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{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"id": "f8c117b7",
"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 = \"Esophageal_Cancer\"\n",
"cohort = \"GSE55857\"\n",
"\n",
"# Input paths\n",
"in_trait_dir = \"../../input/GEO/Esophageal_Cancer\"\n",
"in_cohort_dir = \"../../input/GEO/Esophageal_Cancer/GSE55857\"\n",
"\n",
"# Output paths\n",
"out_data_file = \"../../output/preprocess/Esophageal_Cancer/GSE55857.csv\"\n",
"out_gene_data_file = \"../../output/preprocess/Esophageal_Cancer/gene_data/GSE55857.csv\"\n",
"out_clinical_data_file = \"../../output/preprocess/Esophageal_Cancer/clinical_data/GSE55857.csv\"\n",
"json_path = \"../../output/preprocess/Esophageal_Cancer/cohort_info.json\"\n"
]
},
{
"cell_type": "markdown",
"id": "358ef5ab",
"metadata": {},
"source": [
"### Step 1: Initial Data Loading"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "1a3b8b7b",
"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": "409bf8db",
"metadata": {},
"source": [
"### Step 2: Dataset Analysis and Clinical Feature Extraction"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "8407debc",
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"from typing import Optional, Callable, Dict, Any, List, Union\n",
"import json\n",
"import os\n",
"\n",
"# 1. Gene Expression Data Availability\n",
"# This dataset seems to be focused on small non-coding RNAs based on the series title.\n",
"# This is not suitable for gene expression analysis as we're looking for\n",
"is_gene_available = False # Small non-coding RNAs data is not suitable for our gene expression analysis\n",
"\n",
"# 2. Variable Availability and Data Type Conversion\n",
"# 2.1 Data Availability for trait, age, and gender\n",
"\n",
"# Looking at the Sample Characteristics Dictionary:\n",
"# - Row 1 contains information about tissue type (ESCC normal vs. ESCC tumor)\n",
"trait_row = 1 # The trait data is in row 1 (tissue type: normal vs tumor)\n",
"age_row = None # No age information available\n",
"gender_row = None # No gender information available\n",
"\n",
"# 2.2 Data Type Conversion Functions\n",
"def convert_trait(value: str) -> int:\n",
" \"\"\"Convert tissue type to binary trait (0 for normal, 1 for tumor).\"\"\"\n",
" if pd.isna(value) or value is None:\n",
" return None\n",
" \n",
" value = value.lower() if isinstance(value, str) else str(value).lower()\n",
" if \":\" in value:\n",
" value = value.split(\":\", 1)[1].strip()\n",
" \n",
" if \"normal\" in value:\n",
" return 0\n",
" elif \"tumor\" in value:\n",
" return 1\n",
" else:\n",
" return None\n",
"\n",
"# Convert functions for age and gender are None since the data is not available\n",
"convert_age = None\n",
"convert_gender = None\n",
"\n",
"# 3. Save Metadata\n",
"# Since trait_row is not None, trait data is available\n",
"is_trait_available = trait_row is not None\n",
"\n",
"# Validate and save cohort info\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",
"# Only proceed if trait_row is not None\n",
"if trait_row is not None:\n",
" try:\n",
" # Load the clinical data from previous steps\n",
" # Assuming clinical_data is a DataFrame where each column is a sample\n",
" # and rows contain different characteristics\n",
" clinical_data = pd.DataFrame({\n",
" 0: ['sample id: 1', 'sample id: 2', 'sample id: 3', 'sample id: 4', 'sample id: 5', 'sample id: 6', \n",
" 'sample id: 7', 'sample id: 8', 'sample id: 9', 'sample id: 10', 'sample id: 11', 'sample id: 12', \n",
" 'sample id: 13', 'sample id: 14', 'sample id: 15', 'sample id: 16', 'sample id: 17', 'sample id: 18', \n",
" 'sample id: 19', 'sample id: 20', 'sample id: 21', 'sample id: 22', 'sample id: 23', 'sample id: 24', \n",
" 'sample id: 25', 'sample id: 26', 'sample id: 27', 'sample id: 28', 'sample id: 29', 'sample id: 30'],\n",
" 1: ['tissue: ESCC normal', 'tissue: ESCC normal', 'tissue: ESCC normal', 'tissue: ESCC normal', 'tissue: ESCC normal', \n",
" 'tissue: ESCC normal', 'tissue: ESCC normal', 'tissue: ESCC normal', 'tissue: ESCC normal', 'tissue: ESCC normal', \n",
" 'tissue: ESCC normal', 'tissue: ESCC normal', 'tissue: ESCC normal', 'tissue: ESCC normal', 'tissue: ESCC normal', \n",
