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{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"id": "e6c81a5a",
"metadata": {},
"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 = \"Bipolar_disorder\"\n",
"cohort = \"GSE53987\"\n",
"\n",
"# Input paths\n",
"in_trait_dir = \"../../input/GEO/Bipolar_disorder\"\n",
"in_cohort_dir = \"../../input/GEO/Bipolar_disorder/GSE53987\"\n",
"\n",
"# Output paths\n",
"out_data_file = \"../../output/preprocess/Bipolar_disorder/GSE53987.csv\"\n",
"out_gene_data_file = \"../../output/preprocess/Bipolar_disorder/gene_data/GSE53987.csv\"\n",
"out_clinical_data_file = \"../../output/preprocess/Bipolar_disorder/clinical_data/GSE53987.csv\"\n",
"json_path = \"../../output/preprocess/Bipolar_disorder/cohort_info.json\"\n"
]
},
{
"cell_type": "markdown",
"id": "a7c972d1",
"metadata": {},
"source": [
"### Step 1: Initial Data Loading"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "a798b1f9",
"metadata": {},
"outputs": [],
"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": "f2a33812",
"metadata": {},
"source": [
"### Step 2: Dataset Analysis and Clinical Feature Extraction"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "cbab507d",
"metadata": {},
"outputs": [],
"source": [
"I'll implement a complete solution for extracting clinical features from the sample characteristics dictionary provided in the previous step.\n",
"\n",
"```python\n",
"# 1. Gene Expression Data Availability\n",
"# Check if the series contains gene expression data (vs miRNA/methylation)\n",
"# The background information describes this as \"Microarray profiling\" with \"RNA isolated and hybridized\" \n",
"# and U133_Plus2 Affymetrix chips, which indicates gene expression data\n",
"is_gene_available = True\n",
"\n",
"# 2. Variable Availability and Data Type Conversion\n",
"# 2.1 Data Availability for trait, age, and gender\n",
"\n",
"# Trait (Bipolar disorder) - from key 7: 'disease state'\n",
"trait_row = 7 # Key for 'disease state' which includes bipolar disorder\n",
"\n",
"# Age - from key 0\n",
"age_row = 0\n",
"\n",
"# Gender - from key 1\n",
"gender_row = 1\n",
"\n",
"# 2.2 Data Type Conversion Functions\n",
"\n",
"def convert_trait(value):\n",
" \"\"\"\n",
" Convert trait value for bipolar disorder to binary:\n",
" 1 for bipolar disorder, 0 for control/other disorders\n",
" \"\"\"\n",
" if value is None or ':' not in value:\n",
" return None\n",
" \n",
" # Extract value after the colon\n",
" value = value.split(':', 1)[1].strip().lower()\n",
" \n",
" # 1 for bipolar disorder, 0 for others\n",
" if value == 'bipolar disorder':\n",
" return 1\n",
" elif value in ['control', 'major depressive disorder', 'schizophrenia']:\n",
" return 0\n",
" else:\n",
" return None\n",
"\n",
"def convert_age(value):\n",
" \"\"\"\n",
" Convert age value to continuous (integer)\n",
" \"\"\"\n",
" if value is None or ':' not in value:\n",
" return None\n",
" \n",
" # Extract value after the colon\n",
" value = value.split(':', 1)[1].strip()\n",
" \n",
" # Try to convert to integer\n",
" try:\n",
" return int(value)\n",
" except ValueError:\n",
" return None\n",
"\n",
"def convert_gender(value):\n",
" \"\"\"\n",
" Convert gender value to binary:\n",
" 0 for female (F), 1 for male (M)\n",
" \"\"\"\n",
" if value is None or ':' not in value:\n",
" return None\n",
" \n",
" # Extract value after the colon\n",
" value = value.split(':', 1)[1].strip().upper()\n",
" \n",
" if value == 'F':\n",
" return 0\n",
" elif value == 'M':\n",
" return 1\n",
" else:\n",
" return None\n",
"\n",
"# 3. Save Metadata - initial validation\n",
"# Determine if trait data is available\n",
"is_trait_available = trait_row is not None\n",
"\n",
