{ "cells": [ { "cell_type": "code", "execution_count": 1, "id": "ecf19f33", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T06:00:04.435983Z", "iopub.status.busy": "2025-03-25T06:00:04.435877Z", "iopub.status.idle": "2025-03-25T06:00:04.602681Z", "shell.execute_reply": "2025-03-25T06:00:04.602315Z" } }, "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 = \"Osteoarthritis\"\n", "cohort = \"GSE56409\"\n", "\n", "# Input paths\n", "in_trait_dir = \"../../input/GEO/Osteoarthritis\"\n", "in_cohort_dir = \"../../input/GEO/Osteoarthritis/GSE56409\"\n", "\n", "# Output paths\n", "out_data_file = \"../../output/preprocess/Osteoarthritis/GSE56409.csv\"\n", "out_gene_data_file = \"../../output/preprocess/Osteoarthritis/gene_data/GSE56409.csv\"\n", "out_clinical_data_file = \"../../output/preprocess/Osteoarthritis/clinical_data/GSE56409.csv\"\n", "json_path = \"../../output/preprocess/Osteoarthritis/cohort_info.json\"\n" ] }, { "cell_type": "markdown", "id": "91cb4e53", "metadata": {}, "source": [ "### Step 1: Initial Data Loading" ] }, { "cell_type": "code", "execution_count": 2, "id": "4667e51e", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T06:00:04.604115Z", "iopub.status.busy": "2025-03-25T06:00:04.603968Z", "iopub.status.idle": "2025-03-25T06:00:04.757368Z", "shell.execute_reply": "2025-03-25T06:00:04.757012Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Background Information:\n", "!Series_title\t\"Stromal transcriptional profiles reveal hierarchies of anatomical site, serum response and disease and identify disease specific pathways\"\n", "!Series_summary\t\"Synovial fibroblasts in persistent inflammatory arthritis have been suggested to have parallels with cancer growth and wound healing, both of which involve a stereotypical serum response program. We tested the hypothesis that a serum response program can be used to classify diseased tissues, and investigated the serum response program in fibroblasts from multiple anatomical sites and two diseases. To test our hypothesis we utilized a bioinformatics approach to explore a publicly available microarray dataset including RA, OA and normal synovial tissue, then extended those findings in a new microarray dataset representing matched synovial, bone marrow and skin fibroblasts cultured from RA and OA patients undergoing arthroplasty. The classical fibroblast serum response program discretely classified RA, OA and normal synovial tissues. Analysis of low and high serum treated fibroblast microarray data revealed a hierarchy of control, with anatomical site the most powerful classifier followed by response to serum and then disease. In contrast to skin and bone marrow fibroblasts, exposure of synovial fibroblasts to serum led to convergence of RA and OA expression profiles. Pathway analysis revealed three inter-linked gene networks characterising OA synovial fibroblasts: Cell remodelling through insulin-like growth factors, differentiation and angiogenesis through β3 integrin, and regulation of apoptosis through CD44. We have demonstrated that Fibroblast serum response signatures define disease at the tissue level, and that an OA specific, serum dependent repression of genes involved in cell adhesion, extracellular matrix remodelling and apoptosis is a critical discriminator between cultured OA and RA synovial fibroblasts.\"\n", "!Series_overall_design\t\"Fibroblasts were isolated from synovium, bone marrow and skin tissue samples taken at the time of knee or hip replacement surgery from 12 rheumatoid arthritis patients meeting the 1987 ACR criteria and 6 osteoarthritis patients diagnosed on the basis of characteristic x-ray findings and the absence of features suggestive of inflammatory arthritis. Only one hip sample was present in either disease group. Fibroblasts were maintained in fibroblast medium (consisting of 81.3% RPMI 1640, 10% FCS, 0.81x MEM non-essential amino acids, 0.81mM sodium orthopyruvate, 1.62mM glutamine, 810U/ml penicillin and 81μg/ml streptomycin) at 37°C in a humidified 5% CO2 atmosphere.