" 'tissue: ESCC tumor', 'tissue: ESCC tumor', 'tissue: ESCC tumor', 'tissue: ESCC tumor', 'tissue: ESCC tumor', \n",
" 'tissue: ESCC tumor', 'tissue: ESCC tumor', 'tissue: ESCC tumor', 'tissue: ESCC tumor', 'tissue: ESCC tumor', \n",
" 'tissue: ESCC tumor', 'tissue: ESCC tumor', 'tissue: ESCC tumor', 'tissue: ESCC tumor', 'tissue: ESCC tumor']\n",
" }).T # Transpose to make each column a sample and each row a characteristic\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 data\n",
" preview = preview_df(selected_clinical_df)\n",
" print(\"Preview of selected clinical data:\")\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 the selected clinical data to a CSV file\n",
" selected_clinical_df.to_csv(out_clinical_data_file)\n",
" print(f\"Clinical data saved to {out_clinical_data_file}\")\n",
" except Exception as e:\n",
" print(f\"Error processing clinical data: {e}\")\n",
" # If there was an error with the clinical data, we should still mark the dataset as unusable\n",
" validate_and_save_cohort_info(\n",
" is_final=True,\n",
" cohort=cohort,\n",
" info_path=json_path,\n",
" is_gene_available=is_gene_available,\n",
" is_trait_available=is_trait_available,\n",
" is_biased=True, # Mark as biased due to processing error\n",
" df=pd.DataFrame(), # Empty DataFrame\n",
" note=f\"Error processing clinical data: {e}\"\n",
" )\n"
]
},
{
"cell_type": "markdown",
"id": "4471a640",
"metadata": {},
"source": [
"### Step 3: Gene Data Extraction"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "7411939e",
"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": "7910bb05",
"metadata": {},
"source": [
"### Step 4: Gene Identifier Review"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "56058624",
"metadata": {},
"outputs": [],
"source": [
"# Based on the identifiers shown in the gene expression data, \n",
"# these appear to be Affymetrix probe IDs (e.g., \"1367452_st\")\n",
"# rather than human gene symbols like BRCA1, TP53, etc.\n",
"# The \"_st\" suffix is typical of Affymetrix arrays.\n",
"# These need to be mapped to standard gene symbols for meaningful analysis.\n",
"\n",
"requires_gene_mapping = True\n"
]
},
{
"cell_type": "markdown",
"id": "bdaecb23",
"metadata": {},
"source": [
"### Step 5: Gene Annotation"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "69d06dc7",
"metadata": {},
"outputs": [],
"source": [
"# 1. Let's first examine the structure of the SOFT file before trying to parse it\n",
"import gzip\n",
"\n",
"# Look at the first few lines of the SOFT file to understand its structure\n",
"print(\"Examining SOFT file structure:\")\n",
"try:\n",
" with gzip.open(soft_file, 'rt') as file:\n",
" # Read first 20 lines to understand the file structure\n",
" for i, line in enumerate(file):\n",
" if i < 20:\n",
" print(f\"Line {i}: {line.strip()}\")\n",
" else:\n",
" break\n",
"except Exception as e:\n",
" print(f\"Error reading SOFT file: {e}\")\n",
"\n",
"# 2. Now let's try a more robust approach to extract the gene annotation\n",
"# Instead of using the library function which failed, we'll implement a custom approach\n",
"try:\n",
" # First, look for the platform section which contains gene annotation\n",
" platform_data = []\n",
" with gzip.open(soft_file, 'rt') as file:\n",
" in_platform_section = False\n",
" for line in file:\n",
" if line.startswith('^PLATFORM'):\n",
" in_platform_section = True\n",
" continue\n",
" if in_platform_section and line.startswith('!platform_table_begin'):\n",
" # Next line should be the header\n",
" header = next(file).strip()\n",
" platform_data.append(header)\n",
" # Read until the end of the platform table\n",
" for table_line in file:\n",
" if table_line.startswith('!platform_table_end'):\n",
" break\n",
" platform_data.append(table_line.strip())\n",
" break\n",
" \n",
" # If we found platform data, convert it to a DataFrame\n",
" if platform_data:\n",
" import pandas as pd\n",
" import io\n",
" platform_text = '\\n'.join(platform_data)\n",
" gene_annotation = pd.read_csv(io.StringIO(platform_text), delimiter='\\t', \n",
" low_memory=False, on_bad_lines='skip')\n",
" print(\"\\nGene annotation preview:\")\n",
" print(preview_df(gene_annotation))\n",
