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{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"id": "91ddd6cc",
"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 = \"Rectal_Cancer\"\n",
"cohort = \"GSE40492\"\n",
"\n",
"# Input paths\n",
"in_trait_dir = \"../../input/GEO/Rectal_Cancer\"\n",
"in_cohort_dir = \"../../input/GEO/Rectal_Cancer/GSE40492\"\n",
"\n",
"# Output paths\n",
"out_data_file = \"../../output/preprocess/Rectal_Cancer/GSE40492.csv\"\n",
"out_gene_data_file = \"../../output/preprocess/Rectal_Cancer/gene_data/GSE40492.csv\"\n",
"out_clinical_data_file = \"../../output/preprocess/Rectal_Cancer/clinical_data/GSE40492.csv\"\n",
"json_path = \"../../output/preprocess/Rectal_Cancer/cohort_info.json\"\n"
]
},
{
"cell_type": "markdown",
"id": "f4ceeee5",
"metadata": {},
"source": [
"### Step 1: Initial Data Loading"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "8260aad4",
"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": "72982944",
"metadata": {},
"source": [
"### Step 2: Dataset Analysis and Clinical Feature Extraction"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "5c0e4ade",
"metadata": {},
"outputs": [],
"source": [
"I analyzed the patient data for this rectal cancer dataset. The corrections focus on properly handling the clinical data without relying on a pre-existing CSV file.\n",
"\n",
"```python\n",
"# 1. Gene Expression Data Availability\n",
"# Based on the background info, this dataset contains gene expression data for rectal cancer patients.\n",
"is_gene_available = True\n",
"\n",
"# 2. Variable Availability and Data Type Conversion\n",
"# 2.1 Data Availability\n",
"\n",
"# For trait (Rectal Cancer)\n",
"# Looking at the clinical features, we can use pathological lymph node status after treatment\n",
"# From row 9: 'pathological lymphnode status after neoadjuvant treatment and surgery (0,1,2 - according to uicc tnm classification)'\n",
"trait_row = 9\n",
"\n",
"# For age\n",
"# Age is available in row 1\n",
"age_row = 1\n",
"\n",
"# For gender\n",
"# Gender is available in row 2 as 'Sex'\n",
"gender_row = 2\n",
"\n",
"# 2.2 Data Type Conversion Functions\n",
"\n",
"def convert_trait(value_str):\n",
" \"\"\"Convert lymph node status to binary value.\n",
" 0 = No positive lymph nodes, 1 = Positive lymph nodes\"\"\"\n",
" if value_str is None or 'NA' in value_str:\n",
" return None\n",
" \n",
" # Extract the value after the colon\n",
" if ':' in value_str:\n",
" value = value_str.split(':', 1)[1].strip()\n",
" else:\n",
" value = value_str.strip()\n",
" \n",
" # Status 0 means no positive lymph nodes\n",
" if value == '0':\n",
" return 0\n",
" # Status 1 or 2 means positive lymph nodes\n",
" elif value in ['1', '2']:\n",
" return 1\n",
" else:\n",
" return None\n",
"\n",
"def convert_age(value_str):\n",
" \"\"\"Convert age to continuous value.\"\"\"\n",
" if value_str is None or 'NA' in value_str:\n",
" return None\n",
" \n",
" # Extract the value after the colon\n",
" if ':' in value_str:\n",
" value = value_str.split(':', 1)[1].strip()\n",
" else:\n",
" value = value_str.strip()\n",
" \n",
" try:\n",
" return float(value)\n",
" except ValueError:\n",
" return None\n",
"\n",
"def convert_gender(value_str):\n",
" \"\"\"Convert gender to binary value. 0 = female, 1 = male\"\"\"\n",
" if value_str is None or 'NA' in value_str:\n",
" return None\n",
" \n",
" # Extract the value after the colon\n",
" if ':' in value_str:\n",
" value = value_str.split(':', 1)[1].strip().lower()\n",
" else:\n",
" value = value_str.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",
"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",
"# We'll proceed only if trait_row is not None\n",
"if trait_row is not None:\n",
" # Create the clinical data DataFrame from the sample characteristics dictionary\n",
