{ "cells": [ { "cell_type": "code", "execution_count": 1, "id": "bf3b7d8e", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T08:07:51.746294Z", "iopub.status.busy": "2025-03-25T08:07:51.746060Z", "iopub.status.idle": "2025-03-25T08:07:51.909286Z", "shell.execute_reply": "2025-03-25T08:07:51.908944Z" } }, "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 = \"Metabolic_Rate\"\n", "cohort = \"GSE41168\"\n", "\n", "# Input paths\n", "in_trait_dir = \"../../input/GEO/Metabolic_Rate\"\n", "in_cohort_dir = \"../../input/GEO/Metabolic_Rate/GSE41168\"\n", "\n", "# Output paths\n", "out_data_file = \"../../output/preprocess/Metabolic_Rate/GSE41168.csv\"\n", "out_gene_data_file = \"../../output/preprocess/Metabolic_Rate/gene_data/GSE41168.csv\"\n", "out_clinical_data_file = \"../../output/preprocess/Metabolic_Rate/clinical_data/GSE41168.csv\"\n", "json_path = \"../../output/preprocess/Metabolic_Rate/cohort_info.json\"\n" ] }, { "cell_type": "markdown", "id": "cffc79da", "metadata": {}, "source": [ "### Step 1: Initial Data Loading" ] }, { "cell_type": "code", "execution_count": 2, "id": "6a74a04c", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T08:07:51.910757Z", "iopub.status.busy": "2025-03-25T08:07:51.910614Z", "iopub.status.idle": "2025-03-25T08:07:53.380673Z", "shell.execute_reply": "2025-03-25T08:07:53.380310Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Files in the directory:\n", "['GSE41168_family.soft.gz', 'GSE41168_series_matrix.txt.gz']\n", "SOFT file: ../../input/GEO/Metabolic_Rate/GSE41168/GSE41168_family.soft.gz\n", "Matrix file: ../../input/GEO/Metabolic_Rate/GSE41168/GSE41168_series_matrix.txt.gz\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Background Information:\n", "!Series_title\t\"Resveratrol supplementation does not improve metabolic function in non-obese women with normal glucose tolerance\"\n", "!Series_summary\t\"Resveratrol has been reported to improve metabolic function in metabolically-abnormal rodents and humans, but has not been studied in non-obese people with normal glucose tolerance. We conducted a randomized, double-blind, placebo-controlled trial to evaluate the metabolic effects of 12 weeks of resveratrol supplementation (75 mg/day) in non-obese, postmenopausal women with normal glucose tolerance. Although resveratrol supplementation was well-tolerated and increased plasma resveratrol concentration without adverse effects, it did not change body composition, resting metabolic rate, plasma lipids, or inflammatory markers. A two-stage hyperinsulinemic-euglycemic clamp procedure, in conjunction with stable isotopically-labeled tracer infusions, demonstrated that resveratrol did not increase liver, skeletal muscle, or adipose tissue insulin sensitivity. Consistent with the absence of in vivo metabolic effects, resveratrol did not affect its putative molecular targets, including AMPK, Sirt1, Nampt, and Pgc-1α, in either skeletal muscle or adipose tissue. These findings demonstrate that resveratrol supplementation does not have metabolic effects in non-obese women.\"\n", "!Series_overall_design\t\"We compared gene expression profile in subcutaneous abdominal adipose tissue and skeletal muscle (vastus lateralis) biopsy samples obtained from non-obese people before and after 1) placebo (PLC), 2) resveratrol (RES), and 3) calorie restriction (CR) intervention.\"\n", "Sample Characteristics Dictionary:\n", "{0: ['tissue: muscle', 'tissue: adipose tissue'], 1: ['sample group: calorie restrictive', 'sample group: placebo', 'sample group: resveratrol'], 2: ['treatment: before', 'treatment: after'], 3: ['gender: Female']}\n" ] } ], "source": [ "# 1. Check what files are actually in the directory\n", "import os\n", "print(\"Files in the directory:\")\n", "files = os.listdir(in_cohort_dir)\n", "print(files)\n", "\n", "# 2. Find appropriate files with more flexible pattern matching\n", "soft_file = None\n", "matrix_file = None\n", "\n", "for file in files:\n", " file_path = os.path.join(in_cohort_dir, file)\n", " # Look for files that might contain SOFT or matrix data with various possible extensions\n", " if 'soft' in file.lower() or 'family' in file.lower() or file.endswith('.soft.gz'):\n", " soft_file = file_path\n", " if 'matrix' in file.lower() or file.endswith('.txt.gz') or file.endswith('.tsv.gz'):\n", " matrix_file = file_path\n", "\n", "if not soft_file:\n", " print(\"Warning: Could not find a SOFT file. Using the first .gz file as fallback.\")\n", " gz_files = [f for f in files if f.endswith('.gz')]\n", " if gz_files:\n", " soft_file = os.path.join(in_cohort_dir, gz_files[0])\n", "\n", "if not matrix_file:\n", " print(\"Warning: Could not find a matrix file. Using the second .gz file as fallback if available.