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{
"cells": [
{
"cell_type": "code",
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"id": "d7c20b37",
"metadata": {
"execution": {
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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 = \"Intellectual_Disability\"\n",
"cohort = \"GSE158385\"\n",
"\n",
"# Input paths\n",
"in_trait_dir = \"../../input/GEO/Intellectual_Disability\"\n",
"in_cohort_dir = \"../../input/GEO/Intellectual_Disability/GSE158385\"\n",
"\n",
"# Output paths\n",
"out_data_file = \"../../output/preprocess/Intellectual_Disability/GSE158385.csv\"\n",
"out_gene_data_file = \"../../output/preprocess/Intellectual_Disability/gene_data/GSE158385.csv\"\n",
"out_clinical_data_file = \"../../output/preprocess/Intellectual_Disability/clinical_data/GSE158385.csv\"\n",
"json_path = \"../../output/preprocess/Intellectual_Disability/cohort_info.json\"\n"
]
},
{
"cell_type": "markdown",
"id": "b66bda59",
"metadata": {},
"source": [
"### Step 1: Initial Data Loading"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "1531d515",
"metadata": {
"execution": {
"iopub.execute_input": "2025-03-25T07:09:22.346308Z",
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"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Background Information:\n",
"!Series_title\t\"Apigenin as a Candidate Prenatal Treatment for Trisomy 21: Effects in Human Amniocytes and the Ts1Cje Mouse Model\"\n",
"!Series_summary\t\"This SuperSeries is composed of the SubSeries listed below.\"\n",
"!Series_overall_design\t\"Refer to individual Series\"\n",
"Sample Characteristics Dictionary:\n",
"{0: ['tissue: forebrain'], 1: ['developmental stage: E15'], 2: ['genotype: WT', 'genotype: Ts1Cje'], 3: ['treatment: Powder', 'treatment: Apigenin']}\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": "155ba7cf",
"metadata": {},
"source": [
"### Step 2: Dataset Analysis and Clinical Feature Extraction"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "37d56a9d",
"metadata": {
"execution": {
"iopub.execute_input": "2025-03-25T07:09:22.400266Z",
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"shell.execute_reply": "2025-03-25T07:09:22.407650Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Preview of selected clinical data:\n",
"{'GSM4798553': [nan], 'GSM4798554': [nan], 'GSM4798555': [nan], 'GSM4798556': [nan], 'GSM4798557': [nan], 'GSM4798558': [nan], 'GSM4798559': [nan], 'GSM4798560': [nan], 'GSM4798561': [nan], 'GSM4798562': [nan], 'GSM4798563': [nan], 'GSM4798564': [nan], 'GSM4798565': [nan], 'GSM4798566': [nan], 'GSM4798567': [nan], 'GSM4798568': [nan], 'GSM4798569': [nan], 'GSM4798570': [nan], 'GSM4798571': [nan], 'GSM4798572': [nan]}\n",
"Clinical data saved to ../../output/preprocess/Intellectual_Disability/clinical_data/GSE158385.csv\n"
]
}
],
"source": [
"import pandas as pd\n",
"from typing import Optional, Callable\n",
"import os\n",
"import json\n",
"\n",
"# Check if gene expression data is available\n",
"# From the background information, this appears to be related to trisomy 21 and gene expression\n",
"is_gene_available = True\n",
"\n",
"# Define the row indices for trait, age, and gender\n",
"# trait_row: Karyotype information (row 2) can be used to determine intellectual disability (trisomy 21)\n",
"trait_row = 2 # karyotype information\n",
"age_row = None # No age information available\n",
"gender_row = None # Gender can be inferred from karyotype, but it's not a separate variable for analysis\n",
"\n",
"# Conversion functions\n",
"def convert_trait(value: str) -> Optional[int]:\n",
" \"\"\"Convert karyotype information to binary trait value (1 for T21, 0 for normal)\"\"\"\n",
" if not value or \":\" not in value:\n",
" return None\n",
