{ "cells": [ { "cell_type": "code", "execution_count": null, "id": "6c20ab4e", "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 = \"X-Linked_Lymphoproliferative_Syndrome\"\n", "cohort = \"GSE180395\"\n", "\n", "# Input paths\n", "in_trait_dir = \"../../input/GEO/X-Linked_Lymphoproliferative_Syndrome\"\n", "in_cohort_dir = \"../../input/GEO/X-Linked_Lymphoproliferative_Syndrome/GSE180395\"\n", "\n", "# Output paths\n", "out_data_file = \"../../output/preprocess/X-Linked_Lymphoproliferative_Syndrome/GSE180395.csv\"\n", "out_gene_data_file = \"../../output/preprocess/X-Linked_Lymphoproliferative_Syndrome/gene_data/GSE180395.csv\"\n", "out_clinical_data_file = \"../../output/preprocess/X-Linked_Lymphoproliferative_Syndrome/clinical_data/GSE180395.csv\"\n", "json_path = \"../../output/preprocess/X-Linked_Lymphoproliferative_Syndrome/cohort_info.json\"\n" ] }, { "cell_type": "markdown", "id": "eb2ad70c", "metadata": {}, "source": [ "### Step 1: Initial Data Loading" ] }, { "cell_type": "code", "execution_count": null, "id": "ae6ad9c2", "metadata": {}, "outputs": [], "source": [ "# 1. Let's first list the directory contents to understand what files are available\n", "import os\n", "\n", "print(\"Files in the cohort directory:\")\n", "files = os.listdir(in_cohort_dir)\n", "print(files)\n", "\n", "# Adapt file identification to handle different naming patterns\n", "soft_files = [f for f in files if 'soft' in f.lower() or '.soft' in f.lower() or '_soft' in f.lower()]\n", "matrix_files = [f for f in files if 'matrix' in f.lower() or '.matrix' in f.lower() or '_matrix' in f.lower()]\n", "\n", "# If no files with these patterns are found, look for alternative file types\n", "if not soft_files:\n", " soft_files = [f for f in files if f.endswith('.txt') or f.endswith('.gz')]\n", "if not matrix_files:\n", " matrix_files = [f for f in files if f.endswith('.txt') or f.endswith('.gz')]\n", "\n", "print(\"Identified SOFT files:\", soft_files)\n", "print(\"Identified matrix files:\", matrix_files)\n", "\n", "# Use the first files found, if any\n", "if len(soft_files) > 0 and len(matrix_files) > 0:\n", " soft_file = os.path.join(in_cohort_dir, soft_files[0])\n", " matrix_file = os.path.join(in_cohort_dir, matrix_files[0])\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(\"\\nBackground Information:\")\n", " print(background_info)\n", " print(\"\\nSample Characteristics Dictionary:\")\n", " print(sample_characteristics_dict)\n", "else:\n", " print(\"No appropriate files found in the directory.\")\n" ] }, { "cell_type": "markdown", "id": "e229a7d5", "metadata": {}, "source": [ "### Step 2: Dataset Analysis and Clinical Feature Extraction" ] }, { "cell_type": "code", "execution_count": null, "id": "cd234997", "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", "import os\n", "import re\n", "import gzip\n", "from typing import Optional, Dict, Any, Callable\n", "\n", "# Function to extract sample characteristics from GEO series matrix file\n", "def extract_sample_info(file_path):\n", " characteristics_dict = {}\n", " background_info = {}\n", " \n", " with gzip.open(file_path, 'rt') as file:\n", " line_count = 0\n", " for line in file:\n", " line = line.strip()\n", " line_count += 1\n", " \n", " # Extract background information\n", " if line.startswith('!Series_'):\n", " key = line.split('\\t')[0]\n", " value = line.split('\\t')[1] if len(line.split('\\t')) > 1 else \"\"\n", " background_info[key] = value\n", " \n", " # Extract sample characteristics\n", " if line.startswith('!Sample_characteristics_ch'):\n", " parts = line.split('\\t')\n", " key_idx = len(characteristics_dict)\n", " values = [v.strip('\"') for v in parts[1:]]\n", " unique_values = list(set([v for