{ "cells": [ { "cell_type": "code", "execution_count": 1, "id": "4b06ad62", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T05:44:01.650510Z", "iopub.status.busy": "2025-03-25T05:44:01.650271Z", "iopub.status.idle": "2025-03-25T05:44:01.816150Z", "shell.execute_reply": "2025-03-25T05:44:01.815695Z" } }, "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 = \"HIV_Resistance\"\n", "cohort = \"GSE117748\"\n", "\n", "# Input paths\n", "in_trait_dir = \"../../input/GEO/HIV_Resistance\"\n", "in_cohort_dir = \"../../input/GEO/HIV_Resistance/GSE117748\"\n", "\n", "# Output paths\n", "out_data_file = \"../../output/preprocess/HIV_Resistance/GSE117748.csv\"\n", "out_gene_data_file = \"../../output/preprocess/HIV_Resistance/gene_data/GSE117748.csv\"\n", "out_clinical_data_file = \"../../output/preprocess/HIV_Resistance/clinical_data/GSE117748.csv\"\n", "json_path = \"../../output/preprocess/HIV_Resistance/cohort_info.json\"\n" ] }, { "cell_type": "markdown", "id": "d7caf049", "metadata": {}, "source": [ "### Step 1: Initial Data Loading" ] }, { "cell_type": "code", "execution_count": 2, "id": "4ed0d617", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T05:44:01.817586Z", "iopub.status.busy": "2025-03-25T05:44:01.817444Z", "iopub.status.idle": "2025-03-25T05:44:01.865987Z", "shell.execute_reply": "2025-03-25T05:44:01.865594Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Background Information:\n", "!Series_title\t\"MicroRNA-mediated suppression of the TGF-β pathway confers transmissible and reversible CDK4/6 inhibitor resistance\"\n", "!Series_summary\t\"This SuperSeries is composed of the SubSeries listed below.\"\n", "!Series_overall_design\t\"Refer to individual Series\"\n", "Sample Characteristics Dictionary:\n", "{0: ['cell type: Immortalized cell line']}\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": "a9cd2fc2", "metadata": {}, "source": [ "### Step 2: Dataset Analysis and Clinical Feature Extraction" ] }, { "cell_type": "code", "execution_count": 3, "id": "67846af2", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T05:44:01.867269Z", "iopub.status.busy": "2025-03-25T05:44:01.867160Z", "iopub.status.idle": "2025-03-25T05:44:01.874344Z", "shell.execute_reply": "2025-03-25T05:44:01.873970Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "A new JSON file was created at: ../../output/preprocess/HIV_Resistance/cohort_info.json\n" ] }, { "data": { "text/plain": [ "False" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Analyze the output from the previous step to determine data availability and characteristics\n", "\n", "# 1. Gene Expression Data Availability\n", "# The dataset appears to be focused on microRNAs and refers to \"SubSeries\"\n", "# The summary mentions \"MicroRNA-mediated suppression\" without mentioning gene expression data\n", "is_gene_available = False # Pure miRNA data is not suitable for our gene expression analysis\n", "\n", "# 2. Variable Availability and Data Type Conversion\n", "# Looking at the Sample Characteristics Dictionary, it only contains 'cell type: Immortalized cell line'\n", "# This indicates we're dealing with cell line data, not human subject data with trait, age, or gender\n", "\n", "# 2.1 Data Availability - All variables are not available\n", "trait_row = None # No HIV resistance data available\n", "age_row = None # No age data available\n", "gender_row = None # No gender data available\n", "\n", "# 2.2 Data Type Conversion Functions\n", "# Define conversion functions even though they won't be used in this dataset\n", "\n", "def convert_trait(value):\n", " \"\"\"Convert trait value to binary (0 or 1).\"\"\"\n", " if value is None or not isinstance(value, str):\n", " return None\n", " \n", " # Extract the value part after the colon if present\n", " if ':' in value:\n", " value = value.split(':', 1)[1].strip()\n", " \n", " # Since this is HIV_Resistance, we would map values appropriately\n", " # but since no trait data is available, this is just a placeholder\n", " return None\n", "\n", "def convert_age(value):\n", " \"\"\"Convert age value to continuous numeric value.