" value = value.split(\":\", 1)[1].strip()\n",
" if \"T21\" in value: # Trisomy 21 indicates intellectual disability\n",
" return 1\n",
" elif \"2N\" in value: # Normal karyotype\n",
" return 0\n",
" return None\n",
"\n",
"def convert_age(value: str) -> Optional[float]:\n",
" \"\"\"Convert age value to float\"\"\"\n",
" # Not used but defined for completeness\n",
" if not value or \":\" not in value:\n",
" return None\n",
" value = value.split(\":\", 1)[1].strip()\n",
" try:\n",
" return float(value)\n",
" except:\n",
" return None\n",
"\n",
"def convert_gender(value: str) -> Optional[int]:\n",
" \"\"\"Convert gender value to binary (0 for female, 1 for male)\"\"\"\n",
" # Not used but defined for completeness\n",
" if not value or \":\" not in value:\n",
" return None\n",
" value = value.split(\":\", 1)[1].strip()\n",
" if \"female\" in value.lower() or \"f\" == value.lower():\n",
" return 0\n",
" elif \"male\" in value.lower() or \"m\" == value.lower():\n",
" return 1\n",
" return None\n",
"\n",
"# Check trait availability\n",
"is_trait_available = trait_row is not None\n",
"\n",
"# Save metadata using the validation function\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",
"# Extract clinical features if trait data is available\n",
"# Note: We'll assume the clinical_data is already available as a variable\n",
"# from a previous step, rather than loading from a file\n",
"if trait_row is not None and 'clinical_data' in locals():\n",
" try:\n",
" # Use the geo_select_clinical_features function to extract 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",
" age_row=age_row,\n",
" convert_age=convert_age,\n",
" gender_row=gender_row,\n",
" convert_gender=convert_gender\n",
" )\n",
" \n",
" # Preview the selected clinical data\n",
" print(\"Preview of selected clinical data:\")\n",
" print(preview_df(selected_clinical_df))\n",
" \n",
" # Create the output 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 a CSV file\n",
" selected_clinical_df.to_csv(out_clinical_data_file, index=False)\n",
" print(f\"Clinical data saved to {out_clinical_data_file}\")\n",
" except Exception as e:\n",
" print(f\"Error processing clinical data: {e}\")\n",
"else:\n",
" if trait_row is not None:\n",
" print(\"Clinical data not available in memory. Skipping clinical feature extraction.\")\n",
" else:\n",
" print(\"No trait data available. Skipping clinical feature extraction.\")\n"
]
},
{
"cell_type": "markdown",
"id": "d3282f6e",
"metadata": {},
"source": [
"### Step 3: Gene Data Extraction"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "1b96ef2d",
"metadata": {
"execution": {
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"shell.execute_reply": "2025-03-25T07:09:22.466248Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Extracting gene data from matrix file:\n",
"Successfully extracted gene data with 21225 rows\n",
"First 20 gene IDs:\n",
"Index(['100008567_at', '100009600_at', '100009609_at', '100009614_at',\n",
" '100012_at', '100017_at', '100019_at', '100033459_at', '100034251_at',\n",
" '100034748_at', '100036520_at', '100036521_at', '100036523_at',\n",
" '100036537_at', '100036768_at', '100037258_at', '100037260_at',\n",
" '100037262_at', '100037278_at', '100037396_at'],\n",
" dtype='object', name='ID')\n",
"\n",
"Gene expression data available: True\n"
]
}
],
"source": [
"# 1. Get the file paths for the SOFT file and matrix file\n",
"soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)\n",
"\n",
"# 2. Extract gene expression data from the matrix file\n",
"try:\n",
" print(\"Extracting gene data from matrix file:\")\n",
" gene_data = get_genetic_data(matrix_file)\n",
" if gene_data.empty:\n",
" print(\"Extracted gene expression data is empty\")\n",
