the data\n", " for line in file:\n", " if '!series_matrix_table_begin' in line:\n", " break\n", " \n", " # Read the headers and data\n", " import pandas as pd\n", " df = pd.read_csv(file, sep='\\t', index_col=0)\n", " print(f\"Column names: {df.columns[:5]}\")\n", " print(f\"First 20 row IDs: {df.index[:20]}\")\n", " gene_data = df\n" ] }, { "cell_type": "markdown", "id": "3267a01d", "metadata": {}, "source": [ "### Step 4: Gene Identifier Review" ] }, { "cell_type": "code", "execution_count": 5, "id": "b786db22", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T05:21:57.345903Z", "iopub.status.busy": "2025-03-25T05:21:57.345785Z", "iopub.status.idle": "2025-03-25T05:21:57.347833Z", "shell.execute_reply": "2025-03-25T05:21:57.347487Z" } }, "outputs": [], "source": [ "# Looking at the gene identifiers in the gene expression data\n", "# The identifiers like 'AFFX-BkGr-GC03_st' are Affymetrix probe IDs, not human gene symbols\n", "# These are microarray probe identifiers that need to be mapped to gene symbols\n", "# The \"_st\" suffix indicates these are from an Affymetrix GeneChip ST (Sense Target) array\n", "\n", "requires_gene_mapping = True\n" ] }, { "cell_type": "markdown", "id": "6f987d2b", "metadata": {}, "source": [ "### Step 5: Gene Annotation" ] }, { "cell_type": "code", "execution_count": 6, "id": "a3c2a092", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T05:21:57.348934Z", "iopub.status.busy": "2025-03-25T05:21:57.348828Z", "iopub.status.idle": "2025-03-25T05:22:08.393027Z", "shell.execute_reply": "2025-03-25T05:22:08.392540Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Examining SOFT file structure:\n", "Line 0: ^DATABASE = GeoMiame\n", "Line 1: !Database_name = Gene Expression Omnibus (GEO)\n", "Line 2: !Database_institute = NCBI NLM NIH\n", "Line 3: !Database_web_link = http://www.ncbi.nlm.nih.gov/geo\n", "Line 4: !Database_email = geo@ncbi.nlm.nih.gov\n", "Line 5: ^SERIES = GSE175700\n", "Line 6: !Series_title = Identification of indoleamine 2, 3-dioxygenase 1 (IDO1) regulated genes in human glioblastoma cell line U87\n", "Line 7: !Series_geo_accession = GSE175700\n", "Line 8: !Series_status = Public on Dec 10 2021\n", "Line 9: !Series_submission_date = May 27 2021\n", "Line 10: !Series_last_update_date = Dec 11 2021\n", "Line 11: !Series_pubmed_id = 34479957\n", "Line 12: !Series_summary = Transcriptome analysis of U87 cells under different treatments to identify IDO1-regulated genes\n", "Line 13: !Series_summary = Indoleamine 2, 3-dioxygenase 1 (IDO1) is a tryptophan (Trp) catabolic enzyme that converts Trp into downstream kynurinine (Kyn). Many studies have indicated that IDO1 is a critical suppressive immune checkpoint molecule invovled in various types of cancer. Canonically, the underlying mechanism of IDO1 immunosuppressive role is related with its enzyme activity, that is the depletion of Trp and accumulation of Kyn lead to increased tumor infiltrating suppressive regulatory T cells. Recent studies, however, challenged this hypothesis and imply that tumor cell-derived IDO1 can mediate immunosuppression independent of its enzyme activity. In this study, we aim to identify genes that are regulated by IDO1 in human glioblastoma cells, a gene expression regulatory function of IDO1 that is indepent of its enzyme activity.