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{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"id": "dd6f1b25",
"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 = \"Sickle_Cell_Anemia\"\n",
"cohort = \"GSE84633\"\n",
"\n",
"# Input paths\n",
"in_trait_dir = \"../../input/GEO/Sickle_Cell_Anemia\"\n",
"in_cohort_dir = \"../../input/GEO/Sickle_Cell_Anemia/GSE84633\"\n",
"\n",
"# Output paths\n",
"out_data_file = \"../../output/preprocess/Sickle_Cell_Anemia/GSE84633.csv\"\n",
"out_gene_data_file = \"../../output/preprocess/Sickle_Cell_Anemia/gene_data/GSE84633.csv\"\n",
"out_clinical_data_file = \"../../output/preprocess/Sickle_Cell_Anemia/clinical_data/GSE84633.csv\"\n",
"json_path = \"../../output/preprocess/Sickle_Cell_Anemia/cohort_info.json\"\n"
]
},
{
"cell_type": "markdown",
"id": "be7f2d3d",
"metadata": {},
"source": [
"### Step 1: Initial Data Loading"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "2aa3e4fb",
"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": "7f8e1a4a",
"metadata": {},
"source": [
"### Step 2: Dataset Analysis and Clinical Feature Extraction"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "a998c0bd",
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"import os\n",
"import json\n",
"from typing import Callable, Dict, Any, Optional\n",
"\n",
"# 1. Assess gene expression data availability\n",
"is_gene_available = True # Dataset appears to be gene expression data from PBMCs\n",
"\n",
"# 2. Variable availability and conversion functions\n",
"# 2.1. Trait availability\n",
"trait_row = 2 # From the characteristics dictionary, disease info is at key 2\n",
"\n",
"# Define conversion functions\n",
"def convert_trait(value):\n",
" if value is None:\n",
" return None\n",
" \n",
" # Extract the value after the colon\n",
" if ':' in value:\n",
" value = value.split(':', 1)[1].strip()\n",
" \n",
" # Convert to binary (1 for sickle cell disease, 0 for control)\n",
" if 'sickle cell disease' in value.lower():\n",
" return 1\n",
" elif 'control' in value.lower() or 'healthy' in value.lower():\n",
" return 0\n",
" else:\n",
" return None\n",
"\n",
"# 2.2. Age availability\n",
"age_row = None # Age data not available in the sample characteristics\n",
"\n",
"def convert_age(value):\n",
" # This function won't be used since age data is not available\n",
" if value is None:\n",
" return None\n",
" \n",
" if ':' in value:\n",
" value = value.split(':', 1)[1].strip()\n",
" \n",
" try:\n",
" return float(value)\n",
" except:\n",
" return None\n",
"\n",
"# 2.3. Gender availability\n",
"gender_row = None # Gender data not available in the sample characteristics\n",
"\n",
"def convert_gender(value):\n",
" # This function won't be used since gender data is not available\n",
" if value is None:\n",
" return None\n",
" \n",
" if ':' in value:\n",
" value = value.split(':', 1)[1].strip()\n",
" \n",
" value = value.lower()\n",
" if 'female' in value or 'f' == value:\n",
" return 0\n",
" elif 'male' in value or 'm' == value:\n",
" return 1\n",
" else:\n",
" return None\n",
"\n",
"# 3. Save metadata with initial filtering\n",
"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 (if trait data is available)\n",
"if trait_row is not None:\n",
" try:\n",
" # Extract clinical features - assuming clinical_data variable exists from previous step\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 extracted clinical data\n",
" print(\"Preview of extracted clinical data:\")\n",
" print(preview_df(selected_clinical_df))\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 data to CSV\n",
" selected_clinical_df.to_csv(out_clinical_data_file, index=False)\n",
" print(f\"Clinical data saved to {out_clinical_data_file}\")\n",
" except Exception as e:\n",
" print(f\"Error extracting clinical features: {e}\")\n",
"else:\n",
" print(\"No trait data available, skipping clinical feature extraction.\")\n"
]
},
{
"cell_type": "markdown",
"id": "b443f474",
"metadata": {},
"source": [
"### Step 3: Gene Data Extraction"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "993157f8",
"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": "626dc9c5",
"metadata": {},
"source": [
"### Step 4: Gene Identifier Review"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "dfdba276",
"metadata": {},
"outputs": [],
"source": [
"# Examining the gene identifiers from the previous output\n",
