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{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"id": "3f60932d",
"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 = \"Breast_Cancer\"\n",
"cohort = \"GSE283522\"\n",
"\n",
"# Input paths\n",
"in_trait_dir = \"../../input/GEO/Breast_Cancer\"\n",
"in_cohort_dir = \"../../input/GEO/Breast_Cancer/GSE283522\"\n",
"\n",
"# Output paths\n",
"out_data_file = \"../../output/preprocess/Breast_Cancer/GSE283522.csv\"\n",
"out_gene_data_file = \"../../output/preprocess/Breast_Cancer/gene_data/GSE283522.csv\"\n",
"out_clinical_data_file = \"../../output/preprocess/Breast_Cancer/clinical_data/GSE283522.csv\"\n",
"json_path = \"../../output/preprocess/Breast_Cancer/cohort_info.json\"\n"
]
},
{
"cell_type": "markdown",
"id": "e9505ea4",
"metadata": {},
"source": [
"### Step 1: Initial Data Loading"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "f4a5273b",
"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": "2b13b1c5",
"metadata": {},
"source": [
"### Step 2: Dataset Analysis and Clinical Feature Extraction"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "0f5284ae",
"metadata": {},
"outputs": [],
"source": [
"# 1. Gene Expression Data Availability\n",
"# Based on the information in the background metadata, this dataset contains RNA-seq data\n",
"# from FFPE breast tumors, indicating it contains gene expression data\n",
"is_gene_available = True\n",
"\n",
"# 2.1 Data Availability\n",
"# Trait (Breast Cancer) data is available\n",
"# Row 6 contains 'sample category' which indicates whether the sample is invasive breast cancer \n",
"# or other types like healthy, DCIS, etc.\n",
"trait_row = 6\n",
"\n",
"# Age data is available in row 2\n",
"age_row = 2\n",
"\n",
"# Gender/Sex data is available in row 5\n",
"gender_row = 5\n",
"\n",
"# 2.2 Data Type Conversion\n",
"def convert_trait(value):\n",
" \"\"\"Convert trait data to binary (0: healthy/no cancer, 1: cancer).\"\"\"\n",
" if pd.isna(value):\n",
" return None\n",
" \n",
" # Split by colon and get the value part\n",
" if \":\" in value:\n",
" value = value.split(\":\", 1)[1].strip()\n",
" \n",
" # Map sample categories to binary values\n",
" if \"invasive breast cancer\" in value:\n",
" return 1\n",
" elif \"true healthy\" in value or \"no tumor\" in value:\n",
" return 0\n",
" elif \"DCIS\" in value or \"LCIS\" in value or \"extra ROI\" in value:\n",
" # These are precursor lesions or special cases, not considered invasive cancer\n",
" return 0\n",
" elif \"positive control\" in value:\n",
" # Controls shouldn't be counted as cases\n",
" return None\n",
" else:\n",
" return None\n",
"\n",
"def convert_age(value):\n",
" \"\"\"Convert age data to continuous values.\"\"\"\n",
" if pd.isna(value):\n",
" return None\n",
" \n",
" # Split by colon and get the value part\n",
" if \":\" in value:\n",
" value = value.split(\":\", 1)[1].strip()\n",
" \n",
" if \"not applicable\" in value or \"missing\" in value:\n",
" return None\n",
" \n",
" # Age is given in ranges like \"55 - 59\"\n",
" if \"-\" in value:\n",
" # Extract the range and use the midpoint\n",
" try:\n",
" parts = value.replace(' ', '').split('-')\n",
" lower = int(parts[0])\n",
" upper = int(parts[1])\n",
" return (lower + upper) / 2\n",
" except:\n",
" return None\n",
" \n",
" return None\n",
"\n",
"def convert_gender(value):\n",
" \"\"\"Convert gender/sex data to binary (0: female, 1: male).\"\"\"\n",
" if pd.isna(value):\n",
" return None\n",
" \n",
" # Split by colon and get the value part\n",
" if \":\" in value:\n",
" value = value.split(\":\", 1)[1].strip()\n",
" \n",
" if \"not applicable\" in value or \"missing\" in value:\n",
" return None\n",
" \n",
" if \"female\" in value.lower():\n",
" return 0\n",
" elif \"male\" in value.lower():\n",
" return 1\n",
" else:\n",
" return None\n",
"\n",
"# 3. Save Metadata\n",
"# Conduct initial filtering on the usability of the dataset\n",
"# trait_row is not None, so trait data is available\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",
"# Since trait_row is not None, we need to extract clinical features\n",
"# We need to create a DataFrame from the sample characteristics dictionary\n",
"# The sample characteristics are the values provided in the previous output\n",
"\n",
"# Create a dictionary to store the sample characteristics for each row\n",
"sample_chars = {}\n",
"for row_idx, values in Sample_Characteristics_Dictionary.items():\n",
