{ "cells": [ { "cell_type": "code", "execution_count": 1, "id": "41d473c1", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T05:59:56.829614Z", "iopub.status.busy": "2025-03-25T05:59:56.829512Z", "iopub.status.idle": "2025-03-25T05:59:56.986631Z", "shell.execute_reply": "2025-03-25T05:59:56.986300Z" } }, "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 = \"GSE55457\"\n", "\n", "# Input paths\n", "in_trait_dir = \"../../input/GEO/Osteoarthritis\"\n", "in_cohort_dir = \"../../input/GEO/Osteoarthritis/GSE55457\"\n", "\n", "# Output paths\n", "out_data_file = \"../../output/preprocess/Osteoarthritis/GSE55457.csv\"\n", "out_gene_data_file = \"../../output/preprocess/Osteoarthritis/gene_data/GSE55457.csv\"\n", "out_clinical_data_file = \"../../output/preprocess/Osteoarthritis/clinical_data/GSE55457.csv\"\n", "json_path = \"../../output/preprocess/Osteoarthritis/cohort_info.json\"\n" ] }, { "cell_type": "markdown", "id": "42b7e882", "metadata": {}, "source": [ "### Step 1: Initial Data Loading" ] }, { "cell_type": "code", "execution_count": 2, "id": "e9c4fb4e", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T05:59:56.988038Z", "iopub.status.busy": "2025-03-25T05:59:56.987894Z", "iopub.status.idle": "2025-03-25T05:59:57.037337Z", "shell.execute_reply": "2025-03-25T05:59:57.037020Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Background Information:\n", "!Series_title\t\"Identification of rheumatoid arthritis and osteoarthritis patients by transcriptome-based rule set generation [Jena]\"\n", "!Series_summary\t\"Discrimination of rheumatoid arthritis (RA) patients from patients with other inflammatory/degenerative joint diseases or healthy individuals purely on the basis of genes differentially expressed in high-throughput data has proven very difficult. Thus, the present study sought to achieve such discrimination by employing a novel unbiased approach using rule-based classifiers. Three multi-center genome-wide transcriptomic data sets (Affymetrix HG- U133 A/B) from a total of 79 individuals, including 20 healthy controls (control group - CG), as well as 26 osteoarthritis (OA) and 33 RA patients, were used to infer rule- based classifiers to discriminate the disease groups. The rules were ranked with respect to Kiendl’s statistical relevance index, and the resulting rule set was optimized by pruning. The rule sets were inferred separately from data of one of three centers and applied to the two remaining centers for validation. All rules from the optimized rule sets of all centers were used to analyze their biological relevance applying the software Pathway Studio.\"\n", "!Series_summary\t\"The optimized rule sets for the three centers contained a total of 29, 20, and 8 rules (including 10, 8, and 4 rules for ‘RA’), respectively. The mean sensitivity for the prediction of RA based on six center-to-center tests was 96% (range 90% to 100%), that for OA 86% (range 40% to 100%). The mean specificity for RA prediction was 94% (range 80% to 100%), that for OA 96% (range 83.3% to 100%). The average overall accuracy of the three different rule-based classifiers was 91% (range 80% to 100%). Unbiased analyses by Pathway Studio of the gene sets obtained by discrimination of RA from OA and CG with rule-based classifiers resulted in the identification of the pathogenetically and/or therapeutically relevant interferon-gamma and GM-CSF pathways. First-time application of rule-based classifiers for the discrimination of RA resulted in high performance, with means for all assessment parameters close to or higher than 90%. In addition, this unbiased, new approach resulted in the identification not only of pathways known to be critical to RA, but also of novel molecules such as serine/threonine kinase 10.\"\n", "!Series_overall_design\t\"Three multi-center genome-wide transcriptomic data sets (Affymetrix HG- U133 A/B) from a total of 79 individuals, including 20 healthy controls (control group - CG), as well as 26 osteoarthritis (OA) and 33 RA patients, were used to infer rule- based classifiers to discriminate the disease groups. The rules were ranked with respect to Kiendl’s statistical relevance index, and the resulting rule set was optimized by pruning. The rule sets were inferred separately from data of one of three centers and applied to the two remaining centers for validation. All rules from the optimized rule sets of all centers were used to analyze their biological relevance applying the software Pathway Studio.