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{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"id": "ef013425",
"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 = \"Craniosynostosis\"\n",
"cohort = \"GSE27976\"\n",
"\n",
"# Input paths\n",
"in_trait_dir = \"../../input/GEO/Craniosynostosis\"\n",
"in_cohort_dir = \"../../input/GEO/Craniosynostosis/GSE27976\"\n",
"\n",
"# Output paths\n",
"out_data_file = \"../../output/preprocess/Craniosynostosis/GSE27976.csv\"\n",
"out_gene_data_file = \"../../output/preprocess/Craniosynostosis/gene_data/GSE27976.csv\"\n",
"out_clinical_data_file = \"../../output/preprocess/Craniosynostosis/clinical_data/GSE27976.csv\"\n",
"json_path = \"../../output/preprocess/Craniosynostosis/cohort_info.json\"\n"
]
},
{
"cell_type": "markdown",
"id": "e419df3c",
"metadata": {},
"source": [
"### Step 1: Initial Data Loading"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "d9cf9152",
"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": "47f51e48",
"metadata": {},
"source": [
"### Step 2: Dataset Analysis and Clinical Feature Extraction"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "e76aad6c",
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"import pandas as pd\n",
"import numpy as np\n",
"import json\n",
"import re\n",
"from typing import Optional, Callable, Dict, Any, List, Union\n",
"\n",
"# Sample characteristics from previous output\n",
"sample_characteristics = {\n",
" 0: ['age months: 12.87', 'age months: 10.4', 'age months: 12.3', 'age months: 11.4', 'age months: 10.1', 'age months: 11', 'age months: 4.27', 'age months: 7.97', 'age months: 4.33', 'age months: 9.33', 'age months: 7.93', 'age months: 10.27', 'age months: 10.87', 'age months: 3.87', 'age months: 3.2', 'age months: 13.27', 'age months: 5.6', 'age months: 14.9', 'age months: 3.03', 'age months: 12.4', 'age months: 8.9', 'age months: 14.17', 'age months: 6.33', 'age months: 14.87', 'age months: 8.4', 'age months: 9.07', 'age months: 13.33', 'age months: 10', 'age months: 13.23', 'age months: 10.33'],\n",
" 1: ['gender: F', 'gender: M'],\n",
" 2: ['type: Metopic Synostosis', 'type: Coronal Synostosis R', 'type: Sagittal Synostosis', 'type: Coronal Synostosis L', 'type: Control'],\n",
" 3: ['cell lines: osteoblast'],\n",
" 4: ['tissue: skull']\n",
"}\n",
"\n",
"# 1. Gene Expression Data Availability\n",
"# Based on the background information, this dataset contains gene expression data for craniosynostosis patients\n",
"is_gene_available = True\n",
"\n",
"# 2. Variable Availability and Data Type Conversion\n",
"# Examining the sample characteristics dictionary:\n",
"\n",
"# 2.1 Data Availability\n",
"# Trait data is in row 2 - as \"type\" which indicates craniosynostosis type\n",
"trait_row = 2\n",
"\n",
"# Age data is in row 0 - as \"age months\"\n",
"age_row = 0\n",
"\n",
"# Gender data is in row 1 - as \"gender\"\n",
"gender_row = 1\n",
"\n",
"# 2.2 Data Type Conversion Functions\n",
"\n",
"def convert_trait(value: str) -> int:\n",
" \"\"\"\n",
" Convert craniosynostosis type to binary (0=control, 1=case)\n",
" \"\"\"\n",
" if pd.isna(value) or not isinstance(value, str):\n",
" return None\n",
" \n",
" # Extract the value after the colon\n",
" if \":\" in value:\n",
" value = value.split(\":\", 1)[1].strip()\n",
" \n",
" if \"Control\" in value:\n",
" return 0\n",
" elif \"Synostosis\" in value:\n",
" return 1\n",
" else:\n",
" return None\n",
"\n",
"def convert_age(value: str) -> float:\n",
" \"\"\"\n",
" Convert age in months to a continuous value\n",
" \"\"\"\n",
" if pd.isna(value) or not isinstance(value, str):\n",
" return None\n",
" \n",
" # Extract the value after the colon\n",
" if \":\" in value:\n",
" value = value.split(\":\", 1)[1].strip()\n",
" \n",
" # Extract the numeric part\n",
" match = re.search(r'(\\d+\\.?\\d*)', value)\n",
" if match:\n",
" return float(match.group(1))\n",
" else:\n",
" return None\n",
"\n",
"def convert_gender(value: str) -> int:\n",
" \"\"\"\n",
" Convert gender to binary (0=female, 1=male)\n",
" \"\"\"\n",
" if pd.isna(value) or not isinstance(value, str):\n",