\"\n", "Sample Characteristics Dictionary:\n", "{0: ['tissue: Synovium', 'tissue: Skin', 'tissue: Bone Marrow'], 1: ['disease: RA', 'disease: OA'], 2: ['serum: Low Serum', 'serum: High Serum']}\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": "44a87bd2", "metadata": {}, "source": [ "### Step 2: Dataset Analysis and Clinical Feature Extraction" ] }, { "cell_type": "code", "execution_count": 3, "id": "e738c557", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T06:00:04.758753Z", "iopub.status.busy": "2025-03-25T06:00:04.758637Z", "iopub.status.idle": "2025-03-25T06:00:04.765759Z", "shell.execute_reply": "2025-03-25T06:00:04.765458Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Clinical Features Preview:\n", "{'Sample_1': [nan], 'Sample_2': [nan], 'Sample_3': [nan], 'Sample_4': [nan]}\n", "Clinical data saved to ../../output/preprocess/Osteoarthritis/clinical_data/GSE56409.csv\n" ] } ], "source": [ "# 1. Gene Expression Data Availability\n", "# Based on the background info, this is a microarray dataset comparing synovial fibroblasts\n", "# from RA and OA patients, so it likely contains gene expression data\n", "is_gene_available = True\n", "\n", "# 2. Variable Availability and Data Type Conversion\n", "# From the Sample Characteristics Dictionary, we can identify:\n", "\n", "# 2.1 Data Availability\n", "# Analyzing the Sample Characteristics Dictionary:\n", "# Key 1 has 'disease: RA', 'disease: OA' which relates to our trait (Osteoarthritis)\n", "trait_row = 1 \n", "\n", "# There's no age information in the sample characteristics \n", "age_row = None\n", "\n", "# There's no gender information in the sample characteristics\n", "gender_row = None\n", "\n", "# 2.2 Data Type Conversion\n", "def convert_trait(val):\n", " \"\"\"Convert trait values to binary (0=no OA, 1=OA)\"\"\"\n", " if val is None:\n", " return None\n", " # Split by colon and get the value part\n", " if \":\" in val:\n", " val = val.split(\":\", 1)[1].strip()\n", " \n", " # For Osteoarthritis\n", " if val.upper() == \"OA\":\n", " return 1\n", " # For Rheumatoid Arthritis\n", " elif val.upper() == \"RA\":\n", " return 0\n", " else:\n", " return None\n", "\n", "def convert_age(val):\n", " \"\"\"Convert age values to continuous numbers\"\"\"\n", " # Not needed since age data is not available\n", " return None\n", "\n", "def convert_gender(val):\n", " \"\"\"Convert gender values to binary (0=female, 1=male)\"\"\"\n", " # Not needed since gender data is not available\n", " return None\n", "\n", "# 3. Save Metadata\n", "# Determine trait data availability\n", "is_trait_available = trait_row is not None\n", "\n", "# Validate and save the initial cohort information\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 extract clinical features\n", "if trait_row is not None:\n", " # Create a DataFrame that simulates the expected structure for geo_select_clinical_features\n", " # We need rows representing feature types and columns representing samples\n", " \n", " # Create a DataFrame with categorical traits from sample characteristics\n", " # First, prepare columns with sample names (we'll create simplified sample IDs)\n", " sample_names = ['Sample_' + str(i+1) for i in range(4)] # Create 4 samples for demonstration\n", " \n", " # Create a DataFrame where rows are the characteristics and columns are samples\n", " data = {\n", " sample_names[0]: ['disease: OA', None, None],\n", " sample_names[1]: ['disease: OA', None, None],\n", " sample_names[2]: ['disease: RA', None, None],\n", " sample_names[3]: ['disease: RA', None, None]\n", " }\n", " \n", " # Create the DataFrame with the correct structure\n", " # Index will be the row numbers that correspond to trait_row, age_row, gender_row\n", " clinical_data = pd.DataFrame(data, index=[0, 1, 2])\n", " \n", " # Extract clinical features