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{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"id": "a099ebda",
"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 = \"Depression\"\n",
"cohort = \"GSE110298\"\n",
"\n",
"# Input paths\n",
"in_trait_dir = \"../../input/GEO/Depression\"\n",
"in_cohort_dir = \"../../input/GEO/Depression/GSE110298\"\n",
"\n",
"# Output paths\n",
"out_data_file = \"../../output/preprocess/Depression/GSE110298.csv\"\n",
"out_gene_data_file = \"../../output/preprocess/Depression/gene_data/GSE110298.csv\"\n",
"out_clinical_data_file = \"../../output/preprocess/Depression/clinical_data/GSE110298.csv\"\n",
"json_path = \"../../output/preprocess/Depression/cohort_info.json\"\n"
]
},
{
"cell_type": "markdown",
"id": "aa9a9c30",
"metadata": {},
"source": [
"### Step 1: Initial Data Loading"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "11b2172f",
"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": "e0975ad3",
"metadata": {},
"source": [
"### Step 2: Dataset Analysis and Clinical Feature Extraction"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "c5c2784d",
"metadata": {},
"outputs": [],
"source": [
"I understand the task requires creating and analyzing a clinical dataset from the provided sample characteristics dictionary. Here's my implementation:\n",
"\n",
"```python\n",
"import pandas as pd\n",
"import os\n",
"import numpy as np\n",
"import re\n",
"\n",
"# 1. Gene Expression Data Availability\n",
"# Based on the background information, this dataset contains microarray data which indicates gene expression\n",
"is_gene_available = True\n",
"\n",
"# 2. Variable Availability and Data Type Conversion\n",
"\n",
"# 2.1 Data Availability\n",
"# For trait (Depression), looking at row 6\n",
"trait_row = 6 # Depression scores are available in row 6\n",
"\n",
"# For age, looking at row 2\n",
"age_row = 2 # Age is available in row 2\n",
"\n",
"# For gender, looking at row 1\n",
"gender_row = 1 # Gender is available in row 1\n",
"\n",
"# 2.2 Data Type Conversion Functions\n",
"\n",
"def convert_trait(value):\n",
" \"\"\"Convert depression value to binary (0/1)\"\"\"\n",
" if pd.isna(value) or value is None:\n",
" return None\n",
" \n",
" # Extract numerical value after the colon\n",
" if isinstance(value, str) and \":\" in value:\n",
" value = value.split(\":\", 1)[1].strip()\n",
" \n",
" try:\n",
" depression_score = int(value)\n",
" # Convert to binary: 0 for no depression (score 0), 1 for any depression symptoms (score > 0)\n",
" return 0 if depression_score == 0 else 1\n",
" except (ValueError, TypeError):\n",
" return None\n",
"\n",
"def convert_age(value):\n",
" \"\"\"Convert age to continuous value\"\"\"\n",
" if pd.isna(value) or value is None:\n",
" return None\n",
" \n",
" # Extract numerical value after the colon\n",
" if isinstance(value, str) and \":\" in value:\n",
" value = value.split(\":\", 1)[1].strip()\n",
" \n",
" try:\n",
" return int(value)\n",
" except (ValueError, TypeError):\n",
" return None\n",
"\n",
"def convert_gender(value):\n",
" \"\"\"Convert gender to binary (0 for female, 1 for male)\"\"\"\n",
" if pd.isna(value) or value is None:\n",
" return None\n",
" \n",
" # Extract value after the colon\n",
" if isinstance(value, str) and \":\" in value:\n",
" value = value.split(\":\", 1)[1].strip().lower()\n",
" \n",
" if 'female' in value:\n",
" return 0\n",
" elif 'male' in value:\n",
" return 1\n",
" else:\n",
" return None\n",
"\n",
"# 3. Save Metadata\n",
"# Initial filtering on the usability of the dataset\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",
" # Create clinical data from the sample characteristics dictionary\n",
" # Sample data from the provided dictionary\n",
" sample_characteristics = {\n",
" 0: ['tissue: hippocampus'], \n",
" 1: ['sex (self-reported): female', 'sex (self-reported): male'], \n",
" 2: ['age: 89', 'age: 95', 'age: 84', 'age: 76', 'age: 86', 'age: 96', 'age: 80', 'age: 101', 'age: 85', 'age: 92', 'age: 78', 'age: 88', 'age: 81', 'age: 91', 'age: 99', 'age: 79', 'age: 93', 'age: 87'], \n",
" 3: ['physical activity tier: low activity', 'physical activity tier: moderate activity', 'physical activity tier: high activity'], \n",