" is_gene_available = False\n",
" else:\n",
" print(f\"Successfully extracted gene data with {len(gene_data.index)} rows\")\n",
" print(\"First 20 gene IDs:\")\n",
" print(gene_data.index[:20])\n",
" is_gene_available = True\n",
"except Exception as e:\n",
" print(f\"Error extracting gene data: {e}\")\n",
" print(\"This dataset appears to have an empty or malformed gene expression matrix\")\n",
" is_gene_available = False\n",
"\n",
"print(f\"\\nGene expression data available: {is_gene_available}\")\n"
]
},
{
"cell_type": "markdown",
"id": "12237d86",
"metadata": {},
"source": [
"### Step 4: Gene Identifier Review"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "43ca195f",
"metadata": {
"execution": {
"iopub.execute_input": "2025-03-25T07:09:22.467721Z",
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"shell.execute_reply": "2025-03-25T07:09:22.469095Z"
}
},
"outputs": [],
"source": [
"# Based on my biomedical knowledge, these identifiers (TC01000001.hg.1, etc.) are not standard human gene symbols\n",
"# They appear to be Affymetrix transcript cluster IDs from a human gene array\n",
"# Standard human gene symbols would be like BRCA1, TP53, etc.\n",
"# These IDs need to be mapped to standard gene symbols\n",
"\n",
"requires_gene_mapping = True\n"
]
},
{
"cell_type": "markdown",
"id": "cfdf62a1",
"metadata": {},
"source": [
"### Step 5: Gene Annotation"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "407315bf",
"metadata": {
"execution": {
"iopub.execute_input": "2025-03-25T07:09:22.470375Z",
"iopub.status.busy": "2025-03-25T07:09:22.470276Z",
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"shell.execute_reply": "2025-03-25T07:09:25.432988Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Extracting gene annotation data from SOFT file...\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Successfully extracted gene annotation data with 1647953 rows\n",
"\n",
"Gene annotation preview (first few rows):\n",
"{'ID': ['100008567_at', '100009600_at', '100009609_at', '100009614_at', '100012_at'], 'ENTREZ_GENE_ID': ['100008567', '100009600', '100009609', '100009614', '100012'], 'Description': ['predicted gene 14964', 'zinc finger, GATA-like protein 1', 'vomeronasal 2, receptor 65', 'keratin associated protein LOC100009614', 'oogenesin 3']}\n",
"\n",
"Column names in gene annotation data:\n",
"['ID', 'ENTREZ_GENE_ID', 'Description']\n"
]
}
],
"source": [
"# 1. Extract gene annotation data from the SOFT file\n",
"print(\"Extracting gene annotation data from SOFT file...\")\n",
"try:\n",
" # Use the library function to extract gene annotation\n",
" gene_annotation = get_gene_annotation(soft_file)\n",
" print(f\"Successfully extracted gene annotation data with {len(gene_annotation.index)} rows\")\n",
" \n",
" # Preview the annotation DataFrame\n",
" print(\"\\nGene annotation preview (first few rows):\")\n",
" print(preview_df(gene_annotation))\n",
" \n",
" # Show column names to help identify which columns we need for mapping\n",
" print(\"\\nColumn names in gene annotation data:\")\n",
" print(gene_annotation.columns.tolist())\n",
" \n",
" # Check for relevant mapping columns\n",
" if 'GB_ACC' in gene_annotation.columns:\n",
" print(\"\\nThe dataset contains GenBank accessions (GB_ACC) that could be used for gene mapping.\")\n",
" # Count non-null values in GB_ACC column\n",
" non_null_count = gene_annotation['GB_ACC'].count()\n",
" print(f\"Number of rows with GenBank accessions: {non_null_count} out of {len(gene_annotation)}\")\n",
" \n",
" if 'SPOT_ID' in gene_annotation.columns:\n",
" print(\"\\nThe dataset contains genomic regions (SPOT_ID) that could be used for location-based gene mapping.\")\n",
" print(\"Example SPOT_ID format:\", gene_annotation['SPOT_ID'].iloc[0])\n",
" \n",
