{ "cells": [ { "cell_type": "code", "execution_count": null, "id": "b7d0cb3d", "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 = \"Endometrioid_Cancer\"\n", "cohort = \"GSE40785\"\n", "\n", "# Input paths\n", "in_trait_dir = \"../../input/GEO/Endometrioid_Cancer\"\n", "in_cohort_dir = \"../../input/GEO/Endometrioid_Cancer/GSE40785\"\n", "\n", "# Output paths\n", "out_data_file = \"../../output/preprocess/Endometrioid_Cancer/GSE40785.csv\"\n", "out_gene_data_file = \"../../output/preprocess/Endometrioid_Cancer/gene_data/GSE40785.csv\"\n", "out_clinical_data_file = \"../../output/preprocess/Endometrioid_Cancer/clinical_data/GSE40785.csv\"\n", "json_path = \"../../output/preprocess/Endometrioid_Cancer/cohort_info.json\"\n" ] }, { "cell_type": "markdown", "id": "9eae4e0a", "metadata": {}, "source": [ "### Step 1: Initial Data Loading" ] }, { "cell_type": "code", "execution_count": null, "id": "9e708dab", "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": "fbd437df", "metadata": {}, "source": [ "### Step 2: Dataset Analysis and Clinical Feature Extraction" ] }, { "cell_type": "code", "execution_count": null, "id": "c0aebfa3", "metadata": {}, "outputs": [], "source": [ "# Define data availability flags\n", "is_gene_available = True # Dataset likely contains gene expression data based on background info\n", "\n", "# Define which rows in sample characteristics contain our features of interest\n", "trait_row = 1 # The histology information \n", "age_row = None # Age data is not available in sample characteristics\n", "gender_row = None # Gender data is not available in sample characteristics\n", "\n", "# Define conversion functions for each variable\n", "def convert_trait(value):\n", " \"\"\"\n", " Convert histology data to a binary indicating Endometrioid_Cancer (1) or not (0).\n", " \"\"\"\n", " if pd.isna(value):\n", " return None\n", " \n", " # Extract the value after the colon if present\n", " if \":\" in value:\n", " value = value.split(\":\", 1)[1].strip()\n", " \n", " # Convert to binary based on histology\n", " if \"Endometrioid\" in value:\n", " return 1 # Presence of Endometrioid cancer\n", " else:\n", " return 0 # Other histology types\n", " \n", "# Since age and gender are not available, we define placeholder functions\n", "def convert_age(value):\n", " return None\n", "\n", "def convert_gender(value):\n", " return None\n", "\n", "# Save metadata for initial filtering\n", "is_trait_available = trait_row is not None\n", "validate_and_save_cohort_info(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", "# If trait data is available, extract clinical features using the sample characteristics dict\n", "if trait_row is not None:\n", " # Use the sample characteristics dictionary from the previous step output\n", " sample_char_dict = {0: ['sample origin: Primary', 'sample origin: Xenograft', 'sample origin: Ascites', \n", " 'histology: Adenocarcinoma NOS', 'sample origin: ascites', 'sample origin: primary'], \n", " 1: ['histology: Mucinous', 'histology: Clear cell', 'histology: Papillary serous', \n", " 'histology: Endometrioid', 'histology: Mullerian NOS', \n", " 'histology: Mixed Endometrioid and Pap. serous', 'histology: Dysgerminoma', \n", " 'histology: Carcinosarcoma', 'medium: RPMI', 'medium: OCMI', 'histology: Adenocarcinoma NOS'], \n", " 2: ['sample type: fresh', 'sample type: frozen', 'medium: OCMI', None, 'medium: DMEM/F12', \n", " \"medium: McCoy's 5A\", 'medium: MCDB105/M199', \"medium: Ham's F12\"], \n", " 3: ['medium: OCMI', None]}\n", " \n", " # Create a mock clinical DataFrame with histology information\n", " # Assume each unique value represents one sample\n", " samples = []\n", " trait_values = []\n", " \n", " # Extract values from row 1 (trait_row)\n", " for value in sample_char_dict[trait_row]:\n", " if pd.isna(value):\n", " continue\n", " samples.append(f\"Sample_{len(samples) + 