\")\n", " gz_files = [f for f in files if f.endswith('.gz')]\n", " if len(gz_files) > 1 and soft_file != os.path.join(in_cohort_dir, gz_files[1]):\n", " matrix_file = os.path.join(in_cohort_dir, gz_files[1])\n", " elif len(gz_files) == 1 and not soft_file:\n", " matrix_file = os.path.join(in_cohort_dir, gz_files[0])\n", "\n", "print(f\"SOFT file: {soft_file}\")\n", "print(f\"Matrix file: {matrix_file}\")\n", "\n", "# 3. Read files if found\n", "if soft_file and matrix_file:\n", " # 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", " \n", " try:\n", " background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)\n", " \n", " # Obtain the sample characteristics dictionary from the clinical dataframe\n", " sample_characteristics_dict = get_unique_values_by_row(clinical_data)\n", " \n", " # 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", " except Exception as e:\n", " print(f\"Error processing files: {e}\")\n", " # Try swapping files if first attempt fails\n", " print(\"Trying to swap SOFT and matrix files...\")\n", " temp = soft_file\n", " soft_file = matrix_file\n", " matrix_file = temp\n", " try:\n", " background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)\n", " sample_characteristics_dict = get_unique_values_by_row(clinical_data)\n", " print(\"Background Information:\")\n", " print(background_info)\n", " print(\"Sample Characteristics Dictionary:\")\n", " print(sample_characteristics_dict)\n", " except Exception as e:\n", " print(f\"Still error after swapping: {e}\")\n", "else:\n", " print(\"Could not find necessary files for processing.\")\n" ] }, { "cell_type": "markdown", "id": "742eaa50", "metadata": {}, "source": [ "### Step 2: Dataset Analysis and Clinical Feature Extraction" ] }, { "cell_type": "code", "execution_count": 3, "id": "686ccc5f", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T08:07:53.382031Z", "iopub.status.busy": "2025-03-25T08:07:53.381909Z", "iopub.status.idle": "2025-03-25T08:07:54.171380Z", "shell.execute_reply": "2025-03-25T08:07:54.171014Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Error during data extraction: argument of type 'float' is not iterable\n", "Updated cohort info due to extraction failure.\n" ] } ], "source": [ "import pandas as pd\n", "import numpy as np\n", "import os\n", "import json\n", "from typing import Dict, Any, Callable, Optional, List, Union\n", "\n", "# 1. Evaluate gene expression data availability\n", "# Based on the Series_summary and Series_overall_design, this dataset contains gene expression data from\n", "# subcutaneous abdominal adipose tissue and skeletal muscle samples. It's not just miRNA or methylation data.\n", "is_gene_available = True\n", "\n", "# 2. Variable Availability and Data Type Conversion\n", "# 2.1 Data Availability\n", "\n", "# For trait (Metabolic Rate):\n", "# From the background information, this is a study on metabolic function and resveratrol supplementation\n", "# The \"sample group\" in row 1 indicates different interventions that could affect metabolic rate\n", "trait_row = 1 # sample group: calorie restrictive/placebo/resveratrol\n", "\n", "# For age:\n", "# No explicit age information in the sample characteristics dictionary\n", "age_row = None\n", "\n", "# For gender:\n", "# Gender information is in row 3, but it's constant (all Female)\n", "# Since it's constant, it's not useful for our association study\n", "gender_row = None # Although it exists in row 3, all samples are female\n", "\n", "# 2.2 Data Type Conversion Functions\n", "\n", "def convert_trait(value: str) -> Union[float, None]:\n", " \"\"\"Convert trait values to binary numeric values.\"\"\"\n", " if value is None:\n", " return None\n", " \n", " # Extract value after the colon if present\n", " if \":\" in value:\n", " value = value.split(\":\", 1)[1].strip()\n", " \n", " # Convert sample group to binary based on intervention\n", " # Treatment groups (resveratrol, calorie restriction) are 1, control (placebo) is 0\n", " if \"resveratrol\" in value.lower() or \"calorie restrictive\" in value.lower():\n", " return 1.0\n", " elif \"placebo\" in value.lower():\n", " return 0.0\n", " else:\n", " return None\n", "\n", "def convert_age(value: str) -> Union[float, None]:\n", " \"\"\"Convert age values to numeric values.\"\"\"\n", " # Not used since age_row is None, but defined for compatibility\n", " return None\n", "\n", "def convert_gender(value: str) -> Union[float, None]:\n", " \"\"\"Convert gender values to binary numeric values.\"\"\"\n", " # Not used since gender_row is None, but defined for compatibility\n", " return None\n", "\n", "# 3. Save Metadata\n", "# Determine trait data availability\n", "is_trait_available = trait_row is not None\n", "\n", "# Conduct initial filtering and save cohort information\n", "validate_and_save_cohort_info(\n", " is_final=False,\n", " cohort=cohort,\n", " info_path=json_path,\n", " is_gene_available=is_gene_available,\n", " is_trait_available=is_trait_available\n", ")\n", "\n", "# 4. Clinical Feature Extraction\n", "if trait_row is not None:\n", " try:\n", " # Load the clinical data from the matrix file\n", " # The format of GEO matrix files requires special parsing\n", " clinical_data = pd.read_table(f\"{in_cohort_dir}/GSE41168_series_matrix.txt.gz\", \n", " compression='gzip', comment='!', \n", " header=0)\n", " \n", " # Extract