{ "cells": [ { "cell_type": "code", "execution_count": 1, "id": "9f62ece1", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T08:20:56.069305Z", "iopub.status.busy": "2025-03-25T08:20:56.069012Z", "iopub.status.idle": "2025-03-25T08:20:56.225825Z", "shell.execute_reply": "2025-03-25T08:20:56.225399Z" } }, "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 = \"Chronic_obstructive_pulmonary_disease_(COPD)\"\n", "cohort = \"GSE21359\"\n", "\n", "# Input paths\n", "in_trait_dir = \"../../input/GEO/Chronic_obstructive_pulmonary_disease_(COPD)\"\n", "in_cohort_dir = \"../../input/GEO/Chronic_obstructive_pulmonary_disease_(COPD)/GSE21359\"\n", "\n", "# Output paths\n", "out_data_file = \"../../output/preprocess/Chronic_obstructive_pulmonary_disease_(COPD)/GSE21359.csv\"\n", "out_gene_data_file = \"../../output/preprocess/Chronic_obstructive_pulmonary_disease_(COPD)/gene_data/GSE21359.csv\"\n", "out_clinical_data_file = \"../../output/preprocess/Chronic_obstructive_pulmonary_disease_(COPD)/clinical_data/GSE21359.csv\"\n", "json_path = \"../../output/preprocess/Chronic_obstructive_pulmonary_disease_(COPD)/cohort_info.json\"\n" ] }, { "cell_type": "markdown", "id": "c09dabe8", "metadata": {}, "source": [ "### Step 1: Initial Data Loading" ] }, { "cell_type": "code", "execution_count": 2, "id": "503721f4", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T08:20:56.227270Z", "iopub.status.busy": "2025-03-25T08:20:56.227133Z", "iopub.status.idle": "2025-03-25T08:20:56.544305Z", "shell.execute_reply": "2025-03-25T08:20:56.543920Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Background Information:\n", "!Series_title\t\"Association of CXCL14 in the Human Airway Epithelium with Chronic Obstructive Lung Disease and Lung Cancer\"\n", "!Series_summary\t\"CXCL14, a recently described chemokine constitutively expressed in various epithelia, has multiple putative roles in inflammation and carcinogenesis. Based on the knowledge that cigarette smoking and the smoking-induced disorders, such as chronic obstructive pulmonary disease (COPD) and lung cancer, are associated with inflammation, we hypothesized that the airway epithelium, the primary site of smoking-induced pathologic changes in COPD and adenocarcinoma, responds to cigarette smoking with an altered CXCL14 gene expression as a part of disease-relevant molecular phenotype. Microarray analysis with subsequent TaqMan PCR validation revealed very low constitutive CXCL14 gene expression in the airway epithelium of healthy nonsmokers (n=53) which was strongly up-regulated in healthy smokers ( n=59; p<0.001) and further increased in COPD smokers (n=23; p<10-7 vs nonsmokers; p<0.005 vs healthy smokers). In smokers, CXCL14 expression inversely correlated with lung function parameters FEV1 and FEV1/FVC. Genome-wide analysis also showed that up-regulated correlation of CXCL14 expression with genes related to cell growth and proliferation, squamous differentiation and cancer. The analysis of 193 lung adenocarcinoma samples demonstrated a dramatic up-regulation of CXCL14 in a smoking-dependent manner. [need to include survival data once we get it]. Together, these data suggest that smoking-induced expression of CXCL14 in association with genome-wide reprogramming of processes related to tissue homeostasis, differentiation and tumorigenesis, represents a novel molecular link between cigarette smoking, COPD and lung cancer.