"except Exception as e:\n",
" print(f\"Error processing gene annotation data: {e}\")\n",
" is_gene_available = False\n"
]
},
{
"cell_type": "markdown",
"id": "5c593ed3",
"metadata": {},
"source": [
"### Step 6: Gene Identifier Mapping"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "a5b2974c",
"metadata": {
"execution": {
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"shell.execute_reply": "2025-03-25T07:09:26.413080Z"
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"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Analyzing gene identifiers for mapping...\n",
"\n",
"Creating gene mapping dataframe...\n",
"Created mapping dataframe with 1647953 rows\n",
"Sample mapping entries:\n",
" ID Gene\n",
"0 100008567_at 100008567\n",
"1 100009600_at 100009600\n",
"2 100009609_at 100009609\n",
"3 100009614_at 100009614\n",
"4 100012_at 100012\n",
"\n",
"Applying gene mapping to expression data...\n",
"Overlap between expression data and mapping: 21225 probes out of 21225\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Successfully mapped to 0 genes\n",
"First few gene symbols:\n",
"Index([], dtype='object', name='Gene')\n",
"\n",
"Normalizing gene symbols...\n",
"After normalization: 0 genes\n",
"Gene expression data saved to ../../output/preprocess/Intellectual_Disability/gene_data/GSE158385.csv\n"
]
}
],
"source": [
"# 1. Examine the gene identifiers to determine mapping\n",
"print(\"Analyzing gene identifiers for mapping...\")\n",
"\n",
"# From the previous output, we have gene annotation with ID, ENTREZ_GENE_ID, and Description\n",
"# We'll use the ENTREZ_GENE_ID for mapping since it contains gene identifiers\n",
"\n",
"# Create mapping dataframe using ID and ENTREZ_GENE_ID\n",
"print(\"\\nCreating gene mapping dataframe...\")\n",
"mapping_df = pd.DataFrame({\n",
" 'ID': gene_annotation['ID'],\n",
" 'Gene': gene_annotation['ENTREZ_GENE_ID']\n",
"})\n",
"\n",
"# Keep only rows with valid gene mappings\n",
"mapping_df = mapping_df.dropna(subset=['Gene'])\n",
"mapping_df = mapping_df[mapping_df['Gene'] != '---'] # Remove any placeholder values\n",
"print(f\"Created mapping dataframe with {len(mapping_df)} rows\")\n",
"print(\"Sample mapping entries:\")\n",
"print(mapping_df.head())\n",
"\n",
"# 2. Apply gene mapping to convert probe-level measurements to gene expression data\n",
"try:\n",
" print(\"\\nApplying gene mapping to expression data...\")\n",
" # First, check the overlap between gene expression data IDs and mapping IDs\n",
" overlap_count = sum(gene_data.index.isin(mapping_df['ID']))\n",
" print(f\"Overlap between expression data and mapping: {overlap_count} probes out of {len(gene_data.index)}\")\n",
" \n",
" if overlap_count > 0:\n",
" # Apply gene mapping to convert probe-level measurements to gene expression data\n",
" gene_data = apply_gene_mapping(gene_data, mapping_df)\n",
" print(f\"Successfully mapped to {len(gene_data.index)} genes\")\n",
" print(\"First few gene symbols:\")\n",
" print(gene_data.index[:5])\n",
" \n",
" # Optional: Normalize gene symbols to standard forms\n",
" try:\n",
" print(\"\\nNormalizing gene symbols...\")\n",
" gene_data = normalize_gene_symbols_in_index(gene_data)\n",
" print(f\"After normalization: {len(gene_data.index)} genes\")\n",
" except Exception as e:\n",
" print(f\"Error normalizing gene symbols: {e}\")\n",
" # Continue with unnormalized symbols\n",
" else:\n",
" print(\"No overlap found between expression data IDs and mapping IDs.\")\n",
" print(\"Using probe IDs directly as gene proxies.\")\n",
" # Rename index to Gene for consistency in downstream processing\n",
" gene_data.index.name = 'Gene'\n",
" \n",
" # 3. 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",
" is_gene_available = False\n"
]
},
{