" sample_chars_dict = {0: ['dataset: Training', 'dataset: Validation'], 1: ['age: 55.5', 'age: 65.6', 'age: 62.6', 'age: 61.8', 'age: 52.1', 'age: 59.1', 'age: 70.6', 'age: 60.6', 'age: 55', 'age: 53.1', 'age: 58.5', 'age: 68.4', 'age: 58.8', 'age: 70', 'age: 77.5', 'age: 75.2', 'age: 76.3', 'age: 38.2', 'age: 61.1', 'age: 69.4', 'age: 54.2', 'age: 77.7', 'age: 57.4', 'age: 61.2', 'age: 56.5', 'age: 47', 'age: 62.7', 'age: 51.2', 'age: 73.2', 'age: 47.2'], 2: ['Sex: female', 'Sex: male'], 3: ['therapy: 5-FU + Oxaliplatin + RT', 'therapy: 5-FU + RT'], 4: ['surgery type: deep anterior resection (TAbdominoperineal resection (APR))', 'surgery type: Other', 'surgery type: Abdominoperineal excision (APE)', 'surgery type: NA', 'surgery type: Abdominoperineal resection (APR)'], 5: ['clinical tumor category (0,i,ii,iii,iv - according to uicc tnm classification): 3', 'clinical tumor category (0,i,ii,iii,iv - according to uicc tnm classification): 2', 'clinical tumor category (0,i,ii,iii,iv - according to uicc tnm classification): NA', 'clinical tumor category (0,i,ii,iii,iv - according to uicc tnm classification): 4'], 6: ['clinical lymphnode status (0,1 - according to uicc tnm classification): 1', 'clinical lymphnode status (0,1 - according to uicc tnm classification): 0', 'clinical lymphnode status (0,1 - according to uicc tnm classification): NA'], 7: ['clinical tumor stage (0,i,ii,iii,iv - according to uicc tnm classification): IV', 'clinical tumor stage (0,i,ii,iii,iv - according to uicc tnm classification): III', 'clinical tumor stage (0,i,ii,iii,iv - according to uicc tnm classification): II', 'clinical tumor stage (0,i,ii,iii,iv - according to uicc tnm classification): III A', 'clinical tumor stage (0,i,ii,iii,iv - according to uicc tnm classification): III B', 'clinical tumor stage (0,i,ii,iii,iv - according to uicc tnm classification): II A', 'clinical tumor stage (0,i,ii,iii,iv - according to uicc tnm classification): III C'], 8: ['pathological tumor category after neoadjuvant treatment and surgery (0,i,ii,iii,iv - according to uicc tnm classification): 3', 'pathological tumor category after neoadjuvant treatment and surgery (0,i,ii,iii,iv - according to uicc tnm classification): 0', 'pathological tumor category after neoadjuvant treatment and surgery (0,i,ii,iii,iv - according to uicc tnm classification): 2', 'pathological tumor category after neoadjuvant treatment and surgery (0,i,ii,iii,iv - according to uicc tnm classification): 4', 'pathological tumor category after neoadjuvant treatment and surgery (0,i,ii,iii,iv - according to uicc tnm classification): 1'], 9: ['pathological lymphnode status after neoadjuvant treatment and surgery (0,1,2 - according to uicc tnm classification): 1', 'pathological lymphnode status after neoadjuvant treatment and surgery (0,1,2 - according to uicc tnm classification): 0', 'pathological lymphnode status after neoadjuvant treatment and surgery (0,1,2 - according to uicc tnm classification): 2']}\n",
"\n",
" # Convert the sample characteristics dictionary to a DataFrame\n",
" clinical_data = pd.DataFrame.from_dict(sample_chars_dict, orient='index')\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\n"
]
},
{
"cell_type": "markdown",
"id": "e4c22109",
"metadata": {},
"source": [
"### Step 3: Dataset Analysis and Clinical Feature Extraction"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "199b6cfa",
"metadata": {},
"outputs": [],
"source": [
"I'll analyze the dataset and extract the clinical features as requested:\n",
"\n",
"```python\n",
"import pandas as pd\n",
"import numpy as np\n",
"import os\n",
"import json\n",
"import gzip\n",
"import re\n",
"\n",
"# Let's first look at what files we have in the cohort directory to understand the dataset\n",
"print(f\"Files in {in_cohort_dir}:\")\n",
"for file in os.listdir(in_cohort_dir):\n",
" print(f\"- {file}\")\n",
"\n",
"# Function to extract sample characteristics from series matrix file\n",
"def extract_sample_characteristics(file_path):\n",
" sample_char_dict = {}\n",
" current_row = None\n",
" \n",
" with gzip.open(file_path, 'rt') as f:\n",
" lines = []\n",
" in_char_section = False\n",
" samples = []\n",
" \n",
" for line in f:\n",
" if line.startswith('!Sample_geo_accession'):\n",
" samples = line.strip().split('\\t')[1:]\n",
" \n",
" elif line.startswith('!Sample_characteristics_ch1'):\n",
" if not in_char_section:\n",
" in_char_section = True\n",
" \n",
" parts = line.strip().split('\\t')\n",
" char_value = parts[0].split('!Sample_characteristics_ch1')[1].strip()\n",
" if char_value: # If there's a label in the line\n",
" current_row = len(sample_char_dict)\n",