clinical features using the function from the library\n", " selected_clinical_df = geo_select_clinical_features(\n", " clinical_df=clinical_data,\n", " trait=trait,\n", " trait_row=trait_row,\n", " convert_trait=convert_trait,\n", " age_row=age_row,\n", " convert_age=convert_age,\n", " gender_row=gender_row,\n", " convert_gender=convert_gender\n", " )\n", " \n", " # Preview the extracted clinical data\n", " print(\"Clinical Data Preview:\")\n", " print(preview_df(selected_clinical_df))\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 data to CSV\n", " selected_clinical_df.to_csv(out_clinical_data_file, index=False)\n", " print(f\"Clinical data saved to: {out_clinical_data_file}\")\n", " \n", " except Exception as e:\n", " print(f\"Error during data extraction: {e}\")\n", " # If we can't extract valid trait data, set trait availability to False\n", " is_trait_available = False\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", " print(\"Updated cohort info due to extraction failure.\")\n" ] }, { "cell_type": "markdown", "id": "11741f69", "metadata": {}, "source": [ "### Step 3: Gene Data Extraction" ] }, { "cell_type": "code", "execution_count": 4, "id": "ce013e0c", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T08:07:54.172679Z", "iopub.status.busy": "2025-03-25T08:07:54.172558Z", "iopub.status.idle": "2025-03-25T08:07:54.995396Z", "shell.execute_reply": "2025-03-25T08:07:54.994735Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "This appears to be a SuperSeries. Looking at the SOFT file to find potential subseries:\n", "No subseries references found in the first 1000 lines of the SOFT file.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "Gene data extraction result:\n", "Number of rows: 54613\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. First get the path to the soft and matrix files\n", "soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)\n", "\n", "# 2. Looking more carefully at the background information\n", "# This is a SuperSeries which doesn't contain direct gene expression data\n", "# Need to investigate the soft file to find the subseries\n", "print(\"This appears to be a SuperSeries. Looking at the SOFT file to find potential subseries:\")\n", "\n", "# Open the SOFT file to try to identify subseries\n", "with gzip.open(soft_file, 'rt') as f:\n", " subseries_lines = []\n", " for i, line in enumerate(f):\n", " if 'Series_relation' in line and 'SuperSeries of' in line:\n", " subseries_lines.append(line.strip())\n", " if i > 1000: # Limit search to first 1000 lines\n", " break\n", "\n", "# Display the subseries found\n", "if subseries_lines:\n", " print(\"Found potential subseries references:\")\n", " for line in subseries_lines:\n", " print(line)\n", "else:\n", " print(\"No subseries references found in the first 1000 lines of the SOFT file.\")\n", "\n", "# Despite trying to extract gene data, we expect it might fail because this is a SuperSeries\n", "try:\n", " gene_data = get_genetic_data(matrix_file)\n", " print(\"\\nGene data extraction result:\")\n", " print(\"Number of rows:\", len(gene_data))\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", " print(\"This confirms the dataset is a SuperSeries without direct gene expression data.\")\n" ] }, { "cell_type": "markdown", "id": "824cee51", "metadata": {}, "source": [ "### Step 4: Gene Identifier Review" ] }, { "cell_type": "code", "execution_count": 5, "id": "339d6b51", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T08:07:54.997275Z", "iopub.status.busy": "2025-03-25T08:07:54.997144Z", "iopub.status.idle": "2025-03-25T08:07:54.999659Z", "shell.execute_reply": "2025-03-25T08:07:54.999218Z" } }, "outputs": [], "source": [ "# Examining the gene identifiers from the output\n", "# The identifiers like '1007_s_at', '1053_at', etc. are typical Affymetrix probe IDs,\n", "# not standard human gene symbols (which would look like BRCA1, TP53, etc.)\n", "# These probe IDs need to be mapped to standard gene symbols for meaningful analysis\n", "\n", "# Based on biomedical knowledge, these are Affymetrix probe IDs that require mapping\n", "requires_gene_mapping = True\n" ] }, { "cell_type": "markdown", "id": "e709f161", "metadata": {}, "source": [ "### Step 5: Gene Annotation" ] }, { "cell_type": "code", "execution_count": 6, "id": "03942281", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T08:07:55.001428Z", "iopub.status.busy": "2025-03-25T08:07:55.001289Z", "iopub.status.idle": "2025-03-25T08:08:07.397952Z", "shell.execute_reply": "2025-03-25T08:08:07.397283Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Gene annotation preview:\n", "{'ID': ['1007_s_at', '1053_at', '117_at', '121_at', '1255_g_at'], 'GB_ACC': ['U48705', 'M87338', 'X51757', 'X69699', 'L36861'], 'SPOT_ID': [nan, nan, nan, nan, nan], 'Species Scientific Name': ['Homo sapiens', 'Homo sapiens', 'Homo sapiens', 'Homo sapiens', 'Homo sapiens'], 'Annotation Date': ['Oct 6, 2014', 'Oct 6, 2014', 'Oct 6, 2014', 'Oct 6, 2014', 'Oct 