{ "cells": [ { "cell_type": "code", "execution_count": 1, "id": "5a52db2b", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T04:04:22.419231Z", "iopub.status.busy": "2025-03-25T04:04:22.418912Z", "iopub.status.idle": "2025-03-25T04:04:22.584670Z", "shell.execute_reply": "2025-03-25T04:04:22.584317Z" } }, "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 = \"Stroke\"\n", "cohort = \"GSE161533\"\n", "\n", "# Input paths\n", "in_trait_dir = \"../../input/GEO/Stroke\"\n", "in_cohort_dir = \"../../input/GEO/Stroke/GSE161533\"\n", "\n", "# Output paths\n", "out_data_file = \"../../output/preprocess/Stroke/GSE161533.csv\"\n", "out_gene_data_file = \"../../output/preprocess/Stroke/gene_data/GSE161533.csv\"\n", "out_clinical_data_file = \"../../output/preprocess/Stroke/clinical_data/GSE161533.csv\"\n", "json_path = \"../../output/preprocess/Stroke/cohort_info.json\"\n" ] }, { "cell_type": "markdown", "id": "ab62ad3c", "metadata": {}, "source": [ "### Step 1: Initial Data Loading" ] }, { "cell_type": "code", "execution_count": 2, "id": "adcb98df", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T04:04:22.586124Z", "iopub.status.busy": "2025-03-25T04:04:22.585969Z", "iopub.status.idle": "2025-03-25T04:04:22.836121Z", "shell.execute_reply": "2025-03-25T04:04:22.835649Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Background Information:\n", "!Series_title\t\"Expression data from esophageal squamous cell carcinoma patients\"\n", "!Series_summary\t\"we conducted microarray experiments of 28 stage I-III ESCC patients based on Affymetrix Gene Chip Human Genome U133 plus 2.0 Array, performed enrichment analysis of differentially expressed genes (DEGs) as well as gene set enrichment analysis of all valid genes. Moreover, we summarized the secreted protein-encoding DEGs as well as esophagus-specific DEGs, hoping to offer some hints for early diagnosis and target for more efficacious treatment for ESCC in near future.\"\n", "!Series_overall_design\t\"In total, there were 84 paired normal tissues, paratumor tissues, and tumor tissues from 28 ESCC patients were chosen to perform microarray analysis.\"\n", "Sample Characteristics Dictionary:\n", "{0: ['tissue: normal tissue', 'tissue: paratumor tissue', 'tissue: tumor tissue'], 1: ['Stage: IB', 'Stage: I', 'Stage: IA', 'Stage: IIA', 'Stage: IIB', 'Stage: II', 'Stage: IIIA', 'Stage: IIIB'], 2: ['age: 56', 'age: 57', 'age: 51', 'age: 64', 'age: 54', 'age: 73', 'age: 61', 'age: 71', 'age: 65', 'age: 60', 'age: 69', 'age: 63', 'age: 67', 'age: 70', 'age: 53', 'age: 75', 'age: 74'], 3: ['gender: Male', 'gender: Female'], 4: ['smoking history: None', 'smoking history: 30 years', 'smoking history: 20 years', 'smoking history: 36 years', 'smoking history: 50 years', 'smoking history: 40 years'], 5: ['drinking history: None', 'drinking history: Seldom', 'drinking history: 36 years', 'drinking history: 40 years', 'drinking history: 50 years'], 6: ['disease history: None', 'disease history: Hypertension', 'disease history: Breast cancer', 'disease history: Cerebral infarction', 'disease history: Lymphoma', 'disease history: Hypertension, coronary heart disease, cerebral infarction'], 7: ['family history of cancer: ESCC', 'family history of cancer: None', 'family history of cancer: lung cancer', 'family history of cancer: liver cancer', 'family history of cancer: none', 'family history of cancer: Colorectal cancer', 'family history of cancer: Gastric cancer', 'family history of cancer: cancer']}\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": "98c275a8", "metadata": {}, "source": [ "### Step 2: Dataset Analysis and Clinical Feature Extraction" ] }, { "cell_type": "code", "execution_count": 3, "id": "b3f8942d", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T04:04:22.837827Z", "iopub.status.busy": "2025-03-25T04:04:22.837717Z", "iopub.status.idle": "2025-03-25T04:04:22.849530Z", "shell.execute_reply": "2025-03-25T04:04:22.849239Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Preview