" sample_char_dict[current_row] = {'label': char_value, 'values': parts[1:]}\n",
" else: # If it's a continuation line\n",
" if current_row is not None:\n",
" sample_char_dict[current_row]['values'].extend(parts[1:])\n",
" \n",
" elif in_char_section and not line.startswith('!Sample_'):\n",
" in_char_section = False\n",
" \n",
" # Check for data type description\n",
" if line.startswith('!Series_summary'):\n",
" lines.append(line)\n",
" if line.startswith('!Series_title'):\n",
" lines.append(line)\n",
" if line.startswith('!Series_overall_design'):\n",
" lines.append(line)\n",
" \n",
" # Create DataFrame from the dictionary\n",
" df_columns = samples\n",
" df_index = list(range(len(sample_char_dict)))\n",
" df = pd.DataFrame(index=df_index, columns=df_columns)\n",
" \n",
" for row_idx, row_data in sample_char_dict.items():\n",
" for col_idx, value in enumerate(row_data['values']):\n",
" if col_idx < len(df_columns):\n",
" df.iloc[row_idx, col_idx] = value\n",
" \n",
" return df, lines\n",
"\n",
"# Load the matrix file to check if gene expression data is available\n",
"matrix_file = os.path.join(in_cohort_dir, \"GSE40492_series_matrix.txt.gz\")\n",
"is_gene_available = False\n",
"clinical_data = None\n",
"background_info = []\n",
"\n",
"if os.path.exists(matrix_file):\n",
" # Extract sample characteristics from the matrix file\n",
" clinical_data, background_info = extract_sample_characteristics(matrix_file)\n",
" \n",
" # Check for gene expression data by reading the first few lines of the file\n",
" with gzip.open(matrix_file, 'rt') as f:\n",
" # Skip header lines\n",
" for line in f:\n",
" if line.startswith('!series_matrix_table_begin'):\n",
" break\n",
" \n",
" # Read column headers (should be sample IDs)\n",
" header = next(f)\n",
" \n",
" # Check a few data rows to see if they contain gene expression data\n",
" for _ in range(5):\n",
" line = next(f)\n",
" # If the line contains gene IDs and numeric values, it's likely gene expression data\n",
" if re.match(r'^[A-Za-z0-9_-]+\\t[-+]?[0-9]*\\.?[0-9]+', line):\n",
" is_gene_available = True\n",
" break\n",
"\n",
"# Print background information\n",
"print(\"\\nBackground information:\")\n",
"for line in background_info:\n",
" print(line.strip())\n",
"\n",
"# Print sample characteristics if available\n",
"if clinical_data is not None:\n",
" print(\"\\nSample characteristics shape:\", clinical_data.shape)\n",
" print(\"\\nSample characteristics preview:\")\n",
" print(clinical_data.head(3))\n",
" \n",
" # Print unique values for each row to identify clinical features\n",
" for i in range(len(clinical_data)):\n",
" unique_values = clinical_data.iloc[i].unique()\n",
" print(f\"\\nRow {i}: {len(unique_values)} unique values:\")\n",
" print(unique_values[:10])\n",
"\n",
"# Based on the data exploration, determine clinical features\n",
"# For trait (Rectal Cancer - looking for response status)\n",
"trait_row = None\n",
"age_row = None\n",
"gender_row = None\n",
"\n",
"# Scan through the rows to find relevant clinical features\n",
"if clinical_data is not None:\n",
" for i in range(len(clinical_data)):\n",
" row_values = [str(val).lower() for val in clinical_data.iloc[i] if not pd.isna(val)]\n",
" row_text = ' '.join(row_values)\n",
" \n",
" # Check for response information (trait)\n",
" if any('responder' in val for val in row_values):\n",
" trait_row = i\n",
" \n",
" # Check for age information\n",
" if any('age' in val for val in row_values):\n",
" age_row = i\n",
" \n",
" # Check for gender/sex information\n",
" if any(gender_term in row_text for gender_term in ['gender', 'sex', 'male', 'female']):\n",
" gender_row = i\n",
"\n",
"print(f\"\\nIdentified rows - trait: {trait_row}, age: {age_row}, gender: {gender_row}\")\n",
"\n",
"# Define conversion functions for clinical features\n",
"def convert_trait(value):\n",
" if pd.isna(value):\n",
" return None\n",
" value = str(value).lower()\n",
" if ':' in value:\n",
" value = value.split(':', 1)[1].strip()\n",
" if 'responder' in value:\n",
" return 1 # Responder\n",
" elif 'non-responder' in value:\n",
" return 0 # Non-responder\n",
" else:\n",
" return None\n",
"\n",
"def convert_age(value):\n",
" if pd.isna(value):\n",
" return None\n",
" value = str(value).lower()\n",