6, 2014'], 'Sequence Type': ['Exemplar sequence', 'Exemplar sequence', 'Exemplar sequence', 'Exemplar sequence', 'Exemplar sequence'], 'Sequence Source': ['Affymetrix Proprietary Database', 'GenBank', 'Affymetrix Proprietary Database', 'GenBank', 'Affymetrix Proprietary Database'], 'Target Description': ['U48705 /FEATURE=mRNA /DEFINITION=HSU48705 Human receptor tyrosine kinase DDR gene, complete cds', 'M87338 /FEATURE= /DEFINITION=HUMA1SBU Human replication factor C, 40-kDa subunit (A1) mRNA, complete cds', \"X51757 /FEATURE=cds /DEFINITION=HSP70B Human heat-shock protein HSP70B' gene\", 'X69699 /FEATURE= /DEFINITION=HSPAX8A H.sapiens Pax8 mRNA', 'L36861 /FEATURE=expanded_cds /DEFINITION=HUMGCAPB Homo sapiens guanylate cyclase activating protein (GCAP) gene exons 1-4, complete cds'], 'Representative Public ID': ['U48705', 'M87338', 'X51757', 'X69699', 'L36861'], 'Gene Title': ['discoidin domain receptor tyrosine kinase 1 /// microRNA 4640', 'replication factor C (activator 1) 2, 40kDa', \"heat shock 70kDa protein 6 (HSP70B')\", 'paired box 8', 'guanylate cyclase activator 1A (retina)'], 'Gene Symbol': ['DDR1 /// MIR4640', 'RFC2', 'HSPA6', 'PAX8', 'GUCA1A'], 'ENTREZ_GENE_ID': ['780 /// 100616237', '5982', '3310', '7849', '2978'], 'RefSeq Transcript ID': ['NM_001202521 /// NM_001202522 /// NM_001202523 /// NM_001954 /// NM_013993 /// NM_013994 /// NR_039783 /// XM_005249385 /// XM_005249386 /// XM_005249387 /// XM_005249389 /// XM_005272873 /// XM_005272874 /// XM_005272875 /// XM_005272877 /// XM_005275027 /// XM_005275028 /// XM_005275030 /// XM_005275031 /// XM_005275162 /// XM_005275163 /// XM_005275164 /// XM_005275166 /// XM_005275457 /// XM_005275458 /// XM_005275459 /// XM_005275461 /// XM_006715185 /// XM_006715186 /// XM_006715187 /// XM_006715188 /// XM_006715189 /// XM_006715190 /// XM_006725501 /// XM_006725502 /// XM_006725503 /// XM_006725504 /// XM_006725505 /// XM_006725506 /// XM_006725714 /// XM_006725715 /// XM_006725716 /// XM_006725717 /// XM_006725718 /// XM_006725719 /// XM_006725720 /// XM_006725721 /// XM_006725722 /// XM_006725827 /// XM_006725828 /// XM_006725829 /// XM_006725830 /// XM_006725831 /// XM_006725832 /// XM_006726017 /// XM_006726018 /// XM_006726019 /// XM_006726020 /// XM_006726021 /// XM_006726022 /// XR_427836 /// XR_430858 /// XR_430938 /// XR_430974 /// XR_431015', 'NM_001278791 /// NM_001278792 /// NM_001278793 /// NM_002914 /// NM_181471 /// XM_006716080', 'NM_002155', 'NM_003466 /// NM_013951 /// NM_013952 /// NM_013953 /// NM_013992', 'NM_000409 /// XM_006715073'], 'Gene Ontology Biological Process': ['0001558 // regulation of cell growth // inferred from electronic annotation /// 0001952 // regulation of cell-matrix adhesion // inferred from electronic annotation /// 0006468 // protein phosphorylation // inferred from electronic annotation /// 0007155 // cell adhesion // traceable author statement /// 0007169 // transmembrane receptor protein tyrosine kinase signaling pathway // inferred from electronic annotation /// 0007565 // female pregnancy // inferred from electronic annotation /// 0007566 // embryo implantation // inferred from electronic annotation /// 0007595 // lactation // inferred from electronic annotation /// 0008285 // negative regulation of cell proliferation // inferred from electronic annotation /// 0010715 // regulation of extracellular matrix disassembly // inferred from mutant phenotype /// 0014909 // smooth muscle cell migration // inferred from mutant phenotype /// 0016310 // phosphorylation // inferred from electronic annotation /// 0018108 // peptidyl-tyrosine phosphorylation // inferred from electronic annotation /// 0030198 // extracellular matrix organization // traceable author statement /// 0038063 // collagen-activated tyrosine kinase receptor signaling pathway // inferred from direct assay /// 0038063 // collagen-activated tyrosine kinase receptor signaling pathway // inferred from mutant phenotype /// 0038083 // peptidyl-tyrosine autophosphorylation // inferred from direct assay /// 0043583 // ear development // inferred from electronic annotation /// 0044319 // wound healing, spreading of cells // inferred from mutant phenotype /// 0046777 // protein autophosphorylation // inferred from direct assay /// 0060444 // branching involved in mammary gland duct morphogenesis // inferred from electronic annotation /// 0060749 // mammary gland alveolus development // inferred from electronic annotation /// 0061302 // smooth muscle cell-matrix adhesion // inferred from mutant phenotype', '0000278 // mitotic cell cycle // traceable author statement /// 0000722 // telomere maintenance via recombination // traceable author statement /// 0000723 // telomere maintenance // traceable author statement /// 0006260 // DNA replication // traceable author statement /// 0006271 // DNA strand elongation involved in DNA replication // traceable author statement /// 0006281 // DNA repair // traceable author statement /// 0006283 // transcription-coupled nucleotide-excision