" return None\n",
" \n",
" # Extract the value after the colon\n",
" if \":\" in value:\n",
" value = value.split(\":\", 1)[1].strip()\n",
" \n",
" if value.upper() == 'F':\n",
" return 0\n",
" elif value.upper() == 'M':\n",
" return 1\n",
" else:\n",
" return None\n",
"\n",
"# 3. Save Metadata\n",
"# Determine trait data availability\n",
"is_trait_available = trait_row is not None\n",
"\n",
"# Initial validation and recording of metadata\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 data is available, extract and process clinical features\n",
"if trait_row is not None:\n",
" # Create a suitable dataframe structure for the geo_select_clinical_features function\n",
" # We need to ensure the structure works with get_feature_data called inside geo_select_clinical_features\n",
" \n",
" # The geo_select_clinical_features expects a dataframe where:\n",
" # - Each row corresponds to a feature (age, gender, trait)\n",
" # - The values should be unique values for that feature\n",
" clinical_data = pd.DataFrame(sample_characteristics)\n",
" \n",
" # Extract clinical features\n",
" selected_clinical_df = geo_select_clinical_features(\n",
" clinical_df=clinical_data,\n",
" trait=trait,\n",
" trait_row=trait_row,\n",
" convert_trait=convert_trait,\n",
" 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 features\n",
" preview = preview_df(selected_clinical_df)\n",
" print(\"Preview of selected clinical features:\", preview)\n",
" \n",
" # Create the 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"
]
},
{
"cell_type": "markdown",
"id": "647ea55c",
"metadata": {},
"source": [
"### Step 3: Dataset Analysis and Clinical Feature Extraction"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "bc863e72",
"metadata": {},
"outputs": [],
"source": [
"I understand that we need to properly analyze the dataset to find gene expression data availability and extract clinical features. Here's the corrected code:\n",
"\n",
"```python\n",
"import pandas as pd\n",
"import numpy as np\n",
"import os\n",
"import json\n",
"from typing import Callable, Optional, Dict, Any, Union, List\n",
"\n",
"# Load the necessary data\n",
"print(f\"Files in {in_cohort_dir}:\")\n",
"for f in os.listdir(in_cohort_dir):\n",
" print(f\" {f}\")\n",
"\n",
"# Try to load the clinical data\n",
"clinical_file = os.path.join(in_cohort_dir, \"clinical_data.csv\")\n",
"if os.path.exists(clinical_file):\n",
" clinical_data = pd.read_csv(clinical_file)\n",
" print(f\"Loaded clinical data from {clinical_file}\")\n",
"else:\n",
" clinical_file = os.path.join(in_cohort_dir, f\"{cohort}_sample_characteristics.csv\")\n",
" if os.path.exists(clinical_file):\n",
" clinical_data = pd.read_csv(clinical_file)\n",
" print(f\"Loaded clinical data from {clinical_file}\")\n",
" else:\n",
" # Try to find any CSV file that might contain clinical data\n",
" csv_files = [f for f in os.listdir(in_cohort_dir) if f.endswith('.csv')]\n",
" clinical_data = None\n",
" for f in csv_files:\n",
" try:\n",
" clinical_file = os.path.join(in_cohort_dir, f)\n",
" df = pd.read_csv(clinical_file)\n",
" if 'characteristics_ch1' in df.columns or any('characteristics' in col.lower() for col in df.columns):\n",
" clinical_data = df\n",
" print(f\"Loaded clinical data from {clinical_file}\")\n",
" break\n",
" except:\n",
" continue\n",
" \n",
" if clinical_data is None:\n",
" print(\"No clinical data files found\")\n",
" clinical_data = pd.DataFrame()\n",
"\n",
"# Check if gene expression data is likely available\n",
"gene_files = [f for f in os.listdir(in_cohort_dir) if \n",
" \"gene\" in f.lower() or \n",
" \"expression\" in f.lower() or \n",
" \"series_matrix\" in f.lower() or\n",
" f.endswith('.txt') or \n",
" f.endswith('.tsv')]\n",
"is_gene_available = len(gene_files) > 0\n",
"print(f\"Gene expression data availability: {is_gene_available}\")\n",
"\n",
"# Print the clinical data structure to help us analyze it\n",
"if not clinical_data.empty:\n",
" print(\"\\nClinical data shape:\", clinical_data.shape)\n",
" print(\"\\nClinical data columns:\", clinical_data.columns.tolist())\n",
" print(\"\\nFirst few rows of clinical data:\")\n",
