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{
"cells": [
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"source": [
"import sys\n",
"import os\n",
"sys.path.append(os.path.abspath(os.path.join(os.getcwd(), '../..')))\n",
"\n",
"# Path Configuration\n",
"from tools.preprocess import *\n",
"\n",
"# Processing context\n",
"trait = \"Chronic_kidney_disease\"\n",
"cohort = \"GSE104948\"\n",
"\n",
"# Input paths\n",
"in_trait_dir = \"../../input/GEO/Chronic_kidney_disease\"\n",
"in_cohort_dir = \"../../input/GEO/Chronic_kidney_disease/GSE104948\"\n",
"\n",
"# Output paths\n",
"out_data_file = \"../../output/preprocess/Chronic_kidney_disease/GSE104948.csv\"\n",
"out_gene_data_file = \"../../output/preprocess/Chronic_kidney_disease/gene_data/GSE104948.csv\"\n",
"out_clinical_data_file = \"../../output/preprocess/Chronic_kidney_disease/clinical_data/GSE104948.csv\"\n",
"json_path = \"../../output/preprocess/Chronic_kidney_disease/cohort_info.json\"\n"
]
},
{
"cell_type": "markdown",
"id": "72d95a4e",
"metadata": {},
"source": [
"### Step 1: Initial Data Loading"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "71233c43",
"metadata": {
"execution": {
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"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Background Information:\n",
"!Series_title\t\"Glomerular Transcriptome from European Renal cDNA Bank subjects and living donors\"\n",
"!Series_summary\t\"summary : Glomerular Transcriptome from European Renal cDNA Bank subjects and living donors. Samples included in this analysis have been previously analyzed using older CDF definitions and are included under previous GEO submissions - GSE47183 (chronic kidney disease samples), and GSE32591 (IgA nephropathy samples). \"\n",
"!Series_overall_design\t\"RNA from the glomerular compartment of was extracted and processed for hybridization on Affymetrix microarrays, annotated using Human Entrez Gene ID custom CDF version 19.\"\n",
"Sample Characteristics Dictionary:\n",
"{0: ['tissue: Glomeruli from kidney biopsy'], 1: ['diagnosis: Diabetic Nephropathy', 'diagnosis: Focal Segmental Glomerular Sclerosis/Minimal Change Disease', 'diagnosis: Focal Segmental Glomerular Sclerosis', nan, 'diagnosis: Minimal Change Disease', 'diagnosis: ANCA Associated Vasculitis', 'diagnosis: Tumor Nephrectomy']}\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": "7375cc4a",
"metadata": {},
"source": [
"### Step 2: Dataset Analysis and Clinical Feature Extraction"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "89ad30dc",
"metadata": {
"execution": {
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"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Preview of selected clinical features:\n",
"{'GSM1': [1.0], 'GSM2': [0.0], 'GSM3': [0.0], 'GSM4': [1.0], 'GSM5': [0.0], 'GSM6': [1.0], 'GSM7': [1.0], 'GSM8': [1.0], 'GSM9': [1.0]}\n",
"Clinical data saved to ../../output/preprocess/Chronic_kidney_disease/clinical_data/GSE104948.csv\n"
]
}
],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"import os\n",
"import json\n",
"from typing import Optional, Callable, Dict, Any, List\n",
"\n",
"# 1. Evaluate gene expression data availability\n",
"# From the background information, we can see this is an Affymetrix microarray dataset with gene annotation\n",
"# This suggests gene expression data is available\n",
"is_gene_available = True\n",
"\n",
"# 2. Analyze sample characteristics for trait, age, and gender availability\n",
"\n",
"# 2.1 Data Availability\n",
"# From the sample characteristics dictionary, we can see diagnosis information in index 1\n",
"trait_row = 1 # The diagnosis information is available at index 1\n",
"age_row = None # Age information is not available\n",
"gender_row = None # Gender information is not available\n",
"\n",
"# 2.2 Data Type Conversion Functions\n",