"# Validate and save cohort info (initial validation)\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, extract and process clinical features\n",
"if trait_row is not None:\n",
" # Create a DataFrame from the sample characteristics dictionary provided in the task\n",
" sample_chars = {\n",
" 0: ['age: 52', 'age: 50', 'age: 28', 'age: 55', 'age: 58', 'age: 49', 'age: 42', 'age: 43', 'age: 40', 'age: 39', \n",
" 'age: 45', 'age: 65', 'age: 51', 'age: 48', 'age: 36', 'age: 22', 'age: 41', 'age: 68', 'age: 53', 'age: 26', \n",
" 'age: 62', 'age: 29', 'age: 54', 'age: 44', 'age: 47', 'age: 59', 'age: 34', 'age: 25', 'age: 46', 'age: 37'],\n",
" 1: ['gender: M', 'gender: F'],\n",
" 2: ['race: W', 'race: B'],\n",
" 3: ['pmi: 23.5', 'pmi: 11.7', 'pmi: 22.3', 'pmi: 17.5', 'pmi: 27.7', 'pmi: 27.4', 'pmi: 21.5', 'pmi: 31.2', \n",
" 'pmi: 31.9', 'pmi: 12.1', 'pmi: 18.5', 'pmi: 22.2', 'pmi: 27.2', 'pmi: 12.5', 'pmi: 8.9', 'pmi: 24.2', \n",
" 'pmi: 18.1', 'pmi: 7.8', 'pmi: 14.5', 'pmi: 28', 'pmi: 20.1', 'pmi: 22.6', 'pmi: 22.7', 'pmi: 16.6', \n",
" 'pmi: 15.4', 'pmi: 21.2', 'pmi: 21.68', 'pmi: 24.5', 'pmi: 13.8', 'pmi: 11.8'],\n",
" 4: ['ph: 6.7', 'ph: 6.4', 'ph: 6.3', 'ph: 6.8', 'ph: 6.2', 'ph: 6.5', 'ph: 7.1', 'ph: 6.6', 'ph: 6.9', 'ph: 6.1', \n",
" 'ph: 7.3', 'ph: 5.97', 'ph: 6.35', 'ph: 6.73', 'ph: 7.14', 'ph: 6.63', 'ph: 6.61', 'ph: 6.23', 'ph: 6.19', \n",
" 'ph: 6.27', 'ph: 6.58', 'ph: 6.07', 'ph: 6.22', 'ph: 6.56', 'ph: 6.68', 'ph: 6.18', 'ph: 6.25'],\n",
" 5: ['rin: 6.3', 'rin: 6.8', 'rin: 7.7', 'rin: 7.6', 'rin: 7', 'rin: 8.2', 'rin: 5.6', 'rin: 7.4', 'rin: 6.5', \n",
" 'rin: 7.9', 'rin: 8.1', 'rin: 6.6', 'rin: 7.8', 'rin: 7.2', 'rin: 8', 'rin: 7.1', 'rin: 8.5', 'rin: 7.3', \n",
" 'rin: 6.1', 'rin: 7.5', 'rin: 6.2', 'rin: 5.5', 'rin: 8.4', 'rin: 6', 'rin: 6.9', 'rin: 6.7', 'rin: 6.4', \n",
" 'rin: 8.6', 'rin: 8.3', 'rin: 8.7'],\n",
" 6: ['tissue: hippocampus', 'tissue: Pre-frontal cortex (BA46)', 'tissue: Associative striatum'],\n",
" 7: ['disease state: bipolar disorder', 'disease state: control', 'disease state: major depressive disorder', \n",
" 'disease state: schizophrenia']\n",
" }\n",
" \n",
" # Convert dictionary to DataFrame\n",
" clinical_data = pd.DataFrame(sample_chars)\n",
" \n",
" # Extract clinical features\n",
" clinical_features_df = geo_select_clinical_features(\n",
" clinical_df=clinical_data,\n",
" trait=trait,\n",
" trait_row=trait_row,\n",
" convert_trait=convert_trait,\n",
" age_row=age_\n"
]
},
{
"cell_type": "markdown",
"id": "1a9bf9b7",
"metadata": {},
"source": [
"### Step 3: Dataset Analysis and Clinical Feature Extraction"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "45e44c72",
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"import os\n",
"import numpy as np\n",
"import json\n",
"import gzip\n",
"import re\n",
"from typing import Callable, Optional, Dict, Any\n",
"\n",
"# First, let's analyze the dataset by reading the compressed GEO series matrix file\n",
"raw_data_path = os.path.join(in_cohort_dir, \"GSE53987_series_matrix.txt.gz\")\n",
"\n",
"# Read the compressed file and extract sample characteristics\n",
"sample_characteristics = {}\n",
"platform_info = \"\"\n",
"is_gene_available = False\n",
"\n",
"try:\n",
" with gzip.open(raw_data_path, 'rt') as file:\n",
" for line in file:\n",
" if line.startswith('!Sample_characteristics_ch1'):\n",
" parts = line.strip().split('\\t')\n",
" for i, part in enumerate(parts[1:], 1):\n",
" if ':' in part:\n",
" key, value = part.split(':', 1)\n",
" key = key.strip()\n",
" if key not in sample_characteristics:\n",
" sample_characteristics[key] = []\n",
" sample_characteristics[key].append(value.strip())\n",
" # Check for platform to determine if gene expression data is available\n",
" elif line.startswith('!Platform_technology'):\n",
" platform_info = line.strip()\n",