\n", "Line 14: !Series_overall_design = U87 cells were either non-treated or treated with 20 nM human IDO1-specific siRNA for 16-18 hours, followed by human IFN-g (100 ng/ml) treatment for another 24 hours. Human IDO1 overexpressing U87 (O/E) cells were either non-treated or treated with 20 nM human IDO1-siRNA for 24 hours. At the end of experiment, total RNAs were extracted from the following 6 groups: 1) U87 NT; 2) U87 + IFNg; 3) U87 + siRNA; 4) U87 + siRNA + IFNg; 5) IDO1-O/E U87 NT; 6) IDO1-O/E U87 + siRNA and subject to microarray analysis. Each treatment group has two replicates. Experiment was repeated 3 times. Totally 36 samples were analyzed.\n", "Line 15: !Series_type = Expression profiling by array\n", "Line 16: !Series_contributor = Derek,,Wainwright\n", "Line 17: !Series_contributor = Matthew,,Genet\n", "Line 18: !Series_contributor = Brenda,,Nguyen\n", "Line 19: !Series_contributor = Lijie,,Zhai\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "Gene annotation preview:\n", "{'ID': ['TC0100006432.hg.1', 'TC0100006433.hg.1', 'TC0100006434.hg.1', 'TC0100006435.hg.1', 'TC0100006436.hg.1'], 'probeset_id': ['TC0100006432.hg.1', 'TC0100006433.hg.1', 'TC0100006434.hg.1', 'TC0100006435.hg.1', 'TC0100006436.hg.1'], 'seqname': ['chr1', 'chr1', 'chr1', 'chr1', 'chr1'], 'strand': ['+', '+', '+', '+', '+'], 'start': ['11869', '28046', '29554', '52473', '62948'], 'stop': ['14412', '29178', '31109', '53312', '63887'], 'total_probes': [10, 6, 10, 10, 10], 'gene_assignment': ['NR_046018 // DDX11L1 // DEAD/H (Asp-Glu-Ala-Asp/His) box helicase 11 like 1 // 1p36.33 // 100287102 /// OTTHUMT00000002844 // DDX11L1 // DEAD/H (Asp-Glu-Ala-Asp/His) box helicase 11 like 1 // 1p36.33 // 100287102 /// OTTHUMT00000362751 // DDX11L1 // DEAD/H (Asp-Glu-Ala-Asp/His) box helicase 11 like 1 // 1p36.33 // 100287102', 'spopoybu.aAug10-unspliced // spopoybu // Transcript Identified by AceView // --- // ---', 'NR_036267 // MIR1302-10 // microRNA 1302-10 // --- // 100422834 /// ENST00000607096 // MIR1302-2 // microRNA 1302-2 // --- // 100302278', 'OTTHUMT00000471235 // OR4G4P // olfactory receptor, family 4, subfamily G, member 4 pseudogene // 1p36.33 // 79504', 'ENST00000492842 // OR4G2P // olfactory receptor, family 4, subfamily G, member 2 pseudogene // 15q26 // 26680 /// OTTHUMT00000003224 // OR4G11P // olfactory receptor, family 4, subfamily G, member 11 pseudogene // 1p36.33 // 403263'], '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 // 0 // --- // 0 /// OTTHUMT00000002844 // Havana transcript // DEAD/H (Asp-Glu-Ala-Asp/His) box helicase 11 like 1[gene_biotype:transcribed_unprocessed_pseudogene transcript_biotype:transcribed_unprocessed_pseudogene] // chr1 // 100 // 100 // 0 // --- // 0 /// OTTHUMT00000362751 // Havana transcript // DEAD/H (Asp-Glu-Ala-Asp/His) box helicase 11 like 1[gene_biotype:transcribed_unprocessed_pseudogene transcript_biotype:processed_transcript] // chr1 // 100 // 100 // 0 // --- // 0 /// ENST00000450305 // ENSEMBL // DEAD/H (Asp-Glu-Ala-Asp/His) box helicase 11 like 1 [gene_biotype:transcribed_unprocessed_pseudogene transcript_biotype:transcribed_unprocessed_pseudogene] // chr1 // 100 // 100 // 0 // --- // 0 /// ENST00000456328 // ENSEMBL // DEAD/H (Asp-Glu-Ala-Asp/His) box helicase 11 like 1 [gene_biotype:transcribed_unprocessed_pseudogene transcript_biotype:processed_transcript] // chr1 // 100 // 100 // 0 // --- // 0 /// NONHSAT000001 // lncRNAWiki // Non-coding transcript identified by NONCODE // chr1 // 100 // 100 // 0 // --- // 0 /// NONHSAT000001 // NONCODE // Non-coding transcript identified by NONCODE: Linc // chr1 // 100 // 100 // 0 // --- // 0 /// NONHSAT000002 // lncRNAWiki // Non-coding transcript identified by NONCODE // chr1 // 100 // 100 // 0 // --- // 0 /// NONHSAT000002 // NONCODE // Non-coding transcript identified by NONCODE: Linc // chr1 // 100 // 100 // 0 // --- // 0 /// NONHSAT000003 // lncRNAWiki // Non-coding transcript identified by NONCODE // chr1 // 100 // 100 // 0 // --- // 0 /// NONHSAT000003 // NONCODE // Non-coding transcript identified by NONCODE: Linc // chr1 // 100 // 100 // 0 // --- // 0 /// NONHSAT000004 // lncRNAWiki // Non-coding transcript identified by NONCODE // chr1 // 100 // 100 // 0 // --- // 0 /// NONHSAT000004 // NONCODE // Non-coding transcript identified by NONCODE: Linc // chr1 // 100 // 100 // 0 // --- // 0', 'spopoybu.aAug10-unspliced // Ace View // Transcript Identified by AceView // chr1 // 100 // 100 // 0 // --- // 0', 'NR_036267 // RefSeq // Homo sapiens microRNA 1302-10 (MIR1302-10), microRNA. // chr1 // 100 // 100 // 0 // --- // 0 /// ENST00000607096 // ENSEMBL // microRNA 1302-2 [gene_biotype:miRNA transcript_biotype:miRNA] // chr1 // 100 // 100 // 0 // --- // 0 /// NR_036051_3 // RefSeq // Homo sapiens microRNA 1302-2 (MIR1302-2), microRNA. // chr1 // 100 // 100 // 0 // --- // 0 /// NR_036266_3 // RefSeq // Homo sapiens microRNA 1302-9 (MIR1302-9), microRNA. // chr1 // 100 // 100 // 0 // --- // 0 /// NR_036268_4 // RefSeq // Homo sapiens microRNA 1302-11 (MIR1302-11), microRNA. // chr1 // 100 // 100 // 0 // --- // 0 /// ENST00000469289 // ENSEMBL // havana:known chromosome:GRCh38:1:30267:31109:1 gene:ENSG00000243485 gene_biotype:lincRNA transcript_biotype:lincRNA // chr1 // 100 // 100 // 0 // --- // 0 /// ENST00000473358 // ENSEMBL // havana:known chromosome:GRCh38:1:29554:31097:1 gene:ENSG00000243485 gene_biotype:lincRNA transcript_biotype:lincRNA // chr1 // 100 // 100 // 0 // --- // 0 /// ENST00000469289.1 // lncRNAWiki // microRNA 1302-11 [Source:HGNC Symbol;Acc:HGNC:38246] // chr1 // 100 // 100 // 0 // --- // 0 /// ENST00000473358.1 // lncRNAWiki // microRNA 1302-11 [Source:HGNC Symbol;Acc:HGNC:38246] // chr1 // 100 // 100 // 0 // --- // 0 /// ENST00000607096.1 // lncRNAWiki // microRNA 1302-11 [Source:HGNC Symbol;Acc:38246] // chr1 // 100 // 100 // 0 // --- // 0 /// OTTHUMT00000002840 // Havana transcript // novel transcript // chr1 // 100 // 100 // 0 // --- // 0 /// OTTHUMT00000002841 // Havana transcript // novel transcript // chr1 // 100 // 100 // 0 // --- // 0 /// uc031tlb.1 // UCSC Genes // microRNA 1302-2 [Source:HGNC Symbol;Acc:HGNC:35294] // chr1 // 100 // 100 // 0 // --- // 0 /// uc057aty.1 // UCSC Genes // N/A // chr1 // 100 // 100 // 0 // --- // 0 /// uc057atz.1 // UCSC Genes // N/A // chr1 // 100 // 100 // 0 // --- // 0 /// HG491497.1:1..712:ncRNA // RNACentral // long non-coding RNA OTTHUMT00000002840.1 (RP11-34P13.3 gene // chr1 // 100 // 100 // 0 // --- // 0 /// HG491498.1:1..535:ncRNA // RNACentral // long non-coding RNA OTTHUMT00000002841.2 (RP11-34P13.3 gene // chr1 // 100 // 100 // 0 // --- // 0 /// LM610125.1:1..138:precursor_RNA // RNACentral // microRNA hsa-mir-1302-9 precursor // chr1 // 100 // 100 // 0 // --- // 0 /// NONHSAT000011 // NONCODE // Non-coding transcript identified by NONCODE: Linc // chr1 // 100 // 100 // 0 // --- // 0 /// NONHSAT000012 // NONCODE // Non-coding transcript identified by NONCODE: Linc // chr1 // 100 // 100 // 0 // --- // 0 /// NR_036051.1:1..138:precursor_RNA // RNACentral // microRNA hsa-mir-1302-9 precursor // chr1 // 100 // 100 // 0 // --- // 0', 'OTTHUMT00000471235 // Havana transcript // lfactory receptor, family 4, subfamily G, member 4 pseudogene[gene_biotype:unprocessed_pseudogene transcript_biotype:unprocessed_pseudogene] // chr1 // 100 // 100 // 0 // --- // 0 /// ENST00000606857 // ENSEMBL // olfactory receptor, family 4, subfamily G, member 4 pseudogene [gene_biotype:unprocessed_pseudogene transcript_biotype:unprocessed_pseudogene] // chr1 // 100 // 100 // 0 // --- // 0', 'ENST00000492842 // ENSEMBL // olfactory receptor, family 4, subfamily G, member 11 pseudogene [gene_biotype:unprocessed_pseudogene transcript_biotype:unprocessed_pseudogene] // chr1 // 100 // 100 // 0 // --- // 0 /// OTTHUMT00000003224 // Havana transcript // olfactory receptor, family 4, subfamily G, member 11 pseudogene[gene_biotype:unprocessed_pseudogene transcript_biotype:unprocessed_pseudogene] // chr1 // 100 // 100 // 0 // --- // 0 /// NONHSAT000016 // lncRNAWiki // Non-coding transcript identified by NONCODE // chr1 // 100 // 100 // 0 // --- // 0 /// NONHSAT000016 // NONCODE // Non-coding transcript identified by NONCODE: Linc // chr1 // 100 // 100 // 0 // --- // 0'], 'swissprot': ['NR_046018 // B7ZGX0 /// NR_046018 // B7ZGX2 /// NR_046018 // B7ZGX7 /// NR_046018 // B7ZGX8 /// OTTHUMT00000002844 // B7ZGX0 /// OTTHUMT00000002844 // B7ZGX2 /// OTTHUMT00000002844 // B7ZGX7 /// OTTHUMT00000002844 // B7ZGX8 /// OTTHUMT00000362751 // B7ZGX0 /// OTTHUMT00000362751 // B7ZGX2 /// OTTHUMT00000362751 // B7ZGX7 /// OTTHUMT00000362751 // B7ZGX8 /// ENST00000450305 // B7ZGX0 /// ENST00000450305 // B7ZGX2 /// ENST00000450305 // B7ZGX7 /// ENST00000450305 // B7ZGX8 /// ENST00000450305 // B4E2Z4 /// ENST00000450305 // B7ZGW9 /// ENST00000450305 // Q6ZU42 /// ENST00000450305 // B7ZGX3 /// ENST00000450305 // B5WYT6 /// ENST00000456328 // B7ZGX0 /// ENST00000456328 // B7ZGX2 /// ENST00000456328 // B7ZGX7 /// ENST00000456328 // B7ZGX8 /// ENST00000456328 // B4E2Z4 /// ENST00000456328 // B7ZGW9 /// ENST00000456328 // Q6ZU42 /// ENST00000456328 // B7ZGX3 /// ENST00000456328 // B5WYT6', '---', '---', '---', '---'], 'unigene': ['NR_046018 // Hs.714157 // testis| normal| adult /// OTTHUMT00000002844 // Hs.714157 // testis| normal| adult /// OTTHUMT00000362751 // Hs.714157 // testis| normal| adult /// ENST00000450305 // Hs.719844 // brain| testis| normal /// ENST00000450305 // Hs.714157 // testis| normal| adult /// ENST00000450305 // Hs.740212 // --- /// ENST00000450305 // 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 /// ENST00000456328 // Hs.719844 // brain| testis| normal /// ENST00000456328 // Hs.714157 // testis| normal| adult /// ENST00000456328 // Hs.740212 // --- /// ENST00000456328 // 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', '---', '---', '---', '---'], 'GO_biological_process': ['ENST00000450305 // GO:0006139 // nucleobase-containing compound metabolic process // inferred from electronic annotation /// ENST00000456328 // GO:0006139 // nucleobase-containing compound metabolic process // inferred from electronic annotation', '---', '---', '---', '---'], 'GO_cellular_component': ['---', '---', '---', '---', '---'], 'GO_molecular_function': ['ENST00000450305 // GO:0003676 // nucleic acid binding // inferred from electronic annotation /// ENST00000450305 // GO:0005524 // ATP binding // inferred from electronic annotation /// ENST00000450305 // GO:0008026 // ATP-dependent helicase activity // inferred from electronic annotation /// ENST00000450305 // GO:0016818 // hydrolase activity, acting on acid anhydrides, in phosphorus-containing anhydrides // inferred from electronic annotation /// ENST00000456328 // GO:0003676 // nucleic acid binding // inferred from electronic annotation /// ENST00000456328 // GO:0005524 // ATP binding // inferred from electronic annotation /// ENST00000456328 // GO:0008026 // ATP-dependent helicase activity // inferred from electronic annotation /// ENST00000456328 // GO:0016818 // hydrolase activity, acting on acid anhydrides, in phosphorus-containing anhydrides // inferred from electronic annotation', '---', '---', '---', '---'], 'pathway': ['---', '---', '---', '---', '---'], 'protein_domains': ['---', '---', '---', '---', '---'], 'category': ['main', 'main', 'main', 'main', 'main'], 'locus type': ['Multiple_Complex', 'Coding', 'Multiple_Complex', 'Pseudogene', 'Multiple_Complex'], 'SPOT_ID': ['NR_046018 // RefSeq', 'spopoybu.aAug10-unspliced // Ace View', 'NR_036267 // RefSeq', 'OTTHUMT00000471235 // Havana transcript', 'ENST00000492842 // ENSEMBL']}\n" ] } ], "source": [ "# 1. Let's first examine the structure of the SOFT file before trying to parse it\n", "import gzip\n", "\n", "# Look at the first few lines of the SOFT file to understand its structure\n", "print(\"Examining SOFT file structure:\")\n", "try:\n", " with gzip.open(soft_file, 'rt') as file:\n", " # Read first 20 lines to understand the file structure\n", " for i, line in enumerate(file):\n", " if i < 20:\n", " print(f\"Line {i}: {line.strip()}\")\n", " else:\n", " break\n", "except Exception as e:\n", " print(f\"Error reading SOFT file: {e}\")\n", "\n", "# 2. Now let's try a more robust approach to extract the gene annotation\n", "# Instead of using the library function which failed, we'll implement a custom approach\n", "try:\n", " # First, look for the platform section which contains gene annotation\n", " platform_data = []\n", " with gzip.open(soft_file, 'rt') as file:\n", " in_platform_section = False\n", " for line in file:\n", " if line.startswith('^PLATFORM'):\n", " in_platform_section = True\n", " continue\n", " if in_platform_section and line.startswith('!platform_table_begin'):\n", " # Next line should be the header\n", " header = next(file).strip()\n", " platform_data.append(header)\n", " # Read until the end of the platform table\n", " for table_line in file:\n", " if table_line.startswith('!platform_table_end'):\n", " break\n", " platform_data.append(table_line.strip())\n", " break\n", " \n", " # If we found platform data, convert it to a DataFrame\n", " if platform_data:\n", " import pandas as pd\n", " import io\n", " platform_text = '\\n'.join(platform_data)\n", " gene_annotation = pd.read_csv(io.StringIO(platform_text), delimiter='\\t', \n", " low_memory=False, on_bad_lines='skip')\n", " print(\"\\nGene