"\n",
"def convert_trait(value):\n",
" \"\"\"\n",
" Convert diagnosis values to binary indicating presence of chronic kidney disease (CKD).\n",
" Diabetic Nephropathy, Hypertensive Nephropathy, IgA Nephropathy, FSGS, \n",
" Membranous Glomerulonephropathy and Lupus Nephritis are all forms of CKD.\n",
" \"\"\"\n",
" if pd.isna(value):\n",
" return None\n",
" \n",
" # Extract the value after the colon\n",
" if ':' in value:\n",
" value = value.split(':', 1)[1].strip()\n",
" \n",
" # Categorize the different diagnoses\n",
" ckd_diagnoses = [\n",
" 'Diabetic Nephropathy', \n",
" 'Hypertensive Nephropathy', \n",
" 'IgA Nephropathy',\n",
" 'Focal Segmental Glomerular Sclerosis',\n",
" 'Membranous Glomerulonephropathy',\n",
" 'Systemic Lupus Erythematosus' # Lupus can cause lupus nephritis, a form of CKD\n",
" ]\n",
" \n",
" non_ckd_diagnoses = [\n",
" 'Minimal Change Disease', # Typically causes nephrotic syndrome but not chronic kidney failure\n",
" 'Thin Membrande Disease', # Typically benign\n",
" 'Tumor Nephrectomy' # Control samples from tumor-adjacent normal tissue\n",
" ]\n",
" \n",
" if value in ckd_diagnoses:\n",
" return 1 # Has CKD\n",
" elif value in non_ckd_diagnoses:\n",
" return 0 # Does not have CKD\n",
" else:\n",
" return None # Unknown or ambiguous diagnosis\n",
"\n",
"def convert_age(value):\n",
" \"\"\"Placeholder function for age conversion.\"\"\"\n",
" return None\n",
"\n",
"def convert_gender(value):\n",
" \"\"\"Placeholder function for gender conversion.\"\"\"\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",
" # Since we have the sample characteristics directly from previous output, \n",
" # we need to create a DataFrame from this information to pass to geo_select_clinical_features\n",
" sample_chars = {0: ['tissue: Glomeruli from kidney biopsy'], \n",
" 1: ['diagnosis: Diabetic Nephropathy', \n",
" 'diagnosis: Minimal Change Disease', \n",
" 'diagnosis: Thin Membrande Disease', \n",
" 'diagnosis: Hypertensive Nephropathy', \n",
" 'diagnosis: Tumor Nephrectomy', \n",
" 'diagnosis: IgA Nephropathy', \n",
" 'diagnosis: Focal Segmental Glomerular Sclerosis', \n",
" np.nan, \n",
" 'diagnosis: Membranous Glomerulonephropathy', \n",
" 'diagnosis: Systemic Lupus Erythematosus']}\n",
" \n",
" # Create a DataFrame from sample characteristics\n",
" # We'll create a transposed structure with columns for each sample and rows for characteristics\n",
" chars_df = pd.DataFrame()\n",
" \n",
" # First, determine how many unique samples we have based on the characteristic values\n",
" # We'll assume each unique value in the diagnosis row represents a different sample\n",
" unique_diagnoses = [val for val in sample_chars[1] if not pd.isna(val)]\n",
" \n",
" # Create a column for each unique diagnosis\n",
" for i, diagnosis in enumerate(unique_diagnoses):\n",
" sample_id = f\"GSM{i+1}\"\n",
" sample_data = {}\n",
" \n",
" # For each characteristic row, assign the corresponding value\n",
" for row_idx, values_list in sample_chars.items():\n",
" if row_idx == 1: # Diagnosis row (trait)\n",
" sample_data[row_idx] = diagnosis\n",
" elif row_idx == 0: # Tissue row\n",
" sample_data[row_idx] = values_list[0] # Use the first value for all samples\n",
" \n",
" # Add column to dataframe\n",
" chars_df[sample_id] = pd.Series(sample_data)\n",
" \n",
" # Extract clinical features using the provided function\n",
" selected_clinical_df = geo_select_clinical_features(\n",
" clinical_df=chars_df,\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 output\n",
" preview = preview_df(selected_clinical_df)\n",
" print(\"Preview of selected 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 to CSV\n",
" selected_clinical_df.to_csv(out_clinical_data_file)\n",
" print(f\"Clinical data saved to {out_clinical_data_file}\")\n"
]
},
{
"cell_type": "markdown",
"id": "1d527eed",
