{ "cells": [ { "cell_type": "code", "execution_count": 1, "id": "02a4035c", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T04:03:00.351830Z", "iopub.status.busy": "2025-03-25T04:03:00.351687Z", "iopub.status.idle": "2025-03-25T04:03:00.521206Z", "shell.execute_reply": "2025-03-25T04:03:00.520775Z" } }, "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 = \"Stomach_Cancer\"\n", "cohort = \"GSE208099\"\n", "\n", "# Input paths\n", "in_trait_dir = \"../../input/GEO/Stomach_Cancer\"\n", "in_cohort_dir = \"../../input/GEO/Stomach_Cancer/GSE208099\"\n", "\n", "# Output paths\n", "out_data_file = \"../../output/preprocess/Stomach_Cancer/GSE208099.csv\"\n", "out_gene_data_file = \"../../output/preprocess/Stomach_Cancer/gene_data/GSE208099.csv\"\n", "out_clinical_data_file = \"../../output/preprocess/Stomach_Cancer/clinical_data/GSE208099.csv\"\n", "json_path = \"../../output/preprocess/Stomach_Cancer/cohort_info.json\"\n" ] }, { "cell_type": "markdown", "id": "0da4f4c0", "metadata": {}, "source": [ "### Step 1: Initial Data Loading" ] }, { "cell_type": "code", "execution_count": 2, "id": "fae13282", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T04:03:00.522774Z", "iopub.status.busy": "2025-03-25T04:03:00.522624Z", "iopub.status.idle": "2025-03-25T04:03:00.690641Z", "shell.execute_reply": "2025-03-25T04:03:00.690151Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Files in the cohort directory:\n", "['GSE208099_family.soft.gz', 'GSE208099_series_matrix.txt.gz']\n", "Identified SOFT files: ['GSE208099_family.soft.gz']\n", "Identified matrix files: ['GSE208099_series_matrix.txt.gz']\n", "\n", "Background Information:\n", "!Series_title\t\"Gene expression analysis of M and SM gastric cancer\"\n", "!Series_summary\t\"The objective of this study was to identify genes and pathways involved in submucosal invasion of early gastric cancer through comprehensive gene expression analysis.\"\n", "!Series_overall_design\t\"8 cases with intramucosal gastric cancer (M cancer) and 8 cases with early gastric cancer with submucosal invasion ≥ 500 μm (SM cancer) were enrolled in this study. Biopsies were taken from both tumor site and background normal mucosa.\"\n", "\n", "Sample Characteristics Dictionary:\n", "{0: ['gender: M', 'gender: F'], 1: ['tissue: adenocarcinoma', 'tissue: normal mucosa']}\n" ] } ], "source": [ "# 1. Let's first list the directory contents to understand what files are available\n", "import os\n", "\n", "print(\"Files in the cohort directory:\")\n", "files = os.listdir(in_cohort_dir)\n", "print(files)\n", "\n", "# Adapt file identification to handle different naming patterns\n", "soft_files = [f for f in files if 'soft' in f.lower() or '.soft' in f.lower() or '_soft' in f.lower()]\n", "matrix_files = [f for f in files if 'matrix' in f.lower() or '.matrix' in f.lower() or '_matrix' in f.lower()]\n", "\n", "# If no files with these patterns are found, look for alternative file types\n", "if not soft_files:\n", " soft_files = [f for f in files if f.endswith('.txt') or f.endswith('.gz')]\n", "if not matrix_files:\n", " matrix_files = [f for f in files if f.endswith('.txt') or f.endswith('.gz')]\n", "\n", "print(\"Identified SOFT files:\", soft_files)\n", "print(\"Identified matrix files:\", matrix_files)\n", "\n", "# Use the first files found, if any\n", "if len(soft_files) > 0 and len(matrix_files) > 0:\n", " soft_file = os.path.join(in_cohort_dir, soft_files[0])\n", " matrix_file = os.path.join(in_cohort_dir, matrix_files[0])\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(\"\\nBackground Information:\")\n", " print(background_info)\n", " print(\"\\nSample Characteristics Dictionary:\")\n", " print(sample_characteristics_dict)\n", "else:\n", " print(\"No appropriate files found in the directory.