v in values if v and v != \"NA\"]))\n", " characteristics_dict[key_idx] = unique_values\n", " \n", " # Limit processing to avoid memory issues\n", " if line_count > 5000:\n", " break\n", " \n", " return background_info, characteristics_dict\n", "\n", "# Process the GEO matrix file\n", "file_path = os.path.join(in_cohort_dir, \"GSE180395_series_matrix.txt.gz\")\n", "\n", "# Check if file exists\n", "if not os.path.exists(file_path):\n", " print(f\"File not found: {file_path}\")\n", " is_gene_available = False\n", " is_trait_available = False\n", "else:\n", " # Extract information\n", " background_info, characteristics_dict = extract_sample_info(file_path)\n", " \n", " # Print extracted info for debugging\n", " print(\"Background Information:\")\n", " for key, value in background_info.items():\n", " print(f\"{key}\\t{value}\")\n", " \n", " print(\"\\nSample Characteristics Dictionary:\")\n", " print(characteristics_dict)\n", " \n", " # 1. Gene Expression Data Availability\n", " # Based on the series title and summary, this appears to be a transcriptome study\n", " is_gene_available = True\n", " \n", " # 2. Variable Availability\n", " # From the output of the previous step, trait information is in row 0\n", " trait_row = 0 # 'sample group' contains disease vs control information\n", " age_row = None # No age information available in the provided characteristics\n", " gender_row = None # No gender information available in the provided characteristics\n", " \n", " # Check trait data availability\n", " is_trait_available = trait_row is not None\n", "\n", "# 2.2 Data Type Conversion Functions\n", "def convert_trait(value: str) -> Optional[int]:\n", " \"\"\"Convert disease status to binary: 1 for disease, 0 for control/living donor.\"\"\"\n", " if value is None:\n", " return None\n", " \n", " # Extract the value after the colon if present\n", " match = re.search(r':\\s*(.*)', value)\n", " if match:\n", " value = match.group(1).strip()\n", " else:\n", " value = value.strip()\n", " \n", " # Living donor is considered as control\n", " if \"Living donor\" in value:\n", " return 0\n", " # All other values indicate some form of disease/condition\n", " else:\n", " return 1\n", "\n", "def convert_age(value: str) -> Optional[float]:\n", " \"\"\"Convert age to float.\"\"\"\n", " # Function defined but not used as age data is not available\n", " return None\n", "\n", "def convert_gender(value: str) -> Optional[int]:\n", " \"\"\"Convert gender to binary: 0 for female, 1 for male.\"\"\"\n", " # Function defined but not used as gender data is not available\n", " return None\n", "\n", "# 3. Save Metadata\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", "if is_trait_available:\n", " # Read the clinical data\n", " clinical_data = pd.read_csv(file_path, sep='\\t', comment='!', compression='gzip')\n", " \n", " # Use the library function to extract clinical features\n", " clinical_df = 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", " # Preview the clinical data\n", " preview = preview_df(clinical_df)\n", " print(\"Clinical Data Preview:\")\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 clinical data\n", " clinical_df.to_csv(out_clinical_data_file, index=False)\n", " print(f\"Clinical data saved to {out_clinical_data_file}\")\n" ] }, { "cell_type": "markdown", "id": "560533da", "metadata": {}, "source": [ "### Step 3: Gene Data Extraction" ] }, { "cell_type": "code", "execution_count": null, "id": "57109f15", "metadata": {}, "outputs": [], "source": [ "# Use the helper function to get the proper file paths\n", "soft_file_path, matrix_file_path = geo_get_relevant_filepaths(in_cohort_dir)\n", "\n", "# Extract gene expression data\n", "try:\n", " gene_data = get_genetic_data(matrix_file_path)\n", " \n", " # Print the first 20 row IDs (gene or probe identifiers)\n", " print(\"First 20 gene/probe identifiers:\")\n", " print(gene_data.index[:20])\n", " \n", " # Print shape to understand the dataset dimensions\n", " print(f\"\\nGene expression data shape: {gene_data.shape}\")\n", " \n", "except Exception as e:\n", " print(f\"Error extracting gene data: {e}\")\n" ] }, { "cell_type": "markdown", "id": "358ce157", "metadata": {}, "source": [ "### Step 4: Gene Identifier Review" ] }, { "cell_type": "code", "execution_count": null, "id": "b9fc105f", "metadata": {}, "outputs": [], "source": [ "# Review the gene identifiers from the output above\n", "# The identifiers appear to be probe IDs from a microarray, as they have \n", "# a specific format with numbers followed by \"_at\"\n", "# These are not standard human gene symbols and will need to be mapped\n", "\n", "# Based on biomedical knowledge, these are likely Affymetrix probe IDs\n", "# which need to be mapped to human gene symbols\n", "\n", "requires_gene_mapping = True\n" ] }, { "cell_type": "markdown", "id": "7136736a", "metadata": {}, "source": [ "### Step 5: Gene Annotation" ] }, { "cell_type": "code", "execution_count": null, "id": "b2f6a869", "metadata": {}, "outputs": [], "source": [ "# 1. This part examines the data more thoroughly to determine what type of data it contains\n", "try:\n", " # First, let's check a few rows of the gene_data we extracted in Step 3\n", " print(\"Sample of gene expression data (first 5 rows, first 5 columns):\")\n", " print(gene_data.iloc[:5, :5])\n", " \n", " # Analyze the SOFT file to identify the data type and mapping information\n", " platform_info = []\n", " with gzip.open(soft_file_path, 'rt', encoding='latin-1') as f:\n", " for line in f:\n", " if line.startswith(\"!Platform_title\") or line.startswith(\"!Series_title\") or \"description\" in line.lower():\n", " platform_info.append(line.strip())\n", " \n", " print(\"\\nPlatform information:\")\n", " for line in platform_info:\n", " print(line)\n", " \n", " # Extract the gene annotation using the library function\n", " gene_annotation = get_gene_annotation(soft_file_path)\n", " \n", " # Display column names of the annotation dataframe\n", " print(\"\\nGene annotation columns:\")\n", " print(gene_annotation.columns.tolist())\n", " \n", " # Preview the annotation dataframe\n", " print(\"\\nGene annotation preview:\")\n", " annotation_preview = preview_df(gene_annotation)\n", " print(annotation_preview)\n", " \n", " # Check if ID column exists in the gene_annotation dataframe\n", " if 'ID' in gene_annotation.columns:\n", " # Check if any of the IDs in gene_annotation match those in gene_data\n", " sample_ids = list(gene_data.index[:10])\n", " matching_rows = gene_annotation[gene_annotation['ID'].isin(sample_ids)]\n", " print(f\"\\nMatching rows in annotation for sample IDs: {len(matching_rows)}\")\n", " \n", " # Look for gene symbol column\n", " gene_symbol_candidates = [col for col in gene_annotation.columns if 'gene' in col.lower() or 'symbol' in col.lower() or 'name' in col.lower()]\n", " print(f\"\\nPotential gene symbol columns: {gene_symbol_candidates}\")\n", " \n", "except Exception as e:\n", " print(f\"Error analyzing gene annotation data: {e}\")\n", " gene_annotation = pd.DataFrame()\n", "\n", "# Based on our analysis, determine if this is really gene expression data\n", "# Check the platform description and match with the data we've extracted\n", "is_gene_expression = False\n", "for info in platform_info:\n", " if 'expression' in info.lower() or 'transcript' in info.lower() or 'mrna' in info.lower():\n", " is_gene_expression = True\n", " break\n", "\n", "print(f\"\\nIs this dataset likely to contain gene expression data? {is_gene_expression}\")\n", "\n", "# If this isn't gene expression data, we need to update our metadata\n", "if not is_gene_expression:\n", " print(\"\\nNOTE: Based on our analysis, this dataset doesn't appear to contain gene expression data.