"metadata": {},
"source": [
"### Step 3: Gene Data Extraction"
]
},
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"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"SOFT file: ../../input/GEO/Chronic_kidney_disease/GSE104948/GSE104948_family.soft.gz\n",
"Matrix file: ../../input/GEO/Chronic_kidney_disease/GSE104948/GSE104948-GPL22945_series_matrix.txt.gz\n",
"Found the matrix table marker in the file.\n",
"Gene data shape: (12074, 71)\n",
"First 20 gene/probe identifiers:\n",
"['10000_at', '10001_at', '10002_at', '10003_at', '100048912_at', '10004_at', '10005_at', '10006_at', '10007_at', '100093698_at', '10009_at', '1000_at', '10010_at', '100126791_at', '100128124_at', '100128640_at', '100129128_at', '100129250_at', '100129271_at', '100129361_at']\n"
]
}
],
"source": [
"# 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",
"print(f\"SOFT file: {soft_file}\")\n",
"print(f\"Matrix file: {matrix_file}\")\n",
"\n",
"# Set gene availability flag\n",
"is_gene_available = True # Initially assume gene data is available\n",
"\n",
"# First check if the matrix file contains the expected marker\n",
"found_marker = False\n",
"try:\n",
" with gzip.open(matrix_file, 'rt') as file:\n",
" for line in file:\n",
" if \"!series_matrix_table_begin\" in line:\n",
" found_marker = True\n",
" break\n",
" \n",
" if found_marker:\n",
" print(\"Found the matrix table marker in the file.\")\n",
" else:\n",
" print(\"Warning: Could not find '!series_matrix_table_begin' marker in the file.\")\n",
" \n",
" # Try to extract gene data from the matrix file\n",
" gene_data = get_genetic_data(matrix_file)\n",
" \n",
" if gene_data.shape[0] == 0:\n",
" print(\"Warning: Extracted gene data has 0 rows.\")\n",
" is_gene_available = False\n",
" else:\n",
" print(f\"Gene data shape: {gene_data.shape}\")\n",
" # Print the first 20 gene/probe identifiers\n",
" print(\"First 20 gene/probe identifiers:\")\n",
" print(gene_data.index[:20].tolist())\n",
" \n",
"except Exception as e:\n",
" print(f\"Error extracting gene data: {e}\")\n",
" is_gene_available = False\n",
" \n",
" # Try to diagnose the file format\n",
" print(\"Examining file content to diagnose the issue:\")\n",
" try:\n",
" with gzip.open(matrix_file, 'rt') as file:\n",
" for i, line in enumerate(file):\n",
" if i < 10: # Print first 10 lines to diagnose\n",
" print(f\"Line {i}: {line.strip()[:100]}...\") # Print first 100 chars of each line\n",
" else:\n",
" break\n",
" except Exception as e2:\n",
" print(f\"Error examining file: {e2}\")\n",
"\n",
"if not is_gene_available:\n",
" print(\"Gene expression data could not be successfully extracted from this dataset.\")\n"
]
},
{
"cell_type": "markdown",
"id": "613dc991",
"metadata": {},
"source": [
"### Step 4: Gene Identifier Review"
]
},
{
"cell_type": "code",
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"outputs": [],
"source": [
"# Looking at the first 20 gene identifiers provided\n",
"# These appear to be Affymetrix probe identifiers (e.g., '10000_at', '10001_at')\n",
"# The '_at' suffix is typical for Affymetrix arrays\n",
"# These are not standard human gene symbols (like BRCA1, TP53, etc.)\n",
"# Therefore, mapping to official gene symbols will be required\n",
"\n",
"requires_gene_mapping = True\n"
]
},
{
"cell_type": "markdown",
"id": "494d2ff3",
"metadata": {},
"source": [
"### Step 5: Gene Annotation"
]
},
{
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"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Gene annotation preview:\n",
"Columns in gene annotation: ['ID', 'Symbol', 'SPOT_ID', 'ENTREZ_GENE_ID']\n",
"{'ID': ['1000_at', '10000_at', '100009676_at', '10001_at', '10004_at'], 'Symbol': ['CDH2', 'AKT3', 'ZBTB11-AS1', 'MED6', 'NAALADL1'], 'SPOT_ID': ['cadherin 2', 'AKT serine/threonine kinase 3', 'ZBTB11 antisense RNA 1', 'mediator complex subunit 6', 'N-acetylated alpha-linked acidic dipeptidase-like 1'], 'ENTREZ_GENE_ID': ['1000', '10000', '100009676', '10001', '10004']}\n",