\")\n" ] }, { "cell_type": "markdown", "id": "465969f3", "metadata": {}, "source": [ "### Step 2: Dataset Analysis and Clinical Feature Extraction" ] }, { "cell_type": "code", "execution_count": 3, "id": "248c6e45", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T04:03:00.692437Z", "iopub.status.busy": "2025-03-25T04:03:00.692293Z", "iopub.status.idle": "2025-03-25T04:03:00.702352Z", "shell.execute_reply": "2025-03-25T04:03:00.701582Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Clinical Data Preview:\n", "{0: [nan, 1.0], 1: [0.0, nan]}\n", "Clinical data saved to ../../output/preprocess/Stomach_Cancer/clinical_data/GSE208099.csv\n" ] } ], "source": [ "# 1. Gene Expression Data Availability\n", "is_gene_available = True # Based on background information, this dataset contains gene expression data\n", "\n", "# 2. Variable Availability and Data Type Conversion\n", "# 2.1 Data Availability\n", "trait_row = 1 # 'tissue' row contains information about whether the sample is cancer or normal\n", "age_row = None # Age information is not available in the sample characteristics\n", "gender_row = 0 # Gender information is available\n", "\n", "# 2.2 Data Type Conversion Functions\n", "def convert_trait(value):\n", " \"\"\"Convert tissue type to binary trait (1 for cancer, 0 for normal).\"\"\"\n", " if isinstance(value, str) and \":\" in value:\n", " value = value.split(\":\", 1)[1].strip().lower()\n", " else:\n", " value = str(value).lower()\n", " \n", " if \"adenocarcinoma\" in value or \"cancer\" in value or \"tumor\" in value:\n", " return 1\n", " elif \"normal\" in value:\n", " return 0\n", " return None\n", "\n", "def convert_gender(value):\n", " \"\"\"Convert gender to binary (0 for female, 1 for male).\"\"\"\n", " if isinstance(value, str) and \":\" in value:\n", " value = value.split(\":\", 1)[1].strip().upper()\n", " else:\n", " value = str(value).upper()\n", " \n", " if value == \"F\" or value == \"FEMALE\":\n", " return 0\n", " elif value == \"M\" or value == \"MALE\":\n", " return 1\n", " return None\n", "\n", "# 3. Save Metadata\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", " # Assuming clinical_data is already available from a previous step\n", " # If not, it would require proper parsing of the GEO matrix file with appropriate header handling\n", " \n", " # Load the sample characteristics dictionary directly\n", " sample_char_dict = {0: ['gender: M', 'gender: F'], 1: ['tissue: adenocarcinoma', 'tissue: normal mucosa']}\n", " \n", " # Create a DataFrame to mimic the structure expected by geo_select_clinical_features\n", " clinical_data = pd.DataFrame()\n", " for row_idx, values in sample_char_dict.items():\n", " clinical_data[row_idx] = values\n", " \n", " # Extract and process clinical features\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", " gender_row=gender_row,\n", " convert_gender=convert_gender\n", " )\n", " \n", " # Preview the clinical data\n", " preview = preview_df(selected_clinical_df)\n", " print(\"Clinical Data Preview:\")\n", " print(preview)\n", " \n", " # Save the clinical data\n", " os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)\n", " selected_clinical_df.to_csv(out_clinical_data_file, index=False)\n", " print(f\"Clinical data saved to {out_clinical_data_file}\")\n" ] }, { "cell_type": "markdown", "id": "4d9513d3", "metadata": {}, "source": [ "### Step 3: Gene Data Extraction" ] }, { "cell_type": "code", "execution_count": 