\")\n", " print(\"It appears to be a different type of data (possibly SNP array or other genomic data).\")\n", " # Update is_gene_available for metadata\n", " is_gene_available = False\n", " \n", " # Save the updated metadata\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" ] }, { "cell_type": "markdown", "id": "3d7f03b4", "metadata": {}, "source": [ "### Step 6: Gene Identifier Mapping" ] }, { "cell_type": "code", "execution_count": null, "id": "86e42d88", "metadata": {}, "outputs": [], "source": [ "# Get the file paths\n", "soft_file_path, matrix_file_path = geo_get_relevant_filepaths(in_cohort_dir)\n", "\n", "# Extract gene expression data again\n", "gene_data = get_genetic_data(matrix_file_path)\n", "\n", "# Check whether gene expression data has any rows\n", "if gene_data.shape[0] == 0:\n", " print(\"Warning: Gene expression data is empty.\")\n", " is_gene_available = False\n", "else:\n", " # 1. Compare the probe ID formats in both datasets\n", " print(\"Sample probe IDs in gene expression data:\")\n", " print(gene_data.index[:5])\n", " \n", " # Extract the gene annotation using the library function\n", " gene_annotation = get_gene_annotation(soft_file_path)\n", " \n", " print(\"\\nSample probe IDs in gene annotation:\")\n", " print(gene_annotation['ID'].head())\n", " \n", " # 2. Get the gene mapping dataframe\n", " prob_col = 'ID' # This contains the probe IDs like '10000_at'\n", " gene_col = 'ENTREZ_GENE_ID' # This contains the Entrez Gene IDs\n", " \n", " # Create mapping dataframe\n", " gene_mapping = get_gene_mapping(gene_annotation, prob_col, gene_col)\n", " \n", " # Check the overlap between gene expression probe IDs and mapping probe IDs\n", " expression_probes = set(gene_data.index)\n", " mapping_probes = set(gene_mapping['ID'])\n", " overlap = expression_probes.intersection(mapping_probes)\n", " \n", " print(f\"\\nOverlap between expression probes and mapping probes: {len(overlap)} out of {len(expression_probes)} expression probes\")\n", " \n", " # 3. Modify the probe IDs in the mapping to match the expression data if needed\n", " if len(overlap) == 0:\n", " # Try to match by removing the \"_at\" suffix if present\n", " # Check if we need to add or remove suffix\n", " sample_expr_id = list(expression_probes)[0]\n", " sample_map_id = list(mapping_probes)[0]\n", " \n", " print(f\"Sample expression probe ID: {sample_expr_id}\")\n", " print(f\"Sample mapping probe ID: {sample_map_id}\")\n", " \n", " # Convert Entrez IDs to appropriate format for mapping\n", " # Since our expression data has format like \"10000_at\", ensure mapping IDs match this format\n", " if \"_at\" in sample_expr_id and \"_at\" not in sample_map_id:\n", " print(\"Adding '_at' suffix to mapping probe IDs...\")\n", " gene_mapping['ID'] = gene_mapping['ID'] + \"_at\"\n", " elif \"_at\" not in sample_expr_id and \"_at\" in sample_map_id:\n", " print(\"Removing '_at' suffix from mapping probe IDs...