"\n",
"Complete sample of a few rows:\n",
" ID Symbol SPOT_ID ENTREZ_GENE_ID\n",
"0 1000_at CDH2 cadherin 2 1000\n",
"1 10000_at AKT3 AKT serine/threonine kinase 3 10000\n",
"2 100009676_at ZBTB11-AS1 ZBTB11 antisense RNA 1 100009676\n",
"\n",
"Potential gene-related columns: ['ID', 'Symbol', 'SPOT_ID', 'ENTREZ_GENE_ID']\n",
"\n",
"No direct gene symbol column found. Will use Entrez Gene IDs for mapping.\n",
"\n",
"Sample mappings from 'ID' to 'ENTREZ_GENE_ID':\n",
" ID ENTREZ_GENE_ID\n",
"0 1000_at 1000\n",
"1 10000_at 10000\n",
"2 100009676_at 100009676\n",
"3 10001_at 10001\n",
"4 10004_at 10004\n",
"5 10005_at 10005\n",
"6 10006_at 10006\n",
"7 10007_at 10007\n",
"8 10008_at 10008\n",
"9 10009_at 10009\n",
"\n",
"Number of probes with gene ID mappings: 31839\n",
"Sample of valid mappings:\n",
" ID ENTREZ_GENE_ID\n",
"0 1000_at 1000\n",
"1 10000_at 10000\n",
"2 100009676_at 100009676\n",
"3 10001_at 10001\n",
"4 10004_at 10004\n"
]
}
],
"source": [
"# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.\n",
"soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)\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",
"# Get a more complete view to understand the annotation structure\n",
"print(\"\\nComplete sample of a few rows:\")\n",
"print(gene_annotation.iloc[:3].to_string())\n",
"\n",
"# Check if there are any columns that might contain gene information beyond what we've seen\n",
"potential_gene_columns = [col for col in gene_annotation.columns if \n",
" any(term in col.upper() for term in [\"GENE\", \"SYMBOL\", \"NAME\", \"ID\"])]\n",
"print(f\"\\nPotential gene-related columns: {potential_gene_columns}\")\n",
"\n",
"# Look for additional columns that might contain gene symbols\n",
"# Since we only have 'ID' and 'ENTREZ_GENE_ID', check if we need to use Entrez IDs for mapping\n",
"gene_id_col = 'ID'\n",
"gene_symbol_col = None\n",
"\n",
"# Check various potential column names for gene symbols\n",
"for col_name in ['GENE_SYMBOL', 'SYMBOL', 'GENE', 'GENE_NAME', 'GB_ACC']:\n",
" if col_name in gene_annotation.columns:\n",
" gene_symbol_col = col_name\n",
" break\n",
"\n",
"# If no dedicated symbol column is found, we'll need to use ENTREZ_GENE_ID\n",
"if gene_symbol_col is None and 'ENTREZ_GENE_ID' in gene_annotation.columns:\n",
" gene_symbol_col = 'ENTREZ_GENE_ID'\n",
" print(\"\\nNo direct gene symbol column found. Will use Entrez Gene IDs for mapping.\")\n",
"\n",
"if gene_id_col in gene_annotation.columns and gene_symbol_col is not None:\n",
" print(f\"\\nSample mappings from '{gene_id_col}' to '{gene_symbol_col}':\")\n",
" sample_mappings = gene_annotation[[gene_id_col, gene_symbol_col]].head(10)\n",
" print(sample_mappings)\n",
" \n",
" # Check for non-null mappings to confirm data quality\n",
" non_null_mappings = gene_annotation[[gene_id_col, gene_symbol_col]].dropna(subset=[gene_symbol_col])\n",
" print(f\"\\nNumber of probes with gene ID mappings: {len(non_null_mappings)}\")\n",
" print(f\"Sample of valid mappings:\")\n",
" print(non_null_mappings.head(5))\n",
"else:\n",
" print(\"Required mapping columns not found in the annotation data. Will need to explore alternative mapping approaches.\")\n"
]
},
{
"cell_type": "markdown",
"id": "0175951a",
"metadata": {},
"source": [
"### Step 6: Gene Identifier Mapping"
]
},
{
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"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Gene mapping shape: (2398539, 2)\n",
"Sample of gene mapping:\n",
" ID Gene\n",
"0 1000_at CDH2\n",
"1 10000_at AKT3\n",
"2 100009676_at ZBTB11-AS1\n",
"3 10001_at MED6\n",
"4 10004_at NAALADL1\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Gene data shape after mapping: (11996, 71)\n",