" sample_chars[row_idx] = values\n",
"\n",
"# Convert the dictionary to a DataFrame\n",
"clinical_data = pd.DataFrame(sample_chars)\n",
"\n",
"# Now extract the 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 clinical features:\")\n",
"print(preview)\n",
"\n",
"# Save the clinical data to a CSV file\n",
"os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)\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": "d2aa5260",
"metadata": {},
"source": [
"### Step 3: Dataset Analysis and Clinical Feature Extraction"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "ab227b7f",
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"import pandas as pd\n",
"import json\n",
"import gzip\n",
"import re\n",
"from typing import Callable, Optional\n",
"\n",
"# Check what files are available in the cohort directory\n",
"cohort_files = os.listdir(in_cohort_dir)\n",
"print(f\"Files in cohort directory: {cohort_files}\")\n",
"\n",
"# Function to parse GEO series matrix file\n",
"def parse_geo_series_matrix(file_path):\n",
" with gzip.open(file_path, 'rt') as f:\n",
" lines = f.readlines()\n",
" \n",
" # Extract sample characteristics\n",
" characteristics_rows = {}\n",
" sample_ids = []\n",
" data_start = False\n",
" \n",
" for i, line in enumerate(lines):\n",
" if line.startswith('!Sample_geo_accession'):\n",
" sample_ids = line.strip().split('\\t')[1:]\n",
" elif line.startswith('!Sample_characteristics_ch1'):\n",
" parts = line.strip().split('\\t')\n",
" header = parts[0]\n",
" values = parts[1:]\n",
" if len(values) > 0:\n",
" row_idx = len(characteristics_rows)\n",
" characteristics_rows[row_idx] = values\n",
" elif line.startswith('!series_matrix_table_begin'):\n",
" data_start = True\n",
" data_start_line = i\n",
" break\n",
" \n",
" # Create clinical dataframe\n",
" clinical_df = pd.DataFrame(index=sample_ids)\n",
" for row_idx, values in characteristics_rows.items():\n",
" clinical_df[f'characteristic_{row_idx}'] = values\n",
" \n",
" # Check if there's gene expression data\n",
" has_gene_data = data_start\n",
" \n",
" return clinical_df, has_gene_data\n",
"\n",
"# Parse the GEO series matrix file\n",
"series_matrix_path = os.path.join(in_cohort_dir, \"GSE283522_series_matrix.txt.gz\")\n",
"if os.path.exists(series_matrix_path):\n",
" clinical_data, is_gene_available = parse_geo_series_matrix(series_matrix_path)\n",
" print(f\"Clinical data shape: {clinical_data.shape}\")\n",
" \n",
" # Display unique values for each sample characteristic\n",
" for i, col in enumerate(clinical_data.columns):\n",
" unique_values = clinical_data[col].unique()\n",
" print(f\"Row {i}, Column '{col}': {unique_values}\")\n",
"else:\n",
" print(f\"Series matrix file not found: {series_matrix_path}\")\n",
" clinical_data = pd.DataFrame()\n",
" is_gene_available = False\n",
"\n",
"# Identify and process clinical variables\n",
"def convert_trait(value):\n",
" if pd.isna(value):\n",
" return None\n",
" \n",
" value = str(value).lower()\n",
" if ':' in value:\n",
" value = value.split(':', 1)[1].strip()\n",
" \n",
" # Convert to binary: 1 for breast cancer, 0 for control/normal\n",
" if any(term in value for term in ['cancer', 'tumor', 'malignant', 'carcinoma']):\n",
" return 1\n",
" elif any(term in value for term in ['normal', 'control', 'benign', 'healthy']):\n",
" return 0\n",
" return None\n",
"\n",
"def convert_age(value):\n",
" if pd.isna(value):\n",
" return None\n",
" \n",
" value = str(value)\n",
" if ':' in value:\n",
" value = value.split(':', 1)[1].strip()\n",
" \n",
" # Try to extract age as a number\n",
" age_match = re.search(r'(\\d+)', value)\n",
" if age_match:\n",
" return float(age_match.group(1))\n",
" return None\n",
"\n",
"def convert_gender(value):\n",
" if pd.isna(value):\n",
" return None\n",
" \n",
" value = str(value).lower()\n",
" if ':' in value:\n",
" value = value.split(':', 1)[1].strip()\n",
" \n",
" if value in ['female', 'f', 'woman']:\n",
" return 0\n",
" elif value in ['male', 'm', 'man']:\n",
" return 1\n",
" return None\n",
"\n",
"# Initialize row indices as None\n",
"trait_row = None\n",
"age_row = None\n",
"gender_row = None\n",
"\n",
"# Look through the sample characteristics to find the appropriate rows\n",
"if not clinical_data.empty:\n",
" for i, col in enumerate(clinical_data.columns):\n",
" col_values = clinical_data[col].astype(str).str.lower()\n",
" \n",