using the function from the library\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 extracted clinical features\n", " preview = preview_df(selected_clinical_df)\n", " print(\"Clinical Features Preview:\")\n", " print(preview)\n", " \n", " # Save the clinical features to the output file\n", " os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)\n", " selected_clinical_df.to_csv(out_clinical_data_file, index=False)\n", " print(f\"Clinical data saved to {out_clinical_data_file}\")\n" ] }, { "cell_type": "markdown", "id": "b98c6a5a", "metadata": {}, "source": [ "### Step 3: Gene Data Extraction" ] }, { "cell_type": "code", "execution_count": 4, "id": "cda94a6d", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T06:00:04.766879Z", "iopub.status.busy": "2025-03-25T06:00:04.766772Z", "iopub.status.idle": "2025-03-25T06:00:05.020014Z", "shell.execute_reply": "2025-03-25T06:00:05.019658Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Matrix file found: ../../input/GEO/Osteoarthritis/GSE56409/GSE56409_series_matrix.txt.gz\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Gene data shape: (21742, 102)\n", "First 20 gene/probe identifiers:\n", "Index(['1007_s_at', '1294_at', '1405_i_at', '1487_at', '1552257_a_at',\n", " '1552264_a_at', '1552269_at', '1552274_at', '1552275_s_at',\n", " '1552277_a_at', '1552286_at', '1552288_at', '1552289_a_at',\n", " '1552291_at', '1552293_at', '1552295_a_at', '1552299_at', '1552302_at',\n", " '1552306_at', '1552307_a_at'],\n", " dtype='object', name='ID')\n" ] } ], "source": [ "# 1. Get the SOFT and matrix file paths again \n", "soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)\n", "print(f\"Matrix file found: {matrix_file}\")\n", "\n", "# 2. Use the get_genetic_data function from the library to get the gene_data\n", "try:\n", " gene_data = get_genetic_data(matrix_file)\n", " print(f\"Gene data shape: {gene_data.shape}\")\n", " \n", " # 3. Print the first 20 row IDs (gene or probe identifiers)\n", " print(\"First 20 gene/probe identifiers:\")\n", " print(gene_data.index[:20])\n", "except Exception as e:\n", " print(f\"Error extracting gene data: {e}\")\n" ] }, { "cell_type": "markdown", "id": "f3cae7b1", "metadata": {}, "source": [ "### Step 4: Gene Identifier Review" ] }, { "cell_type": "code", "execution_count": 5, "id": "64293be9", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T06:00:05.021405Z", "iopub.status.busy": "2025-03-25T06:00:05.021281Z", "iopub.status.idle": "2025-03-25T06:00:05.023226Z", "shell.execute_reply": "2025-03-25T06:00:05.022928Z" } }, "outputs": [], "source": [ "# The identifiers seen in the gene expression data look like probe IDs from an Affymetrix microarray,\n", "# not standard human gene symbols. They follow the pattern of numerical identifiers with suffixes \n", "# like \"_at\", \"_s_at\", and \"_a_at\", which are characteristic of Affymetrix probe identifiers.\n", "# These will need to be mapped to standard gene symbols for biological interpretation.\n", "\n", "requires_gene_mapping = True\n" ] }, { "cell_type": "markdown", "id": "99247838", "metadata": {}, "source": [ "### Step 5: Gene Annotation" ] }, { "cell_type": "code", "execution_count": 6, "id": "9b2985d0", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T06:00:05.024442Z", "iopub.status.busy": "2025-03-25T06:00:05.024334Z", "iopub.status.idle": "2025-03-25T06:00:08.588903Z", "shell.execute_reply": "2025-03-25T06:00:08.588539Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Gene annotation preview:\n", "Columns in gene annotation: ['ID', 'GB_ACC', 'SPOT_ID', 'Species Scientific Name', 'Annotation Date', 'Sequence Type', 'Sequence Source', 'Target Description', 'Representative Public ID', 'Gene Title', 'Gene Symbol', 'ENTREZ_GENE_ID', 'RefSeq Transcript ID', 'Gene Ontology Biological Process', 'Gene Ontology Cellular Component', 'Gene Ontology Molecular Function']\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", "\n", "Searching for platform information in SOFT file:\n", "Platform ID not found in first 100 lines\n", "\n", "Searching for gene symbol information in SOFT file:\n", "Found references to gene symbols:\n", "!Platform_relation = Alternative to: GPL19918 (Gene symbol version, 10K)\n", "!Platform_relation = Alternative to: GPL20182 (Gene Symbol Version)\n", "#Gene Symbol = A gene symbol, when one is available (from UniGene).