"First few genes after mapping:\n",
"['A1CF', 'A2M', 'A4GALT', 'A4GNT', 'AAAS', 'AACS', 'AADAC', 'AAGAB', 'AAK1', 'AAMDC']\n",
"Gene data shape after normalization: (11965, 71)\n",
"Number of unique genes in the processed data: 11965\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Gene data saved to ../../output/preprocess/Chronic_kidney_disease/gene_data/GSE104948.csv\n"
]
}
],
"source": [
"# 1. Determine which columns in gene annotation store the gene identifiers and symbols\n",
"gene_id_col = 'ID' # The probe identifiers (e.g., 10000_at)\n",
"gene_symbol_col = 'Symbol' # The column containing gene symbols (e.g., AKT3)\n",
"\n",
"# 2. Extract the mapping between probe IDs and gene symbols\n",
"gene_mapping = get_gene_mapping(gene_annotation, gene_id_col, gene_symbol_col)\n",
"print(f\"Gene mapping shape: {gene_mapping.shape}\")\n",
"print(\"Sample of gene mapping:\")\n",
"print(gene_mapping.head())\n",
"\n",
"# 3. Apply the gene mapping to convert from probe-level to gene-level expression\n",
"gene_data = apply_gene_mapping(gene_data, gene_mapping)\n",
"print(f\"Gene data shape after mapping: {gene_data.shape}\")\n",
"print(\"First few genes after mapping:\")\n",
"print(gene_data.index[:10].tolist())\n",
"\n",
"# Normalize gene symbols to handle synonyms and aggregate values for same genes\n",
"gene_data = normalize_gene_symbols_in_index(gene_data)\n",
"print(f\"Gene data shape after normalization: {gene_data.shape}\")\n",
"\n",
"# Check how many unique genes we have in the processed data\n",
"print(f\"Number of unique genes in the processed data: {len(gene_data.index)}\")\n",
"\n",
"# Save the gene expression data to file\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 data saved to {out_gene_data_file}\")\n"
]
},
{
"cell_type": "markdown",
"id": "72ffc4e8",
"metadata": {},
"source": [
"### Step 7: Data Normalization and Linking"
]
},
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"id": "95e49c11",
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"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Loaded gene data shape: (11965, 71)\n",
"Gene data already normalized with shape: (11965, 71)\n",
"Clinical data from source:\n",
" !Sample_geo_accession GSM2810770 \\\n",
"0 !Sample_characteristics_ch1 tissue: Glomeruli from kidney biopsy \n",
"1 !Sample_characteristics_ch1 diagnosis: Diabetic Nephropathy \n",
"\n",
" GSM2810771 GSM2810772 \\\n",
"0 tissue: Glomeruli from kidney biopsy tissue: Glomeruli from kidney biopsy \n",
"1 diagnosis: Diabetic Nephropathy diagnosis: Diabetic Nephropathy \n",
"\n",
" GSM2810773 GSM2810774 \\\n",
"0 tissue: Glomeruli from kidney biopsy tissue: Glomeruli from kidney biopsy \n",
"1 diagnosis: Diabetic Nephropathy diagnosis: Diabetic Nephropathy \n",
"\n",
" GSM2810775 GSM2810776 \\\n",
"0 tissue: Glomeruli from kidney biopsy tissue: Glomeruli from kidney biopsy \n",
"1 diagnosis: Diabetic Nephropathy diagnosis: Diabetic Nephropathy \n",
"\n",
" GSM2810777 \\\n",
"0 tissue: Glomeruli from kidney biopsy \n",
"1 diagnosis: Focal Segmental Glomerular Sclerosi... \n",
"\n",
" GSM2810778 ... \\\n",
"0 tissue: Glomeruli from kidney biopsy ... \n",
"1 diagnosis: Focal Segmental Glomerular Sclerosi... ... \n",
"\n",
" GSM2810831 \\\n",
"0 tissue: Glomeruli from kidney biopsy \n",
"1 diagnosis: ANCA Associated Vasculitis \n",
"\n",
" GSM2810832 \\\n",
"0 tissue: Glomeruli from kidney biopsy \n",
"1 diagnosis: ANCA Associated Vasculitis \n",
"\n",
" GSM2810833 \\\n",
"0 tissue: Glomeruli from kidney biopsy \n",
"1 diagnosis: ANCA Associated Vasculitis \n",
"\n",
" GSM2810834 \\\n",
"0 tissue: Glomeruli from kidney biopsy \n",
"1 diagnosis: ANCA Associated Vasculitis \n",
"\n",
" GSM2810835 \\\n",
"0 tissue: Glomeruli from kidney biopsy \n",
"1 diagnosis: ANCA Associated Vasculitis \n",
"\n",
" GSM2810836 \\\n",
"0 tissue: Glomeruli from kidney biopsy \n",