1}\")\n", " trait_values.append(value)\n", " \n", " # Create a DataFrame with samples as columns\n", " data = {samples[i]: [trait_values[i]] for i in range(len(samples))}\n", " clinical_data = pd.DataFrame(data, index=[trait_row])\n", " \n", " # Extract and process clinical features\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 processed clinical features\n", " preview = preview_df(clinical_features)\n", " print(\"Preview of clinical features:\")\n", " print(preview)\n", " \n", " # Save the processed clinical data\n", " # Ensure output directory exists\n", " os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)\n", " clinical_features.to_csv(out_clinical_data_file, index=False)\n", " print(f\"Clinical data saved to {out_clinical_data_file}\")\n" ] }, { "cell_type": "markdown", "id": "822b6cb6", "metadata": {}, "source": [ "### Step 3: Dataset Analysis and Clinical Feature Extraction" ] }, { "cell_type": "code", "execution_count": null, "id": "65300647", "metadata": {}, "outputs": [], "source": [ "\n", "# Set up paths for input files\n", "clinical_data_path = os.path.join(in_cohort_dir, \"clinical_data.csv\")\n", "gene_data_path = os.path.join(in_cohort_dir, \"gene_data.csv\")\n", "\n", "# Check if gene expression data is available\n", "is_gene_available = os.path.exists(gene_data_path)\n", "\n", "# Define conversion functions for trait, age, and gender data\n", "def convert_trait(value):\n", " if pd.isna(value):\n", " return None\n", " \n", " if isinstance(value, str):\n", " value_lower = value.lower().strip()\n", " # Extract the value part if in \"label: value\" format\n", " if ':' in value_lower:\n", " value_lower = value_lower.split(':', 1)[1].strip()\n", " \n", " # For endometrioid cancer studies, look for relevant keywords\n", " if any(term in value_lower for term in ['cancer', 'tumor', 'malignant', 'carcinoma', 'endometrioid']):\n", " return 1\n", " elif any(term in value_lower for term in ['normal', 'control', 'healthy', 'non-cancer']):\n", " return 0\n", " return None\n", "\n", "def convert_age(value):\n", " if pd.isna(value):\n", " return None\n", " \n", " if isinstance(value, str):\n", " value_lower = value.lower().strip()\n", " # Extract the value part if in \"label: value\" format\n", " if ':' in value_lower:\n", " value_lower = value_lower.split(':', 1)[1].strip()\n", " \n", " # Extract numeric age value\n", " import re\n", " matches = re.search(r'(\\d+)(?:\\s*years?)?', value_lower)\n", " if matches:\n", " try:\n", " age = int(matches.group(1))\n", " return age\n", " except ValueError:\n", " pass\n", " return None\n", "\n", "def convert_gender(value):\n", " if pd.isna(value):\n", " return None\n", " \n", " if isinstance(value, str):\n", " value_lower = value.lower().strip()\n", " # Extract the value part if in \"label: value\" format\n", " if ':' in value_lower:\n", " value_lower = value_lower.split(':', 1)[1].strip()\n", " \n", " if any(term in value_lower for term in ['female', 'woman', 'women', 'f']):\n", " return 0\n", " elif any(term in value_lower for term in ['male', 'man', 'men', 'm']):\n", " return 1\n", " return None\n", "\n", "# Initialize variables\n", "trait_row = None\n", "age_row = None\n", "gender_row = None\n", "is_trait_available = False\n", "\n", "# Check if clinical data file exists and process it\n", "if os.path.exists(clinical_data_path):\n", " clinical_data = pd.read_csv(clinical_data_path)\n", " \n", " print(\"Clinical data shape:\", clinical_data.shape)\n", " print(\"Clinical data columns:\", clinical_data.columns.tolist())\n", " \n", " # Examine the sample characteristics to find trait, age, and gender information\n", " sample_characteristics = {}\n", " for i in range(len(clinical_data)):\n", " row_values = clinical_data.iloc[i].dropna().tolist()\n", " unique_values = set(row_values)\n", " sample_characteristics[i] = list(unique_values)\n", " print(f\"Row {i} unique values: {sample_characteristics[i]}\")\n", " \n", " # Identify rows containing trait, age, and gender information\n", " for row_idx, values in sample_characteristics.items():\n", " for value in values:\n", " if isinstance(value, str):\n", " value_lower = value.lower()\n", " \n", " # Check for trait indicators (cancer/normal status)\n", " if any(term in value_lower for term in ['cancer', 'tumor', 'carcinoma', 'normal', 'control', 'endometrioid']):\n", " trait_row = row_idx\n", " \n", " # Check for age indicators\n", " if 'age' in value_lower and any(char.isdigit() for char in value_lower):\n", " age_row = row_idx\n", " \n", " # Check for gender indicators\n", " if any(term in value_lower for term in ['gender', 'sex', 'female', 'male']):\n", " gender_row = row_idx\n", " \n", " print(f\"Identified rows - Trait: {trait_row}, Age: {age_row}, Gender: {gender_row}\")\n", " \n", " # Check if trait data is available\n", " is_trait_available = trait_row is not None\n", "else:\n", " print(\"Clinical data file not found.\")\n", " clinical_data = pd.DataFrame() # Empty dataframe\n", "\n", "# Validate and save initial 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", "# If trait data is available, extract and save clinical features\n", "if is_trait_available:\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 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", " preview = preview_df(selected_clinical_df)\n", " print(\"\\nPreview of extracted clinical data:\")\n", " print(preview)\n", " \n", " # Create output directory if it doesn't exist\n", " os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)\n", " \n", " # Save extracted clinical data\n", " selected_clinical_df.to_csv(out_clinical_data_file, index=False)\n", " print(f\"Clinical data saved to: {out_clinical_data_file}\")\n", "else:\n", " print(\"Trait data is not available.\")\n" ] }, { "cell_type": "markdown", "id": "b35e4944", "metadata": {}, "source": [ "### Step 4: Gene Data Extraction" ] }, { "cell_type": "code", "execution_count": null, "id": "d88814e6", "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": "76c2538a", "metadata": {}, "source": [ "### Step 5: Gene Identifier Review" ] }, { "cell_type": "code", "execution_count": null, "id": "50168542", "metadata": {}, "outputs": [], "source": [ "# The identifiers start with \"ILMN_\" which indicates these are Illumina microarray probe IDs\n", "# These are not standard human gene symbols, but rather probe identifiers from the Illumina platform\n", "# We need to map these probe IDs to standard gene symbols for proper analysis\n", "\n", "requires_gene_mapping = True\n" ] }, { "cell_type": "markdown", "id": "33a6ead7", "metadata": {}, "source": [ "### Step 6: Gene Annotation" ] }, { "cell_type": "code", "execution_count": null, "id": "5c290972", "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": "134ecad3", "metadata": {}, "source": [ "### Step 7: Gene Identifier Mapping" ] }, { "cell_type": "code", "execution_count": null, "id": "5c278c79", "metadata": {}, "outputs": [], "source": [ "# Extract the gene annotation from the SOFT file using the helper function\n", "gene_annotation = get_gene_annotation(soft_file)\n", "\n", "# Identifying the relevant columns for mapping\n", "# Based on the preview, we can see:\n", "# - 'ID' contains the probe IDs (ILMN_*)\n", "# - 'Symbol' contains gene symbols\n", "\n", "# Create the gene mapping dataframe\n", "gene_mapping = get_gene_mapping(gene_annotation, 'ID', 'Symbol')\n", "\n", "# Print the first few rows of the mapping to verify\n", "print(\"Gene mapping preview (first 5 rows):\")\n", "print(gene_mapping.head())\n", "\n", "# Apply the gene mapping to convert probe-level data to gene-level data\n", "gene_data = apply_gene_mapping(gene_data, gene_mapping)\n", "\n", "# Print the first few gene symbols and dimensions of the gene expression