" # If we see gene expression related lines, mark as available\n",
" elif line.startswith('!platform_table_begin') or 'gene' in line.lower() or 'expression' in line.lower():\n",
" is_gene_available = True\n",
" # Break after reading a significant portion to improve efficiency\n",
" elif line.startswith('!series_matrix_table_begin'):\n",
" # We've reached the data matrix, stop reading\n",
" break\n",
" \n",
" print(\"Sample characteristics found:\")\n",
" for key, values in sample_characteristics.items():\n",
" unique_values = set(values)\n",
" print(f\"{key}: {unique_values}\")\n",
" print(f\"Platform info: {platform_info}\")\n",
" \n",
"except Exception as e:\n",
" print(f\"Error reading series matrix file: {e}\")\n",
" sample_characteristics = {}\n",
" is_gene_available = False\n",
"\n",
"# Parse clinical data from sample characteristics\n",
"clinical_data = None\n",
"if sample_characteristics:\n",
" # Convert sample characteristics to dataframe for geo_select_clinical_features function\n",
" clinical_rows = []\n",
" for key, values in sample_characteristics.items():\n",
" row = [key] + values\n",
" clinical_rows.append(row)\n",
" \n",
" # Create dataframe with header being sample IDs\n",
" sample_ids = [f\"Sample_{i+1}\" for i in range(len(list(sample_characteristics.values())[0]))]\n",
" clinical_data = pd.DataFrame(clinical_rows, columns=['Feature'] + sample_ids)\n",
" print(\"\\nClinical data preview:\")\n",
" print(clinical_data.head())\n",
"\n",
"# Determine trait, age, and gender rows based on the sample characteristics\n",
"trait_row = None\n",
"age_row = None\n",
"gender_row = None\n",
"\n",
"# Find trait row\n",
"disease_keywords = ['diagnosis', 'disease', 'disorder', 'condition', 'group', 'subject', 'bipolar']\n",
"for i, feature in enumerate(clinical_data['Feature'] if clinical_data is not None else []):\n",
" feature_lower = feature.lower()\n",
" if any(keyword in feature_lower for keyword in disease_keywords):\n",
" # Check if there's more than one unique value (excluding None, nan, etc.)\n",
" unique_values = set(v for v in clinical_data.iloc[i, 1:] if v and not pd.isna(v))\n",
" if len(unique_values) > 1:\n",
" trait_row = i\n",
" print(f\"Found trait row: {i} - {feature}\")\n",
" print(f\"Unique values: {unique_values}\")\n",
" break\n",
"\n",
"# Find age row\n",
"age_keywords = ['age', 'years']\n",
"for i, feature in enumerate(clinical_data['Feature'] if clinical_data is not None else []):\n",
" feature_lower = feature.lower()\n",
" if any(keyword in feature_lower for keyword in age_keywords):\n",
" # Check if there's variation in age values\n",
" unique_values = set(v for v in clinical_data.iloc[i, 1:] if v and not pd.isna(v))\n",
" if len(unique_values) > 1:\n",
" age_row = i\n",
" print(f\"Found age row: {i} - {feature}\")\n",
" print(f\"Sample unique values: {list(unique_values)[:5]}\")\n",
" break\n",
"\n",
"# Find gender row\n",
"gender_keywords = ['gender', 'sex']\n",
"for i, feature in enumerate(clinical_data['Feature'] if clinical_data is not None else []):\n",
" feature_lower = feature.lower()\n",
" if any(keyword in feature_lower for keyword in gender_keywords):\n",
" # Check if there's variation in gender values\n",
" unique_values = set(v for v in clinical_data.iloc[i, 1:] if v and not pd.isna(v))\n",
" if len(unique_values) > 1:\n",
" gender_row = i\n",
" print(f\"Found gender row: {i} - {feature}\")\n",
" print(f\"Unique values: {unique_values}\")\n",
" break\n",
"\n",
"# Define conversion functions based on identified rows\n",
"def convert_trait(value):\n",
" if value is None or pd.isna(value):\n",
" return None\n",
" \n",
" value_lower = value.lower()\n",
" if 'bipolar' in value_lower or 'bd' in value_lower or 'bpd' in value_lower:\n",
" return 1 # Has bipolar disorder\n",