\"\n", "Sample Characteristics Dictionary:\n", "{0: ['gender: male', 'gender: female'], 1: ['age: 61', 'age: 64', 'age: 78', 'age: 65', 'age: 53', 'age: 68', 'age: 29', 'age: 17', 'age: 39', 'age: 36', 'age: 75', 'age: 79', 'age: 63', 'age: 66', 'age: 46', 'age: 71', 'age: 72', 'age: 2', 'age: 47', 'age: 59', 'age: 73', 'age: 77', 'age: 76', 'age: 69', 'age: 80'], 2: ['clinical status: normal control', 'clinical status: rheumatoid arthritis', 'clinical status: osteoarthritis']}\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": "63c5e878", "metadata": {}, "source": [ "### Step 2: Dataset Analysis and Clinical Feature Extraction" ] }, { "cell_type": "code", "execution_count": 3, "id": "7796af52", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T05:59:57.038630Z", "iopub.status.busy": "2025-03-25T05:59:57.038527Z", "iopub.status.idle": "2025-03-25T05:59:57.048663Z", "shell.execute_reply": "2025-03-25T05:59:57.048387Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Preview of selected clinical features:\n", "{'GSM1337304': [0.0, 61.0, 1.0], 'GSM1337305': [0.0, 64.0, 1.0], 'GSM1337306': [0.0, 78.0, 0.0], 'GSM1337307': [0.0, 65.0, 1.0], 'GSM1337308': [0.0, 53.0, 1.0], 'GSM1337309': [0.0, 68.0, 1.0], 'GSM1337310': [0.0, 29.0, 0.0], 'GSM1337311': [0.0, 17.0, 1.0], 'GSM1337312': [0.0, 39.0, 1.0], 'GSM1337313': [0.0, 36.0, 1.0], 'GSM1337314': [0.0, 75.0, 1.0], 'GSM1337315': [0.0, 79.0, 0.0], 'GSM1337316': [0.0, 63.0, 0.0], 'GSM1337317': [0.0, 66.0, 0.0], 'GSM1337318': [0.0, 64.0, 0.0], 'GSM1337319': [0.0, 63.0, 0.0], 'GSM1337320': [0.0, 46.0, 0.0], 'GSM1337321': [0.0, 71.0, 0.0], 'GSM1337322': [0.0, 72.0, 0.0], 'GSM1337323': [0.0, 2.0, 0.0], 'GSM1337324': [0.0, 47.0, 1.0], 'GSM1337325': [0.0, 59.0, 1.0], 'GSM1337326': [0.0, 73.0, 0.0], 'GSM1337327': [1.0, 77.0, 0.0], 'GSM1337328': [1.0, 71.0, 0.0], 'GSM1337329': [1.0, 76.0, 0.0], 'GSM1337330': [1.0, 61.0, 0.0], 'GSM1337331': [1.0, 75.0, 0.0], 'GSM1337332': [1.0, 78.0, 1.0], 'GSM1337333': [1.0, 69.0, 1.0], 'GSM1337334': [1.0, 71.0, 0.0], 'GSM1337335': [1.0, 80.0, 0.0], 'GSM1337336': [1.0, 66.0, 0.0]}\n", "Clinical data saved to ../../output/preprocess/Osteoarthritis/clinical_data/GSE55457.csv\n" ] } ], "source": [ "import os\n", "import json\n", "import pandas as pd\n", "from typing import Dict, Any, Optional, Callable\n", "\n", "# 1. Gene Expression Data Availability\n", "# Based on the background information, this dataset contains genome-wide transcriptomic data from Affymetrix HG-U133 A/B\n", "# which means it contains gene expression data\n", "is_gene_available = True\n", "\n", "# 2.1 Data Availability\n", "# Examining the sample characteristics dictionary:\n", "# - trait: in key 2 as 'clinical status', which includes 'osteoarthritis' (our trait of interest)\n", "# - age: in key 1 as age\n", "# - gender: in key 0 as gender\n", "trait_row = 2\n", "age_row = 1\n", "gender_row = 0\n", "\n", "# 2.2 Data Type Conversion\n", "def convert_trait(value):\n", " \"\"\"Convert trait value to binary (1 for osteoarthritis, 0 for others)\"\"\"\n", " if value is None:\n", " return None\n", " # Extract the value after colon if present\n", " if ':' in value:\n", " value = value.split(':', 1)[1].strip().lower()\n", " else:\n", " value = value.strip().lower()\n", " \n", " # Return 1 for osteoarthritis, 0 for others\n", " if 'osteoarthritis' in value:\n", " return 1\n", " elif 'normal control' in value or 'rheumatoid arthritis' in value:\n", " return 0\n", " else:\n", " return None\n", "\n", "def convert_age(value):\n", " \"\"\"Convert age value to continuous numerical value\"\"\"\n", " if value is None:\n", " return None\n", " # Extract the value after colon if present\n", " if ':' in value:\n", " value = value.split(':', 1)[1].strip()\n", " else:\n", " value = value.