" 4: ['physical activity: Actical average counts/day: 1.02', 'physical activity: Actical average counts/day: 0.74', 'physical activity: Actical average counts/day: 1.05', 'physical activity: Actical average counts/day: 0.71', 'physical activity: Actical average counts/day: 0.13', 'physical activity: Actical average counts/day: 1.06', 'physical activity: Actical average counts/day: 0.37', 'physical activity: Actical average counts/day: 0.89', 'physical activity: Actical average counts/day: 0.94', 'physical activity: Actical average counts/day: 0.65', 'physical activity: Actical average counts/day: 0.57', 'physical activity: Actical average counts/day: 1.75', 'physical activity: Actical average counts/day: 2', 'physical activity: Actical average counts/day: 1.48', 'physical activity: Actical average counts/day: 1.72', 'physical activity: Actical average counts/day: 2.06', 'physical activity: Actical average counts/day: 1.4', 'physical activity: Actical average counts/day: 1.8', 'physical activity: Actical average counts/day: 1.99', 'physical activity: Actical average counts/day: 1.87', 'physical activity: Actical average counts/day: 1.44', 'physical activity: Actical average counts/day: 2.32', 'physical activity: Actical average counts/day: 2.6', 'physical activity: Actical average counts/day: 3.53', 'physical activity: Actical average counts/day: 2.82', 'physical activity: Actical average counts/day: 4.19', 'physical activity: Actical average counts/day: 5.6', 'physical activity: Actical average counts/day: 3.26', 'physical activity: Actical average counts/day: 2.99', 'physical activity: Actical average counts/day: 4.7'], \n",
" 5: ['education: 10', 'education: 14', 'education: 16', 'education: 12', 'education: 13', 'education: 21', 'education: 11', 'education: 18', 'education: 20', 'education: 15', 'education: 19', 'education: 22'], \n",
" 6: ['depression: 0', 'depression: 2', 'depression: 8', 'depression: 1', 'depression: 3', 'depression: 4'], \n",
" 7: ['cognitive frequency: 2.429', 'cognitive frequency: 3.143', 'cognitive frequency: 3', 'cognitive frequency: 3.714', 'cognitive frequency: NA', 'cognitive frequency: 1', 'cognitive frequency: 1.14', 'cognitive frequency: 2.29', 'cognitive frequency: 2.57', 'cognitive frequency: 3.57', 'cognitive frequency: 3.71', 'cognitive frequency: 3.571', 'cognitive frequency: 3.86', 'cognitive frequency: 2.71', 'cognitive frequency: 4.29', 'cognitive frequency: 3.429', 'cognitive frequency: 3.857', 'cognitive frequency: 4.143', 'cognitive frequency: 2.286', 'cognitive frequency: 2.86', 'cognitive frequency: 4', 'cognitive frequency: 2.43'], \n",
" 8: ['social frequency: 2.167', 'social frequency: 1.333', 'social frequency: 2.333', 'social frequency: 3', 'social frequency: NA', 'social frequency: 1.83', 'social frequency: 2.83', 'social frequency: 2.17', 'social frequency: 1.5', 'social frequency: 2.33', 'social frequency: 2.5', 'social frequency: 1.833', 'social frequency: 2', 'social frequency: 2.67', 'social frequency: 3.167', 'social frequency: 3.333', 'social frequency: 2.667', 'social frequency: 1.67']\n",
" }\n",
" \n",
" # Convert the sample characteristics dictionary to a DataFrame format expected by geo_select_clinical_features\n",
" # We want each row to represent a characteristic and each column to represent a sample\n",
" # First, determine\n"
]
},
{
"cell_type": "markdown",
"id": "68e5dce7",
"metadata": {},
"source": [
"### Step 3: Dataset Analysis and Clinical Feature Extraction"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "30600a63",
"metadata": {},
"outputs": [],
"source": [
"# Import necessary libraries\n",
"import pandas as pd\n",
"import os\n",
"import json\n",
"from typing import Callable, Optional, Dict, Any\n",
"\n",
"# Since we don't have output from a previous step, let's first check if the files exist\n",
"# before making assumptions about the data\n",
"\n",
"# Check for matrix file which typically contains sample information\n",
"matrix_files = [f for f in os.listdir(in_cohort_dir) if f.endswith(\"_matrix.txt\")]\n",
"sample_files = [f for f in os.listdir(in_cohort_dir) if \"sample\" in f.lower() and f.endswith(\".txt\")]\n",
"clinical_files = [f for f in os.listdir(in_cohort_dir) if \"clinical\" in f.lower()]\n",
"\n",
"# Print available files to understand what we're working with\n",
"print(f\"Files in {in_cohort_dir}:\")\n",
"for file in os.listdir(in_cohort_dir):\n",
" print(f\" - {file}\")\n",
"\n",
"# 1. Determine gene expression data availability\n",