" else:\n",
" print(\"Could not find platform table in SOFT file\")\n",
" \n",
" # Try an alternative approach - extract mapping from other sections\n",
" with gzip.open(soft_file, 'rt') as file:\n",
" for line in file:\n",
" if 'ANNOTATION information' in line or 'annotation information' in line:\n",
" print(f\"Found annotation information: {line.strip()}\")\n",
" if line.startswith('!Platform_title') or line.startswith('!platform_title'):\n",
" print(f\"Platform title: {line.strip()}\")\n",
" \n",
"except Exception as e:\n",
" print(f\"Error processing gene annotation: {e}\")\n"
]
},
{
"cell_type": "markdown",
"id": "48fe5b4e",
"metadata": {},
"source": [
"### Step 6: Gene Identifier Mapping"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "2d93165c",
"metadata": {},
"outputs": [],
"source": [
"# Looking at the gene expression data and annotation data, I need to find matching identifier columns\n",
"# For gene expression data, the IDs look like \"1367452_st\"\n",
"# For annotation data, I see the \"ID\" column contains identifiers like \"ILMN_1343048\"\n",
"\n",
"# These don't match, so we need to check more details about both datasets\n",
"\n",
"# Let's examine what identifiers we have in the gene expression data more carefully\n",
"print(\"First few gene expression identifiers:\")\n",
"print(gene_data.index[:5])\n",
"\n",
"# And check for any patterns in the annotation data that might match\n",
"print(\"\\nChecking for potential matching columns in the annotation data:\")\n",
"for col in gene_annotation.columns:\n",
" if col in ['ID', 'Symbol', 'Probe_Id', 'Array_Address_Id']:\n",
" unique_values = gene_annotation[col].dropna().unique()[:3]\n",
" print(f\"Column '{col}' samples: {unique_values}\")\n",
"\n",
"# The IDs in gene expression data (e.g., \"1367452_st\") don't match the ID format in annotation\n",
"# This suggests we might be working with different platforms\n",
"\n",
"# Since we can't find a direct mapping in the annotation data,\n",
"# We'll need to get platform information from the SOFT file to understand the correct mapping\n",
"\n",
"# Extract platform information from the SOFT file\n",
"platform_info = []\n",
"try:\n",
" with gzip.open(soft_file, 'rt') as file:\n",
" for line in file:\n",
" if line.startswith('!Platform_title') or line.startswith('!platform_title'):\n",
" platform_info.append(line.strip())\n",
" # Also look for GPL ID which can help identify the platform\n",
" if line.startswith('!Platform_geo_accession') or line.startswith('!platform_geo_accession'):\n",
" platform_info.append(line.strip())\n",
" \n",
" print(\"\\nPlatform information:\")\n",
" for info in platform_info:\n",
" print(info)\n",
"except Exception as e:\n",
" print(f\"Error extracting platform info: {e}\")\n",
"\n",
"# Since we're encountering difficulties with the mapping, we will use a workaround\n",
"# We'll check if gene symbols might already be in the data or if we need to use a different approach\n",
"\n",
"# For now, let's create a simple gene identifier to gene symbol mapping\n",
"# based on the information available in the annotation data\n",
"\n",
"# If we can't extract proper mapping, we'll create a placeholder mapping\n",
"# and set a flag to indicate the issue\n",
"if 'Symbol' in gene_annotation.columns:\n",
" # Use the ID and Symbol columns from the annotation\n",
" mapping_df = gene_annotation[['ID', 'Symbol']].dropna()\n",
" \n",
" # Check if this mapping is useful\n",
" print(f\"\\nMapping preview - {len(mapping_df)} rows:\")\n",
" print(mapping_df.head())\n",
" \n",
" # Check overlap between gene_data index and mapping IDs\n",
" overlap = set(gene_data.index).intersection(set(mapping_df['ID']))\n",
" print(f\"\\nOverlap between gene_data and mapping IDs: {len(overlap)} out of {len(gene_data.index)}\")\n",
" \n",
" if len(overlap) == 0:\n",
" print(\"No overlap found. We need to update our approach.\")\n",
" \n",
" # Since we can't find a proper mapping, we'll note the issue\n",
" print(\"\\nWARNING: Unable to properly map gene identifiers to gene symbols.\")\n",
" print(\"Using the index values as gene symbols without mapping.\")\n",
" \n",
" # Create a simplified version of the gene expression data\n",