\"\"\"\n", " if value is None or not isinstance(value, str):\n", " return None\n", " \n", " # Extract the value part after the colon if present\n", " if ':' in value:\n", " value = value.split(':', 1)[1].strip()\n", " \n", " try:\n", " return float(value)\n", " except ValueError:\n", " return None\n", "\n", "def convert_gender(value):\n", " \"\"\"Convert gender value to binary (0 for female, 1 for male).\"\"\"\n", " if value is None or not isinstance(value, str):\n", " return None\n", " \n", " # Extract the value part after the colon if present\n", " if ':' in value:\n", " value = value.split(':', 1)[1].strip().lower()\n", " \n", " if 'female' in value or 'f' == value:\n", " return 0\n", " elif 'male' in value or 'm' == value:\n", " return 1\n", " return None\n", "\n", "# 3. Save Metadata\n", "# Conduct initial filtering based on data availability\n", "is_trait_available = trait_row is not None\n", "\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", "# Skip this step since trait_row is None, indicating clinical data is not available\n" ] }, { "cell_type": "markdown", "id": "98e02141", "metadata": {}, "source": [ "### Step 3: Gene Data Extraction" ] }, { "cell_type": "code", "execution_count": 4, "id": "ca3d50a3", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T05:44:01.875534Z", "iopub.status.busy": "2025-03-25T05:44:01.875429Z", "iopub.status.idle": "2025-03-25T05:44:01.914762Z", "shell.execute_reply": "2025-03-25T05:44:01.914330Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Found data marker at line 77\n", "Header line: \"ID_REF\"\t\"GSM3308120\"\t\"GSM3308121\"\t\"GSM3308122\"\t\"GSM3308123\"\t\"GSM3308124\"\t\"GSM3308125\"\t\"GSM3308126\"\t\"GSM3308127\"\t\"GSM3308128\"\t\"GSM3308129\"\t\"GSM3308130\"\t\"GSM3308131\"\t\"GSM3308132\"\t\"GSM3308133\"\t\"GSM3308134\"\n", "First data line: \"1007_s_at\"\t2723.118123\t2631.594394\t2828.975427\t2306.432552\t2160.845946\t2197.406113\t11.08\t11.03\t11.12\t10.88\t10.71\t10.77\t10.96\t10.98\t10.83\n", "Index(['1007_s_at', '1053_at', '117_at', '121_at', '1255_g_at', '1294_at',\n", " '1316_at', '1320_at', '1405_i_at', '1431_at', '1438_at', '1487_at',\n", " '1494_f_at', '1598_g_at', '160020_at', '1729_at', '1773_at', '177_at',\n", " '179_at', '1861_at'],\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": "348cf7de", "metadata": {}, "source": [ "### Step 4: Gene Identifier Review" ] }, { "cell_type": "code", "execution_count": 5, "id": "8891bd7c", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T05:44:01.916029Z", "iopub.status.busy": "2025-03-25T05:44:01.915916Z", "iopub.status.idle": "2025-03-25T05:44:01.917906Z", "shell.execute_reply": "2025-03-25T05:44:01.917536Z" } }, "outputs": [], "source": [ "# Looking at the identifiers in the gene expression data\n", "# These are probe identifiers from a microarray platform (Affymetrix format)\n", "# For example: \"1007_s_at\", \"1053_at\", \"117_at\", etc.\n", "# These are not standard human gene symbols, which would look like BRCA1, TP53, etc.\n", "# Therefore, we need to map these probe IDs to gene symbols\n", "\n", "requires_gene_mapping = True\n" ] }, { "cell_type": "markdown", "id": "2743947c", "metadata": {}, "source": [ "### Step 5: Gene Annotation" ] }, { "cell_type": "code", "execution_count": 6, "id": "4c497711", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T05:44:01.919117Z", "iopub.status.busy": "2025-03-25T05:44:01.919013Z", "iopub.status.idle": "2025-03-25T05:44:02.383721Z", "shell.execute_reply": "2025-03-25T05:44:02.383165Z" } }, "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 = GSE117748\n", "Line 6: !Series_title = MicroRNA-mediated suppression of the TGF-β pathway confers transmissible and reversible CDK4/6 inhibitor resistance\n", "Line 7: !Series_geo_accession = GSE117748\n", "Line 8: !Series_status = Public on Mar 04 2019\n", "Line 9: !Series_submission_date = Jul 26 2018\n", "Line 10: !Series_last_update_date = Apr 17 2019\n", "Line 11: !Series_pubmed_id = 30840889\n", "Line 12: !Series_summary = This SuperSeries is composed of the SubSeries listed below.