of clinical features:\n", "{0: [0.0, 56.0, 1.0], 1: [0.0, 57.0, 0.0], 2: [0.0, 51.0, nan], 3: [0.0, 64.0, nan], 4: [0.0, 54.0, nan], 5: [0.0, 73.0, nan], 6: [nan, 61.0, nan], 7: [nan, 71.0, nan], 8: [nan, 65.0, nan], 9: [nan, 60.0, nan], 10: [nan, 69.0, nan], 11: [nan, 63.0, nan], 12: [nan, 67.0, nan], 13: [nan, 70.0, nan], 14: [nan, 53.0, nan], 15: [nan, 75.0, nan], 16: [nan, 74.0, nan]}\n", "Clinical features saved to ../../output/preprocess/Stroke/clinical_data/GSE161533.csv\n" ] } ], "source": [ "import pandas as pd\n", "import numpy as np\n", "import os\n", "import re\n", "from typing import Optional, Callable, Dict, Any\n", "\n", "# Create a DataFrame from the sample characteristics dictionary provided in the previous step\n", "sample_characteristics = {\n", " 0: ['tissue: normal tissue', 'tissue: paratumor tissue', 'tissue: tumor tissue'], \n", " 1: ['Stage: IB', 'Stage: I', 'Stage: IA', 'Stage: IIA', 'Stage: IIB', 'Stage: II', 'Stage: IIIA', 'Stage: IIIB'], \n", " 2: ['age: 56', 'age: 57', 'age: 51', 'age: 64', 'age: 54', 'age: 73', 'age: 61', 'age: 71', 'age: 65', 'age: 60', 'age: 69', 'age: 63', 'age: 67', 'age: 70', 'age: 53', 'age: 75', 'age: 74'], \n", " 3: ['gender: Male', 'gender: Female'], \n", " 4: ['smoking history: None', 'smoking history: 30 years', 'smoking history: 20 years', 'smoking history: 36 years', 'smoking history: 50 years', 'smoking history: 40 years'], \n", " 5: ['drinking history: None', 'drinking history: Seldom', 'drinking history: 36 years', 'drinking history: 40 years', 'drinking history: 50 years'], \n", " 6: ['disease history: None', 'disease history: Hypertension', 'disease history: Breast cancer', 'disease history: Cerebral infarction', 'disease history: Lymphoma', 'disease history: Hypertension, coronary heart disease, cerebral infarction'], \n", " 7: ['family history of cancer: ESCC', 'family history of cancer: None', 'family history of cancer: lung cancer', 'family history of cancer: liver cancer', 'family history of cancer: none', 'family history of cancer: Colorectal cancer', 'family history of cancer: Gastric cancer', 'family history of cancer: cancer']\n", "}\n", "\n", "# Convert sample characteristics dictionary to a DataFrame to use with geo_select_clinical_features\n", "clinical_data = pd.DataFrame.from_dict(sample_characteristics, orient='index')\n", "\n", "# 1. Gene Expression Data Availability\n", "# Affymetrix Gene Chip Human Genome U133 plus 2.0 Array is a gene expression microarray\n", "is_gene_available = True\n", "\n", "# 2.1 Data Availability\n", "# Checking if stroke-related data is available in the sample characteristics\n", "# The disease history field (index 6) contains \"Cerebral infarction\" which is related to stroke\n", "trait_row = 6 # Disease history contains stroke-related information\n", "age_row = 2 # Age information is available\n", "gender_row = 3 # Gender information is available\n", "\n", "# 2.2 Data Type Conversion Functions\n", "def convert_trait(value: str) -> Optional[int]:\n", " \"\"\"\n", " Convert disease history to binary stroke status.\n", " 1 if cerebral infarction (stroke) is mentioned, 0 otherwise.\n", " \"\"\"\n", " if pd.isna(value) 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 cerebral infarction is mentioned\n", " if \"cerebral infarction\" in value.lower():\n", " return 1\n", " else:\n", " return 0\n", "\n", "def convert_age(value: str) -> Optional[float]:\n", " \"\"\"\n", " Convert age string to numeric value.\n", " \"\"\"\n", " if pd.isna(value) 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", " # Extract numeric age\n", " match = re.search(r'\\d+', value)\n", " if match:\n", " return float(match.group())\n", " else:\n", " return None\n", "\n", "def convert_gender(value: str) -> Optional[int]:\n", " \"\"\"\n", " Convert gender string to binary: 0 for female, 1 for male.