"cell_type": "markdown",
"id": "690ec6b7",
"metadata": {},
"source": [
"### Step 7: Data Normalization and Linking"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "2602f583",
"metadata": {
"execution": {
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"shell.execute_reply": "2025-03-25T07:09:26.731633Z"
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},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Handling gene data...\n",
"No valid gene symbols after mapping. Using original probe data as gene proxies...\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Gene data saved to: ../../output/preprocess/Intellectual_Disability/gene_data/GSE158385.csv with 21225 features\n",
"\n",
"Loading clinical data and linking with genetic data...\n",
"Loaded clinical data with shape: (1, 19)\n",
"Clinical data columns: Index(['GSM4798554', 'GSM4798555', 'GSM4798556', 'GSM4798557', 'GSM4798558',\n",
" 'GSM4798559', 'GSM4798560', 'GSM4798561', 'GSM4798562', 'GSM4798563',\n",
" 'GSM4798564', 'GSM4798565', 'GSM4798566', 'GSM4798567', 'GSM4798568',\n",
" 'GSM4798569', 'GSM4798570', 'GSM4798571', 'GSM4798572'],\n",
" dtype='object')\n",
"Clinical data index: Index([nan], dtype='float64', name='GSM4798553')\n",
"Updated clinical data index: Index(['Intellectual_Disability'], dtype='object')\n",
"First few clinical sample IDs: ['GSM4798554', 'GSM4798555', 'GSM4798556', 'GSM4798557', 'GSM4798558']\n",
"First few genetic sample IDs: ['GSM4798553', 'GSM4798554', 'GSM4798555', 'GSM4798556', 'GSM4798557']\n",
"Found 19 common samples between clinical and genetic data\n",
"Linked data shape: (19, 21226)\n",
"Linked data columns: Index(['Intellectual_Disability', '100008567_at', '100009600_at',\n",
" '100009609_at', '100009614_at', '100012_at', '100017_at', '100019_at',\n",
" '100033459_at', '100034251_at'],\n",
" dtype='object')\n",
"\n",
"Handling missing values...\n",
"After handling missing values, data shape: (0, 1)\n",
"\n",
"Checking for bias in features...\n",
"Quartiles for 'Intellectual_Disability':\n",
" 25%: nan\n",
" 50% (Median): nan\n",
" 75%: nan\n",
"Min: nan\n",
"Max: nan\n",
"The distribution of the feature 'Intellectual_Disability' in this dataset is fine.\n",
"\n",
"\n",
"Performing final validation...\n",
"Abnormality detected in the cohort: GSE158385. Preprocessing failed.\n",
"Dataset not usable for Intellectual_Disability association studies. Data not saved.\n"
]
}
],
"source": [
"# 1. Use the original gene expression data with probe IDs since normalization gave 0 genes\n",
"print(\"\\nHandling gene data...\")\n",
"try:\n",
" # Load original gene data from previous step if it exists\n",
" if 'gene_data' not in locals() or len(gene_data.index) == 0:\n",
" print(\"No valid gene symbols after mapping. Using original probe data as gene proxies...\")\n",
" # Get original gene expression data again\n",
" gene_data = get_genetic_data(matrix_file)\n",
" # Rename index to Gene for consistency\n",
" gene_data.index.name = 'Gene'\n",
" \n",
" # Save the gene 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 data saved to: {out_gene_data_file} with {len(gene_data.index)} features\")\n",
" is_gene_available = len(gene_data.index) > 0\n",
"except Exception as e:\n",
" print(f\"Error handling gene data: {e}\")\n",
" is_gene_available = False\n",
"\n",
"# 2. Load the clinical data and link with genetic data\n",
"print(\"\\nLoading clinical data and linking with genetic data...\")\n",
"try:\n",
" # Load the clinical data\n",
" clinical_df = pd.read_csv(out_clinical_data_file)\n",
" \n",
" # If clinical_df doesn't have an index column, set the first column as index\n",
" if not clinical_df.index.name and len(clinical_df.columns) > 1:\n",
" clinical_df = clinical_df.set_index(clinical_df.columns[0])\n",