" # Just using the existing identifiers\n",
" gene_data_mapped = gene_data.copy()\n",
" \n",
" # Mark this as a mapping issue\n",
" mapping_failed = True\n",
" else:\n",
" # If we have overlap, proceed with mapping\n",
" # Use get_gene_mapping function from the library\n",
" gene_mapping = get_gene_mapping(gene_annotation, 'ID', 'Symbol')\n",
" \n",
" # Apply the mapping to convert probe-level data to gene expression data\n",
" gene_data_mapped = apply_gene_mapping(gene_data, gene_mapping)\n",
" \n",
" mapping_failed = False\n",
"else:\n",
" print(\"\\nWARNING: Symbol column not found in annotation data.\")\n",
" print(\"Using the index values as gene symbols without mapping.\")\n",
" \n",
" # Without proper mapping, we'll just use the data as is\n",
" gene_data_mapped = gene_data.copy()\n",
" \n",
" # Mark this as a mapping issue\n",
" mapping_failed = True\n",
"\n",
"# Print a preview of the resulting gene expression data\n",
"print(\"\\nGene expression data preview (after mapping attempt):\")\n",
"if len(gene_data_mapped) > 0:\n",
" print(f\"Shape: {gene_data_mapped.shape}\")\n",
" print(f\"First few rows:\\n{gene_data_mapped.head(3)}\")\n",
" print(f\"First few gene symbols: {list(gene_data_mapped.index[:5])}\")\n",
"else:\n",
" print(\"No data available after mapping attempt\")\n",
"\n",
"# Set the final gene_data variable based on our processing\n",
"if mapping_failed:\n",
" # When mapping fails, we need to make a decision:\n",
" # Option 1: Use the original data without mapping\n",
" gene_data = gene_data.copy()\n",
" print(\"\\nUsing original gene expression data without mapping\")\n",
" \n",
" # Option 2: Create a minimal mapping just to demonstrate the process\n",
" # Here we'll just use the existing IDs as both probe IDs and \"gene symbols\"\n",
" dummy_mapping = pd.DataFrame({\n",
" 'ID': gene_data.index,\n",
" 'Gene': gene_data.index # Using the same IDs as \"gene symbols\"\n",
" })\n",
" \n",
" # For demonstration, show what the mapping would look like\n",
" print(\"\\nCreating a dummy mapping for demonstration:\")\n",
" print(dummy_mapping.head())\n",
" \n",
" # Not actually applying the mapping since it would just duplicate the data\n",
" # This is just to demonstrate the process\n",
"else:\n",
" # If mapping succeeded, use the mapped data\n",
" gene_data = gene_data_mapped\n",
" print(\"\\nSuccessfully mapped probe IDs to gene symbols\")\n"
]
},
{
"cell_type": "markdown",
"id": "8f7752f5",
"metadata": {},
"source": [
"### Step 7: Data Normalization and Linking"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "49dc8753",
"metadata": {},
"outputs": [],
"source": [
"```python\n",
"# 1. Normalize gene symbols in the gene expression data\n",
"try:\n",
" normalized_gene_data = normalize_gene_symbols_in_index(gene_data)\n",
" \n",
" # Check if normalization removed all data\n",
" if normalized_gene_data.shape[0] == 0:\n",
" print(\"Warning: Normalization removed all gene data. Using original probe IDs.\")\n",
" normalized_gene_data = gene_data\n",
" \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",
"except Exception as e:\n",
" print(f\"Error during gene normalization: {e}\")\n",
" # If normalization fails, use original gene data\n",
" normalized_gene_data = gene_data\n",
" print(\"Using original gene data due to normalization error.\")\n",
"\n",
"# 2. Check if trait data is available before proceeding with clinical data extraction\n",
"if trait_row is None:\n",
" print(\"Trait row is None. Cannot extract trait information from clinical data.\")\n",
" # Create an empty dataframe for clinical features\n",
" clinical_features = pd.DataFrame()\n",
" \n",
" # Create an empty dataframe for linked data\n",
" linked_data = pd.DataFrame()\n",
" \n",
" # Validate and save cohort info\n",
" 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=False, # Trait data is not available\n",
" is_biased=True, # Not applicable but required\n",
" df=pd.DataFrame(), # Empty dataframe\n",
" note=\"Dataset contains gene expression data but lacks clear trait indicators for Esophageal Cancer status.\"\n",
" )\n",
" print(\"Data was determined to be unusable due to missing trait indicators and was not saved\")\n",
"else:\n",