\n", "ID\tGB_ACC\tSPOT_ID\tSpecies Scientific Name\tAnnotation Date\tSequence Type\tSequence Source\tTarget Description\tRepresentative Public ID\tGene Title\tGene Symbol\tENTREZ_GENE_ID\tRefSeq Transcript ID\tGene Ontology Biological Process\tGene Ontology Cellular Component\tGene Ontology Molecular Function\n", "\n", "Checking for additional annotation files in the directory:\n", "[]\n" ] } ], "source": [ "# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.\n", "gene_annotation = get_gene_annotation(soft_file)\n", "\n", "# 2. Analyze the gene annotation dataframe to identify which columns contain the gene identifiers and gene symbols\n", "print(\"\\nGene annotation preview:\")\n", "print(f\"Columns in gene annotation: {gene_annotation.columns.tolist()}\")\n", "print(preview_df(gene_annotation, n=5))\n", "\n", "# Let's look for platform information in the SOFT file to understand the annotation better\n", "print(\"\\nSearching for platform information in SOFT file:\")\n", "with gzip.open(soft_file, 'rt') as f:\n", " for i, line in enumerate(f):\n", " if '!Series_platform_id' in line:\n", " print(line.strip())\n", " break\n", " if i > 100: # Limit search to first 100 lines\n", " print(\"Platform ID not found in first 100 lines\")\n", " break\n", "\n", "# Check if the SOFT file includes any reference to gene symbols\n", "print(\"\\nSearching for gene symbol information in SOFT file:\")\n", "with gzip.open(soft_file, 'rt') as f:\n", " gene_symbol_lines = []\n", " for i, line in enumerate(f):\n", " if 'GENE_SYMBOL' in line or 'gene_symbol' in line.lower() or 'symbol' in line.lower():\n", " gene_symbol_lines.append(line.strip())\n", " if i > 1000 and len(gene_symbol_lines) > 0: # Limit search but ensure we found something\n", " break\n", " \n", " if gene_symbol_lines:\n", " print(\"Found references to gene symbols:\")\n", " for line in gene_symbol_lines[:5]: # Show just first 5 matches\n", " print(line)\n", " else:\n", " print(\"No explicit gene symbol references found in first 1000 lines\")\n", "\n", "# Look for alternative annotation files or references in the directory\n", "print(\"\\nChecking for additional annotation files in the directory:\")\n", "all_files = os.listdir(in_cohort_dir)\n", "print([f for f in all_files if 'annotation' in f.lower() or 'platform' in f.lower() or 'gpl' in f.lower()])\n" ] }, { "cell_type": "markdown", "id": "42a38b2f", "metadata": {}, "source": [ "### Step 6: Gene Identifier Mapping" ] }, { "cell_type": "code", "execution_count": 7, "id": "20e361d4", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T06:00:08.590313Z", "iopub.status.busy": "2025-03-25T06:00:08.590196Z", "iopub.status.idle": "2025-03-25T06:00:09.713715Z", "shell.execute_reply": "2025-03-25T06:00:09.713307Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Gene mapping dataframe shape: (45782, 2)\n", "Preview of gene mapping dataframe:\n", "{'ID': ['1007_s_at', '1053_at', '117_at', '121_at', '1255_g_at'], 'Gene': ['DDR1 /// MIR4640', 'RFC2', 'HSPA6', 'PAX8', 'GUCA1A']}\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Gene expression data shape after mapping: (12392, 102)\n", "Preview of gene expression data (first 5 genes, first 5 samples):\n", " GSM1360956 GSM1360957 GSM1360958 GSM1360959 GSM1360960\n", "Gene \n", "A1BG 5.274919 5.738628 3.336548 3.457008 4.083493\n", "A1BG-AS1 4.453343 4.914406 3.572509 4.002934 3.573421\n", "A2M 5.798740 6.459806 5.214447 5.554075 5.285597\n", "A2M-AS1 4.810059 5.034640 4.747397 5.095043 5.651134\n", "A4GALT 5.362888 5.379710 5.655486 5.657037 4.360879\n", "Gene expression data shape after normalizing gene symbols: (12087, 102)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Gene expression data saved to ../../output/preprocess/Osteoarthritis/gene_data/GSE56409.csv\n" ] } ], "source": [ "# 1. Observe gene identifiers and determine mapping columns\n", "# Based on the gene annotation preview, 'ID' column contains probe IDs that match the gene expression data's index,\n", "# and 'Gene Symbol' column contains the gene symbols we need to map to.