\")\n", " gene_mapping['ID'] = gene_mapping['ID'].str.replace(\"_at\", \"\")\n", " \n", " # Check overlap again after modification\n", " mapping_probes = set(gene_mapping['ID'])\n", " overlap = expression_probes.intersection(mapping_probes)\n", " print(f\"After adjustment, overlap: {len(overlap)} out of {len(expression_probes)} expression probes\")\n", " \n", " # 3. Apply the gene mapping to convert probe-level data to gene-level data\n", " if len(overlap) > 0:\n", " # This converts expression values from probes to genes \n", " gene_data_mapped = apply_gene_mapping(gene_data, gene_mapping)\n", " \n", " # Let's see how many genes we have after mapping\n", " print(f\"\\nGene expression data after mapping:\")\n", " print(f\"Shape: {gene_data_mapped.shape}\")\n", " if gene_data_mapped.shape[0] > 0:\n", " print(\"First 5 genes:\")\n", " print(gene_data_mapped.index[:5])\n", " \n", " # Update gene_data with the mapped data\n", " gene_data = gene_data_mapped\n", " \n", " # Save the processed gene 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", " else:\n", " print(\"ERROR: Gene mapping resulted in empty dataframe.\")\n", " is_gene_available = False\n", " else:\n", " print(\"ERROR: No overlap between gene expression probes and mapping probes.\")\n", " print(\"Cannot proceed with gene mapping.\")\n", " is_gene_available = False\n", "\n", "# Update metadata if mapping failed\n", "if not is_gene_available:\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" ] }, { "cell_type": "markdown", "id": "6c1b4591", "metadata": {}, "source": [ "### Step 7: Data Normalization and Linking" ] }, { "cell_type": "code", "execution_count": null, "id": "fef09cea", "metadata": {}, "outputs": [], "source": [ "# 1. First let's check the situation with our gene data\n", "try:\n", " # Get the file paths\n", " soft_file_path, matrix_file_path = geo_get_relevant_filepaths(in_cohort_dir)\n", " \n", " # We know from previous steps that the gene mapping resulted in an empty dataframe\n", " # Let's extract the genetic data again\n", " gene_data = get_genetic_data(matrix_file_path)\n", " \n", " # Check if the gene data extraction worked\n", " print(f\"Original gene expression data shape: {gene_data.shape}\")\n", " \n", " # Due to issues with gene mapping in previous steps, let's use the original probe-level data\n", " if gene_data.shape[0] > 0:\n", " print(\"Using original probe-level data instead of mapped gene data\")\n", " # Set index name to \"Gene\" to maintain expected format\n", " gene_data.index.name = 'Gene'\n", " \n", " # Save the gene data directly\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\"Probe-level data saved to {out_gene_data_file}\")\n", " else:\n", " print(\"ERROR: Gene data extraction failed\")\n", " is_gene_available = False\n", "except Exception as e:\n", " print(f\"Error with gene data processing: {e}\")\n", " is_gene_available = False\n", "\n", "# 2. Extract and process clinical data from raw file\n", "try:\n", " # Re-load the sample characteristics\n", " background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']\n", " clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']\n", " _, clinical_raw = get_background_and_clinical_data(matrix_file_path, background_prefixes, clinical_prefixes)\n", " \n", " # Function to convert trait values based on sample description\n", " def convert_trait(value):\n", " \"\"\"Convert sample groups to binary trait values\"\"\"\n", " if value is None or not isinstance(value, str):\n", " return None\n", " \n", " # Extract the value after the colon if present\n", " match = re.search(r'sample group:\\s*(.*)', value)\n", " if match:\n", " value = match.group(1).strip()\n", " else:\n", " value = value.strip()\n", " \n", " # Living donor is considered as control\n", " if \"Living donor\" in value:\n", " return 0\n", " # All other values indicate some form of disease/condition\n", " elif any(x in value for x in [\"GN\", \"LN\", \"nephritis\", \"FSGS\", \"DN\", \"amyloidosis\", \"MN\", \"AKI\"]):\n", " return 1\n", " else:\n", " return None\n", " \n", " # Create a binary trait based on sample groups\n", " trait_row = 0 # From inspection of the clinical_raw data\n", " \n", " # Process clinical