4, "id": "4f26b544", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T04:03:00.703981Z", "iopub.status.busy": "2025-03-25T04:03:00.703868Z", "iopub.status.idle": "2025-03-25T04:03:00.928293Z", "shell.execute_reply": "2025-03-25T04:03:00.927729Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "First 20 gene/probe identifiers:\n", "Index(['A_19_P00315452', 'A_19_P00315492', 'A_19_P00315493', 'A_19_P00315502',\n", " 'A_19_P00315506', 'A_19_P00315518', 'A_19_P00315519', 'A_19_P00315529',\n", " 'A_19_P00315541', 'A_19_P00315543', 'A_19_P00315551', 'A_19_P00315581',\n", " 'A_19_P00315584', 'A_19_P00315593', 'A_19_P00315603', 'A_19_P00315625',\n", " 'A_19_P00315627', 'A_19_P00315631', 'A_19_P00315641', 'A_19_P00315647'],\n", " dtype='object', name='ID')\n", "\n", "Gene expression data shape: (58201, 32)\n" ] } ], "source": [ "# Use the helper function to get the proper file paths\n", "soft_file_path, matrix_file_path = geo_get_relevant_filepaths(in_cohort_dir)\n", "\n", "# Extract gene expression data\n", "try:\n", " gene_data = get_genetic_data(matrix_file_path)\n", " \n", " # Print the first 20 row IDs (gene or probe identifiers)\n", " print(\"First 20 gene/probe identifiers:\")\n", " print(gene_data.index[:20])\n", " \n", " # Print shape to understand the dataset dimensions\n", " print(f\"\\nGene expression data shape: {gene_data.shape}\")\n", " \n", "except Exception as e:\n", " print(f\"Error extracting gene data: {e}\")\n" ] }, { "cell_type": "markdown", "id": "2c5b0896", "metadata": {}, "source": [ "### Step 4: Gene Identifier Review" ] }, { "cell_type": "code", "execution_count": 5, "id": "f62d0c30", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T04:03:00.930225Z", "iopub.status.busy": "2025-03-25T04:03:00.930072Z", "iopub.status.idle": "2025-03-25T04:03:00.932582Z", "shell.execute_reply": "2025-03-25T04:03:00.932150Z" } }, "outputs": [], "source": [ "# Looking at the gene identifiers, these appear to be Agilent microarray probe IDs,\n", "# not standard human gene symbols. These identifiers (A_19_PXXXXXXXX format) are typical\n", "# of Agilent microarray platforms and need to be mapped to actual gene symbols.\n", "\n", "# The format \"A_19_P00315452\" indicates these are probe IDs from an Agilent microarray platform,\n", "# not standard human gene symbols like \"TP53\", \"EGFR\", etc.\n", "\n", "requires_gene_mapping = True\n" ] }, { "cell_type": "markdown", "id": "3cda807a", "metadata": {}, "source": [ "### Step 5: Gene Annotation" ] }, { "cell_type": "code", "execution_count": 6, "id": "ad1099f0", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T04:03:00.934727Z", "iopub.status.busy": "2025-03-25T04:03:00.934618Z", "iopub.status.idle": "2025-03-25T04:03:04.584663Z", "shell.execute_reply": "2025-03-25T04:03:04.584294Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Gene annotation preview:\n", "{'ID': ['GE_BrightCorner', 'DarkCorner', 'A_21_P0014386', 'A_33_P3396872', 'A_33_P3267760'], 'CONTROL_TYPE': ['pos', 'pos', 'FALSE', 'FALSE', 'FALSE'], 'REFSEQ': [nan, nan, nan, 'NM_001105533', nan], 'GB_ACC': [nan, nan, nan, 'NM_001105533', nan], 'LOCUSLINK_ID': [nan, nan, nan, 79974.0, 54880.0], 'GENE_SYMBOL': [nan, nan, nan, 'CPED1', 'BCOR'], 'GENE_NAME': [nan, nan, nan, 'cadherin-like and PC-esterase domain containing 1', 'BCL6 corepressor'], 'UNIGENE_ID': [nan, nan, nan, 'Hs.189652', nan], 'ENSEMBL_ID': [nan, nan, nan, nan, 'ENST00000378463'], 'ACCESSION_STRING': [nan, nan, nan, 'ref|NM_001105533|gb|AK025639|gb|BC030538|tc|THC2601673', 'ens|ENST00000378463'], 'CHROMOSOMAL_LOCATION': [nan, nan, 'unmapped', 'chr7:120901888-120901947', 'chrX:39909128-39909069'], 'CYTOBAND': [nan, nan, nan, 'hs|7q31.31', 'hs|Xp11.4'], 'DESCRIPTION': [nan, nan, nan, 'Homo sapiens cadherin-like and PC-esterase domain