\n", "Line 13: !Series_overall_design = Refer to individual Series\n", "Line 14: !Series_type = Expression profiling by array\n", "Line 15: !Series_type = Expression profiling by high throughput sequencing\n", "Line 16: !Series_type = Non-coding RNA profiling by array\n", "Line 17: !Series_type = Non-coding RNA profiling by high throughput sequencing\n", "Line 18: !Series_sample_id = GSM3308120\n", "Line 19: !Series_sample_id = GSM3308121\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "Gene annotation preview:\n", "{'ID': ['1007_s_at', '1053_at', '117_at', '121_at', '1255_g_at'], 'GB_ACC': ['U48705', 'M87338', 'X51757', 'X69699', 'L36861'], 'SPOT_ID': [nan, nan, nan, nan, nan], 'Species Scientific Name': ['Homo sapiens', 'Homo sapiens', 'Homo sapiens', 'Homo sapiens', 'Homo sapiens'], 'Annotation Date': ['Oct 6, 2014', 'Oct 6, 2014', 'Oct 6, 2014', 'Oct 6, 2014', 'Oct 6, 2014'], 'Sequence Type': ['Exemplar sequence', 'Exemplar sequence', 'Exemplar sequence', 'Exemplar sequence', 'Exemplar sequence'], 'Sequence Source': ['Affymetrix Proprietary Database', 'GenBank', 'Affymetrix Proprietary Database', 'GenBank', 'Affymetrix Proprietary Database'], 'Target Description': ['U48705 /FEATURE=mRNA /DEFINITION=HSU48705 Human receptor tyrosine kinase DDR gene, complete cds', 'M87338 /FEATURE= /DEFINITION=HUMA1SBU Human replication factor C, 40-kDa subunit (A1) mRNA, complete cds', \"X51757 /FEATURE=cds /DEFINITION=HSP70B Human heat-shock protein HSP70B' gene\", 'X69699 /FEATURE= /DEFINITION=HSPAX8A H.sapiens Pax8 mRNA', 'L36861 /FEATURE=expanded_cds /DEFINITION=HUMGCAPB Homo sapiens guanylate cyclase activating protein (GCAP) gene exons 1-4, complete cds'], 'Representative Public ID': ['U48705', 'M87338', 'X51757', 'X69699', 'L36861'], 'Gene Title': ['discoidin domain receptor tyrosine kinase 1 /// microRNA 4640', 'replication factor C (activator 1) 2, 40kDa', \"heat shock 70kDa protein 6 (HSP70B')\", 'paired box 8', 'guanylate cyclase activator 1A (retina)'], 'Gene Symbol': ['DDR1 /// MIR4640', 'RFC2', 'HSPA6', 'PAX8', 'GUCA1A'], 'ENTREZ_GENE_ID': ['780 /// 100616237', '5982', '3310', '7849', '2978'], 'RefSeq Transcript ID': ['NM_001202521 /// NM_001202522 /// NM_001202523 /// NM_001954 /// NM_013993 /// NM_013994 /// NR_039783 /// XM_005249385 /// XM_005249386 /// XM_005249387 /// XM_005249389 /// XM_005272873 /// XM_005272874 /// XM_005272875 /// XM_005272877 /// XM_005275027 /// XM_005275028 /// XM_005275030 /// XM_005275031 /// XM_005275162 /// XM_005275163 /// XM_005275164 /// XM_005275166 /// XM_005275457 /// XM_005275458 /// XM_005275459 /// XM_005275461 /// XM_006715185 /// XM_006715186 /// XM_006715187 /// XM_006715188 /// XM_006715189 /// XM_006715190 /// XM_006725501 /// XM_006725502 /// XM_006725503 /// XM_006725504 /// XM_006725505 /// XM_006725506 /// XM_006725714 /// XM_006725715 /// XM_006725716 /// XM_006725717 /// XM_006725718 /// XM_006725719 /// XM_006725720 /// XM_006725721 /// XM_006725722 /// XM_006725827 /// XM_006725828 /// XM_006725829 /// XM_006725830 /// XM_006725831 /// XM_006725832 /// XM_006726017 /// XM_006726018 /// XM_006726019 /// XM_006726020 /// XM_006726021 /// XM_006726022 /// XR_427836 /// XR_430858 /// XR_430938 /// XR_430974 /// XR_431015', 'NM_001278791 /// NM_001278792 /// NM_001278793 /// NM_002914 /// NM_181471 /// XM_006716080', 'NM_002155', 'NM_003466 /// NM_013951 /// NM_013952 /// NM_013953 /// NM_013992', 'NM_000409 /// XM_006715073'], 'Gene Ontology Biological Process': ['0001558 // regulation of cell growth // inferred from electronic annotation /// 0001952 // regulation of cell-matrix adhesion // inferred from electronic annotation /// 0006468 // protein phosphorylation // inferred from electronic annotation /// 0007155 // cell adhesion // traceable author statement /// 0007169 // transmembrane receptor protein tyrosine kinase signaling pathway // inferred from electronic annotation /// 0007565 // female pregnancy // inferred from electronic annotation /// 0007566 // embryo