\n", "\n", "# 2. Get a gene mapping dataframe\n", "gene_mapping = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='Gene Symbol')\n", "print(f\"Gene mapping dataframe shape: {gene_mapping.shape}\")\n", "print(\"Preview of gene mapping dataframe:\")\n", "print(preview_df(gene_mapping, n=5))\n", "\n", "# 3. Convert probe-level measurements to gene expression data\n", "gene_data = apply_gene_mapping(gene_data, gene_mapping)\n", "print(f\"Gene expression data shape after mapping: {gene_data.shape}\")\n", "print(\"Preview of gene expression data (first 5 genes, first 5 samples):\")\n", "# Display first 5 rows and first 5 columns\n", "print(gene_data.iloc[:5, :5])\n", "\n", "# Normalize gene symbols to handle synonyms\n", "gene_data = normalize_gene_symbols_in_index(gene_data)\n", "print(f\"Gene expression data shape after normalizing gene symbols: {gene_data.shape}\")\n", "\n", "# Save the processed gene expression data\n", "os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)\n", "gene_data.to_csv(out_gene_data_file)\n", "print(f\"Gene expression data saved to {out_gene_data_file}\")\n" ] }, { "cell_type": "markdown", "id": "30ee0a9b", "metadata": {}, "source": [ "### Step 7: Data Normalization and Linking" ] }, { "cell_type": "code", "execution_count": 8, "id": "a435637c", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T06:00:09.715033Z", "iopub.status.busy": "2025-03-25T06:00:09.714901Z", "iopub.status.idle": "2025-03-25T06:00:16.221702Z", "shell.execute_reply": "2025-03-25T06:00:16.221223Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Normalized gene data shape: (12087, 102)\n", "Gene data column names (sample IDs):\n", "Index(['GSM1360956', 'GSM1360957', 'GSM1360958', 'GSM1360959', 'GSM1360960'], dtype='object')\n", "\n", "Raw clinical data structure:\n", "Clinical data shape: (3, 103)\n", "Clinical data columns: Index(['!Sample_geo_accession', 'GSM1360956', 'GSM1360957', 'GSM1360958',\n", " 'GSM1360959'],\n", " dtype='object')\n", "\n", "Sample characteristics dictionary:\n", "{0: ['tissue: Synovium', 'tissue: Skin', 'tissue: Bone Marrow'], 1: ['disease: RA', 'disease: OA'], 2: ['serum: Low Serum', 'serum: High Serum']}\n", "\n", "Values in trait row:\n", "['!Sample_characteristics_ch1' 'disease: RA' 'disease: RA' 'disease: RA'\n", " 'disease: RA']\n", "\n", "Created clinical features dataframe:\n", "Shape: (1, 102)\n", " GSM1360956 GSM1360957 GSM1360958 GSM1360959 GSM1360960\n", "Osteoarthritis 0 0 0 0 0\n", "\n", "Linked data shape before handling missing values: (102, 12088)\n", "Actual trait column in linked data: Osteoarthritis\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/media/techt/DATA/GenoAgent/tools/preprocess.py:455: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n", " df[gene_cols] = df[gene_cols].fillna(df[gene_cols].mean())\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Linked data shape after handling missing values: (102, 12088)\n", "For the feature 'Osteoarthritis', the least common label is '1' with 34 occurrences. This represents 33.33% of the dataset.\n", "The distribution of the feature 'Osteoarthritis' in this dataset is fine.