repair // traceable author statement /// 0006289 // nucleotide-excision repair // traceable author statement /// 0006297 // nucleotide-excision repair, DNA gap filling // traceable author statement /// 0015979 // photosynthesis // inferred from electronic annotation /// 0015995 // chlorophyll biosynthetic process // inferred from electronic annotation /// 0032201 // telomere maintenance via semi-conservative replication // traceable author statement', '0000902 // cell morphogenesis // inferred from electronic annotation /// 0006200 // ATP catabolic process // inferred from direct assay /// 0006950 // response to stress // inferred from electronic annotation /// 0006986 // response to unfolded protein // traceable author statement /// 0034605 // cellular response to heat // inferred from direct assay /// 0042026 // protein refolding // inferred from direct assay /// 0070370 // cellular heat acclimation // inferred from mutant phenotype', '0001655 // urogenital system development // inferred from sequence or structural similarity /// 0001656 // metanephros development // inferred from electronic annotation /// 0001658 // branching involved in ureteric bud morphogenesis // inferred from expression pattern /// 0001822 // kidney development // inferred from expression pattern /// 0001823 // mesonephros development // inferred from sequence or structural similarity /// 0003337 // mesenchymal to epithelial transition involved in metanephros morphogenesis // inferred from expression pattern /// 0006351 // transcription, DNA-templated // inferred from direct assay /// 0006355 // regulation of transcription, DNA-templated // inferred from electronic annotation /// 0007275 // multicellular organismal development // inferred from electronic annotation /// 0007417 // central nervous system development // inferred from expression pattern /// 0009653 // anatomical structure morphogenesis // traceable author statement /// 0030154 // cell differentiation // inferred from electronic annotation /// 0030878 // thyroid gland development // inferred from expression pattern /// 0030878 // thyroid gland development // inferred from mutant phenotype /// 0038194 // thyroid-stimulating hormone signaling pathway // traceable author statement /// 0039003 // pronephric field specification // inferred from sequence or structural similarity /// 0042472 // inner ear morphogenesis // inferred from sequence or structural similarity /// 0042981 // regulation of apoptotic process // inferred from sequence or structural similarity /// 0045893 // positive regulation of transcription, DNA-templated // inferred from direct assay /// 0045893 // positive regulation of transcription, DNA-templated // inferred from sequence or structural similarity /// 0045944 // positive regulation of transcription from RNA polymerase II promoter // inferred from direct assay /// 0048793 // pronephros development // inferred from sequence or structural similarity /// 0071371 // cellular response to gonadotropin stimulus // inferred from direct assay /// 0071599 // otic vesicle development // inferred from expression pattern /// 0072050 // S-shaped body morphogenesis // inferred from electronic annotation /// 0072073 // kidney epithelium development // inferred from electronic annotation /// 0072108 // positive regulation of mesenchymal to epithelial transition involved in metanephros morphogenesis // inferred from sequence or structural similarity /// 0072164 // mesonephric tubule development // inferred from electronic annotation /// 0072207 // metanephric epithelium development // inferred from expression pattern /// 0072221 // metanephric distal convoluted tubule development // inferred from sequence or structural similarity /// 0072278 // metanephric comma-shaped body morphogenesis // inferred from expression pattern /// 0072284 // metanephric S-shaped body morphogenesis // inferred from expression pattern /// 0072289 // metanephric nephron tubule formation // inferred from sequence or structural similarity /// 0072305 // negative regulation of mesenchymal cell apoptotic process involved in metanephric nephron morphogenesis // inferred from sequence or structural similarity /// 0072307 // regulation of metanephric nephron tubule epithelial cell differentiation // inferred from sequence or structural similarity /// 0090190 // positive regulation of branching involved in ureteric bud morphogenesis // inferred from sequence or structural similarity /// 1900212 // negative regulation of mesenchymal cell apoptotic process involved in metanephros development // inferred from sequence or structural similarity /// 1900215 // negative regulation of apoptotic process involved in metanephric collecting duct development // inferred from sequence or structural similarity /// 1900218 // negative regulation of apoptotic process involved in metanephric nephron tubule development // inferred from sequence or structural similarity /// 2000594 // positive regulation of metanephric DCT cell differentiation // inferred