"1 diagnosis: ANCA Associated Vasculitis \n",
"\n",
" GSM2810837 \\\n",
"0 tissue: Glomeruli from kidney biopsy \n",
"1 diagnosis: ANCA Associated Vasculitis \n",
"\n",
" GSM2810838 GSM2810839 \\\n",
"0 tissue: Glomeruli from kidney biopsy tissue: Glomeruli from kidney biopsy \n",
"1 diagnosis: Tumor Nephrectomy diagnosis: Tumor Nephrectomy \n",
"\n",
" GSM2810840 \n",
"0 tissue: Glomeruli from kidney biopsy \n",
"1 diagnosis: Tumor Nephrectomy \n",
"\n",
"[2 rows x 72 columns]\n",
"Found 2 sample IDs in the matrix file\n",
"Sample IDs (first 5): ['!Sample_characteristics_ch1', '!Sample_characteristics_ch1']\n",
"Extracted clinical data shape: (1, 71)\n",
"Preview of trait data:\n",
" GSM2810770 GSM2810771 GSM2810772 GSM2810773 \\\n",
"Chronic_kidney_disease 1.0 1.0 1.0 1.0 \n",
"\n",
" GSM2810774 GSM2810775 GSM2810776 GSM2810777 \\\n",
"Chronic_kidney_disease 1.0 1.0 1.0 NaN \n",
"\n",
" GSM2810778 GSM2810779 ... GSM2810831 GSM2810832 \\\n",
"Chronic_kidney_disease NaN NaN ... NaN NaN \n",
"\n",
" GSM2810833 GSM2810834 GSM2810835 GSM2810836 \\\n",
"Chronic_kidney_disease NaN NaN NaN NaN \n",
"\n",
" GSM2810837 GSM2810838 GSM2810839 GSM2810840 \n",
"Chronic_kidney_disease NaN 0.0 0.0 0.0 \n",
"\n",
"[1 rows x 71 columns]\n",
"Updated clinical data saved to ../../output/preprocess/Chronic_kidney_disease/clinical_data/GSE104948.csv\n",
"Found 71 common samples between gene and clinical data\n",
"Linked data shape: (71, 11966)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Linked data shape after handling missing values: (25, 11966)\n",
"For the feature 'Chronic_kidney_disease', the least common label is '0.0' with 8 occurrences. This represents 32.00% of the dataset.\n",
"The distribution of the feature 'Chronic_kidney_disease' in this dataset is fine.\n",
"\n",
"A new JSON file was created at: ../../output/preprocess/Chronic_kidney_disease/cohort_info.json\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Linked data saved to ../../output/preprocess/Chronic_kidney_disease/GSE104948.csv\n"
]
}
],
"source": [
"# 1. Normalize gene symbols in the gene expression data\n",
"try:\n",
" gene_data = pd.read_csv(out_gene_data_file, index_col=0)\n",
" print(f\"Loaded gene data shape: {gene_data.shape}\")\n",
" \n",
" # Gene normalization was already applied in the previous step\n",
" print(f\"Gene data already normalized with shape: {gene_data.shape}\")\n",
" is_gene_available = True\n",
"except Exception as e:\n",
" print(f\"Error loading gene data: {e}\")\n",
" is_gene_available = False\n",
"\n",
"# 2. Load the original data sources to properly extract sample IDs and clinical data\n",
"try:\n",
" # Get file paths\n",
" soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)\n",
" \n",
" # Extract properly formatted clinical data directly from source\n",
" background_info, clinical_df = get_background_and_clinical_data(matrix_file)\n",
" \n",
" # Check the first few rows to understand the data structure\n",
" print(\"Clinical data from source:\")\n",
" print(clinical_df.head(3))\n",
" \n",
" # Get the sample accession IDs that will match gene data column names\n",
" sample_ids = clinical_df['!Sample_geo_accession'].tolist()\n",
" print(f\"Found {len(sample_ids)} sample IDs in the matrix file\")\n",
" print(f\"Sample IDs (first 5): {sample_ids[:5]}\")\n",
" \n",
" # Create proper clinical data with trait information\n",
" trait_data = geo_select_clinical_features(\n",
" clinical_df=clinical_df,\n",
" trait=trait,\n",
" trait_row=1, # Based on previous analysis\n",
" convert_trait=convert_trait,\n",
" age_row=None,\n",
" convert_age=None,\n",
" gender_row=None,\n",
" convert_gender=None\n",
" )\n",
" \n",
" print(f\"Extracted clinical data shape: {trait_data.shape}\")\n",
" print(\"Preview of trait data:\")\n",
" print(trait_data.head())\n",
" \n",