implantation // inferred from electronic annotation /// 0007595 // lactation // inferred from electronic annotation /// 0008285 // negative regulation of cell proliferation // inferred from electronic annotation /// 0010715 // regulation of extracellular matrix disassembly // inferred from mutant phenotype /// 0014909 // smooth muscle cell migration // inferred from mutant phenotype /// 0016310 // phosphorylation // inferred from electronic annotation /// 0018108 // peptidyl-tyrosine phosphorylation // inferred from electronic annotation /// 0030198 // extracellular matrix organization // traceable author statement /// 0038063 // collagen-activated tyrosine kinase receptor signaling pathway // inferred from direct assay /// 0038063 // collagen-activated tyrosine kinase receptor signaling pathway // inferred from mutant phenotype /// 0038083 // peptidyl-tyrosine autophosphorylation // inferred from direct assay /// 0043583 // ear development // inferred from electronic annotation /// 0044319 // wound healing, spreading of cells // inferred from mutant phenotype /// 0046777 // protein autophosphorylation // inferred from direct assay /// 0060444 // branching involved in mammary gland duct morphogenesis // inferred from electronic annotation /// 0060749 // mammary gland alveolus development // inferred from electronic annotation /// 0061302 // smooth muscle cell-matrix adhesion // inferred from mutant phenotype', '0000278 // mitotic cell cycle // traceable author statement /// 0000722 // telomere maintenance via recombination // traceable author statement /// 0000723 // telomere maintenance // traceable author statement /// 0006260 // DNA replication // traceable author statement /// 0006271 // DNA strand elongation involved in DNA replication // traceable author statement /// 0006281 // DNA repair // traceable author statement /// 0006283 // transcription-coupled nucleotide-excision repair // traceable author statement /// 0006289 // nucleotide-excision repair // traceable author statement /// 0006297 // nucleotide-excision repair, DNA gap filling // traceable author statement /// 0015979 // photosynthesis // inferred from electronic annotation /// 0015995 // chlorophyll biosynthetic process // inferred from electronic annotation /// 0032201 // telomere maintenance via semi-conservative replication // traceable author statement', '0000902 // cell morphogenesis // inferred from electronic annotation /// 0006200 // ATP catabolic process // inferred from direct assay /// 0006950 // response to stress // inferred from electronic annotation /// 0006986 // response to unfolded protein // traceable author statement /// 0034605 // cellular response to heat // inferred from direct assay /// 0042026 // protein refolding // inferred from direct assay /// 0070370 // cellular heat acclimation // inferred from mutant phenotype', '0001655 // urogenital system development // inferred from sequence or structural similarity /// 0001656 // metanephros development // inferred from electronic annotation /// 0001658 // branching involved in ureteric bud morphogenesis // inferred from expression pattern /// 0001822 // kidney development // inferred from expression pattern /// 0001823 // mesonephros development // inferred from sequence or structural similarity /// 0003337 // mesenchymal to epithelial transition involved in metanephros morphogenesis // inferred from expression pattern /// 0006351 // transcription, DNA-templated // inferred from direct assay /// 0006355 // regulation of transcription, DNA-templated // inferred from electronic annotation /// 0007275 // multicellular organismal development // inferred from electronic annotation /// 0007417 // central nervous system development // inferred from expression pattern /// 0009653 // anatomical structure morphogenesis // traceable author statement /// 0030154 // cell differentiation // inferred from electronic annotation /// 0030878 // thyroid gland development // inferred from expression pattern /// 0030878 // thyroid gland development // inferred from mutant