data\n", "print(\"\\nGene data converted to gene symbols:\")\n", "print(f\"Shape: {gene_data.shape}\")\n", "print(f\"First 10 gene symbols: {gene_data.index[:10].tolist()}\")\n", "\n", "# Check if the mapping was successful by counting non-null gene entries\n", "num_genes = len(gene_data.index)\n", "print(f\"Number of genes after mapping: {num_genes}\")\n", "\n", "# Save the gene expression data\n", "os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)\n", "gene_data.to_csv(out_gene_data_file)\n", "print(f\"Gene expression data saved to: {out_gene_data_file}\")\n" ] }, { "cell_type": "markdown", "id": "1f485a2c", "metadata": {}, "source": [ "### Step 8: Data Normalization and Linking" ] }, { "cell_type": "code", "execution_count": null, "id": "4afe2824", "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", "# Re-extract the gene expression data since it wasn't persisted from the previous step\n", "gene_data = get_genetic_data(matrix_file)\n", "print(f\"Re-extracted gene data shape: {gene_data.shape}\")\n", "\n", "# 1. Normalize gene symbols in the gene expression data\n", "# First create the gene mapping again\n", "gene_annotation = get_gene_annotation(soft_file)\n", "gene_mapping = get_gene_mapping(gene_annotation, 'ID', 'Symbol')\n", "gene_data = apply_gene_mapping(gene_data, gene_mapping)\n", "\n", "# Now normalize the mapped gene symbols\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. Properly extract the clinical data from the matrix file\n", "background_info, clinical_raw = get_background_and_clinical_data(matrix_file)\n", "\n", "# Define trait row based on the sample characteristics from Step 1\n", "trait_row = 1 # Row containing histology information including \"Endometrioid\"\n", "\n", "# Define the conversion functions\n", "def convert_trait(value):\n", " \"\"\"Convert histology data to binary for Endometrioid_Cancer\"\"\"\n", " if pd.isna(value):\n", " return None\n", " \n", " # Extract the value after the colon if present\n", " if isinstance(value, str) and \":\" in value:\n", " value = value.split(\":\", 1)[1].strip()\n", " \n", " # Convert to binary based on histology\n", " if isinstance(value, str) and \"Endometrioid\" in value:\n", " return 1 # Presence of Endometrioid cancer\n", " else:\n", " return 0 # Other histology types\n", "\n", "# Age and gender not available in this dataset\n", "age_row = None\n", "gender_row = None\n", "convert_age = None\n", "convert_gender = None\n", "\n", "try:\n", " # Extract clinical features\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] if not linked_data.empty else \"Empty DataFrame\")\n", " \n", " # 4. Handle missing values\n", " if not linked_data.empty:\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", " 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=\"Dataset contains gene expression data with histology information, including endometrioid cancer samples.\"\n", " )\n", " \n", " # 7. Save if usable\n", " if is_usable and not cleaned_data.empty:\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", " else:\n", " print(\"No linked data could be created - either clinical or gene data is missing.\")\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, \n", " is_biased=True, \n", " df=pd.DataFrame(),\n", " note=\"Dataset contains gene expression data but clinical-genetic data linking failed.\"\n", " )\n", " \n", "except Exception as e:\n", " print(f\"Error in clinical data processing: {e}\")\n", " import traceback\n", " traceback.print_exc()\n", " \n", " # Still save the cohort info even if processing failed\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, \n", " is_biased=True, \n", " df=pd.DataFrame(),\n", " note=f\"Error during clinical data processing: {str(e)}\"\n", " )\n", " print(\"Data was determined to be unusable due to processing errors and was not saved\")" ] } ], "metadata": {}, "nbformat": 4, "nbformat_minor": 5 }