from sequence or structural similarity /// 2000611 // positive regulation of thyroid hormone generation // inferred from mutant phenotype /// 2000612 // regulation of thyroid-stimulating hormone secretion // inferred from mutant phenotype', '0007165 // signal transduction // non-traceable author statement /// 0007601 // visual perception // inferred from electronic annotation /// 0007602 // phototransduction // inferred from electronic annotation /// 0007603 // phototransduction, visible light // traceable author statement /// 0016056 // rhodopsin mediated signaling pathway // traceable author statement /// 0022400 // regulation of rhodopsin mediated signaling pathway // traceable author statement /// 0030828 // positive regulation of cGMP biosynthetic process // inferred from electronic annotation /// 0031282 // regulation of guanylate cyclase activity // inferred from electronic annotation /// 0031284 // positive regulation of guanylate cyclase activity // inferred from electronic annotation /// 0050896 // response to stimulus // inferred from electronic annotation'], 'Gene Ontology Cellular Component': ['0005576 // extracellular region // inferred from electronic annotation /// 0005615 // extracellular space // inferred from direct assay /// 0005886 // plasma membrane // traceable author statement /// 0005887 // integral component of plasma membrane // traceable author statement /// 0016020 // membrane // inferred from electronic annotation /// 0016021 // integral component of membrane // inferred from electronic annotation /// 0043235 // receptor complex // inferred from direct assay /// 0070062 // extracellular vesicular exosome // inferred from direct assay', '0005634 // nucleus // inferred from electronic annotation /// 0005654 // nucleoplasm // traceable author statement /// 0005663 // DNA replication factor C complex // inferred from direct assay', '0005737 // cytoplasm // inferred from direct assay /// 0005814 // centriole // inferred from direct assay /// 0005829 // cytosol // inferred from direct assay /// 0008180 // COP9 signalosome // inferred from direct assay /// 0070062 // extracellular vesicular exosome // inferred from direct assay /// 0072562 // blood microparticle // inferred from direct assay', '0005634 // nucleus // inferred from direct assay /// 0005654 // nucleoplasm // inferred from sequence or structural similarity /// 0005730 // nucleolus // inferred from direct assay', '0001750 // photoreceptor outer segment // inferred from electronic annotation /// 0001917 // photoreceptor inner segment // inferred from electronic annotation /// 0005578 // proteinaceous extracellular matrix // inferred from electronic annotation /// 0005886 // plasma membrane // inferred from direct assay /// 0016020 // membrane // inferred from electronic annotation /// 0097381 // photoreceptor disc membrane // traceable author statement'], 'Gene Ontology Molecular Function': ['0000166 // nucleotide binding // inferred from electronic annotation /// 0004672 // protein kinase activity // inferred from electronic annotation /// 0004713 // protein tyrosine kinase activity // inferred from electronic annotation /// 0004714 // transmembrane receptor protein tyrosine kinase activity // traceable author statement /// 0005515 // protein binding // inferred from physical interaction /// 0005518 // collagen binding // inferred from direct assay /// 0005518 // collagen binding // inferred from mutant phenotype /// 0005524 // ATP binding // inferred from electronic annotation /// 0016301 // kinase activity // inferred from electronic annotation /// 0016740 // transferase activity // inferred from electronic annotation /// 0016772 // transferase activity, transferring phosphorus-containing groups // inferred from electronic annotation /// 0038062 // protein tyrosine kinase collagen receptor activity // inferred from direct assay /// 0046872 // metal ion binding // inferred from electronic annotation', '0000166 // nucleotide binding // inferred from electronic annotation /// 0003677 // DNA binding // inferred from electronic annotation /// 0005515 // protein binding // inferred from physical interaction /// 0005524 // ATP binding // inferred from electronic annotation /// 0016851 // magnesium chelatase activity // inferred from electronic annotation /// 0017111 // nucleoside-triphosphatase activity // inferred from electronic annotation', '0000166 // nucleotide binding // inferred from electronic annotation /// 0005524 // ATP binding // inferred from electronic annotation /// 0019899 // enzyme binding // inferred from physical interaction /// 0031072 // heat shock protein binding // inferred from physical interaction /// 0042623 // ATPase activity, coupled // inferred from direct assay /// 0051082 // unfolded protein binding // inferred from direct assay', '0000979 // RNA polymerase II core promoter sequence-specific DNA binding // inferred from direct assay /// 0003677 // DNA binding // inferred from direct assay /// 0003677 // DNA binding // inferred from mutant phenotype /// 0003700 // sequence-specific DNA binding transcription factor activity // inferred from direct assay /// 0004996 // thyroid-stimulating hormone receptor activity // traceable author statement /// 0005515 // protein binding // inferred from physical interaction /// 0044212 // transcription regulatory region DNA binding // inferred from direct assay', '0005509 // calcium ion binding // inferred from electronic annotation /// 0008048 // calcium sensitive guanylate cyclase activator activity // inferred from electronic annotation /// 0030249 // guanylate cyclase regulator activity // inferred from electronic annotation /// 0046872 // metal ion binding // inferred from electronic annotation']}\n" ] } ], "source": [ "# 1. 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. Use the 'preview_df' function from the library to preview the data and print out the results.\n", "print(\"Gene annotation preview:\")\n", "print(preview_df(gene_annotation))\n" ] }, { "cell_type": "markdown", "id": "26df4537", "metadata": {}, "source": [ "### Step 6: Gene Identifier Mapping" ] }, { "cell_type": "code", "execution_count": 7, "id": "e639e3cc", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T08:08:07.399887Z", "iopub.status.busy": "2025-03-25T08:08:07.399751Z", "iopub.status.idle": "2025-03-25T08:08:09.974380Z", "shell.execute_reply": "2025-03-25T08:08:09.973726Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Mapping dataframe preview:\n", "{'ID': ['1007_s_at', '1053_at', '117_at', '121_at', '1255_g_at'], 'Gene': ['DDR1 /// MIR4640', 'RFC2', 'HSPA6', 'PAX8', 'GUCA1A']}\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "Gene expression data preview:\n", "Number of genes: 21278\n", "Sample of gene symbols: ['A1BG', 'A1BG-AS1', 'A1CF', 'A2M', 'A2M-AS1', 'A2ML1', 'A2MP1', 'A4GALT', 'A4GNT', 'AA06']\n", "First 5 rows and 5 columns:\n", " GSM1009750 GSM1009751 GSM1009752 GSM1009753 GSM1009754\n", "Gene \n", "A1BG 0.441593 0.352073 1.117518 0.955207 0.434547\n", "A1BG-AS1 1.048902 0.755683 0.995292 1.054459 1.099495\n", "A1CF 0.678257 0.384777 1.069259 0.453898 0.494588\n", "A2M 7.708893 7.795638 9.462428 9.861125 7.292437\n", "A2M-AS1 2.338703 2.537749 3.986187 3.813036 2.212537\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Gene expression data saved to: ../../output/preprocess/Metabolic_Rate/gene_data/GSE41168.csv\n" ] } ], "source": [ "# 1. Decide which keys in the gene annotation dictionary correspond to identifiers and gene symbols\n", "# From the preview, 'ID' column matches the gene identifiers from gene expression data\n", "# 'Gene Symbol' column contains the corresponding gene symbols\n", "prob_col = 'ID'\n", "gene_col = 'Gene Symbol'\n", "\n", "# 2. Extract the mapping between probes and gene symbols from gene annotation\n", "mapping_df = get_gene_mapping(gene_annotation, prob_col, gene_col)\n", "\n", "print(\"Mapping dataframe preview:\")\n", "print(preview_df(mapping_df))\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", "\n", "print(\"\\nGene expression data preview:\")\n", "print(f\"Number of genes: {len(gene_data)}\")\n", "print(f\"Sample of gene symbols: {list(gene_data.index[:10])}\")\n", "print(f\"First 5 rows and 5 columns:\\n{gene_data.iloc[:5, :5]}\")\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 gene expression data to CSV\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": "ed3d418a", "metadata": {}, "source": [ "### Step 7: Data Normalization and Linking" ] }, { "cell_type": "code", "execution_count": 8, "id": "8d69158d", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T08:08:09.976353Z", "iopub.status.busy": "2025-03-25T08:08:09.976196Z", "iopub.status.idle": "2025-03-25T08:08:28.094926Z", "shell.execute_reply": "2025-03-25T08:08:28.094251Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Shape of gene data after normalization: (19845, 140)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Saved normalized gene data to ../../output/preprocess/Metabolic_Rate/gene_data/GSE41168.csv\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Sample characteristics dictionary:\n", "{0: ['tissue: muscle', 'tissue: adipose tissue'], 1: ['sample group: calorie restrictive', 'sample group: placebo', 'sample group: resveratrol'], 2: ['treatment: before', 'treatment: after'], 3: ['gender: Female']}\n", "Clinical data preview:\n", "{'Metabolic_Rate': [1.0, 1.0, 0.0, 0.0, 0.0], 'Gender': [0.0, 0.0, 0.0, 0.0, 0.0]}\n", "Saved clinical data to ../../output/preprocess/Metabolic_Rate/clinical_data/GSE41168.csv\n", "Shape of linked data: (140, 19847)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Shape of linked data after handling missing values: (140, 19847)\n", "For the feature 'Metabolic_Rate', the least common label is '0.0' with 46 occurrences. This represents 32.86% of the dataset.\n", "The distribution of the feature 'Metabolic_Rate' in this dataset is fine.\n", "\n", "For the feature 'Gender', the least common label is '0.0' with 140 occurrences. This represents 100.00% of the dataset.