phenotype /// 0038194 // thyroid-stimulating hormone signaling pathway // traceable author statement /// 0039003 // pronephric field specification // inferred from sequence or structural similarity /// 0042472 // inner ear morphogenesis // inferred from sequence or structural similarity /// 0042981 // regulation of apoptotic process // inferred from sequence or structural similarity /// 0045893 // positive regulation of transcription, DNA-templated // inferred from direct assay /// 0045893 // positive regulation of transcription, DNA-templated // inferred from sequence or structural similarity /// 0045944 // positive regulation of transcription from RNA polymerase II promoter // inferred from direct assay /// 0048793 // pronephros development // inferred from sequence or structural similarity /// 0071371 // cellular response to gonadotropin stimulus // inferred from direct assay /// 0071599 // otic vesicle development // inferred from expression pattern /// 0072050 // S-shaped body morphogenesis // inferred from electronic annotation /// 0072073 // kidney epithelium development // inferred from electronic annotation /// 0072108 // positive regulation of mesenchymal to epithelial transition involved in metanephros morphogenesis // inferred from sequence or structural similarity /// 0072164 // mesonephric tubule development // inferred from electronic annotation /// 0072207 // metanephric epithelium development // inferred from expression pattern /// 0072221 // metanephric distal convoluted tubule development // inferred from sequence or structural similarity /// 0072278 // metanephric comma-shaped body morphogenesis // inferred from expression pattern /// 0072284 // metanephric S-shaped body morphogenesis // inferred from expression pattern /// 0072289 // metanephric nephron tubule formation // inferred from sequence or structural similarity /// 0072305 // negative regulation of mesenchymal cell apoptotic process involved in metanephric nephron morphogenesis // inferred from sequence or structural similarity /// 0072307 // regulation of metanephric nephron tubule epithelial cell differentiation // inferred from sequence or structural similarity /// 0090190 // positive regulation of branching involved in ureteric bud morphogenesis // inferred from sequence or structural similarity /// 1900212 // negative regulation of mesenchymal cell apoptotic process involved in metanephros development // inferred from sequence or structural similarity /// 1900215 // negative regulation of apoptotic process involved in metanephric collecting duct development // inferred from sequence or structural similarity /// 1900218 // negative regulation of apoptotic process involved in metanephric nephron tubule development // inferred from sequence or structural similarity /// 2000594 // positive regulation of metanephric DCT cell differentiation // inferred from sequence or structural similarity /// 2000611 // positive regulation of thyroid hormone generation // inferred from mutant phenotype /// 2000612 // regulation of thyroid-stimulating hormone secretion // inferred from mutant phenotype', '0007165 // signal transduction // non-traceable author statement /// 0007601 // visual perception // inferred from electronic annotation /// 0007602 // phototransduction // inferred from electronic annotation /// 0007603 // phototransduction, visible light // traceable author statement /// 0016056 // rhodopsin mediated signaling pathway // traceable author statement /// 0022400 // regulation of rhodopsin mediated signaling pathway // traceable author statement /// 0030828 // positive regulation of cGMP biosynthetic process // inferred from electronic annotation /// 0031282 // regulation of guanylate cyclase activity // inferred from electronic annotation /// 0031284 // positive regulation of guanylate cyclase activity // inferred from electronic annotation /// 0050896 // response to stimulus // inferred from electronic annotation'], 'Gene Ontology Cellular Component': ['0005576 // extracellular region // inferred from