\"\n", "!Series_overall_design\t\"Affymetrix arrays were used to assess the expression of CXCL14 gene expression data in small airway epithelium obtained by fiberoptic bronchoscopy of 53 healthy non-smokers and 59 healthy smokers and 23 smokers with COPD.\"\n", "Sample Characteristics Dictionary:\n", "{0: ['Age: 41', 'age: 35', 'age: 61', 'age: 37', 'age: 47', 'age: 38', 'age: 49', 'age: 45', 'age: 36', 'age: 46', 'age: 48', 'age: 50', 'age: 56', 'age: 59', 'age: 34', 'age: 44', 'Age: 45', 'age: 29', 'age: 42', 'Age: 47', 'age: 55', 'age: 51', 'age: 60', 'age: 52', 'age: 40', 'age: 41', 'age: 43', 'age: 31', 'age: 53', 'age: 62'], 1: ['Sex: M', 'sex: M', 'sex: F', 'Sex: F'], 2: ['Ethnic group: black', 'ethnic group: black', 'ethnic group: white', 'ethnic group: hispanic', 'Ethnic group: white', 'ethnic group: black/hispanic', 'ethnic group: asian'], 3: ['Smoking Status: non-smoker', 'smoking status: non-smoker', 'smoking status: smoker, 21 pack-years', 'smoking status: smoker, 23 pack-years', 'smoking status: smoker, 28 pack-years', 'smoking status: smoker, 20 pack-years', 'smoking status: smoker, 38 pack-years', 'smoking status: smoker, 80 pack-years', 'smoking status: smoker, 60 pack-years', 'Smoking status: non-smoker', 'smoking status: COPD, GOLD-I, 50 pack-years', 'Smoking status: COPD, GOLD-II, 33 pack-years', 'smoking status: COPD, GOLD-II, 35 pack-years', 'smoking status: COPD, GOLD-II, 20 pack-years', 'smoking status: COPD, GOLD-I, 48 pack-years', 'smoking status: COPD, GOLD-II, 75 pack-years', 'smoking status: COPD, GOLD-II, 27 pack-years', 'smoking status: COPD, GOLD-II, 60 pack-years', 'smoking status: COPD, GOLD-III, 110 pack-years', 'smoking status: COPD, GOLD-I, 22 pack-years', 'smoking status: COPD, GOLD-I, 23 pack-years', 'smoking status: smoker, 24 pack-years', 'smoking status: smoker, 29 pack-years', 'smoking status: smoker, 45 pack-years', 'smoking status: smoker, 32 pack-years', 'smoking status: smoker, 36 pack-years', 'smoking status: smoker, 15 pack-years', 'smoking status: smoker, 22 pack-years', 'smoking status: smoker, 33 pack-years', 'smoking status: smoker, 16 pack-years']}\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": "57a8c06c", "metadata": {}, "source": [ "### Step 2: Dataset Analysis and Clinical Feature Extraction" ] }, { "cell_type": "code", "execution_count": 3, "id": "daea0c4b", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T08:20:56.545664Z", "iopub.status.busy": "2025-03-25T08:20:56.545550Z", "iopub.status.idle": "2025-03-25T08:20:56.556109Z", "shell.execute_reply": "2025-03-25T08:20:56.555783Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Clinical Features Preview:\n", "{0: [nan, 41.0, nan], 1: [nan, nan, 1.0], 2: [nan, nan, nan], 3: [0.0, nan, nan]}\n", "Clinical features saved to ../../output/preprocess/Chronic_obstructive_pulmonary_disease_(COPD)/clinical_data/GSE21359.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", "# This dataset appears to be using Affymetrix arrays to assess gene expression, \n", "# so it's likely to contain gene expression data\n", "is_gene_available = True\n", "\n", "# 2.1 Data Availability\n", "# Trait (COPD) information is in row 3 under \"smoking status\"\n", "trait_row = 3\n", "# Age information is in row 0\n", "age_row = 0\n", "# Gender information is in row 1\n", "gender_row = 1\n", "\n", "# 2.2 Data Type Conversion Functions\n", "def convert_trait(value):\n", " \"\"\"Convert trait information to binary (0: non-COPD, 1: COPD)\"\"\"\n", " if value is