" \n",
" print(f\"Loaded clinical data with shape: {clinical_df.shape}\")\n",
" print(f\"Clinical data columns: {clinical_df.columns}\")\n",
" print(f\"Clinical data index: {clinical_df.index}\")\n",
" \n",
" # Set the appropriate name for the trait in clinical data\n",
" # Since we're working with one trait row from earlier steps\n",
" clinical_df.index = [trait]\n",
" print(f\"Updated clinical data index: {clinical_df.index}\")\n",
" \n",
" # Ensure we have gene data\n",
" if is_gene_available and not gene_data.empty:\n",
" # Print sample IDs from both datasets for debugging\n",
" print(\"First few clinical sample IDs:\", list(clinical_df.columns)[:5])\n",
" print(\"First few genetic sample IDs:\", list(gene_data.columns)[:5])\n",
" \n",
" # Check and align sample IDs if needed\n",
" common_samples = set(clinical_df.columns).intersection(set(gene_data.columns))\n",
" if len(common_samples) > 0:\n",
" print(f\"Found {len(common_samples)} common samples between clinical and genetic data\")\n",
" # Keep only common samples\n",
" clinical_subset = clinical_df[list(common_samples)]\n",
" gene_data_subset = gene_data[list(common_samples)]\n",
" \n",
" # Link clinical and genetic data\n",
" linked_data = pd.concat([clinical_subset, gene_data_subset], axis=0).T\n",
" is_trait_available = True\n",
" print(f\"Linked data shape: {linked_data.shape}\")\n",
" print(f\"Linked data columns: {linked_data.columns[:10]}\") # Print first 10 columns\n",
" \n",
" # 3. Handle missing values systematically\n",
" print(\"\\nHandling missing values...\")\n",
" try:\n",
" # Make sure the trait column exists in the linked data\n",
" if trait not in linked_data.columns:\n",
" print(f\"Warning: {trait} column not found in linked data. Available columns: {linked_data.columns[:5]}\")\n",
" # If the first column is our trait data, rename it\n",
" linked_data.rename(columns={linked_data.columns[0]: trait}, inplace=True)\n",
" print(f\"Renamed first column to {trait}\")\n",
" \n",
" linked_data = handle_missing_values(linked_data, trait)\n",
" print(f\"After handling missing values, data shape: {linked_data.shape}\")\n",
" \n",
" # 4. Determine whether the trait and demographic features are biased\n",
" print(\"\\nChecking for bias in features...\")\n",
" is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)\n",
" except Exception as e:\n",
" print(f\"Error handling missing values: {e}\")\n",
" linked_data = pd.DataFrame()\n",
" is_trait_available = False\n",
" is_biased = True\n",
" else:\n",
" print(\"No common samples found between clinical and genetic data\")\n",
" linked_data = pd.DataFrame()\n",
" is_trait_available = False\n",
" is_biased = True\n",
" else:\n",
" print(\"No valid gene expression data available\")\n",
" linked_data = pd.DataFrame()\n",
" is_trait_available = False\n",
" is_biased = True\n",
" \n",
"except Exception as e:\n",
" print(f\"Error in linking clinical and genetic data: {e}\")\n",
" linked_data = pd.DataFrame()\n",
" is_trait_available = False\n",
" is_biased = True\n",
"\n",
"# 5. Final quality validation\n",
"print(\"\\nPerforming final validation...\")\n",
"note = \"Dataset is about trisomy 21 (Down syndrome) which is associated with intellectual disability\"\n",
"\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",
"# 6. Save linked data if usable\n",
"if is_usable:\n",
" # Create directory if it doesn't exist\n",
" os.makedirs(os.path.dirname(out_data_file), exist_ok=True)\n",
" \n",
" # Save linked data\n",
" linked_data.to_csv(out_data_file)\n",
" print(f\"Linked data saved to {out_data_file}\")\n",
"else:\n",
" print(f\"Dataset not usable for {trait} association studies. Data not saved.\")"
]
}
],
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