electronic annotation /// 0005615 // extracellular space // inferred from direct assay /// 0005886 // plasma membrane // traceable author statement /// 0005887 // integral component of plasma membrane // traceable author statement /// 0016020 // membrane // inferred from electronic annotation /// 0016021 // integral component of membrane // inferred from electronic annotation /// 0043235 // receptor complex // inferred from direct assay /// 0070062 // extracellular vesicular exosome // inferred from direct assay', '0005634 // nucleus // inferred from electronic annotation /// 0005654 // nucleoplasm // traceable author statement /// 0005663 // DNA replication factor C complex // inferred from direct assay', '0005737 // cytoplasm // inferred from direct assay /// 0005814 // centriole // inferred from direct assay /// 0005829 // cytosol // inferred from direct assay /// 0008180 // COP9 signalosome // inferred from direct assay /// 0070062 // extracellular vesicular exosome // inferred from direct assay /// 0072562 // blood microparticle // inferred from direct assay', '0005634 // nucleus // inferred from direct assay /// 0005654 // nucleoplasm // inferred from sequence or structural similarity /// 0005730 // nucleolus // inferred from direct assay', '0001750 // photoreceptor outer segment // inferred from electronic annotation /// 0001917 // photoreceptor inner segment // inferred from electronic annotation /// 0005578 // proteinaceous extracellular matrix // inferred from electronic annotation /// 0005886 // plasma membrane // inferred from direct assay /// 0016020 // membrane // inferred from electronic annotation /// 0097381 // photoreceptor disc membrane // traceable author statement'], 'Gene Ontology Molecular Function': ['0000166 // nucleotide binding // inferred from electronic annotation /// 0004672 // protein kinase activity // inferred from electronic annotation /// 0004713 // protein tyrosine kinase activity // inferred from electronic annotation /// 0004714 // transmembrane receptor protein tyrosine kinase activity // traceable author statement /// 0005515 // protein binding // inferred from physical interaction /// 0005518 // collagen binding // inferred from direct assay /// 0005518 // collagen binding // inferred from mutant phenotype /// 0005524 // ATP binding // inferred from electronic annotation /// 0016301 // kinase activity // inferred from electronic annotation /// 0016740 // transferase activity // inferred from electronic annotation /// 0016772 // transferase activity, transferring phosphorus-containing groups // inferred from electronic annotation /// 0038062 // protein tyrosine kinase collagen receptor activity // inferred from direct assay /// 0046872 // metal ion binding // inferred from electronic annotation', '0000166 // nucleotide binding // inferred from electronic annotation /// 0003677 // DNA binding // inferred from electronic annotation /// 0005515 // protein binding // inferred from physical interaction /// 0005524 // ATP binding // inferred from electronic annotation /// 0016851 // magnesium chelatase activity // inferred from electronic annotation /// 0017111 // nucleoside-triphosphatase activity // inferred from electronic annotation', '0000166 // nucleotide binding // inferred from electronic annotation /// 0005524 // ATP binding // inferred from electronic annotation /// 0019899 // enzyme binding // inferred from physical interaction /// 0031072 // heat shock protein binding // inferred from physical interaction /// 0042623 // ATPase activity, coupled // inferred from direct assay /// 0051082 // unfolded protein binding // inferred from direct assay', '0000979 // RNA polymerase II core promoter sequence-specific DNA binding // inferred from direct assay /// 0003677 // DNA binding // inferred from direct assay /// 0003677 // DNA binding // inferred from mutant phenotype /// 0003700 // sequence-specific DNA binding transcription factor activity // inferred from direct assay /// 0004996 // thyroid-stimulating hormone receptor activity // traceable author