None or not isinstance(value, str):\n", " return None\n", " \n", " # Extract value after colon\n", " if ':' in value:\n", " value = value.split(':', 1)[1].strip()\n", " \n", " # Check if the person has COPD\n", " if 'COPD' in value:\n", " return 1\n", " elif 'non-smoker' in value or 'smoker,' in value:\n", " return 0\n", " else:\n", " return None\n", "\n", "def convert_age(value):\n", " \"\"\"Convert age information to continuous values\"\"\"\n", " if value is None or not isinstance(value, str):\n", " return None\n", " \n", " # Extract value after colon\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 information to binary (0: female, 1: male)\"\"\"\n", " if value is None or not isinstance(value, str):\n", " return None\n", " \n", " # Extract value after colon\n", " if ':' in value:\n", " value = value.split(':', 1)[1].strip()\n", " \n", " # Convert gender to binary\n", " if value.upper() == 'F':\n", " return 0\n", " elif value.upper() == 'M':\n", " return 1\n", " else:\n", " return None\n", "\n", "# 3. Save Metadata\n", "# Check if trait data is available\n", "is_trait_available = trait_row is not None\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", "if trait_row is not None:\n", " # Create DataFrame from the sample characteristics dictionary\n", " sample_char_dict = {0: ['Age: 41', 'age: 35', 'age: 61', 'age: 37', 'age: 47', 'age: 38', 'age: 49', 'age: 45', 'age: 36', 'age: 46', 'age: 48', 'age: 50', 'age: 56', 'age: 59', 'age: 34', 'age: 44', 'Age: 45', 'age: 29', 'age: 42', 'Age: 47', 'age: 55', 'age: 51', 'age: 60', 'age: 52', 'age: 40', 'age: 41', 'age: 43', 'age: 31', 'age: 53', 'age: 62'], \n", " 1: ['Sex: M', 'sex: M', 'sex: F', 'Sex: F'], \n", " 2: ['Ethnic group: black', 'ethnic group: black', 'ethnic group: white', 'ethnic group: hispanic', 'Ethnic group: white', 'ethnic group: black/hispanic', 'ethnic group: asian'], \n", " 3: ['Smoking Status: non-smoker', 'smoking status: non-smoker', 'smoking status: smoker, 21 pack-years', 'smoking status: smoker, 23 pack-years', 'smoking status: smoker, 28 pack-years', 'smoking status: smoker, 20 pack-years', 'smoking status: smoker, 38 pack-years', 'smoking status: smoker, 80 pack-years', 'smoking status: smoker, 60 pack-years', 'Smoking status: non-smoker', 'smoking status: COPD, GOLD-I, 50 pack-years', 'Smoking status: COPD, GOLD-II, 33 pack-years', 'smoking status: COPD, GOLD-II, 35 pack-years', 'smoking status: COPD, GOLD-II, 20 pack-years', 'smoking status: COPD, GOLD-I, 48 pack-years', 'smoking status: COPD, GOLD-II, 75 pack-years', 'smoking status: COPD, GOLD-II, 27 pack-years', 'smoking status: COPD, GOLD-II, 60 pack-years', 'smoking status: COPD, GOLD-III, 110 pack-years', 'smoking status: COPD, GOLD-I, 22 pack-years', 'smoking status: COPD, GOLD-I, 23 pack-years', 'smoking status: smoker, 24 pack-years', 'smoking status: smoker, 29 pack-years', 'smoking status: smoker, 45 pack-years', 'smoking status: smoker, 32 pack-years', 'smoking status: smoker, 36 pack-years', 'smoking status: smoker, 15 pack-years', 'smoking status: smoker, 22 pack-years', 'smoking status: smoker, 33 pack-years', 'smoking status: smoker, 16 pack-years']}\n", " \n", " # Convert dict to DataFrame\n", " clinical_data = pd.DataFrame.from_dict(sample_char_dict, orient='index')\n", " clinical_data = clinical_data.transpose()\n", " \n", " # Extract clinical features\n", " clinical_features_df = geo_select_clinical_features(\n", " clinical_df=clinical_data,\n", " trait=trait,\n", " trait_row=trait_row,\n", " convert_trait=convert_trait,\n", " age_row=age_row,\n", " convert_age=convert_age,\n", " gender_row=gender_row,\n", " convert_gender=convert_gender\n", " )\n", " \n", " # Preview the extracted features\n", " preview = preview_df(clinical_features_df)\n", " print(\"Clinical Features Preview:\")\n", " print(preview)\n", " \n", " # Create directory if it doesn't exist\n", " os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)\n", " \n", " # Save the clinical features to CSV\n", " clinical_features_df.to_csv(out_clinical_data_file, index=False)\n", " print(f\"Clinical features saved to {out_clinical_data_file}\")\n" ] }, { "cell_type": "markdown", "id": "5bf6c1c6", "metadata": {}, "source": [ "### Step 3: Gene Data Extraction" ] }, { "cell_type": "code", "execution_count": 4, "id": "cdd316a1", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T08:20:56.557235Z", "iopub.status.busy": "2025-03-25T08:20:56.557128Z", "iopub.status.idle": "2025-03-25T08:20:57.163134Z", "shell.execute_reply": "2025-03-25T08:20:57.162448Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Matrix file found: ../../input/GEO/Chronic_obstructive_pulmonary_disease_(COPD)/GSE21359/GSE21359_series_matrix.txt.gz\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Gene data shape: (54675, 135)\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', '1552256_a_at', '1552257_a_at', '1552258_at', '1552261_at',\n", " '1552263_at', '1552264_a_at', '1552266_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": "b38bf927", "metadata": {}, "source": [ "### Step 4: Gene Identifier Review" ] }, { "cell_type": "code", "execution_count": 5, "id": "92473627", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T08:20:57.164533Z", "iopub.status.busy": "2025-03-25T08:20:57.164406Z", "iopub.status.idle": "2025-03-25T08:20:57.166696Z", "shell.execute_reply": "2025-03-25T08:20:57.166231Z" } }, "outputs": [], "source": [ "# Examining the gene identifiers shows that they are in the format of Affymetrix probe IDs (e.g., 1007_s_at)\n", "# rather than standard human gene symbols (like BRCA1, TP53, etc.)\n", "# These probe IDs need to be mapped to human gene symbols for biological interpretation\n", "\n", "requires_gene_mapping = True\n" ] }, { "cell_type": "markdown", "id": "b99398bd", "metadata": {}, "source": [ "### Step 5: Gene Annotation" ] }, { "cell_type": "code", "execution_count": 6, "id": "00c20b68", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T08:20:57.168092Z", "iopub.status.busy": "2025-03-25T08:20:57.167985Z", "iopub.status.idle": "2025-03-25T08:21:08.269103Z", "shell.execute_reply": "2025-03-25T08:21:08.268466Z" } }, "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": "48b0e844", "metadata": {}, "source": [ "### Step 6: Gene Identifier Mapping" ] }, { "cell_type": "code", "execution_count": 7, "id": "19d4593a", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T08:21:08.270422Z", "iopub.status.busy": "2025-03-25T08:21:08.270292Z", "iopub.status.idle": "2025-03-25T08:21:10.343563Z", "shell.execute_reply": "2025-03-25T08:21:10.342897Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Mapping dataframe shape: (45782, 2)\n", "Mapping sample:\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" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Gene expression data shape after mapping: (21278, 135)\n", "First few rows of gene expression data:\n", " GSM101095 GSM101096 GSM101097 