"# These look like numeric identifiers (e.g., 2315554, 2315633), not standard human gene symbols\n",
"# Standard human gene symbols would be alphanumeric like BRCA1, TP53, etc.\n",
"# These numeric IDs are likely probe IDs that need mapping to gene symbols\n",
"\n",
"requires_gene_mapping = True\n"
]
},
{
"cell_type": "markdown",
"id": "08c99b99",
"metadata": {},
"source": [
"### Step 5: Gene Annotation"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "379f9a6f",
"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": "9ce4a1cd",
"metadata": {},
"source": [
"### Step 6: Gene Identifier Mapping"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "0d7bb96e",
"metadata": {},
"outputs": [],
"source": [
"# 1. Determine which columns in the gene annotation contain our probe IDs and gene symbols\n",
"# From the previous output, we can see:\n",
"# - 'ID' column in gene_annotation contains probe IDs like 2315100, matching what we saw in gene_data\n",
"# - 'gene_assignment' column contains gene symbol information\n",
"\n",
"# Define the columns for mapping\n",
"probe_id_column = 'ID'\n",
"gene_symbol_column = 'gene_assignment'\n",
"\n",
"# 2. Create a mapping dataframe\n",
"mapping_df = gene_annotation[[probe_id_column, gene_symbol_column]].copy()\n",
"mapping_df = mapping_df.dropna() # Remove rows with missing gene symbols\n",
"mapping_df = mapping_df.rename(columns={gene_symbol_column: 'Gene'}).astype({probe_id_column: 'str'})\n",
"\n",
"# First let's inspect some examples of the gene assignment strings\n",
"print(\"Example gene assignments:\")\n",
"for i in range(3):\n",
" if i < len(mapping_df) and isinstance(mapping_df.iloc[i]['Gene'], str):\n",
" print(f\"Example {i+1}: {mapping_df.iloc[i]['Gene'][:200]}...\")\n",
"\n",
"# Apply the extract_human_gene_symbols function to get gene symbols\n",
"mapping_df['Gene'] = mapping_df['Gene'].apply(extract_human_gene_symbols)\n",
"\n",
"# Remove rows with empty gene lists\n",
"mapping_df = mapping_df[mapping_df['Gene'].apply(len) > 0]\n",
"\n",
"# Preview the mapping dataframe after extraction\n",
"print(\"\\nGene mapping preview after extraction:\")\n",
"print(mapping_df.head(10))\n",
"print(f\"Total mappings with gene symbols: {len(mapping_df)}\")\n",
"\n",
"# 3. Apply gene mapping to convert probe measurements to gene expression\n",
"# Convert probe IDs in gene_data to strings to match the mapping\n",
"gene_data.index = gene_data.index.astype(str)\n",
"\n",
"# Check overlap between datasets\n",
"common_probes = set(gene_data.index) & set(mapping_df['ID'])\n",
"print(f\"\\nNumber of common probes between expression data and mapping: {len(common_probes)}\")\n",
"\n",
"# Apply the mapping to get gene expression values\n",
"gene_data_mapped = apply_gene_mapping(gene_data, mapping_df)\n",
"\n",
"# Preview the mapped gene expression data\n",
"print(\"\\nGene expression data after mapping:\")\n",
"print(f\"Shape: {gene_data_mapped.shape}\")\n",
"if not gene_data_mapped.empty:\n",
" print(f\"First few gene symbols: {gene_data_mapped.index[:5]}\")\n",
" print(f\"Number of genes: {len(gene_data_mapped)}\")\n",
"else:\n",
" print(\"Warning: No gene symbols were mapped successfully.\")\n",
" \n",
" # If first approach fails, try normalizing the gene symbols\n",
" print(\"\\nAttempting to normalize gene symbols...\")\n",
" if len(gene_data_mapped) > 0:\n",
" gene_data_mapped = normalize_gene_symbols_in_index(gene_data_mapped)\n",
" print(f\"After normalization - Number of genes: {len(gene_data_mapped)}\")\n",
"\n",
"# Save the gene expression data\n",
"os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)\n",
"gene_data_mapped.to_csv(out_gene_data_file)\n",
"print(f\"\\nGene expression data saved to {out_gene_data_file}\")\n"
]
},
{
"cell_type": "markdown",
"id": "f8954bd2",
"metadata": {},
"source": [
"### Step 7: Data Normalization and Linking"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "c7f33553",
"metadata": {},
"outputs": [],
"source": [
"# 1. Let's try a manual approach to normalize the gene data\n",
"# First, extract the gene_data which should be the original probe-level expression data\n",
"gene_data_index = gene_data.index.tolist()\n",
"print(f\"Original gene_data shape: {gene_data.shape}\")\n",
"print(f\"First 10 probes: {gene_data_index[:10]}\")\n",
"\n",
"# Check if original gene_data is available and well-formed\n",
"if gene_data.shape[0] == 0 or gene_data.shape[1] == 0:\n",