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:\n", " return None\n", " # Extract the value after colon if present\n", " if ':' in value:\n", " value = value.split(':', 1)[1].strip().lower()\n", " else:\n", " value = value.strip().lower()\n", " \n", " if 'female' in value:\n", " return 0\n", " elif 'male' in value:\n", " return 1\n", " else:\n", " return None\n", "\n", "# 3. Save Metadata\n", "# Determine trait data availability\n", "is_trait_available = trait_row is not None\n", "validate_and_save_cohort_info(is_final=False, cohort=cohort, info_path=json_path, \n", " is_gene_available=is_gene_available, \n", " is_trait_available=is_trait_available)\n", "\n", "# 4. Clinical Feature Extraction\n", "# Since trait_row is not None, we need to extract clinical features\n", "if trait_row is not None:\n", " # Load the sample characteristics file from the previous step\n", " # We're assuming clinical_data is already loaded from previous steps\n", " # Or that it will be provided to us in the execution environment\n", " try:\n", " # First try loading a pre-existing clinical_data variable\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 DataFrame\n", " preview = preview_df(selected_clinical_df)\n", " print(\"Preview of selected clinical features:\")\n", " print(preview)\n", " \n", " # Create the directory if it doesn't exist\n", " os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)\n", " \n", " # Save the DataFrame to CSV\n", " selected_clinical_df.to_csv(out_clinical_data_file)\n", " print(f\"Clinical data saved to {out_clinical_data_file}\")\n", " except Exception as e:\n", " print(f\"Error processing clinical data: {e}\")\n", " print(\"Clinical data processing failed. Check if clinical_data is properly loaded.\")\n" ] }, { "cell_type": "markdown", "id": "d671da00", "metadata": {}, "source": [ "### Step 3: Gene Data Extraction" ] }, { "cell_type": "code", "execution_count": 4, "id": "b12fd502", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T05:59:57.049603Z", "iopub.status.busy": "2025-03-25T05:59:57.049504Z", "iopub.status.idle": "2025-03-25T05:59:57.109307Z", "shell.execute_reply": "2025-03-25T05:59:57.109007Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Matrix file found: ../../input/GEO/Osteoarthritis/GSE55457/GSE55457_series_matrix.txt.gz\n", "Gene data shape: (22283, 33)\n", "First 20 gene/probe identifiers:\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 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": "afc1e793", "metadata": {}, "source": [ "### Step 4: Gene Identifier Review" ] }, { "cell_type": "code", "execution_count": 5, "id": "5e00e818", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T05:59:57.110429Z", "iopub.status.busy": "2025-03-25T05:59:57.110322Z", "iopub.status.idle": "2025-03-25T05:59:57.112120Z", "shell.execute_reply": "2025-03-25T05:59:57.111843Z" } }, "outputs": [], "source": [ "# Based on the gene identifiers observed (e.g., '1007_s_at', '1053_at'), these appear to be \n", "# Affymetrix probe IDs rather than human gene symbols.\n", "# Affymetrix IDs need to be mapped to standard gene symbols for consistent analysis.\n", "\n", "requires_gene_mapping = True\n" ] }, { "cell_type": "markdown", "id": "ae3e3bda", "metadata": {}, "source": [ "### Step 5: Gene Annotation" ] }, { "cell_type": "code", "execution_count": 6, "id": "1a60f58a", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T05:59:57.113181Z", "iopub.status.busy": "2025-03-25T05:59:57.113086Z", "iopub.status.idle": "2025-03-25T05:59:58.477261Z", "shell.execute_reply": "2025-03-25T05:59:58.476805Z" } }, "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", "!Series_platform_id = GPL96\n", "\n", "Searching for gene symbol information in SOFT file:\n", "Found references to gene symbols:\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": "592bf352", "metadata": {}, "source": [ "### Step 6: Gene Identifier Mapping" ] }, { "cell_type": "code", "execution_count": 7, "id": "2af57673", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T05:59:58.478717Z", "iopub.status.busy": "2025-03-25T05:59:58.478591Z", "iopub.status.idle": "2025-03-25T05:59:58.839142Z", "shell.execute_reply": "2025-03-25T05:59:58.838760Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Using ID as probe identifier column and Gene Symbol as gene symbol column\n", "Gene mapping dataframe shape: (21225, 2)\n", "First 5 rows of mapping dataframe:\n", " ID Gene\n", "0 1007_s_at DDR1 /// MIR4640\n", "1 1053_at RFC2\n", "2 117_at