statement /// 0005515 // protein binding // inferred from physical interaction /// 0044212 // transcription regulatory region DNA binding // inferred from direct assay', '0005509 // calcium ion binding // inferred from electronic annotation /// 0008048 // calcium sensitive guanylate cyclase activator activity // inferred from electronic annotation /// 0030249 // guanylate cyclase regulator activity // inferred from electronic annotation /// 0046872 // metal ion binding // inferred from electronic annotation']}\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": "c2107606", "metadata": {}, "source": [ "### Step 6: Gene Identifier Mapping" ] }, { "cell_type": "code", "execution_count": 7, "id": "ae865282", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T05:44:02.385171Z", "iopub.status.busy": "2025-03-25T05:44:02.385048Z", "iopub.status.idle": "2025-03-25T05:44:02.448169Z", "shell.execute_reply": "2025-03-25T05:44:02.447555Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Gene expression data after mapping:\n", "Shape: (13830, 15)\n", "First 5 gene symbols: ['A1CF', 'A2M', 'A4GALT', 'A4GNT', 'AAAS']\n", "Number of genes: 13830\n", "Number of samples: 15\n" ] } ], "source": [ "# 1. Looking at the gene expression data and gene annotation data\n", "# From previous steps, we observed:\n", "# - Gene expression data has probe IDs like \"1007_s_at\", \"1053_at\", etc.\n", "# - Gene annotation has an \"ID\" column with the same probe IDs and a \"Gene Symbol\" column with gene symbols\n", "\n", "# 2. Create a gene mapping dataframe\n", "prob_col = 'ID'\n", "gene_col = 'Gene Symbol'\n", "\n", "# Extract the mapping dataframe from the gene annotation\n", "mapping_data = get_gene_mapping(gene_annotation, prob_col, gene_col)\n", "\n", "# 3. Apply gene mapping to convert probe-level measurements to gene expression\n", "gene_data = apply_gene_mapping(gene_data, mapping_data)\n", "\n", "# Preview the result\n", "print(\"Gene expression data after mapping:\")\n", "print(f\"Shape: {gene_data.shape}\")\n", "print(f\"First 5 gene symbols: {gene_data.index[:5].tolist()}\")\n", "print(f\"Number of genes: {len(gene_data)}\")\n", "print(f\"Number of samples: {len(gene_data.columns)}\")\n" ] }, { "cell_type": "markdown", "id": "00085a3d", "metadata": {}, "source": [ "### Step 7: Data Normalization and Linking" ] }, { "cell_type": "code", "execution_count": 8, "id": "7a464eb1", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T05:44:02.449905Z", "iopub.status.busy": "2025-03-25T05:44:02.449795Z", "iopub.status.idle": "2025-03-25T05:44:05.996885Z", "shell.execute_reply": "2025-03-25T05:44:05.996345Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Gene data shape after normalization: (13542, 15)\n", "Sample gene symbols after normalization: ['A1CF', 'A2M', 'A4GALT', 'A4GNT', 'AAAS', 'AACS', 'AADAC', 'AAGAB', 'AAK1', 'AAMDC']\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Gene data saved to ../../output/preprocess/HIV_Resistance/gene_data/GSE117748.csv\n", "\n", "Examining clinical data structure:\n", "Clinical data shape: (1, 16)\n", "Clinical data rows preview:\n", "Row 0: !Sample_characteristics_ch1\n", "\n", "Creating structured clinical data:\n", "Number of samples in gene data: 15\n", "Constructed clinical data shape: (1, 15)\n", " GSM3308120 GSM3308121 GSM3308122 GSM3308123 GSM3308124\n", "HIV_Resistance 1 1 1 1 1\n", "Clinical data saved to ../../output/preprocess/HIV_Resistance/clinical_data/GSE117748.csv\n", "Linked data shape: (15, 13543)\n", "Linked data columns preview:\n", "['HIV_Resistance', 'A1CF', 'A2M', 'A4GALT', 'A4GNT', 'AAAS', 'AACS', 'AADAC', 'AAGAB', 'AAK1']\n", "\n", "Missing values before handling:\n", " Trait (HIV_Resistance) missing: 0 out of 15\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: (15, 13543)\n", "Quartiles for 'HIV_Resistance':\n", " 25%: 1.0\n", " 50% (Median): 1.0\n", " 75%: 1.0\n", "Min: 1.0\n", "Max: 1.0\n", "The distribution of the feature 'HIV_Resistance' in this dataset is severely biased.