" if ':' in value:\n",
" value = value.split(':', 1)[1].strip()\n",
" # Try to extract numeric age\n",
" match = re.search(r'(\\d+)', value)\n",
" if match:\n",
" return float(match.group(1))\n",
" return None\n",
"\n",
"def convert_gender(value):\n",
" if pd.isna(value):\n",
" return None\n",
" value = str(value).lower()\n",
" if ':' in value:\n",
" value = value.split(':', 1)[1].strip()\n",
" if any(female_term in value for female_term in ['female', 'f', 'woman']):\n",
" return 0\n",
" elif any(male_term in value for male_term in ['male', 'm', 'man']):\n",
" return 1\n",
" else:\n",
" return None\n",
"\n",
"# Validate and save cohort info\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",
"# Extract clinical features if trait data is available\n",
"if trait_row is not None and clinical_data is not None:\n",
" clinical_features = 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 clinical features\n",
" print(\"\\nExtracted clinical features preview:\")\n",
" preview = preview_df(clinical_features)\n",
" print(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 clinical features to CSV\n",
" clinical_features.to_csv(out_clinical_data_file)\n",
" print(f\"\\nClinical\n"
]
},
{
"cell_type": "markdown",
"id": "394abeb6",
"metadata": {},
"source": [
"### Step 4: Gene Data Extraction"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "59183ede",
"metadata": {},
"outputs": [],
"source": [
"# 1. First get the file paths\n",
"soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)\n",
"\n",
"# 2. Use the get_genetic_data function from the library to get the gene_data\n",
"gene_data = get_genetic_data(matrix_file)\n",
"\n",
"# 3. Print the first 20 row IDs (gene or probe identifiers) for future observation\n",
"print(gene_data.index[:20])\n"
]
},
{
"cell_type": "markdown",
"id": "faaf9393",
"metadata": {},
"source": [
"### Step 5: Gene Identifier Review"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "390e74e8",
"metadata": {},
"outputs": [],
"source": [
"# Examining the gene identifiers revealed numeric values ('12', '13', '14', etc.)\n",
"# These are clearly not standard human gene symbols (which would be alphanumeric like BRCA1, TP53, etc.)\n",
"# These appear to be probe or array identifiers that will need to be mapped to proper gene symbols\n",
"\n",
"requires_gene_mapping = True\n"
]
},
{
"cell_type": "markdown",
"id": "cf97a7be",
"metadata": {},
"source": [
"### Step 6: Gene Annotation"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "efc22ded",
"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": "762d35e2",
"metadata": {},
"source": [
"### Step 7: Gene Identifier Mapping"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "9ef11ef6",
"metadata": {},
"outputs": [],
"source": [
"# 1. Determine which columns in gene_annotation contain probe IDs and gene symbols\n",
"# Looking at the gene_annotation preview, the 'ID' column contains numeric identifiers similar to \n",
"# the gene_data index. For gene symbols, the 'GENE_SYMBOL' column appears to be the appropriate one.\n",
"prob_col = 'ID'\n",
"gene_col = 'GENE_SYMBOL'\n",
"\n",
"# 2. Get gene mapping dataframe with the two columns\n",
"mapping_df = get_gene_mapping(gene_annotation, prob_col, gene_col)\n",
"\n",
"# 3. Convert probe-level measurements to gene-level expression data\n",
"# The apply_gene_mapping function splits probe signals equally among mapped genes\n",
"# and then sums up contributions to each gene\n",
"gene_data = apply_gene_mapping(gene_data, mapping_df)\n",
"\n",
"# Let's check the first few rows of the mapped gene data to verify the transformation\n",
"print(\"Gene expression data after mapping to gene symbols:\")\n",
"print(gene_data.head())\n",
"print(f\"Shape of gene expression data: {gene_data.shape}\")\n"
]
},
{
"cell_type": "markdown",
"id": "a91e5ce9",
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"### Step 8: Data Normalization and Linking"
]
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"# First, we need to re-establish all the required variables and data from previous steps\n",
"\n",
"# 1. Get file paths\n",
"soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)\n",
"\n",
"# 2. Get background info and clinical data\n",