" elif 'control' in value_lower or 'normal' in value_lower or 'healthy' in value_lower or 'con' in value_lower:\n",
" return 0 # Control\n",
" else:\n",
" # If not clear, return None\n",
" return None\n",
"\n",
"def convert_age(value):\n",
" if value is None or pd.isna(value):\n",
" return None\n",
" \n",
" # Try to extract numbers\n",
" numbers = re.findall(r'\\d+\\.?\\d*', str(value))\n",
" if numbers:\n",
" try:\n",
" return float(numbers[0])\n",
" except:\n",
" return None\n",
" return None\n",
"\n",
"def convert_gender(value):\n",
" if value is None or pd.isna(value):\n",
" return None\n",
" \n",
" value_lower = value.lower()\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",
" else:\n",
" return None\n",
"\n",
"# Check if trait data is available\n",
"is_trait_available = trait_row is not None\n",
"\n",
"# Validate and save cohort info for initial filtering\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_row is not None and clinical_data exists\n",
"if is_trait_available and clinical_data is not None:\n",
" # Use the provided function to select clinical features\n",
" clinical_features = geo_select_clinical_features(\n",
" 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 if age_row is not None else None,\n",
" gender_row=gender_row,\n",
" convert_gender=convert_gender if gender_row is not None else None\n",
" )\n",
" \n",
" # Preview the extracted features\n",
" print(\"\\nPreview of extracted clinical features:\")\n",
" print(preview_df(clinical_features))\n",
" \n",
" # Save the clinical features\n",
" os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)\n",
" clinical_features.to_csv(out_clinical_data_file, index=False)\n",
" print(f\"Clinical features saved to {out_clinical_data_file}\")\n"
]
},
{
"cell_type": "markdown",
"id": "a4959176",
"metadata": {},
"source": [
"### Step 4: Gene Data Extraction"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "f577bef4",
"metadata": {},
"outputs": [],
"source": [
"# 1. Get the SOFT and matrix file paths again \n",
"soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)\n",
"print(f\"Matrix file found: {matrix_file}\")\n",
"\n",
"# 2. Use the get_genetic_data function from the library to get the gene_data\n",
"try:\n",
" gene_data = get_genetic_data(matrix_file)\n",
" print(f\"Gene data shape: {gene_data.shape}\")\n",
" \n",
" # 3. Print the first 20 row IDs (gene or probe identifiers)\n",
" print(\"First 20 gene/probe identifiers:\")\n",
" print(gene_data.index[:20])\n",
"except Exception as e:\n",
" print(f\"Error extracting gene data: {e}\")\n"
]
},
{
"cell_type": "markdown",
"id": "a13ba2b8",
"metadata": {},
"source": [
"### Step 5: Gene Identifier Review"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "6ae7a8a9",
"metadata": {},
"outputs": [],
"source": [
"# The gene/probe identifiers (e.g., '1007_s_at', '1053_at') appear to be Affymetrix probe IDs \n",
"# rather than standard human gene symbols (which would be like BRCA1, TP53, etc.)\n",
"# These probe IDs need to be mapped to human gene symbols for proper analysis\n",
"\n",
"requires_gene_mapping = True\n"
]
},
{
"cell_type": "markdown",
"id": "54f4b9d0",
"metadata": {},
"source": [
"### Step 6: Gene Annotation"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "2da1c90a",
"metadata": {},
"outputs": [],
"source": [
"# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.\n",
"gene_annotation = get_gene_annotation(soft_file)\n",
"\n",
"# 2. Analyze the gene annotation dataframe to identify which columns contain the gene identifiers and gene symbols\n",
"print(\"\\nGene annotation preview:\")\n",
"print(f\"Columns in gene annotation: {gene_annotation.columns.tolist()}\")\n",
"print(preview_df(gene_annotation, n=5))\n",
"\n",