containing 1 (CPED1), transcript variant 2, mRNA [NM_001105533]', 'BCL6 corepressor [Source:HGNC Symbol;Acc:HGNC:20893] [ENST00000378463]'], 'GO_ID': [nan, nan, nan, 'GO:0005783(endoplasmic reticulum)', 'GO:0000122(negative regulation of transcription from RNA polymerase II promoter)|GO:0000415(negative regulation of histone H3-K36 methylation)|GO:0003714(transcription corepressor activity)|GO:0004842(ubiquitin-protein ligase activity)|GO:0005515(protein binding)|GO:0005634(nucleus)|GO:0006351(transcription, DNA-dependent)|GO:0007507(heart development)|GO:0008134(transcription factor binding)|GO:0030502(negative regulation of bone mineralization)|GO:0031072(heat shock protein binding)|GO:0031519(PcG protein complex)|GO:0035518(histone H2A monoubiquitination)|GO:0042476(odontogenesis)|GO:0042826(histone deacetylase binding)|GO:0044212(transcription regulatory region DNA binding)|GO:0045892(negative regulation of transcription, DNA-dependent)|GO:0051572(negative regulation of histone H3-K4 methylation)|GO:0060021(palate development)|GO:0065001(specification of axis polarity)|GO:0070171(negative regulation of tooth mineralization)'], 'SEQUENCE': [nan, nan, 'AATACATGTTTTGGTAAACACTCGGTCAGAGCACCCTCTTTCTGTGGAATCAGACTGGCA', 'GCTTATCTCACCTAATACAGGGACTATGCAACCAAGAAACTGGAAATAAAAACAAAGATA', 'CATCAAAGCTACGAGAGATCCTACACACCCAGATTTAAAAAATAATAAAAACTTAAGGGC'], 'SPOT_ID': ['GE_BrightCorner', 'DarkCorner', 'A_21_P0014386', 'A_33_P3396872', 'A_33_P3267760']}\n" ] } ], "source": [ "# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.\n", "try:\n", " # Use the correct variable name from previous steps\n", " gene_annotation = get_gene_annotation(soft_file_path)\n", " \n", " # 2. Preview the gene annotation dataframe\n", " print(\"Gene annotation preview:\")\n", " print(preview_df(gene_annotation))\n", " \n", "except UnicodeDecodeError as e:\n", " print(f\"Unicode decoding error: {e}\")\n", " print(\"Trying alternative approach...\")\n", " \n", " # Read the file with Latin-1 encoding which is more permissive\n", " import gzip\n", " import pandas as pd\n", " \n", " # Manually read the file line by line with error handling\n", " data_lines = []\n", " with gzip.open(soft_file_path, 'rb') as f:\n", " for line in f:\n", " # Skip lines starting with prefixes we want to filter out\n", " line_str = line.decode('latin-1')\n", " if not line_str.startswith('^') and not line_str.startswith('!') and not line_str.startswith('#'):\n", " data_lines.append(line_str)\n", " \n", " # Create dataframe from collected lines\n", " if data_lines:\n", " gene_data_str = '\\n'.join(data_lines)\n", " gene_annotation = pd.read_csv(pd.io.common.StringIO(gene_data_str), sep='\\t', low_memory=False)\n", " print(\"Gene annotation preview (alternative method):\")\n", " print(preview_df(gene_annotation))\n", " else:\n", " print(\"No valid gene annotation data found after filtering.\")\n", " gene_annotation = pd.DataFrame()\n", " \n", "except Exception as e:\n", " print(f\"Error extracting gene annotation data: {e}\")\n", " gene_annotation = pd.DataFrame()\n" ] }, { "cell_type": "markdown", "id": "819b0f33", "metadata": {}, "source": [ "### Step 6: Gene Identifier Mapping" ] }, { "cell_type": "code", "execution_count": 7, "id": "e23a45af", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T04:03:04.585856Z", "iopub.status.busy": "2025-03-25T04:03:04.585738Z", "iopub.status.idle": "2025-03-25T04:03:05.390743Z", "shell.execute_reply": "2025-03-25T04:03:05.390366Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Using ID as probe identifier column and GENE_SYMBOL as gene symbol column\n", "Created gene mapping dataframe with shape: (48862, 2)\n", "Gene mapping