annotation preview:\")\n", " print(preview_df(gene_annotation))\n", " else:\n", " print(\"Could not find platform table in SOFT file\")\n", " \n", " # Try an alternative approach - extract mapping from other sections\n", " with gzip.open(soft_file, 'rt') as file:\n", " for line in file:\n", " if 'ANNOTATION information' in line or 'annotation information' in line:\n", " print(f\"Found annotation information: {line.strip()}\")\n", " if line.startswith('!Platform_title') or line.startswith('!platform_title'):\n", " print(f\"Platform title: {line.strip()}\")\n", " \n", "except Exception as e:\n", " print(f\"Error processing gene annotation: {e}\")\n" ] }, { "cell_type": "markdown", "id": "b23fd413", "metadata": {}, "source": [ "### Step 6: Gene Identifier Mapping" ] }, { "cell_type": "code", "execution_count": 7, "id": "2038e9f9", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T05:22:08.394376Z", "iopub.status.busy": "2025-03-25T05:22:08.394266Z", "iopub.status.idle": "2025-03-25T05:22:10.426787Z", "shell.execute_reply": "2025-03-25T05:22:10.426302Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Gene expression data after mapping:\n", "Shape: (73312, 36)\n", "First 5 gene symbols: ['A-', 'A-52', 'A-E', 'A-I', 'A-II']\n", "Sample of gene expression values:\n", " GSM5344433 GSM5344434 GSM5344435 GSM5344436 GSM5344437\n", "Gene \n", "A- 33.056562 33.313759 33.639137 33.114868 33.792869\n", "A-52 19.138190 18.546140 19.292170 19.040045 19.265255\n", "A-E 0.769540 0.632636 0.748591 0.716990 0.893139\n", "A-I 3.273666 3.064109 3.105160 3.134560 3.060834\n", "A-II 1.306777 1.430533 1.328100 1.298530 1.352080\n", "\n", "After normalizing gene symbols:\n", "Shape: (34003, 36)\n", "First 5 gene symbols: ['A1BG', 'A1BG-AS1', 'A1CF', 'A2M', 'A2M-AS1']\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Gene expression data saved to ../../output/preprocess/Glioblastoma/gene_data/GSE175700.csv\n" ] } ], "source": [ "# 1. Identify which columns in gene_annotation contain probe IDs and gene symbols\n", "# From examining the gene_annotation dataframe:\n", "# 'ID' column contains the probe IDs that match the gene expression data\n", "# 'gene_assignment' column contains gene symbol information, but needs to be processed\n", "\n", "# 2. Create a gene mapping dataframe with probe IDs and gene symbols\n", "# First, let's extract the columns and rename gene_assignment to 'Gene' as required by apply_gene_mapping\n", "mapping_data = gene_annotation[['ID', 'gene_assignment']].copy()\n", "mapping_data = mapping_data.rename(columns={'gene_assignment': 'Gene'})\n", "\n", "# 3. Apply the mapping to convert probe-level measurements to gene expression\n", "# Use the apply_gene_mapping function, which will:\n", "# - Extract human gene symbols from the gene_assignment text\n", "# - Handle the many-to-many mapping between probes and genes\n", "# - Split probe values proportionally when a probe maps to multiple genes\n", "# - Sum all probe contributions for each gene\n", "gene_data = apply_gene_mapping(gene_data, mapping_data)\n", "\n", "# Add validation to check if the mapping was successful\n", "if gene_data.empty:\n", " print(\"Warning: No genes were successfully mapped. Check the gene annotation format.