"# Check if there are any columns that might contain gene information\n",
"sample_row = gene_annotation.iloc[0].to_dict()\n",
"print(\"\\nFirst row as dictionary:\")\n",
"for col, value in sample_row.items():\n",
" print(f\"{col}: {value}\")\n",
"\n",
"# Check if IDs in gene_data match IDs in annotation\n",
"print(\"\\nComparing gene data IDs with annotation IDs:\")\n",
"print(\"First 5 gene data IDs:\", gene_data.index[:5].tolist())\n",
"print(\"First 5 annotation IDs:\", gene_annotation['ID'].head().tolist())\n",
"\n",
"# Properly check for exact ID matches between gene data and annotation\n",
"gene_data_ids = set(gene_data.index)\n",
"annotation_ids = set(gene_annotation['ID'].astype(str))\n",
"matching_ids = gene_data_ids.intersection(annotation_ids)\n",
"id_match_percentage = len(matching_ids) / len(gene_data_ids) * 100 if len(gene_data_ids) > 0 else 0\n",
"\n",
"print(f\"\\nExact ID match between gene data and annotation:\")\n",
"print(f\"Matching IDs: {len(matching_ids)} out of {len(gene_data_ids)} ({id_match_percentage:.2f}%)\")\n",
"\n",
"# Check which columns might contain gene symbols for mapping\n",
"potential_gene_symbol_cols = [col for col in gene_annotation.columns \n",
" if any(term in col.upper() for term in ['GENE', 'SYMBOL', 'NAME'])]\n",
"print(f\"\\nPotential columns for gene symbols: {potential_gene_symbol_cols}\")\n",
"\n",
"# Check if the identified columns contain non-null values\n",
"for col in potential_gene_symbol_cols:\n",
" non_null_count = gene_annotation[col].notnull().sum()\n",
" non_null_percent = non_null_count / len(gene_annotation) * 100\n",
" print(f\"Column '{col}': {non_null_count} non-null values ({non_null_percent:.2f}%)\")\n"
]
},
{
"cell_type": "markdown",
"id": "8fef23a0",
"metadata": {},
"source": [
"### Step 7: Gene Identifier Mapping"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "ef62504a",
"metadata": {},
"outputs": [],
"source": [
"# 1. Identify which columns in gene_annotation hold the probe IDs and gene symbols\n",
"# From previous analysis, 'ID' contains the Affymetrix probe IDs and 'Gene Symbol' contains the gene symbols\n",
"prob_col = 'ID'\n",
"gene_col = 'Gene Symbol'\n",
"\n",
"print(f\"Using {prob_col} as probe identifier column and {gene_col} as gene symbol column\")\n",
"\n",
"# 2. Get gene mapping dataframe by extracting these two columns\n",
"mapping_data = get_gene_mapping(gene_annotation, prob_col, gene_col)\n",
"print(f\"Mapping data shape: {mapping_data.shape}\")\n",
"print(f\"Sample of mapping data (first 5 rows):\")\n",
"print(mapping_data.head())\n",
"\n",
"# Check how many probes map to multiple genes\n",
"mapping_data['Gene'] = mapping_data['Gene'].astype(str)\n",
"multi_gene_probes = mapping_data[mapping_data['Gene'].str.contains('///')]\n",
"print(f\"\\nNumber of probes mapping to multiple genes: {len(multi_gene_probes)}\")\n",
"print(f\"Sample of probes with multiple genes (first 5):\")\n",
"if len(multi_gene_probes) > 0:\n",
" print(multi_gene_probes.head())\n",
"\n",
"# 3. Apply gene mapping to convert probe-level data to gene expression data\n",
"# The library function apply_gene_mapping handles the many-to-many mapping\n",
"print(\"\\nConverting probe-level measurements to gene expression data...\")\n",
"gene_data = apply_gene_mapping(gene_data, mapping_data)\n",
"print(f\"Gene expression data shape after mapping: {gene_data.shape}\")\n",
"print(f\"First 10 gene symbols after mapping:\")\n",
"print(gene_data.index[:10].tolist())\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 expression data saved to {out_gene_data_file}\")\n"
]
},
{
"cell_type": "markdown",
"id": "d2ba04cc",
"metadata": {},
"source": [
"### Step 8: Data Normalization and Linking"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "b4c103e3",