" print(clinical_data.head())\n",
" \n",
" # Look for sample characteristics\n",
" if 'characteristics_ch1' in clinical_data.columns:\n",
" unique_values = {}\n",
" for i in range(len(clinical_data)):\n",
" val = clinical_data.loc[i, 'characteristics_ch1']\n",
" if i not in unique_values:\n",
" unique_values[i] = set()\n",
" unique_values[i].add(val)\n",
" \n",
" for row_idx, values in unique_values.items():\n",
" print(f\"Row {row_idx} unique values:\", values)\n",
" \n",
" # Or check for any columns that might contain sample characteristics\n",
" sample_cols = [col for col in clinical_data.columns if 'characteristics' in col.lower()]\n",
" for col in sample_cols:\n",
" print(f\"\\nUnique values in {col}:\")\n",
" for val in clinical_data[col].unique():\n",
" print(f\" {val}\")\n",
"\n",
"# Based on our inspection, set the row indices for trait, age, and gender\n",
"# Setting these based on the Craniosynostosis dataset patterns\n",
"# After reviewing the data, these values should be updated\n",
"trait_row = 1 # Sample row index where craniosynostosis status can be found\n",
"age_row = 2 # Sample row index where age information can be found\n",
"gender_row = 3 # Sample row index where gender information can be found\n",
"\n",
"def convert_trait(value: str) -> int:\n",
" \"\"\"\n",
" Convert craniosynostosis information to binary format.\n",
" \n",
" Args:\n",
" value: The raw value from the clinical data\n",
" \n",
" Returns:\n",
" 1 for cases, 0 for controls, None for unknown\n",
" \"\"\"\n",
" if pd.isna(value) or value is None:\n",
" return None\n",
" \n",
" value = str(value).lower()\n",
" \n",
" # Extract the actual value if it's in format \"label: value\"\n",
" if ':' in value:\n",
" value = value.split(':', 1)[1].strip()\n",
" \n",
" if 'case' in value or 'patient' in value or 'craniosynostosis' in value or 'affected' in value:\n",
" return 1\n",
" elif 'control' in value or 'normal' in value or 'unaffected' in value or 'healthy' in value:\n",
" return 0\n",
" else:\n",
" return None\n",
"\n",
"def convert_age(value: str) -> float:\n",
" \"\"\"\n",
" Convert age information to numerical format.\n",
" \n",
" Args:\n",
" value: The raw age value from the clinical data\n",
" \n",
" Returns:\n",
" Age as a float, None for unknown\n",
" \"\"\"\n",
" if pd.isna(value) or value is None:\n",
" return None\n",
" \n",
" value = str(value).lower()\n",
" \n",
" # Extract the actual value if it's in format \"label: value\"\n",
" if ':' in value:\n",
" value = value.split(':', 1)[1].strip()\n",
" \n",
" # Try to extract age\n",
" import re\n",
" \n",
" # Try to find a number, potentially followed by time units\n",
" age_match = re.search(r'(\\d+\\.?\\d*)\\s*(years?|yr|y|months?|mo|days?|d|weeks?|wk)?', value)\n",
" if age_match:\n",
" age_val = float(age_match.group(1))\n",
" unit = age_match.group(2) if age_match.group(2) else 'years'\n",
" \n",
" # Convert to years if needed\n",
" if 'month' in unit or unit == 'mo':\n",
" age_val /= 12\n",
" elif 'day' in unit or unit == 'd':\n",
" age_val /= 365\n",
" elif 'week' in unit or unit == 'wk':\n",
" age_val /= 52\n",
" \n",
" return age_val\n",
" \n",
" return None\n",
"\n",
"def convert_gender(value: str) -> int:\n",
" \"\"\"\n",
" Convert gender information to binary format.\n",
" \n",
" Args:\n",
" value: The raw gender value from the clinical data\n",
" \n",
" Returns:\n",
" 0 for female, 1 for male, None for unknown\n",
" \"\"\"\n",
" if pd.isna(value) or value is None:\n",
" return None\n",
" \n",
" value = str(value).lower()\n",
" \n",
" # Extract the actual value if it's in format \"label: value\"\n",
" if ':' in value:\n",
" value = value.split(':', 1)[1].strip()\n",
" \n",
" if 'female' in value or 'f' == value.strip() or 'woman' in value or 'girl' in value:\n",
" return 0\n",
" elif 'male' in value or 'm' == value.strip() or 'man' in value or 'boy' in value:\n",
" return 1\n",
" else:\n",
" return None\n",
"\n",
"# Determine trait data availability\n",
"is_trait_available = trait_row is not None\n",
"\n",
"# Save initial metadata\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",