\")\n", "else:\n", " # Preview the results\n", " print(\"Gene expression data after mapping:\")\n", " print(f\"Shape: {gene_data.shape}\")\n", " print(f\"First 5 gene symbols: {list(gene_data.index[:5])}\")\n", " print(\"Sample of gene expression values:\")\n", " print(gene_data.iloc[:5, :5])\n", "\n", " # Normalize gene symbols in the index to handle synonyms and aggregate rows with same symbol\n", " gene_data = normalize_gene_symbols_in_index(gene_data)\n", " print(\"\\nAfter normalizing gene symbols:\")\n", " print(f\"Shape: {gene_data.shape}\")\n", " print(f\"First 5 gene symbols: {list(gene_data.index[:5])}\")\n", "\n", " # Save the 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\"Gene expression data saved to {out_gene_data_file}\")\n" ] }, { "cell_type": "markdown", "id": "d48796b6", "metadata": {}, "source": [ "### Step 7: Data Normalization and Linking" ] }, { "cell_type": "code", "execution_count": 8, "id": "632e63c4", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T05:22:10.428159Z", "iopub.status.busy": "2025-03-25T05:22:10.428035Z", "iopub.status.idle": "2025-03-25T05:22:24.117604Z", "shell.execute_reply": "2025-03-25T05:22:24.117197Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Loaded gene data shape: (34003, 36)\n", "Normalized gene data shape: (34003, 36)\n", "Sample gene symbols after normalization: ['A1BG', 'A1BG-AS1', 'A1CF', 'A2M', 'A2M-AS1', 'A2ML1', 'A2ML1-AS1', 'A2ML1-AS2', 'A2MP1', 'A3GALT2']\n", "\n", "Creating mock clinical data - this is a cell line experiment with no clinical variation\n", "Mock clinical data shape: (36, 1)\n", "Mock clinical data preview:\n", " Glioblastoma\n", "GSM5344433 1\n", "GSM5344434 1\n", "GSM5344435 1\n", "GSM5344436 1\n", "GSM5344437 1\n", "Linked data shape: (36, 34004)\n", "Linked data preview (first 5 rows, first 5 columns):\n", " Glioblastoma A1BG A1BG-AS1 A1CF A2M\n", "GSM5344433 1 3.306317 2.422265 0.951533 4.913935\n", "GSM5344434 1 3.350438 2.392850 0.988175 4.899650\n", "GSM5344435 1 3.103491 2.201615 0.952673 4.918110\n", "GSM5344436 1 3.343382 2.464650 0.902613 4.953450\n", "GSM5344437 1 3.484879 2.545615 1.205907 5.221790\n", "\n", "Missing values before handling:\n", " Trait (Glioblastoma) missing: 0 out of 36\n", " Genes with >20% missing: 0\n", " Samples with >5% missing genes: 0\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Data shape after handling missing values: (36, 34004)\n", "\n", "Evaluating trait bias:\n", "All samples in this dataset are glioblastoma cell lines under different experimental conditions.\n", "Since there is no variation in the trait, this dataset is biased and not suitable for associational studies.\n", "This dataset is not usable for trait-gene association studies as it lacks trait variation.\n" ] } ], "source": [ "# 1. Load the gene expression data saved in step 6\n", "gene_data = pd.read_csv(out_gene_data_file, index_col=0)\n", "print(f\"Loaded gene data shape: {gene_data.shape}\")\n", "\n", "# Normalize gene symbols using NCBI Gene database (already done in step 6, so we don't need to do it again)\n", "# We'll use the normalized gene data directly from the previous step\n", "normalized_gene_data = gene_data\n", "print(f\"Normalized gene data shape: {normalized_gene_data.shape}\")\n", "print(f\"Sample gene symbols after normalization: {list(normalized_gene_data.index[:10])}\")\n", "\n", "# 2. Create mock clinical data since this dataset doesn't have real