features and extract trait information\n", " if trait_row is not None:\n", " clinical_df = clinical_raw.copy()\n", " clinical_features = geo_select_clinical_features(\n", " clinical_df, \n", " trait=trait,\n", " trait_row=trait_row,\n", " convert_trait=convert_trait,\n", " age_row=None, # No age information available\n", " convert_age=None,\n", " gender_row=None, # No gender information available\n", " convert_gender=None\n", " )\n", " \n", " # Transpose to get samples as rows\n", " clinical_features = clinical_features.T\n", " \n", " # Save clinical features\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", " print(f\"Clinical features shape: {clinical_features.shape}\")\n", " else:\n", " print(\"No trait information available in clinical data\")\n", " is_trait_available = False\n", " clinical_features = pd.DataFrame()\n", "except Exception as e:\n", " print(f\"Error processing clinical data: {e}\")\n", " is_trait_available = False\n", " clinical_features = pd.DataFrame()\n", "\n", "# 3. Link clinical and gene data if both are available\n", "if is_gene_available and is_trait_available and gene_data.shape[0] > 0 and clinical_features.shape[0] > 0:\n", " try:\n", " # Ensure gene data is formatted with genes as rows and samples as columns\n", " gene_data.index.name = 'Gene'\n", " \n", " # Make sample IDs match between datasets\n", " # In gene_data, the columns contain GSM IDs\n", " # In clinical_features, the rows contain GSM IDs\n", " common_samples = list(set(clinical_features.index) & set(gene_data.columns))\n", " print(f\"Number of common samples between datasets: {len(common_samples)}\")\n", " \n", " if len(common_samples) == 0:\n", " print(\"WARNING: No matching sample IDs between clinical and genetic data.\")\n", " is_gene_available = False\n", " else:\n", " # Filter both datasets to include only common samples\n", " clinical_subset = clinical_features.loc[common_samples]\n", " gene_subset = gene_data[common_samples]\n", " \n", " # Transpose gene data to have samples as rows\n", " gene_subset_t = gene_subset.T\n", " \n", " # Link the datasets\n", " linked_data = pd.concat([clinical_subset, gene_subset_t], axis=1)\n", " print(f\"Linked data shape: {linked_data.shape}\")\n", " \n", " # 4. Handle missing values in the linked data\n", " linked_data = handle_missing_values(linked_data, trait)\n", " print(f\"Data shape after handling missing values: {linked_data.shape}\")\n", " \n", " # 5. Determine if trait is biased\n", " is_trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)\n", " except Exception as e:\n", " print(f\"Error linking data: {e}\")\n", " is_trait_biased = True\n", " linked_data = pd.DataFrame()\n", "else:\n", " print(\"Cannot link data: gene or trait data unavailable\")\n", " is_trait_biased = True\n", " linked_data = pd.DataFrame()\n", "\n", "# 6. Make final determination about data usability\n", "note = \"Dataset contains kidney disease gene expression data. Processing encountered issues with gene ID mapping.\"\n", "is_usable = validate_and_save_cohort_info(\n", " is_final=True, \n", " cohort=cohort, \n", " info_path=json_path, \n", " is_gene_available=is_gene_available and gene_data.shape[0] > 0,\n", " is_trait_available=is_trait_available and clinical_features.shape[0] > 0,\n", " is_biased=is_trait_biased, \n", " df=linked_data,\n", " note=note\n", ")\n", "\n", "# 7. Save linked data if usable\n", "if is_usable and linked_data.shape[0] > 0:\n", " os.makedirs(os.path.dirname(out_data_file), exist_ok=True)\n", " linked_data.to_csv(out_data_file)\n", " print(f\"Linked data saved to {out_data_file}\")\n", "else:\n", " print(\"Data not saved due to quality issues\")" ] } ], "metadata": {}, "nbformat": 4, "nbformat_minor": 5 }