HSPA6\n", "3 121_at PAX8\n", "4 1255_g_at GUCA1A\n", "Gene expression data shape after mapping: (13830, 33)\n", "First 10 gene symbols in the mapped gene expression data:\n", "Index(['A1CF', 'A2M', 'A4GALT', 'A4GNT', 'AAAS', 'AACS', 'AADAC', 'AAGAB',\n", " 'AAK1', 'AAMDC'],\n", " dtype='object', name='Gene')\n", "Gene expression data shape after normalization: (13542, 33)\n", "First 10 gene symbols after normalization:\n", "Index(['A1CF', 'A2M', 'A4GALT', 'A4GNT', 'AAAS', 'AACS', 'AADAC', 'AAGAB',\n", " 'AAK1', 'AAMDC'],\n", " dtype='object', name='Gene')\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Gene expression data saved to ../../output/preprocess/Osteoarthritis/gene_data/GSE55457.csv\n" ] } ], "source": [ "# 1. Determine the columns for gene identifiers and gene symbols in the annotation dataframe\n", "# Based on the preview, we can see:\n", "# - 'ID' column contains probe IDs (e.g., '1007_s_at') matching the gene expression data index\n", "# - 'Gene Symbol' column contains gene symbols (e.g., 'DDR1 /// MIR4640')\n", "probe_col = 'ID'\n", "gene_col = 'Gene Symbol'\n", "\n", "print(f\"Using {probe_col} as probe identifier column and {gene_col} as gene symbol column\")\n", "\n", "# 2. Get mapping dataframe from gene annotation\n", "mapping_df = get_gene_mapping(gene_annotation, probe_col, gene_col)\n", "print(f\"Gene mapping dataframe shape: {mapping_df.shape}\")\n", "print(\"First 5 rows of mapping dataframe:\")\n", "print(mapping_df.head())\n", "\n", "# 3. Apply gene mapping to convert probe-level measurements to gene-level expression\n", "gene_data = apply_gene_mapping(gene_data, mapping_df)\n", "print(f\"Gene expression data shape after mapping: {gene_data.shape}\")\n", "print(\"First 10 gene symbols in the mapped gene expression data:\")\n", "print(gene_data.index[:10])\n", "\n", "# 4. Normalize gene symbols to handle duplicates and synonyms\n", "gene_data = normalize_gene_symbols_in_index(gene_data)\n", "print(f\"Gene expression data shape after normalization: {gene_data.shape}\")\n", "print(\"First 10 gene symbols after normalization:\")\n", "print(gene_data.index[:10])\n", "\n", "# 5. 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": "84bc4446", "metadata": {}, "source": [ "### Step 7: Data Normalization and Linking" ] }, { "cell_type": "code", "execution_count": 8, "id": "c06e1c6c", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T05:59:58.840717Z", "iopub.status.busy": "2025-03-25T05:59:58.840580Z", "iopub.status.idle": "2025-03-25T06:00:03.685057Z", "shell.execute_reply": "2025-03-25T06:00:03.684686Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Normalized gene data shape: (13542, 33)\n", "Gene data column names (sample IDs):\n", "Index(['GSM1337304', 'GSM1337305', 'GSM1337306', 'GSM1337307', 'GSM1337308'], dtype='object')\n", "\n", "Raw clinical data structure:\n", "Clinical data shape: (3, 34)\n", "Clinical data columns: Index(['!Sample_geo_accession', 'GSM1337304', 'GSM1337305', 'GSM1337306',\n", " 'GSM1337307'],\n", " dtype='object')\n", "\n", "Sample characteristics dictionary:\n", "{0: ['gender: male', 'gender: female'], 1: ['age: 61', 'age: 64', 'age: 78', 'age: 65', 'age: 53', 'age: 68', 'age: 29', 'age: 17', 'age: 39', 'age: 36', 'age: 75', 'age: 79', 'age: 63', 'age: 66', 'age: 46', 'age: 71', 'age: 72', 'age: 2', 'age: 47', 'age: 59', 'age: 73', 'age: 77', 'age: 76', 'age: 69', 'age: 80'], 2: ['clinical status: normal control', 'clinical status: rheumatoid arthritis', 'clinical status: osteoarthritis']}\n", "\n", "Values in trait row:\n", "['!Sample_characteristics_ch1' 'clinical status: normal control'\n", " 'clinical status: normal control' 'clinical status: normal control'\n", " 'clinical status: normal control']\n", "\n", "Created clinical features dataframe:\n", "Shape: (1, 33)\n", " GSM1337304 GSM1337305 GSM1337306 GSM1337307 GSM1337308\n", "Osteoarthritis 0 0 0 0 0\n", "\n", "Linked data shape before handling missing values: (33, 13543)\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: (33, 13543)\n", "For the feature 'Osteoarthritis', the least common label is '1' with 10 occurrences. This represents 30.30% 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/GSE55457.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 }