clinical features\n", "# Based on the background information, this is a cell line study with no clinical variation\n", "print(\"\\nCreating mock clinical data - this is a cell line experiment with no clinical variation\")\n", "# Create a clinical DataFrame with just the trait column (all samples have glioblastoma)\n", "sample_ids = normalized_gene_data.columns\n", "mock_clinical_data = pd.DataFrame(\n", " {trait: [1] * len(sample_ids)}, # All samples are glioblastoma cell lines\n", " index=sample_ids\n", ")\n", "print(f\"Mock clinical data shape: {mock_clinical_data.shape}\")\n", "print(\"Mock clinical data preview:\")\n", "print(mock_clinical_data.head())\n", "\n", "# Save the mock clinical data\n", "os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)\n", "mock_clinical_data.to_csv(out_clinical_data_file)\n", "\n", "# 3. Link clinical and genetic data\n", "linked_data = pd.concat([mock_clinical_data, normalized_gene_data.T], axis=1)\n", "print(f\"Linked data shape: {linked_data.shape}\")\n", "print(\"Linked data preview (first 5 rows, first 5 columns):\")\n", "if linked_data.shape[1] >= 5:\n", " print(linked_data.iloc[:5, :5])\n", "else:\n", " print(linked_data.head())\n", "\n", "# 4. Handle missing values\n", "print(\"\\nMissing values before handling:\")\n", "print(f\" Trait ({trait}) missing: {linked_data[trait].isna().sum()} out of {len(linked_data)}\")\n", "gene_cols = [col for col in linked_data.columns if col != trait]\n", "if gene_cols:\n", " print(f\" Genes with >20% missing: {sum(linked_data[gene_cols].isna().mean() > 0.2)}\")\n", " print(f\" Samples with >5% missing genes: {sum(linked_data[gene_cols].isna().mean(axis=1) > 0.05)}\")\n", "\n", "cleaned_data = handle_missing_values(linked_data, trait)\n", "print(f\"Data shape after handling missing values: {cleaned_data.shape}\")\n", "\n", "# 5. Evaluate bias in trait and demographic features\n", "is_trait_biased = True # Set to True since all samples have the same trait value (all are glioblastoma)\n", "print(\"\\nEvaluating trait bias:\")\n", "print(f\"All samples in this dataset are glioblastoma cell lines under different experimental conditions.\")\n", "print(f\"Since there is no variation in the trait, this dataset is biased and not suitable for associational studies.\")\n", "\n", "# 6. Final validation and save\n", "is_usable = validate_and_save_cohort_info(\n", " is_final=True, \n", " cohort=cohort, \n", " info_path=json_path, \n", " is_gene_available=len(normalized_gene_data) > 0, \n", " is_trait_available=True, # The trait is available, but lacks variation\n", " is_biased=is_trait_biased, \n", " df=cleaned_data,\n", " note=\"Dataset contains gene expression from glioblastoma cell line U87 under different treatments, but lacks trait variation.\"\n", ")\n", "\n", "# 7. Since this is a cell line experiment without clinical variation,\n", "# we won't save the linked data as it's not suitable for trait-gene association studies\n", "if is_usable and len(cleaned_data) > 0:\n", " os.makedirs(os.path.dirname(out_data_file), exist_ok=True)\n", " cleaned_data.to_csv(out_data_file)\n", " print(f\"Linked data saved to {out_data_file}\")\n", "else:\n", " print(\"This dataset is not usable for trait-gene association studies as it lacks trait variation.\")" ] } ], "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 }