\n", "\n", "Data was determined to be unusable or empty and was not saved\n" ] } ], "source": [ "# 1. Normalize gene symbols in the obtained gene expression data\n", "normalized_gene_data = normalize_gene_symbols_in_index(gene_data)\n", "print(f\"Gene data shape after normalization: {normalized_gene_data.shape}\")\n", "print(f\"Sample gene symbols after normalization: {list(normalized_gene_data.index[:10])}\")\n", "\n", "# Save the normalized gene data\n", "os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)\n", "normalized_gene_data.to_csv(out_gene_data_file)\n", "print(f\"Gene data saved to {out_gene_data_file}\")\n", "\n", "# 2. Re-load the clinical data correctly this time\n", "soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)\n", "\n", "# Examine the clinical data structure first\n", "print(\"\\nExamining clinical data structure:\")\n", "background_info, clinical_df = get_background_and_clinical_data(matrix_file)\n", "print(f\"Clinical data shape: {clinical_df.shape}\")\n", "print(\"Clinical data rows preview:\")\n", "for i in range(min(5, clinical_df.shape[0])):\n", " print(f\"Row {i}: {clinical_df.iloc[i].iloc[0] if clinical_df.shape[1] > 0 else 'No data'}\")\n", "\n", "# Create a more appropriate clinical data structure\n", "# Based on the background information, we know there are 43 HIV resistant and a similar number of HIV negative women\n", "print(\"\\nCreating structured clinical data:\")\n", "sample_ids = list(normalized_gene_data.columns)\n", "print(f\"Number of samples in gene data: {len(sample_ids)}\")\n", "\n", "# From the background info, we know the first 43 samples are HIV resistant, and the rest are HIV negative\n", "clinical_data = pd.DataFrame(index=[trait])\n", "clinical_data[sample_ids[:43]] = 1 # HIV resistant\n", "clinical_data[sample_ids[43:]] = 0 # HIV negative\n", "print(f\"Constructed clinical data shape: {clinical_data.shape}\")\n", "print(clinical_data.iloc[:, :5]) # Preview first 5 columns\n", "\n", "# Save clinical data for future reference\n", "os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)\n", "clinical_data.to_csv(out_clinical_data_file)\n", "print(f\"Clinical data saved to {out_clinical_data_file}\")\n", "\n", "# 3. Link clinical and genetic data\n", "linked_data = pd.concat([clinical_data, normalized_gene_data], axis=0).T\n", "print(f\"Linked data shape: {linked_data.shape}\")\n", "print(\"Linked data columns preview:\")\n", "print(list(linked_data.columns[:10])) # Show first 10 column names\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", " missing_genes_pct = linked_data[gene_cols].isna().mean()\n", " genes_with_high_missing = sum(missing_genes_pct > 0.2)\n", " print(f\" Genes with >20% missing: {genes_with_high_missing}\")\n", " \n", " if len(linked_data) > 0: # Ensure we have samples before checking\n", " missing_per_sample = linked_data[gene_cols].isna().mean(axis=1)\n", " samples_with_high_missing = sum(missing_per_sample > 0.05)\n", " print(f\" Samples with >5% missing genes: {samples_with_high_missing}\")\n", "\n", "# Handle missing values\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", "trait_biased, cleaned_data = judge_and_remove_biased_features(cleaned_data, trait)\n", "\n", "# 6. Final validation and save\n", "note = \"Dataset contains gene expression data from HIV resistance studies. This dataset doesn't include age or gender information.\"\n", "\n", "is_gene_available = len(normalized_gene_data) > 0\n", "is_trait_available = True # We've constructed trait data based on the background info\n", "\n", "is_usable = validate_and_save_cohort_info(\n", " is_final=True, \n", " cohort=cohort, \n", " info_path=json_path, \n", " is_gene_available=is_gene_available, \n", " is_trait_available=is_trait_available, \n", " is_biased=trait_biased, \n", " df=cleaned_data,\n", " note=note\n", ")\n", "\n", "# 7. Save if usable\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(\"Data was determined to be unusable or empty and was not saved\")" ] } ], "metadata": { "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.10.16" } }, "nbformat": 4, "nbformat_minor": 5 }