"# If trait data is available, extract clinical features\n",
"if is_trait_available and not clinical_data.empty:\n",
" # Extract clinical features\n",
" selected_clinical_df = geo_select_clinical_features(\n",
" clinical_df=clinical_data,\n",
" trait=trait,\n",
" trait_row=trait_row,\n",
" convert_trait=convert_trait,\n",
" age_row=age_row,\n",
" convert_age=convert_age,\n",
" gender_row=gender_row,\n",
" convert_gender=convert_gender\n",
" )\n",
" \n",
" # Preview the extracted features\n"
]
},
{
"cell_type": "markdown",
"id": "8f58aa24",
"metadata": {},
"source": [
"### Step 4: Gene Data Extraction"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "0c80a868",
"metadata": {},
"outputs": [],
"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. First, let's examine the structure of the matrix file to understand its format\n",
"import gzip\n",
"\n",
"# Peek at the first few lines of the file to understand its structure\n",
"with gzip.open(matrix_file, 'rt') as file:\n",
" # Read first 100 lines to find the header structure\n",
" for i, line in enumerate(file):\n",
" if '!series_matrix_table_begin' in line:\n",
" print(f\"Found data marker at line {i}\")\n",
" # Read the next line which should be the header\n",
" header_line = next(file)\n",
" print(f\"Header line: {header_line.strip()}\")\n",
" # And the first data line\n",
" first_data_line = next(file)\n",
" print(f\"First data line: {first_data_line.strip()}\")\n",
" break\n",
" if i > 100: # Limit search to first 100 lines\n",
" print(\"Matrix table marker not found in first 100 lines\")\n",
" break\n",
"\n",
"# 3. Now try to get the genetic data with better error handling\n",
"try:\n",
" gene_data = get_genetic_data(matrix_file)\n",
" print(gene_data.index[:20])\n",
"except KeyError as e:\n",
" print(f\"KeyError: {e}\")\n",
" \n",
" # Alternative approach: manually extract the data\n",
" print(\"\\nTrying alternative approach to read the gene data:\")\n",
" with gzip.open(matrix_file, 'rt') as file:\n",
" # Find the start of the data\n",
" for line in file:\n",
" if '!series_matrix_table_begin' in line:\n",
" break\n",
" \n",
" # Read the headers and data\n",
" import pandas as pd\n",
" df = pd.read_csv(file, sep='\\t', index_col=0)\n",
" print(f\"Column names: {df.columns[:5]}\")\n",
" print(f\"First 20 row IDs: {df.index[:20]}\")\n",
" gene_data = df\n"
]
},
{
"cell_type": "markdown",
"id": "6707fb59",
"metadata": {},
"source": [
"### Step 5: Gene Identifier Review"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "fc4c675a",
"metadata": {},
"outputs": [],
"source": [
"# Looking at the identifier format (7892501, 7892502, etc), these appear to be probe IDs from a microarray\n",
"# platform rather than standard human gene symbols (which typically have alphabetic characters like BRCA1, TP53).\n",
"# \n",
"# These numeric identifiers likely come from an Affymetrix or Illumina microarray platform and need to be\n",
"# mapped to standard gene symbols for proper analysis.\n",
"\n",
"requires_gene_mapping = True\n"
]
},
{
"cell_type": "markdown",
"id": "c1f3f8e4",
"metadata": {},
"source": [
"### Step 6: Gene Annotation"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "f88cefde",
"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. Use the 'preview_df' function from the library to preview the data and print out the results.\n",
"print(\"Gene annotation preview:\")\n",
"print(preview_df(gene_annotation))\n"
]
},
{
"cell_type": "markdown",
"id": "396a3fb6",
"metadata": {},
"source": [
"### Step 7: Gene Identifier Mapping"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "c02a7c72",
"metadata": {},
"outputs": [],
"source": [
"# 1. Determine which columns contain identifiers and gene symbols\n",
"# Examining the gene_annotation dataframe:\n",
"# - 'ID' column contains probe identifiers matching the gene expression data indices\n",
"# - 'gene_assignment' column contains gene symbol information\n",
"\n",
"print(\"Examining mapping columns:\")\n",
"print(f\"First few IDs: {gene_annotation['ID'].head(3).tolist()}\")\n",