"# For GEO datasets, we typically look for series matrix files which contain gene expression data\n",
"# Let's check if any files suggest gene expression data\n",
"is_gene_available = any(f for f in os.listdir(in_cohort_dir) if \"matrix\" in f.lower() or \"expression\" in f.lower())\n",
"print(f\"Gene expression data available: {is_gene_available}\")\n",
"\n",
"# 2. Variable Availability - Since we don't have sample characteristics data yet, we'll set these to None\n",
"# We'll need to analyze the actual data in future steps\n",
"trait_row = None\n",
"age_row = None\n",
"gender_row = None\n",
"\n",
"# Define conversion functions that will be used if we find the relevant data\n",
"def convert_trait(value: str) -> int:\n",
" \"\"\"Convert depression status to binary (0: Control, 1: Depression).\"\"\"\n",
" if value is None:\n",
" return None\n",
" \n",
" # Extract value after colon if present\n",
" if ':' in value:\n",
" value = value.split(':', 1)[1].strip().lower()\n",
" else:\n",
" value = value.strip().lower()\n",
" \n",
" # Convert to binary\n",
" if 'depression' in value or 'mdd' in value or 'case' in value or 'patient' in value:\n",
" return 1\n",
" elif 'control' in value or 'healthy' in value or 'normal' in value:\n",
" return 0\n",
" else:\n",
" return None\n",
"\n",
"def convert_age(value: str) -> float:\n",
" \"\"\"Convert age to float.\"\"\"\n",
" if value is None:\n",
" return None\n",
" \n",
" # Extract value after colon if present\n",
" if ':' in value:\n",
" value = value.split(':', 1)[1].strip()\n",
" else:\n",
" value = value.strip()\n",
" \n",
" # Try to extract numeric value\n",
" try:\n",
" # Handle ranges (e.g., \"25-30\") by taking average\n",
" if '-' in value:\n",
" parts = value.split('-')\n",
" return (float(parts[0]) + float(parts[1])) / 2\n",
" \n",
" # Handle years notation\n",
" if 'years' in value or 'y' in value:\n",
" return float(value.replace('years', '').replace('y', '').strip())\n",
" \n",
" return float(value)\n",
" except (ValueError, TypeError):\n",
" return None\n",
"\n",
"def convert_gender(value: str) -> int:\n",
" \"\"\"Convert gender to binary (0: Female, 1: Male).\"\"\"\n",
" if value is None:\n",
" return None\n",
" \n",
" # Extract value after colon if present\n",
" if ':' in value:\n",
" value = value.split(':', 1)[1].strip().lower()\n",
" else:\n",
" value = value.strip().lower()\n",
" \n",
" # Convert to binary\n",
" if 'female' in value or 'f' == value:\n",
" return 0\n",
" elif 'male' in value or 'm' == value:\n",
" return 1\n",
" else:\n",
" return None\n",
"\n",
"# 3. Save Metadata with initial filtering\n",
"# As we don't have enough information yet, we'll set is_trait_available based on trait_row\n",
"is_trait_available = trait_row is not None\n",
"print(f\"Trait data available: {is_trait_available}\")\n",
"\n",
"# Validate and save cohort information with is_final=False for 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. Skip clinical feature extraction for now since we don't have confirmed data availability\n",
"print(\"Clinical feature extraction will be performed in a later step once data availability is confirmed.\")\n"
]
},
{
"cell_type": "markdown",
"id": "1a6d9584",
"metadata": {},
"source": [
"### Step 4: Gene Data Extraction"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "c2131edc",
"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": "24032189",
"metadata": {},
"source": [
"### Step 5: Gene Identifier Review"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "5bf5e3ce",
"metadata": {},
"outputs": [],
"source": [
"# Reviewing the gene identifiers\n",
"# These identifiers appear to be probe IDs from Affymetrix microarray platform\n",
"# Format like \"1007_s_at\" is typical of Affymetrix array probes\n",
"# These are not standard human gene symbols (like BRCA1, TP53, etc.)\n",
"# Therefore, they need to be mapped to standard gene symbols\n",
"\n",
"requires_gene_mapping = True\n"
]
},
{
"cell_type": "markdown",
"id": "0f7dccb2",
"metadata": {},
"source": [
"### Step 6: Gene Annotation"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "57a5b062",
"metadata": {},
"outputs": [],
"source": [
"# Get the SOFT and matrix file paths\n",
"soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)\n",
"\n",
"# 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",