" print(\"WARNING: Original gene_data is empty. Using original matrix file to get gene data again.\")\n",
" # Try to reload gene data from matrix file\n",
" with gzip.open(matrix_file, 'rt') as file:\n",
" for line in file:\n",
" if '!series_matrix_table_begin' in line:\n",
" break\n",
" # Skip the header line\n",
" next(file)\n",
" # Read the data\n",
" import pandas as pd\n",
" gene_data = pd.read_csv(file, sep='\\t', index_col=0)\n",
" print(f\"Reloaded gene_data shape: {gene_data.shape}\")\n",
"\n",
"# Count the mappable genes\n",
"mapping_count = mapping_df.groupby('Gene').size().sort_values(ascending=False)\n",
"print(f\"Top 10 mapped gene symbols: {mapping_count.head(10)}\")\n",
"\n",
"# Try another approach for mapping: keep the original probe IDs if mapping fails\n",
"# This means we'll use probe IDs as substitutes for gene symbols\n",
"print(\"\\nAttempting to create linked data using probe IDs...\")\n",
"\n",
"# 2. Link clinical and expression data\n",
"clinical_features = pd.read_csv(out_clinical_data_file)\n",
"clinical_features = clinical_features.set_index(clinical_features.columns[0]) # Set first column as index\n",
"\n",
"# Transpose gene_data for linking (so samples are rows)\n",
"gene_data_t = gene_data.T\n",
"print(f\"Transposed gene_data shape: {gene_data_t.shape}\")\n",
"\n",
"# Link the data\n",
"linked_data = pd.concat([clinical_features.T, gene_data_t], axis=1)\n",
"print(f\"Linked data shape with probe IDs: {linked_data.shape}\")\n",
"\n",
"# Check for missing values\n",
"print(\"\\nMissing values summary:\")\n",
"trait_missing = linked_data[trait].isna().sum()\n",
"print(f\" Trait ({trait}) missing: {trait_missing} out of {len(linked_data)}\")\n",
"\n",
"# Handle missing values\n",
"if linked_data.shape[0] > 0:\n",
" # Get gene columns (all columns except trait, Age, Gender)\n",
" covariate_cols = [trait]\n",
" if 'Age' in linked_data.columns:\n",
" covariate_cols.append('Age')\n",
" if 'Gender' in linked_data.columns:\n",
" covariate_cols.append('Gender')\n",
" \n",
" gene_cols = [col for col in linked_data.columns if col not in covariate_cols]\n",
" \n",
" # Print missing value statistics before cleaning\n",
" gene_missing_pct = linked_data[gene_cols].isna().mean()\n",
" print(f\" Genes with >20% missing: {sum(gene_missing_pct > 0.2)} out of {len(gene_cols)}\")\n",
" \n",
" sample_missing_pct = linked_data[gene_cols].isna().mean(axis=1)\n",
" print(f\" Samples with >5% missing genes: {sum(sample_missing_pct > 0.05)} out of {len(linked_data)}\")\n",
" \n",
" # Apply missing value handling\n",
" cleaned_data = handle_missing_values(linked_data, trait)\n",
" print(f\"Data shape after handling missing values: {cleaned_data.shape}\")\n",
" \n",
" # Evaluate bias in trait and demographic features\n",
" if len(cleaned_data) > 0:\n",
" is_trait_biased, cleaned_data = judge_and_remove_biased_features(cleaned_data, trait)\n",
" \n",
" # 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=f\"Dataset contains only {trait} patients with no controls. Used probe IDs instead of gene symbols.\"\n",
" )\n",
" \n",
" # 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 biased or empty and was not saved\")\n",
" else:\n",
" print(\"No data remains after handling missing values.\")\n",
" # Record 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=True, \n",
" is_biased=True, \n",
" df=pd.DataFrame(),\n",
" note=f\"Dataset produced empty dataframe after handling missing values.\"\n",
" )\n",
"else:\n",
" print(\"Linked data is empty.\")\n",
" # Record cohort info\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, \n",
" df=pd.DataFrame(),\n",
" note=f\"Failed to create valid linked data with gene expression values.\"\n",
" )\n"
]
},
{
"cell_type": "markdown",
"id": "4b7e5dd9",
"metadata": {},
"source": [
"### Step 8: Data Normalization and Linking"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "8d27acd0",
"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=f\"Dataset contains gene expression data but lacks clear trait indicators for {trait} 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=f\"Dataset contains only {trait} patients with no healthy controls, making it unsuitable for case-control analysis.\"\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 for {trait}: {str(e)}\"\n",
" )\n",
" print(\"Data was determined to be unusable and was not saved\")"
]
}
],
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|