\n", " \"\"\"\n", " if pd.isna(value) 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 to binary\n", " if \"male\" in value.lower() and \"female\" not in value.lower():\n", " return 1\n", " elif \"female\" in value.lower():\n", " return 0\n", " else:\n", " return None\n", "\n", "# 3. Save Metadata\n", "# Check if trait data is available (trait_row is not None)\n", "is_trait_available = trait_row is not None\n", "\n", "# Validate and save cohort info\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", " # Extract clinical features using the geo_select_clinical_features function\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 clinical features\n", " preview = preview_df(clinical_features_df)\n", " print(\"Preview of clinical features:\")\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": "9e6b5382", "metadata": {}, "source": [ "### Step 3: Gene Data Extraction" ] }, { "cell_type": "code", "execution_count": 4, "id": "8eb35c0e", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T04:04:22.850977Z", "iopub.status.busy": "2025-03-25T04:04:22.850874Z", "iopub.status.idle": "2025-03-25T04:04:23.251392Z", "shell.execute_reply": "2025-03-25T04:04:23.251014Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Matrix file found: ../../input/GEO/Stroke/GSE161533/GSE161533_series_matrix.txt.gz\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Gene data shape: (54675, 84)\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": "2b60e4d2", "metadata": {}, "source": [ "### Step 4: Gene Identifier Review" ] }, { "cell_type": "code", "execution_count": 5, "id": "a17d93f9", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T04:04:23.253081Z", "iopub.status.busy": "2025-03-25T04:04:23.252957Z", "iopub.status.idle": "2025-03-25T04:04:23.254892Z", "shell.execute_reply": "2025-03-25T04:04:23.254609Z" } }, "outputs": [], "source": [ "# Looking at the gene identifiers, they appear to be Affymetrix probe IDs (like '1007_s_at', '1053_at') \n", "# rather than standard human gene symbols (which would look like BRCA1, TP53, etc.)\n", "# These probe IDs need to be mapped to human gene symbols for meaningful analysis\n", "\n", "requires_gene_mapping = True\n" ] }, { "cell_type": "markdown", "id": "7be73898", "metadata": {}, "source": [ "### Step 5: Gene Annotation" ] }, { "cell_type": "code", "execution_count": 6, "id": "5669a5aa", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T04:04:23.256442Z", "iopub.status.busy": "2025-03-25T04:04:23.256311Z", "iopub.status.idle": "2025-03-25T04:04:29.849710Z", "shell.execute_reply": "2025-03-25T04:04:29.849159Z" } }, "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": "294246d3", "metadata": {}, "source": [ "### Step 6: Gene Identifier Mapping" ] }, { "cell_type": "code", "execution_count": 7, "id": "9da3eb9a", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T04:04:29.851188Z", "iopub.status.busy": "2025-03-25T04:04:29.851066Z", "iopub.status.idle": "2025-03-25T04:04:31.200574Z", "shell.execute_reply": "2025-03-25T04:04:31.199939Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Gene mapping shape: (45782, 2)\n", "Gene mapping sample (first 5 rows):\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", "After mapping: Gene expression data shape: (21278, 84)\n", "First 10 gene symbols after mapping:\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/Stroke/gene_data/GSE161533.csv\n" ] } ], "source": [ "# 1. Identify columns for mapping\n", "# Based on the preview of gene annotation data, we can see:\n", "# - 'ID' column contains probe IDs (e.g., '1007_s_at') which match the gene expression data indices\n", "# - 'Gene Symbol' column contains the human gene symbols we need (e.g., 'DDR1 /// MIR4640')\n", "\n", "# 2. Get gene mapping dataframe\n", "gene_mapping = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='Gene Symbol')\n", "print(f\"Gene mapping shape: {gene_mapping.shape}\")\n", "print(\"Gene mapping sample (first 5 rows):\")\n", "print(gene_mapping.head())\n", "\n", "# 3. Apply gene mapping to convert probe-level data to gene-level data\n", "gene_data = apply_gene_mapping(gene_data, gene_mapping)\n", "print(f\"After mapping: Gene expression data shape: {gene_data.shape}\")\n", "print(\"First 10 gene symbols after mapping:\")\n", "print(gene_data.index[:10])\n", "\n", "# Save the gene data to CSV\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": "0bf168a8", "metadata": {}, "source": [ "### Step 7: Data Normalization and Linking" ] }, { "cell_type": "code", "execution_count": 8, "id": "09e39777", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T04:04:31.202054Z", "iopub.status.busy": "2025-03-25T04:04:31.201924Z", "iopub.status.idle": "2025-03-25T04:04:38.298271Z", "shell.execute_reply": "2025-03-25T04:04:38.297713Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Original gene data shape: (21278, 84)\n", "Normalized gene data shape: (19845, 84)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Gene expression data saved to ../../output/preprocess/Stroke/gene_data/GSE161533.csv\n", "Clinical features shape: (3, 84)\n", "Clinical features preview:\n", " GSM4909553 GSM4909554 GSM4909555 GSM4909556 GSM4909557 \\\n", "Stroke 0.0 0.0 0.0 0.0 0.0 \n", "Age 56.0 57.0 51.0 64.0 54.0 \n", "Gender 1.0 1.0 1.0 0.0 1.0 \n", "\n", " GSM4909558 GSM4909559 GSM4909560 GSM4909561 GSM4909562 ... \\\n", "Stroke 0.0 0.0 0.0 0.0 0.0 ... \n", "Age 64.0 73.0 73.0 61.0 71.0 ... \n", "Gender 0.0 0.0 1.0 1.0 1.0 ... \n", "\n", " GSM4909627 GSM4909628 GSM4909629 GSM4909630 GSM4909631 \\\n", "Stroke 0.0 0.0 0.0 0.0 0.0 \n", "Age 64.0 57.0 67.0 70.0 53.0 \n", "Gender 1.0 1.0 1.0 1.0 1.0 \n", "\n", " GSM4909632 GSM4909633 GSM4909634 GSM4909635 GSM4909636 \n", "Stroke 0.0 0.0 0.0 0.0 0.0 \n", "Age 65.0 64.0 75.0 75.0 74.0 \n", "Gender 1.0 1.0 0.0 1.0 1.0 \n", "\n", "[3 rows x 84 columns]\n", "Clinical data saved to ../../output/preprocess/Stroke/clinical_data/GSE161533.csv\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Linked data shape: (84, 19848)\n", "Linked data preview (first 5 rows, 5 columns):\n", " Stroke Age Gender A1BG A1BG-AS1\n", "GSM4909553 0.0 56.0 1.0 20.7429 21.2433\n", "GSM4909554 0.0 57.0 1.0 14.0490 16.5552\n", "GSM4909555 0.0 51.0 1.0 12.3174 16.3096\n", "GSM4909556 0.0 64.0 0.0 17.3028 19.4446\n", "GSM4909557 0.0 54.0 1.0 16.6225 14.8843\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Linked data shape after handling missing values: (84, 19848)\n", "Quartiles for 'Stroke':\n", " 25%: 0.0\n", " 50% (Median): 0.0\n", " 75%: 0.0\n", "Min: 0.0\n", "Max: 0.0\n", "The distribution of the feature 'Stroke' in this dataset is severely biased.\n", "\n", "Quartiles for 'Age':\n", " 25%: 59.25\n", " 50% (Median): 64.0\n", " 75%: 69.25\n", "Min: 51.0\n", "Max: 75.0\n", "The distribution of the feature 'Age' in this dataset is fine.\n", "\n", "For the feature 'Gender', the least common label is '0.0' with 21 occurrences. This represents 25.00% of the dataset.\n", "The distribution of the feature 'Gender' in this dataset is fine.\n", "\n", "Dataset deemed not usable for associative studies. Linked data not saved.\n" ] } ], "source": [ "# 1. Normalize gene symbols\n", "print(f\"Original gene data shape: {gene_data.shape}\")\n", "\n", "try:\n", " # Attempt to normalize gene symbols\n", " gene_data_normalized = normalize_gene_symbols_in_index(gene_data)\n", " print(f\"Normalized gene data shape: {gene_data_normalized.shape}\")\n", "except Exception as e:\n", " print(f\"Gene normalization failed: {e}\")\n", " # If normalization fails, use the original gene data\n", " gene_data_normalized = gene_data.copy()\n", " print(f\"Using original gene data with shape: {gene_data_normalized.shape}\")\n", "\n", "# Save the gene expression data \n", "os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)\n", "gene_data_normalized.to_csv(out_gene_data_file)\n", "print(f\"Gene