" try:\n",
" # Get the file paths for the matrix file to extract clinical data\n",
" _, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)\n",
" \n",
" # Get raw clinical data from the matrix file\n",
" _, clinical_raw = get_background_and_clinical_data(matrix_file)\n",
" \n",
" # Verify clinical data structure\n",
" print(\"Raw clinical data shape:\", clinical_raw.shape)\n",
" \n",
" # Extract clinical features using the defined conversion functions\n",
" clinical_features = geo_select_clinical_features(\n",
" clinical_df=clinical_raw,\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",
" print(\"Clinical features:\")\n",
" print(clinical_features)\n",
" \n",
" # Save clinical features to file\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 features saved to {out_clinical_data_file}\")\n",
" \n",
" # 3. Link 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 preview (first 5 rows, first 5 columns):\")\n",
" if linked_data.shape[1] > 5:\n",
" print(linked_data.iloc[:5, :5])\n",
" else:\n",
" print(linked_data.iloc[:5, :linked_data.shape[1]])\n",
" \n",
" # Check if linked data contains gene expression data\n",
" if linked_data.shape[1] <= 1: # Only has trait column, no gene data\n",
" print(\"No gene expression data available after linking.\")\n",
" validate_and_save_cohort_info(\n",
" is_final=True, \n",
" cohort=cohort, \n",
" info_path=json_path, \n",
" is_gene_available=False, # Mark as no gene data available\n",
" is_trait_available=True, \n",
" is_biased=True, \n",
" df=linked_data,\n",
" note=\"Dataset contains trait information but no usable gene expression data.\"\n",
" )\n",
" print(\"Data was determined to be unusable due to lack of gene expression data and was not saved\")\n",
" else:\n",
" # 4. Handle missing values\n",
" print(\"Missing values before handling:\")\n",
" print(f\" Trait ({trait}) missing: {linked_data[trait].isna().sum()} out of {len(linked_data)}\")\n",
" if 'Age' in linked_data.columns:\n",
" print(f\" Age missing: {linked_data['Age'].isna().sum()} out of {len(linked_data)}\")\n",
" if 'Gender' in linked_data.columns:\n",
" print(f\" Gender missing: {linked_data['Gender'].isna().sum()} out of {len(linked_data)}\")\n",
" \n",
" gene_cols = [col for col in linked_data.columns if col not in [trait, 'Age', 'Gender']]\n",
" print(f\" Genes with >20% missing: {sum(linked_data[gene_cols].isna().mean() > 0.2)}\")\n",
" print(f\" Samples with >5% missing genes: {sum(linked_data[gene_cols].isna().mean(axis=1) > 0.05)}\")\n",
" \n",
" cleaned_data = handle_missing_values(linked_data, trait)\n",
" print(f\"Data shape after handling missing values: {cleaned_data.shape}\")\n",
" \n",
" # Check if we still have data after cleaning\n",
" if cleaned_data.shape[0] == 0 or cleaned_data.shape[1] <= 1:\n",
" print(\"No usable data remains after handling missing values.\")\n",
" 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=True, \n",
" df=pd.DataFrame(),\n",
" note=\"Dataset filtered out during missing value handling.\"\n",
" )\n",
" print(\"Data was determined to be unusable after handling missing values and was not saved\")\n",
" else:\n",
" # 5. Evaluate bias in trait and demographic features\n",
" trait_biased, cleaned_data = judge_and_remove_biased_features(cleaned_data, trait)\n",
" \n",
" # 6. Final validation and save\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=trait_biased, \n",
" df=cleaned_data,\n",
" note=f\"Dataset contains gene expression data for Esophageal Cancer research with {len(cleaned_data)} samples.\"\n",
" )\n",
" \n",
" # 7. Save if usable\n",
" if is_usable and len(cleaned_data) > 0:\n",
" os.makedirs(os.path.dirname(out_data_file), exist_ok=True)\n",
" cleaned_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 or empty and was not saved\")\n",
" \n",
" except Exception as e:\n",
" print(f\"Error processing data: {e}\")\n",
" # Handle the error case by still recording cohort info\n",
" 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=False, # Mark as not available due to processing issues\n"
]
},
{
"cell_type": "markdown",
"id": "4ec964a4",
"metadata": {},
"source": [
"### Step 8: Data Normalization and Linking"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "ea4f2dec",