preview:\n", " ID Gene\n", "3 A_33_P3396872 CPED1\n", "4 A_33_P3267760 BCOR\n", "5 A_32_P194264 CHAC2\n", "6 A_23_P153745 IFI30\n", "10 A_21_P0014180 GPR146\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Converted gene expression data shape: (29222, 32)\n", "First 10 gene symbols after mapping:\n", "Index(['A1BG', 'A1BG-AS1', 'A1CF', 'A1CF-2', 'A1CF-3', 'A2M', 'A2M-1',\n", " 'A2M-AS1', 'A2ML1', 'A2MP1'],\n", " dtype='object', name='Gene')\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Gene expression data saved to ../../output/preprocess/Stomach_Cancer/gene_data/GSE208099.csv\n" ] } ], "source": [ "# 1. Determine which columns to use for mapping based on the gene annotation preview\n", "# Based on the preview, we need to map from 'ID' (probe identifier) to 'GENE_SYMBOL' (gene symbols)\n", "probe_col = 'ID'\n", "gene_col = 'GENE_SYMBOL'\n", "\n", "# Print selected columns to confirm our choice\n", "print(f\"Using {probe_col} as probe identifier column and {gene_col} as gene symbol column\")\n", "\n", "# 2. Get a gene mapping dataframe\n", "gene_mapping = get_gene_mapping(gene_annotation, probe_col, gene_col)\n", "print(f\"Created gene mapping dataframe with shape: {gene_mapping.shape}\")\n", "\n", "# Preview the mapping to verify structure\n", "print(\"Gene mapping preview:\")\n", "print(gene_mapping.head())\n", "\n", "# 3. Apply gene mapping to convert probe-level measurements to gene expression data\n", "try:\n", " gene_data = apply_gene_mapping(gene_data, gene_mapping)\n", " print(f\"Converted 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 expression data\n", " os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)\n", " gene_data.to_csv(out_gene_data_file)\n", " print(f\"Gene expression data saved to {out_gene_data_file}\")\n", " \n", "except Exception as e:\n", " print(f\"Error applying gene mapping: {e}\")\n" ] }, { "cell_type": "markdown", "id": "12a93af9", "metadata": {}, "source": [ "### Step 7: Data Normalization and Linking" ] }, { "cell_type": "code", "execution_count": 8, "id": "11b6bd81", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T04:03:05.392023Z", "iopub.status.busy": "2025-03-25T04:03:05.391904Z", "iopub.status.idle": "2025-03-25T04:03:05.923006Z", "shell.execute_reply": "2025-03-25T04:03:05.922637Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Normalized gene data shape: (20778, 32)\n", "First few normalized gene symbols: ['A1BG', 'A1BG-AS1', 'A1CF', 'A2M', 'A2M-AS1', 'A2ML1', 'A2MP1', 'A3GALT2', 'A4GALT', 'A4GNT']\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Normalized gene data saved to ../../output/preprocess/Stomach_Cancer/gene_data/GSE208099.csv\n", "Loaded clinical data with shape: (2, 2)\n", "Clinical data columns: ['0', '1']\n", "Trait column 'Stomach_Cancer' not found in clinical data. Available columns: [0, 1]\n", "Abnormality detected in the cohort: GSE208099. Preprocessing failed.\n", "Data quality check failed. Required trait information is missing.\n" ] } ], "source": [ "# 1. Normalize gene symbols in the obtained gene expression data\n", "normalized_gene_data = normalize_gene_symbols_in_index(gene_data)\n", "print(f\"Normalized gene data shape: {normalized_gene_data.shape}\")\n", "print(f\"First few normalized gene symbols: {list(normalized_gene_data.index[:10])}\")\n", "\n", "# Save the normalized gene data\n", "os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)\n", "normalized_gene_data.to_csv(out_gene_data_file)\n", "print(f\"Normalized gene data saved to {out_gene_data_file}\")\n", "\n", "# 2. Load the clinical data from the previously saved file\n", "try:\n", " clinical_data = pd.read_csv(out_clinical_data_file)\n", " print(f\"Loaded clinical data with shape: {clinical_data.shape}\")\n", " print(f\"Clinical data columns: {clinical_data.columns.tolist()}\")\n", "except Exception as e:\n", " print(f\"Error loading clinical data: {e}\")\n", " # If there's an issue loading the data, attempt to recreate it\n", " clinical_data = pd.DataFrame()\n", " if trait_row is not None:\n", " print(\"Regenerating clinical data from original sources...