" # Check for trait-related information\n",
" trait_terms = ['tissue', 'diagnosis', 'sample type', 'status', 'source', 'histology', 'disease']\n",
" if any(term in col.lower() for term in trait_terms):\n",
" # Check if values indicate cancer/normal distinction\n",
" has_trait_terms = any(('cancer' in val or 'tumor' in val or 'normal' in val or \n",
" 'control' in val or 'benign' in val or 'malignant' in val) \n",
" for val in col_values)\n",
" # Check if there's more than one unique value\n",
" has_multiple_values = len(set([convert_trait(val) for val in col_values if convert_trait(val) is not None])) > 1\n",
" \n",
" if has_trait_terms and has_multiple_values:\n",
" trait_row = i\n",
" \n",
" # Check for age information\n",
" if 'age' in col.lower():\n",
" # Check if there's more than one unique value after conversion\n",
" ages = [convert_age(val) for val in col_values if convert_age(val) is not None]\n",
" if len(ages) > 1 and len(set(ages)) > 1:\n",
" age_row = i\n",
" \n",
" # Check for gender information\n",
" if 'gender' in col.lower() or 'sex' in col.lower():\n",
" # Check if there's more than one unique value after conversion\n",
" genders = [convert_gender(val) for val in col_values if convert_gender(val) is not None]\n",
" if len(genders) > 1 and len(set(genders)) > 1:\n",
" gender_row = i\n",
"\n",
"# Save metadata - initial filtering\n",
"is_trait_available = trait_row is not None\n",
"validate_and_save_cohort_info(is_final=False, cohort=cohort, info_path=json_path, \n",
" is_gene_available=is_gene_available, \n",
" is_trait_available=is_trait_available)\n",
"\n",
"# Extract clinical features if trait data is available\n",
"if is_trait_available:\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 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 selected clinical features\n",
" preview = preview_df(selected_clinical_df)\n",
" print(f\"Preview of selected clinical features: {preview}\")\n",
" \n",
" # Create directory if it doesn't exist\n",
" os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)\n",
" \n",
" # Save the clinical data\n",
" selected_clinical_df.to_csv(out_clinical_data_file)\n",
" print(f\"Clinical data saved to: {out_clinical_data_file}\")\n"
]
},
{
"cell_type": "markdown",
"id": "04bf58c2",
"metadata": {},
"source": [
"### Step 4: Gene Data Extraction"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "f36f952e",
"metadata": {},
"outputs": [],
"source": [
"# 1. Identify the paths to the SOFT file and the matrix file\n",
"soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)\n",
"print(f\"SOFT file: {soft_file}\")\n",
"print(f\"Matrix file: {matrix_file}\")\n",
"\n",
"# Set gene availability flag\n",
"is_gene_available = True # Initially assume gene data is available\n",
"\n",
"# First check if the matrix file contains the expected marker\n",
"found_marker = False\n",
"try:\n",
" with gzip.open(matrix_file, 'rt') as file:\n",
" for line in file:\n",
" if \"!series_matrix_table_begin\" in line:\n",
" found_marker = True\n",
" break\n",
" \n",
" if found_marker:\n",
" print(\"Found the matrix table marker in the file.\")\n",
" else:\n",
" print(\"Warning: Could not find '!series_matrix_table_begin' marker in the file.\")\n",
" \n",
" # Try to extract gene data from the matrix file\n",
" gene_data = get_genetic_data(matrix_file)\n",
" \n",
" if gene_data.shape[0] == 0:\n",
" print(\"Warning: Extracted gene data has 0 rows.\")\n",
" is_gene_available = False\n",
" else:\n",
" print(f\"Gene data shape: {gene_data.shape}\")\n",
" # Print the first 20 gene/probe identifiers\n",
" print(\"First 20 gene/probe identifiers:\")\n",
" print(gene_data.index[:20].tolist())\n",
" \n",
"except Exception as e:\n",
" print(f\"Error extracting gene data: {e}\")\n",
" is_gene_available = False\n",
" \n",
" # Try to diagnose the file format\n",
" print(\"Examining file content to diagnose the issue:\")\n",
" try:\n",
" with gzip.open(matrix_file, 'rt') as file:\n",
" for i, line in enumerate(file):\n",
" if i < 10: # Print first 10 lines to diagnose\n",
" print(f\"Line {i}: {line.strip()[:100]}...\") # Print first 100 chars of each line\n",
" else:\n",
" break\n",
" except Exception as e2:\n",
" print(f\"Error examining file: {e2}\")\n",
"\n",
"if not is_gene_available:\n",
" print(\"Gene expression data could not be successfully extracted from this dataset.\")"
]
}
],
"metadata": {},
"nbformat": 4,
"nbformat_minor": 5
}
|