GSM101098 GSM101100 \\\n", "Gene \n", "A1BG 26.664608 23.200108 23.611168 20.731066 22.466328 \n", "A1BG-AS1 38.430410 37.644897 27.018625 25.424614 33.169040 \n", "A1CF 32.887757 27.871269 27.148127 24.667728 23.662186 \n", "A2M 305.641970 1104.150857 162.984248 257.923840 254.933860 \n", "A2M-AS1 20.335550 27.066956 28.000036 32.134990 39.643590 \n", "\n", " GSM101101 GSM101102 GSM101103 GSM101104 GSM101105 ... \\\n", "Gene ... \n", "A1BG 25.030586 25.183960 22.589863 23.743465 27.456318 ... \n", "A1BG-AS1 26.234987 23.183222 24.775188 20.421490 25.106298 ... \n", "A1CF 27.690921 32.027879 26.455109 29.115901 29.179587 ... \n", "A2M 162.423000 205.575187 184.443614 171.407947 88.020766 ... \n", "A2M-AS1 47.090164 40.219450 32.511940 27.311834 26.748950 ... \n", "\n", " GSM434061 GSM434062 GSM434063 GSM434064 GSM458579 GSM458580 \\\n", "Gene \n", "A1BG 36.4369 86.7034 69.8861 116.91500 55.5067 90.07020 \n", "A1BG-AS1 11.9127 89.5264 67.4591 28.44960 17.9411 9.96002 \n", "A1CF 152.4898 76.8428 153.6378 46.09965 137.8570 169.70480 \n", "A2M 405.1700 504.6519 817.4321 862.06154 1040.7970 295.14000 \n", "A2M-AS1 313.0060 215.3160 251.0310 201.64300 205.5960 180.92900 \n", "\n", " GSM458581 GSM458582 GSM469991 GSM470000 \n", "Gene \n", "A1BG 53.5092 83.99710 130.17200 72.42720 \n", "A1BG-AS1 84.5107 48.65930 29.05250 248.15300 \n", "A1CF 41.3142 154.99446 126.77519 113.47501 \n", "A2M 1105.5896 271.15290 2166.79520 1080.31820 \n", "A2M-AS1 221.6670 204.65500 291.91100 277.13000 \n", "\n", "[5 rows x 135 columns]\n", "Gene expression data shape after normalization: (19845, 135)\n", "First few genes after normalization:\n", "Index(['A1BG', 'A1BG-AS1', 'A1CF', 'A2M', 'A2M-AS1', 'A2ML1', 'A2MP1',\n", " 'A4GALT', 'A4GNT', 'AA06'],\n", " dtype='object', name='Gene')\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Gene expression data saved to ../../output/preprocess/Chronic_obstructive_pulmonary_disease_(COPD)/gene_data/GSE21359.csv\n" ] } ], "source": [ "# 1. Identify the correct columns for mapping\n", "prob_col = \"ID\" # This is the column containing probe IDs in the gene annotation\n", "gene_col = \"Gene Symbol\" # This is the column containing gene symbols\n", "\n", "# 2. Get the gene mapping dataframe \n", "mapping_df = get_gene_mapping(gene_annotation, prob_col, gene_col)\n", "print(f\"Mapping dataframe shape: {mapping_df.shape}\")\n", "print(\"Mapping sample:\")\n", "print(mapping_df.head())\n", "\n", "# 3. Convert probe-level measurements to gene expression data\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 few rows of gene expression data:\")\n", "print(gene_data.head())\n", "\n", "# Let's normalize the gene symbols in the index\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 few genes after normalization:\")\n", "print(gene_data.index[:10])\n", "\n", "# Create directory if it doesn't exist\n", "os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)\n", "\n", "# Save the gene expression data\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": "91a6276c", "metadata": {}, "source": [ "### Step 7: Data Normalization and Linking" ] }, { "cell_type": "code", "execution_count": 8, "id": "9e333f42", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T08:21:10.345103Z", "iopub.status.busy": "2025-03-25T08:21:10.344967Z", "iopub.status.idle": "2025-03-25T08:21:26.235333Z", "shell.execute_reply": "2025-03-25T08:21:26.234691Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Normalized gene