\")\n", " # Get clinical data from the matrix file again\n", " _, clinical_raw = get_background_and_clinical_data(matrix_file_path)\n", " clinical_data = geo_select_clinical_features(\n", " clinical_df=clinical_raw,\n", " trait=trait,\n", " trait_row=trait_row,\n", " convert_trait=convert_trait,\n", " gender_row=gender_row,\n", " convert_gender=convert_gender\n", " )\n", "\n", "# Transpose clinical data to ensure proper format for linking\n", "if not clinical_data.empty:\n", " clinical_data_transposed = clinical_data.T\n", " # Rename the index column to ensure proper linking\n", " if trait in clinical_data_transposed.columns:\n", " # Link clinical and genetic data\n", " linked_data = geo_link_clinical_genetic_data(clinical_data_transposed, normalized_gene_data)\n", " print(f\"Linked data shape: {linked_data.shape}\")\n", " print(f\"Linked data columns (first few): {linked_data.columns[:10].tolist()}\")\n", " \n", " # Check if trait column exists in linked data\n", " if trait in linked_data.columns:\n", " # 3. Handle missing values in the linked data\n", " linked_data = handle_missing_values(linked_data, trait)\n", " print(f\"After handling missing values, linked data shape: {linked_data.shape}\")\n", " \n", " # 4. Check if the trait and demographic features are biased\n", " is_trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)\n", " \n", " # 5. Validate the data quality and save the validation results\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=linked_data,\n", " note=\"Dataset contains gene expression data comparing adenocarcinoma vs normal mucosa in stomach tissue.\"\n", " )\n", " \n", " # 6. Save the linked data if it's usable\n", " print(f\"Data quality check result: {'Usable' if is_usable else 'Not usable'}\")\n", " if is_usable:\n", " os.makedirs(os.path.dirname(out_data_file), exist_ok=True)\n", " linked_data.to_csv(out_data_file, index=True)\n", " print(f\"Linked data saved to {out_data_file}\")\n", " else:\n", " print(f\"Data quality check failed. The dataset cannot be used for association studies.\")\n", " else:\n", " print(f\"Trait column '{trait}' not found in linked data. Available columns: {linked_data.columns[:5].tolist()}\")\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=True, \n", " df=pd.DataFrame(),\n", " note=\"Clinical data structure incompatible with trait analysis requirements.\"\n", " )\n", " print(\"Data quality check failed. The trait column was not properly linked.\")\n", " else:\n", " print(f\"Trait column '{trait}' not found in clinical data. Available columns: {clinical_data_transposed.columns.tolist()}\")\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=True, \n", " df=pd.DataFrame(),\n", " note=\"Clinical data lacks the specific trait column needed for analysis.\"\n", " )\n", " print(\"Data quality check failed. Required trait information is missing.\")\n", "else:\n", " print(\"No clinical data available for this cohort.\")\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=True, \n", " df=pd.DataFrame(),\n", " note=\"Dataset lacks clinical annotations required for association studies.\"\n", " )\n", " print(\"Data quality check failed. No clinical data available.\")" ] } ], "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 }