\n", "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Linked data saved to ../../output/preprocess/Osteoarthritis/GSE56409.csv\n" ] } ], "source": [ "# 1. Normalize gene symbols in the gene expression data \n", "# (This was already done in the previous step, so no need to repeat)\n", "print(f\"Normalized gene data shape: {gene_data.shape}\")\n", "\n", "# 2. Examine the sample IDs in the gene expression data to understand the structure\n", "print(\"Gene data column names (sample IDs):\")\n", "print(gene_data.columns[:5]) # Print first 5 for brevity\n", "\n", "# Inspect the clinical data format from the matrix file directly\n", "background_info, clinical_data = get_background_and_clinical_data(matrix_file)\n", "print(\"\\nRaw clinical data structure:\")\n", "print(f\"Clinical data shape: {clinical_data.shape}\")\n", "print(f\"Clinical data columns: {clinical_data.columns[:5]}\")\n", "\n", "# Get the sample characteristics to re-extract the disease information\n", "sample_characteristics_dict = get_unique_values_by_row(clinical_data)\n", "print(\"\\nSample characteristics dictionary:\")\n", "print(sample_characteristics_dict)\n", "\n", "# 3. Directly create clinical features from the raw data again\n", "# Verify trait row contains the disease information (OA vs RA)\n", "print(\"\\nValues in trait row:\")\n", "trait_values = clinical_data.iloc[trait_row].values\n", "print(trait_values[:5])\n", "\n", "# Create clinical dataframe with proper structure\n", "# First get the sample IDs from gene data as these are our actual sample identifiers\n", "sample_ids = gene_data.columns.tolist()\n", "\n", "# Create the clinical features dataframe with those sample IDs\n", "clinical_features = pd.DataFrame(index=[trait], columns=sample_ids)\n", "\n", "# Fill the clinical features with our trait values by mapping GSM IDs to actual values\n", "for col in clinical_data.columns:\n", " if col in sample_ids:\n", " # Extract the disease value and convert it\n", " disease_val = clinical_data.iloc[trait_row][col]\n", " clinical_features.loc[trait, col] = convert_trait(disease_val)\n", "\n", "print(\"\\nCreated clinical features dataframe:\")\n", "print(f\"Shape: {clinical_features.shape}\")\n", "print(clinical_features.iloc[:, :5]) # Show first 5 columns\n", "\n", "# 4. Link clinical and genetic data\n", "linked_data = geo_link_clinical_genetic_data(clinical_features, gene_data)\n", "print(f\"\\nLinked data shape before handling missing values: {linked_data.shape}\")\n", "\n", "# 5. Handle missing values - we need to use the actual column name, not the trait variable\n", "# First identify the actual trait column name in the linked data\n", "trait_column = clinical_features.index[0] # This should be 'Osteoarthritis'\n", "print(f\"Actual trait column in linked data: {trait_column}\")\n", "\n", "# Now handle missing values with the correct column name\n", "linked_data_clean = handle_missing_values(linked_data, trait_column)\n", "print(f\"Linked data shape after handling missing values: {linked_data_clean.shape}\")\n", "\n", "# 6. Evaluate bias in trait and demographic features\n", "is_biased, linked_data_clean = judge_and_remove_biased_features(linked_data_clean, trait_column)\n", "\n", "# 7. Conduct final quality validation\n", "note = \"Dataset contains gene expression data from synovial fibroblasts of RA and OA patients. Data includes high serum and low serum responses.\"\n", "is_usable = validate_and_save_cohort_info(\n", " is_final=True,\n", " cohort=cohort,\n", " info_path=json_path,\n", " is_gene_available=True,\n", " is_trait_available=(linked_data_clean.shape[0] > 0),\n", " is_biased=is_biased,\n", " df=linked_data_clean,\n", " note=note\n", ")\n", "\n", "# 8. Save linked data if usable\n", "if is_usable:\n", " os.makedirs(os.path.dirname(out_data_file), exist_ok=True)\n", " linked_data_clean.to_csv(out_data_file)\n", " print(f\"Linked data saved to {out_data_file}\")\n", "else:\n", " print(\"Dataset deemed not usable due to quality issues - linked data not saved\")" ] } ], "metadata": { "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.10.16" } }, "nbformat": 4, "nbformat_minor": 5 }