data shape: (19845, 135)\n", "Gene data column names (sample IDs):\n", "Index(['GSM101095', 'GSM101096', 'GSM101097', 'GSM101098', 'GSM101100'], dtype='object')\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "Raw clinical data structure:\n", "Clinical data shape: (4, 136)\n", "Clinical data columns: Index(['!Sample_geo_accession', 'GSM101095', 'GSM101096', 'GSM101097',\n", " 'GSM101098'],\n", " dtype='object')\n", "\n", "Sample characteristics dictionary:\n", "{0: ['Age: 41', 'age: 35', 'age: 61', 'age: 37', 'age: 47', 'age: 38', 'age: 49', 'age: 45', 'age: 36', 'age: 46', 'age: 48', 'age: 50', 'age: 56', 'age: 59', 'age: 34', 'age: 44', 'Age: 45', 'age: 29', 'age: 42', 'Age: 47', 'age: 55', 'age: 51', 'age: 60', 'age: 52', 'age: 40', 'age: 41', 'age: 43', 'age: 31', 'age: 53', 'age: 62'], 1: ['Sex: M', 'sex: M', 'sex: F', 'Sex: F'], 2: ['Ethnic group: black', 'ethnic group: black', 'ethnic group: white', 'ethnic group: hispanic', 'Ethnic group: white', 'ethnic group: black/hispanic', 'ethnic group: asian'], 3: ['Smoking Status: non-smoker', 'smoking status: non-smoker', 'smoking status: smoker, 21 pack-years', 'smoking status: smoker, 23 pack-years', 'smoking status: smoker, 28 pack-years', 'smoking status: smoker, 20 pack-years', 'smoking status: smoker, 38 pack-years', 'smoking status: smoker, 80 pack-years', 'smoking status: smoker, 60 pack-years', 'Smoking status: non-smoker', 'smoking status: COPD, GOLD-I, 50 pack-years', 'Smoking status: COPD, GOLD-II, 33 pack-years', 'smoking status: COPD, GOLD-II, 35 pack-years', 'smoking status: COPD, GOLD-II, 20 pack-years', 'smoking status: COPD, GOLD-I, 48 pack-years', 'smoking status: COPD, GOLD-II, 75 pack-years', 'smoking status: COPD, GOLD-II, 27 pack-years', 'smoking status: COPD, GOLD-II, 60 pack-years', 'smoking status: COPD, GOLD-III, 110 pack-years', 'smoking status: COPD, GOLD-I, 22 pack-years', 'smoking status: COPD, GOLD-I, 23 pack-years', 'smoking status: smoker, 24 pack-years', 'smoking status: smoker, 29 pack-years', 'smoking status: smoker, 45 pack-years', 'smoking status: smoker, 32 pack-years', 'smoking status: smoker, 36 pack-years', 'smoking status: smoker, 15 pack-years', 'smoking status: smoker, 22 pack-years', 'smoking status: smoker, 33 pack-years', 'smoking status: smoker, 16 pack-years']}\n", "\n", "Values in trait row:\n", "['!Sample_characteristics_ch1' 'Smoking Status: non-smoker'\n", " 'smoking status: non-smoker' 'smoking status: non-smoker'\n", " 'smoking status: non-smoker']\n", "\n", "Created clinical features dataframe:\n", "Shape: (1, 135)\n", " GSM101095 GSM101096 GSM101097 \\\n", "Chronic_obstructive_pulmonary_disease_(COPD) 0 0 0 \n", "\n", " GSM101098 GSM101100 \n", "Chronic_obstructive_pulmonary_disease_(COPD) 0 0 \n", "\n", "Linked data shape before handling missing values: (135, 19846)\n", "Actual trait column in linked data: Chronic_obstructive_pulmonary_disease_(COPD)\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: (135, 19846)\n", "For the feature 'Chronic_obstructive_pulmonary_disease_(COPD)', the least common label is '1' with 23 occurrences. This represents 17.04% of the dataset.\n", "The distribution of the feature 'Chronic_obstructive_pulmonary_disease_(COPD)' in this dataset is fine.\n", "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Linked data saved to ../../output/preprocess/Chronic_obstructive_pulmonary_disease_(COPD)/GSE21359.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 }