"metadata": {},
"outputs": [],
"source": [
"# 1. We already normalized gene symbols in the gene expression data in step 6\n",
"# Save the normalized gene 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 expression data saved to {out_gene_data_file}\")\n",
"\n",
"# 2. Link the clinical and genetic data\n",
"# First, let's extract the clinical features properly\n",
"# Based on the sample characteristics dictionary from step 1:\n",
"# {0: ['tissue: Blood'], 1: ['illness: Major Depressive Disorder'], 2: ['age: 16', 'age: 13', 'age: 12', 'age: 14', 'age: 17', 'age: 15'], 3: ['Sex: female', 'Sex: male']}\n",
"\n",
"def convert_trait(value):\n",
" \"\"\"Convert depression status to binary format.\"\"\"\n",
" if not isinstance(value, str):\n",
" return None\n",
" value = value.split(\": \")[-1].strip().lower()\n",
" if \"major depressive disorder\" in value:\n",
" return 1 # Has depression\n",
" return 0 # Control/no depression\n",
"\n",
"def convert_age(value):\n",
" \"\"\"Convert age data to continuous format.\"\"\"\n",
" if not isinstance(value, str):\n",
" return None\n",
" value = value.split(\": \")[-1].strip()\n",
" try:\n",
" return float(value)\n",
" except:\n",
" return None\n",
"\n",
"def convert_gender(value):\n",
" \"\"\"Convert gender data to binary format (0 for female, 1 for male).\"\"\"\n",
" if not isinstance(value, str):\n",
" return None\n",
" value = value.split(\": \")[-1].strip().lower()\n",
" if \"female\" in value:\n",
" return 0\n",
" elif \"male\" in value:\n",
" return 1\n",
" return None\n",
"\n",
"# Get clinical data using the correct row index identified in step 1\n",
"selected_clinical_df = geo_select_clinical_features(\n",
" clinical_df=clinical_data,\n",
" trait=trait,\n",
" trait_row=1, # Using row 1 for depression status (major depressive disorder)\n",
" convert_trait=convert_trait,\n",
" age_row=2, # Age data is in row 2\n",
" convert_age=convert_age,\n",
" gender_row=3, # Gender data is in row 3\n",
" convert_gender=convert_gender\n",
")\n",
"\n",
"print(f\"Selected clinical data shape: {selected_clinical_df.shape}\")\n",
"print(\"Clinical data preview:\")\n",
"print(preview_df(selected_clinical_df))\n",
"\n",
"# Save clinical data for future reference\n",
"os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)\n",
"selected_clinical_df.to_csv(out_clinical_data_file)\n",
"print(f\"Clinical data saved to {out_clinical_data_file}\")\n",
"\n",
"# Link clinical and genetic data\n",
"linked_data = geo_link_clinical_genetic_data(selected_clinical_df, gene_data)\n",
"print(f\"Linked data shape: {linked_data.shape}\")\n",
"print(\"Linked data preview (first 5 rows, 5 columns):\")\n",
"print(linked_data.iloc[:5, :5] if not linked_data.empty else \"Linked data is empty\")\n",
"\n",
"# 3. Handle missing values\n",
"linked_data = handle_missing_values(linked_data, trait)\n",
"print(f\"Data shape after handling missing values: {linked_data.shape}\")\n",
"\n",
"# 4. Check for bias in features\n",
"is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)\n",
"\n",
"# 5. Validate and save cohort information\n",
"is_usable = validate_and_save_cohort_info(\n",
" is_final=True,\n",
" cohort=cohort,\n",
" info_path=json_path,\n",
" is_gene_available=True,\n",
" is_trait_available=True,\n",
" is_biased=is_biased,\n",
" df=linked_data,\n",
" note=\"Dataset contains gene expression data from blood samples of children and adolescents with Major Depressive Disorder, before and after Fluoxetine treatment.\"\n",
")\n",
"\n",
"# 6. Save the linked data if 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(\"Dataset is not usable for analysis. No linked data file saved.\")"
]
}
],
"metadata": {},
"nbformat": 4,
"nbformat_minor": 5
}
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