\n", "The distribution of the feature 'Gender' in this dataset is severely biased.\n", "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Saved processed linked data to ../../output/preprocess/Metabolic_Rate/GSE41168.csv\n" ] } ], "source": [ "# 1. Normalize gene symbols in the gene expression data\n", "gene_data_normalized = normalize_gene_symbols_in_index(gene_data)\n", "print(f\"Shape of gene data after normalization: {gene_data_normalized.shape}\")\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\"Saved normalized gene data to {out_gene_data_file}\")\n", "\n", "# 2. Re-examine the clinical data from the matrix file\n", "soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)\n", "background_info, clinical_data = get_background_and_clinical_data(matrix_file)\n", "\n", "# Print out the sample characteristics to verify available rows\n", "characteristics_dict = get_unique_values_by_row(clinical_data)\n", "print(\"Sample characteristics dictionary:\")\n", "print(characteristics_dict)\n", "\n", "# Based on the background information, this is a study about metabolic function\n", "# but the specific metabolic rate values may not be explicitly stored in the characteristics\n", "# We'll use the sample group (row 1) as our trait, as it indicates different interventions\n", "# that would affect metabolic rate (calorie restriction, resveratrol, placebo)\n", "\n", "# Define conversion functions for the clinical features\n", "def convert_treatment_to_trait(value):\n", " if not isinstance(value, str):\n", " return None\n", " \n", " # Extract value after the colon if present\n", " if \":\" in value:\n", " value = value.split(\":\", 1)[1].strip().lower()\n", " else:\n", " value = value.lower()\n", " \n", " # Treatment groups (resveratrol, calorie restriction) are 1, control (placebo) is 0\n", " if \"resveratrol\" in value or \"calorie restrictive\" in value:\n", " return 1.0\n", " elif \"placebo\" in value:\n", " return 0.0\n", " else:\n", " return None\n", "\n", "def convert_gender(value):\n", " if not isinstance(value, str):\n", " return None\n", " \n", " if \":\" in value:\n", " value = value.split(\":\", 1)[1].strip().lower()\n", " else:\n", " value = value.lower()\n", " \n", " if 'female' in value:\n", " return 0.0\n", " elif 'male' in value:\n", " return 1.0\n", " else:\n", " return None\n", "\n", "# Create the clinical dataframe using the correct row indices based on sample characteristics\n", "try:\n", " clinical_df = geo_select_clinical_features(\n", " clinical_data,\n", " trait=\"Metabolic_Rate\",\n", " trait_row=1, # Row 1 contains sample group (intervention type)\n", " convert_trait=convert_treatment_to_trait,\n", " gender_row=3 if 3 in characteristics_dict and 'gender' in str(characteristics_dict[3]).lower() else None, # Row 3 for gender if it exists\n", " convert_gender=convert_gender,\n", " age_row=None # No age information available\n", " )\n", " \n", " print(\"Clinical data preview:\")\n", " print(preview_df(clinical_df.T)) # Transpose for better viewing\n", " \n", " # Save the clinical data\n", " os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)\n", " clinical_df.T.to_csv(out_clinical_data_file)\n", " print(f\"Saved clinical data to {out_clinical_data_file}\")\n", " \n", " # 3. Link clinical and genetic data\n", " linked_data = geo_link_clinical_genetic_data(clinical_df, gene_data_normalized)\n", " print(f\"Shape of linked data: {linked_data.shape}\")\n", " \n", " # 4. Handle missing values in the linked data\n", " linked_data_cleaned = handle_missing_values(linked_data, 'Metabolic_Rate')\n", " print(f\"Shape of linked data after handling missing values: {linked_data_cleaned.shape}\")\n", " \n", " # 5. Check if the trait and demographic features are biased\n", " is_trait_biased, unbiased_linked_data = judge_and_remove_biased_features(linked_data_cleaned, 'Metabolic_Rate')\n", " \n", " # 6. Validate the dataset and save cohort information\n", " note = \"Dataset contains gene expression data from muscle and adipose tissue samples, comparing placebo, resveratrol supplementation, and calorie restriction interventions.\"\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_trait_biased,\n", " df=unbiased_linked_data,\n", " note=note\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", " unbiased_linked_data.to_csv(out_data_file)\n", " print(f\"Saved processed linked data to {out_data_file}\")\n", " else:\n", " print(\"Dataset validation failed. Final linked data not saved.\")\n", " \n", "except Exception as e:\n", " print(f\"Error in processing clinical data: {e}\")\n", " # If we failed to extract clinical data, update the cohort info\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=False,\n", " is_biased=None,\n", " df=pd.DataFrame(),\n", " note=\"Failed to extract clinical data. Gene expression data is available but missing trait information.\"\n", " )\n", " print(\"Dataset validation failed due to missing clinical data. Only gene data 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 }