"print(f\"First gene_assignment (partial): {str(gene_annotation['gene_assignment'].iloc[0])[:100]}...\")\n",
"\n",
"# 2. Get a gene mapping dataframe with the probe ID and gene symbol columns\n",
"gene_mapping = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='gene_assignment')\n",
"\n",
"# Print a sample of the mapping to verify\n",
"print(\"\\nSample of gene mapping:\")\n",
"print(gene_mapping.head(3))\n",
"print(f\"Number of probes with gene mappings: {len(gene_mapping)}\")\n",
"\n",
"# 3. Apply the gene mapping to convert probe-level data to gene expression data\n",
"# This uses apply_gene_mapping function that handles many-to-many relationships\n",
"gene_data = apply_gene_mapping(gene_data, gene_mapping)\n",
"\n",
"# Print summary of the gene expression data after mapping\n",
"print(\"\\nGene expression data after mapping:\")\n",
"print(f\"Shape of gene data: {gene_data.shape}\")\n",
"print(f\"Sample gene symbols: {list(gene_data.index[:5])}\")\n",
"\n",
"# Save the gene expression data to a CSV 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": "890b3e33",
"metadata": {},
"source": [
"### Step 8: Data Normalization and Linking"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "196c82f6",
"metadata": {},
"outputs": [],
"source": [
"# 1. Normalize gene symbols in the gene expression data\n",
"normalized_gene_data = normalize_gene_symbols_in_index(gene_data)\n",
"print(f\"Normalized gene data shape: {normalized_gene_data.shape}\")\n",
"print(\"First few genes with their expression values after normalization:\")\n",
"print(normalized_gene_data.head())\n",
"\n",
"# Save the normalized gene data\n",
"os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)\n",
"normalized_gene_data.to_csv(out_gene_data_file)\n",
"print(f\"Normalized gene data saved to {out_gene_data_file}\")\n",
"\n",
"# 2. Extract clinical features using the functions defined in step 2\n",
"# First, let's load the clinical data again to ensure we have the latest version\n",
"soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)\n",
"background_info, clinical_data = get_background_and_clinical_data(matrix_file)\n",
"\n",
"# Extract clinical features using melanoma vs normal tissue as the binary trait\n",
"selected_clinical_df = geo_select_clinical_features(\n",
" clinical_data, \n",
" trait=\"Melanoma\", \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",
"# Save the clinical data\n",
"os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)\n",
"selected_clinical_df.to_csv(out_clinical_data_file)\n",
"print(f\"Clinical data saved to {out_clinical_data_file}\")\n",
"print(\"Clinical data preview:\")\n",
"print(preview_df(selected_clinical_df))\n",
"\n",
"# 3. Link the clinical and genetic data\n",
"# Transpose normalized gene data for linking\n",
"gene_data_t = normalized_gene_data.T\n",
"\n",
"# Link the clinical and genetic data\n",
"linked_data = geo_link_clinical_genetic_data(selected_clinical_df, normalized_gene_data)\n",
"print(f\"Linked data shape (before handling missing values): {linked_data.shape}\")\n",
"\n",
"# 4. Handle missing values in the linked data\n",
"linked_data = handle_missing_values(linked_data, \"Melanoma\")\n",
"print(f\"Data after handling missing values: {linked_data.shape}\")\n",
"\n",
"# 5. Determine whether the trait and demographic features are biased\n",
"is_trait_biased, unbiased_linked_data = judge_and_remove_biased_features(linked_data, \"Melanoma\")\n",
"\n",
"# 6. Conduct final quality validation 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_trait_biased, \n",
" df=unbiased_linked_data,\n",
" note=\"Dataset contains gene expression data comparing melanoma (primary and metastatic) with normal tissue/nevi.\"\n",
")\n",
"\n",
"# 7. If the linked data is usable, save it\n",
"if is_usable:\n",
" os.makedirs(os.path.dirname(out_data_file), exist_ok=True)\n",
" unbiased_linked_data.to_csv(out_data_file)\n",
" print(f\"Linked data saved to {out_data_file}\")\n",
"else:\n",
" print(\"Data was determined to be unusable and was not saved\")"
]
}
],
"metadata": {},
"nbformat": 4,
"nbformat_minor": 5
}
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