"background_info, clinical_data = get_background_and_clinical_data(matrix_file)\n",
"\n",
"# 3. Define the necessary row indices based on analysis of clinical data\n",
"# From the sample characteristics dictionary, we can determine:\n",
"# Trait: pathological lymph node status is at row 9\n",
"# Age: age information is at row 1\n",
"# Gender: sex information is at row 2\n",
"trait_row = 9\n",
"age_row = 1\n",
"gender_row = 2\n",
"\n",
"# 4. Define conversion functions for clinical features\n",
"def convert_trait(value_str):\n",
" \"\"\"Convert lymph node status to binary value.\n",
" 0 = No positive lymph nodes, 1 = Positive lymph nodes\"\"\"\n",
" if value_str is None or 'NA' in value_str:\n",
" return None\n",
" \n",
" # Extract the value after the colon\n",
" if ':' in value_str:\n",
" value = value_str.split(':', 1)[1].strip()\n",
" else:\n",
" value = value_str.strip()\n",
" \n",
" # Status 0 means no positive lymph nodes\n",
" if value == '0':\n",
" return 0\n",
" # Status 1 or 2 means positive lymph nodes\n",
" elif value in ['1', '2']:\n",
" return 1\n",
" else:\n",
" return None\n",
"\n",
"def convert_age(value_str):\n",
" \"\"\"Convert age to continuous value.\"\"\"\n",
" if value_str is None or 'NA' in value_str:\n",
" return None\n",
" \n",
" # Extract the value after the colon\n",
" if ':' in value_str:\n",
" value = value_str.split(':', 1)[1].strip()\n",
" else:\n",
" value = value_str.strip()\n",
" \n",
" try:\n",
" return float(value)\n",
" except ValueError:\n",
" return None\n",
"\n",
"def convert_gender(value_str):\n",
" \"\"\"Convert gender to binary value. 0 = female, 1 = male\"\"\"\n",
" if value_str is None or 'NA' in value_str:\n",
" return None\n",
" \n",
" # Extract the value after the colon\n",
" if ':' in value_str:\n",
" value = value_str.split(':', 1)[1].strip().lower()\n",
" else:\n",
" value = value_str.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",
"# Reload the gene expression data from the matrix file\n",
"gene_data = get_genetic_data(matrix_file)\n",
"\n",
"# Get gene annotation data\n",
"gene_annotation = get_gene_annotation(soft_file)\n",
"prob_col = 'ID'\n",
"gene_col = 'GENE_SYMBOL'\n",
"mapping_df = get_gene_mapping(gene_annotation, prob_col, gene_col)\n",
"\n",
"# Apply gene mapping to convert probe-level data to gene-level\n",
"gene_data = apply_gene_mapping(gene_data, mapping_df)\n",
"\n",
"# Now proceed with the data normalization and linking steps\n",
"\n",
"# 1. Extract 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,\n",
" gender_row=gender_row, \n",
" convert_gender=convert_gender\n",
")\n",
"\n",
"# Save the clinical features data\n",
"os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)\n",
"clinical_features.to_csv(out_clinical_data_file)\n",
"print(f\"Clinical data saved to {out_clinical_data_file}\")\n",
"\n",
"# 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(f\"First few normalized gene symbols: {list(normalized_gene_data.index[:10])}\")\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. Link the clinical and genetic data\n",
"linked_data = geo_link_clinical_genetic_data(clinical_features, normalized_gene_data)\n",
"print(f\"Linked data shape: {linked_data.shape}\")\n",
"print(linked_data.head())\n",
"\n",
"# 3. Handle missing values in the linked data\n",
"linked_data = handle_missing_values(linked_data, trait)\n",
"print(f\"Shape after handling missing values: {linked_data.shape}\")\n",
"\n",
"# 4. Determine whether the trait and demographic features are severely biased\n",
"is_trait_biased, unbiased_linked_data = judge_and_remove_biased_features(linked_data, trait)\n",
"\n",
"# 5. Conduct quality check and save the 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=f\"Dataset contains rectal cancer patients with clinical annotations including pathological lymph node status.\"\n",
")\n",
"\n",
"# 6. Save the data if it's 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",
" # Save the data\n",
" unbiased_linked_data.to_csv(out_data_file)\n",
" print(f\"Linked data saved to {out_data_file}\")\n",
"else:\n",
" print(f\"Data quality check failed. The dataset is not suitable for association studies.\")"
]
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