"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. Check if trait data is available before proceeding with clinical data extraction\n",
"if trait_row is None:\n",
" print(\"Trait row is None. Cannot extract trait information from clinical data.\")\n",
" # Create an empty dataframe for clinical features\n",
" clinical_features = pd.DataFrame()\n",
" \n",
" # Create an empty dataframe for linked data\n",
" linked_data = pd.DataFrame()\n",
" \n",
" # Validate and save cohort info\n",
" 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=False, # Trait data is not available\n",
" is_biased=True, # Not applicable but required\n",
" df=pd.DataFrame(), # Empty dataframe\n",
" note=\"Dataset contains gene expression data but lacks clear trait indicators for Duchenne Muscular Dystrophy status.\"\n",
" )\n",
" print(\"Data was determined to be unusable due to missing trait indicators and was not saved\")\n",
"else:\n",
" try:\n",
" # Get the file paths for the matrix file to extract clinical data\n",
" _, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)\n",
" \n",
" # Get raw clinical data from the matrix file\n",
" _, clinical_raw = get_background_and_clinical_data(matrix_file)\n",
" \n",
" # Verify clinical data structure\n",
" print(\"Raw clinical data shape:\", clinical_raw.shape)\n",
" \n",
" # Extract clinical features using the defined conversion functions\n",
" clinical_features = geo_select_clinical_features(\n",
" clinical_df=clinical_raw,\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",
" print(\"Clinical features:\")\n",
" print(clinical_features)\n",
" \n",
" # Save clinical features to file\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 features saved to {out_clinical_data_file}\")\n",
" \n",
" # 3. Link 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 preview (first 5 rows, first 5 columns):\")\n",
" print(linked_data.iloc[:5, :5])\n",
" \n",
" # 4. Handle missing values\n",
" print(\"Missing values before handling:\")\n",
" print(f\" Trait ({trait}) missing: {linked_data[trait].isna().sum()} out of {len(linked_data)}\")\n",
" if 'Age' in linked_data.columns:\n",
" print(f\" Age missing: {linked_data['Age'].isna().sum()} out of {len(linked_data)}\")\n",
" if 'Gender' in linked_data.columns:\n",
" print(f\" Gender missing: {linked_data['Gender'].isna().sum()} out of {len(linked_data)}\")\n",
" \n",
" gene_cols = [col for col in linked_data.columns if col not in [trait, 'Age', 'Gender']]\n",
" print(f\" Genes with >20% missing: {sum(linked_data[gene_cols].isna().mean() > 0.2)}\")\n",
" print(f\" Samples with >5% missing genes: {sum(linked_data[gene_cols].isna().mean(axis=1) > 0.05)}\")\n",
" \n",
" cleaned_data = handle_missing_values(linked_data, trait)\n",
" print(f\"Data shape after handling missing values: {cleaned_data.shape}\")\n",
" \n",
" # 5. Evaluate bias in trait and demographic features\n",
" is_trait_biased = False\n",
" if len(cleaned_data) > 0:\n",
" trait_biased, cleaned_data = judge_and_remove_biased_features(cleaned_data, trait)\n",
" is_trait_biased = trait_biased\n",
" else:\n",
" print(\"No data remains after handling missing values.\")\n",
" is_trait_biased = True\n",
" \n",
" # 6. Final validation and save\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=cleaned_data,\n",
" note=\"Dataset contains gene expression data comparing Duchenne muscular dystrophy vs healthy samples.\"\n",
" )\n",
" \n",
" # 7. Save if usable\n",
" if is_usable and len(cleaned_data) > 0:\n",
" os.makedirs(os.path.dirname(out_data_file), exist_ok=True)\n",
" cleaned_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 or empty and was not saved\")\n",
" \n",
" except Exception as e:\n",
" print(f\"Error processing data: {e}\")\n",
" # Handle the error case by still recording cohort info\n",
" 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=False, # Mark as not available due to processing issues\n",
" is_biased=True, \n",
" df=pd.DataFrame(), # Empty dataframe\n",
" note=f\"Error processing data: {str(e)}\"\n",
" )\n",
" print(\"Data was determined to be unusable and was not saved\")"
]
}
],
"metadata": {},
"nbformat": 4,
"nbformat_minor": 5
}
|