expression data saved to {out_gene_data_file}\")\n", "\n", "# 2. Load the clinical data from Step 2\n", "# Get the clinical data from the matrix file\n", "soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)\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", "# Define conversion functions as in Step 2\n", "def convert_trait(value: str) -> Optional[int]:\n", " \"\"\"Convert disease history to binary stroke status (1 if cerebral infarction is mentioned, 0 otherwise)\"\"\"\n", " if pd.isna(value) or not isinstance(value, str):\n", " return None\n", " # Extract value after colon\n", " if \":\" in value:\n", " value = value.split(\":\", 1)[1].strip()\n", " # Check if cerebral infarction is mentioned\n", " if \"cerebral infarction\" in value.lower():\n", " return 1\n", " else:\n", " return 0\n", "\n", "def convert_age(value: str) -> Optional[float]:\n", " \"\"\"Convert age string to numeric value\"\"\"\n", " if pd.isna(value) or not isinstance(value, str):\n", " return None\n", " # Extract value after colon\n", " if \":\" in value:\n", " value = value.split(\":\", 1)[1].strip()\n", " # Extract numeric age\n", " match = re.search(r'\\d+', value)\n", " if match:\n", " return float(match.group())\n", " else:\n", " return None\n", "\n", "def convert_gender(value: str) -> Optional[int]:\n", " \"\"\"Convert gender string to binary (0 for female, 1 for male)\"\"\"\n", " if pd.isna(value) or not isinstance(value, str):\n", " return None\n", " # Extract value after colon\n", " if \":\" in value:\n", " value = value.split(\":\", 1)[1].strip()\n", " # Convert to binary\n", " if \"male\" in value.lower() and \"female\" not in value.lower():\n", " return 1\n", " elif \"female\" in value.lower():\n", " return 0\n", " else:\n", " return None\n", "\n", "# Extract clinical features using the correct trait_row (6 for disease history)\n", "clinical_features = geo_select_clinical_features(\n", " clinical_data, \n", " trait=trait, \n", " trait_row=6, # Using disease history which contains cerebral infarction (stroke) information\n", " convert_trait=convert_trait,\n", " age_row=2, # Age information\n", " convert_age=convert_age,\n", " gender_row=3, # Gender information\n", " convert_gender=convert_gender\n", ")\n", "\n", "print(f\"Clinical features shape: {clinical_features.shape}\")\n", "print(\"Clinical features preview:\")\n", "print(clinical_features.head())\n", "\n", "# Save the clinical data\n", "os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)\n", "clinical_features.to_csv(out_clinical_data_file)\n", "print(f\"Clinical data saved to {out_clinical_data_file}\")\n", "\n", "# 3. Link clinical and genetic data\n", "linked_data = geo_link_clinical_genetic_data(clinical_features, gene_data_normalized)\n", "print(f\"Linked data shape: {linked_data.shape}\")\n", "print(\"Linked data preview (first 5 rows, 5 columns):\")\n", "print(linked_data.iloc[:5, :5])\n", "\n", "# 4. Handle missing values\n", "linked_data_clean = handle_missing_values(linked_data, trait)\n", "print(f\"Linked data shape after handling missing values: {linked_data_clean.shape}\")\n", "\n", "# 5. Check for bias in the dataset\n", "is_biased, linked_data_clean = judge_and_remove_biased_features(linked_data_clean, trait)\n", "\n", "# 6. Conduct final quality validation\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=True,\n", " is_biased=is_biased,\n", " df=linked_data_clean,\n", " note=\"Dataset contains gene expression data from esophageal squamous cell carcinoma patients. The 'Stroke' trait was extracted from disease history field, identifying patients with cerebral infarction as stroke cases.\"\n", ")\n", "\n", "# 7. Save the linked data if it's 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, index=True)\n", " print(f\"Linked data saved to {out_data_file}\")\n", "else:\n", " print(\"Dataset deemed not usable for associative studies. 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 }