"# Look at the columns available and choose the correct ones for mapping\n",
"# Based on the columns in the preview, 'ID' is for probe IDs and 'Gene Symbol' contains gene symbols\n",
"prob_col = 'ID'\n",
"gene_col = 'Gene Symbol'\n",
"print(f\"\\nWill use '{prob_col}' for probe IDs and '{gene_col}' for gene symbols in mapping step\")\n",
"\n",
"# Test extracting mapping data\n",
"mapping_data = get_gene_mapping(gene_annotation, prob_col, gene_col)\n",
"print(f\"\\nMapping data shape: {mapping_data.shape}\")\n",
"print(\"First few rows of mapping data:\")\n",
"print(preview_df(mapping_data, n=3))\n"
]
},
{
"cell_type": "markdown",
"id": "1845b87c",
"metadata": {},
"source": [
"### Step 7: Gene Identifier Mapping"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "b2c774a7",
"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",
"\n",
"# 2. Load the gene expression data again\n",
"genetic_data = get_genetic_data(matrix_file)\n",
"print(f\"Loaded genetic data with shape: {genetic_data.shape}\")\n",
"\n",
"# 3. Extract gene annotation data from the SOFT file\n",
"gene_annotation = get_gene_annotation(soft_file)\n",
"print(f\"Loaded gene annotation data with shape: {gene_annotation.shape}\")\n",
"\n",
"# 4. Perform gene identifier mapping\n",
"print(\"\\nPerforming gene identifier mapping...\")\n",
"\n",
"# Create mapping from probe IDs to gene symbols\n",
"mapping_data = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='Gene Symbol')\n",
"print(f\"Created mapping data with shape: {mapping_data.shape}\")\n",
"print(f\"Sample of mapping data: {preview_df(mapping_data, n=3)}\")\n",
"\n",
"# Apply the mapping to convert probe-level measurements to gene-level expression data\n",
"gene_data = apply_gene_mapping(expression_df=genetic_data, mapping_df=mapping_data)\n",
"print(f\"Transformed gene data shape after mapping: {gene_data.shape}\")\n",
"print(\"First 10 gene symbols after mapping:\")\n",
"print(gene_data.index[:10])\n",
"\n",
"# Preview a sample of the mapped gene expression data\n",
"gene_sample = gene_data.iloc[:5, :5]\n",
"print(\"\\nSample of gene expression data (first 5 genes, first 5 samples):\")\n",
"print(gene_sample)\n"
]
},
{
"cell_type": "markdown",
"id": "b8fb2cb4",
"metadata": {},
"source": [
"### Step 8: Data Normalization and Linking"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "c9abddc7",
"metadata": {},
"outputs": [],
"source": [
"# 1. Get the SOFT and matrix file paths\n",
"soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)\n",
"\n",
"# Re-extract gene expression data and create the gene_data\n",
"# Load the gene expression data\n",
"genetic_data = get_genetic_data(matrix_file)\n",
"gene_annotation = get_gene_annotation(soft_file)\n",
"mapping_data = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='Gene Symbol')\n",
"gene_data = apply_gene_mapping(expression_df=genetic_data, mapping_df=mapping_data)\n",
"\n",
"# Normalize gene symbols in the gene expression data\n",
"gene_data = normalize_gene_symbols_in_index(gene_data)\n",
"print(f\"Gene data shape after normalization: {gene_data.shape}\")\n",
"\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. Re-extract the clinical 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",
"# Define conversion functions for clinical features\n",
"def convert_trait(value):\n",
" \"\"\"Convert depression score to binary format.\"\"\"\n",
" if not isinstance(value, str):\n",
" return None\n",
" value = value.split(\": \")[-1].strip()\n",
" try:\n",
" # Depression is measured as a score, converting to binary\n",
" # 0 = no depression, 1 = any depression symptoms\n",
" depression_score = int(value)\n",
" return 0 if depression_score == 0 else 1\n",
" except:\n",
" return None\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 identified row indices\n",
"selected_clinical_df = geo_select_clinical_features(\n",
" clinical_df=clinical_data,\n",
" trait=trait,\n",
" trait_row=6, # Depression scores in row 6\n",
" convert_trait=convert_trait,\n",
" age_row=2, # Age data in row 2\n",
" convert_age=convert_age,\n",
" gender_row=1, # Gender data in row 1\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\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 hippocampus samples of elderly subjects with depression scores.\"\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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