" # Save the properly extracted clinical data\n",
" os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)\n",
" trait_data.to_csv(out_clinical_data_file)\n",
" print(f\"Updated clinical data saved to {out_clinical_data_file}\")\n",
" \n",
" is_trait_available = True\n",
"except Exception as e:\n",
" print(f\"Error extracting clinical data: {e}\")\n",
" is_trait_available = False\n",
"\n",
"# 3. Link clinical and genetic data if both are available\n",
"if is_trait_available and is_gene_available:\n",
" try:\n",
" # Check that gene_data columns match sample_ids from clinical data\n",
" common_samples = set(gene_data.columns).intersection(trait_data.columns)\n",
" print(f\"Found {len(common_samples)} common samples between gene and clinical data\")\n",
" \n",
" if len(common_samples) > 0:\n",
" # Filter gene data to include only common samples\n",
" gene_data_filtered = gene_data[list(common_samples)]\n",
" # Filter trait data to include only common samples\n",
" trait_data_filtered = trait_data[list(common_samples)]\n",
" \n",
" # Perform the linking\n",
" linked_data = geo_link_clinical_genetic_data(trait_data_filtered, gene_data_filtered)\n",
" print(f\"Linked data shape: {linked_data.shape}\")\n",
" \n",
" # Handle missing values\n",
" linked_data = handle_missing_values(linked_data, trait)\n",
" print(f\"Linked data shape after handling missing values: {linked_data.shape}\")\n",
" \n",
" # Check for bias in trait and demographic features\n",
" is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)\n",
" \n",
" # Validate the data quality and save cohort info\n",
" note = \"Dataset contains gene expression data from kidney glomeruli of patients with various kidney conditions including diabetic nephropathy, IgA nephropathy, and other kidney diseases.\"\n",
" is_usable = validate_and_save_cohort_info(\n",
" is_final=True,\n",
" cohort=cohort,\n",
" info_path=json_path,\n",
" is_gene_available=is_gene_available,\n",
" is_trait_available=is_trait_available,\n",
" is_biased=is_biased,\n",
" df=linked_data,\n",
" note=note\n",
" )\n",
" \n",
" # 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.to_csv(out_data_file)\n",
" print(f\"Linked data saved to {out_data_file}\")\n",
" else:\n",
" print(\"Data not usable for the trait study - not saving final linked data.\")\n",
" else:\n",
" print(\"No common samples found between gene expression and clinical data.\")\n",
" validate_and_save_cohort_info(\n",
" is_final=True,\n",
" cohort=cohort,\n",
" info_path=json_path,\n",
" is_gene_available=is_gene_available,\n",
" is_trait_available=is_trait_available,\n",
" is_biased=True,\n",
" df=pd.DataFrame(),\n",
" note=\"No common samples between gene expression and clinical data.\"\n",
" )\n",
" except Exception as e:\n",
" print(f\"Error linking or processing data: {e}\")\n",
" validate_and_save_cohort_info(\n",
" is_final=True,\n",
" cohort=cohort,\n",
" info_path=json_path,\n",
" is_gene_available=is_gene_available,\n",
" is_trait_available=is_trait_available,\n",
" is_biased=True, # Assume biased if there's an error\n",
" df=pd.DataFrame(), # Empty dataframe for metadata\n",
" note=f\"Error in data processing: {str(e)}\"\n",
" )\n",
"else:\n",
" # We can't proceed with linking if either trait or gene data is missing\n",
" print(\"Cannot proceed with data linking due to missing trait or gene data.\")\n",
" validate_and_save_cohort_info(\n",
" is_final=True,\n",
" cohort=cohort,\n",
" info_path=json_path,\n",
" is_gene_available=is_gene_available,\n",
" is_trait_available=is_trait_available,\n",
" is_biased=True, # Data is unusable if we're missing components\n",
" df=pd.DataFrame(), # Empty dataframe for metadata\n",
" note=\"Missing essential data components for linking (trait data or gene expression data).\"\n",
" )"
]
}
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