{ "cells": [ { "cell_type": "code", "execution_count": null, "id": "8e02a120", "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 = \"Vitamin_D_Levels\"\n", "cohort = \"GSE129604\"\n", "\n", "# Input paths\n", "in_trait_dir = \"../../input/GEO/Vitamin_D_Levels\"\n", "in_cohort_dir = \"../../input/GEO/Vitamin_D_Levels/GSE129604\"\n", "\n", "# Output paths\n", "out_data_file = \"../../output/preprocess/Vitamin_D_Levels/GSE129604.csv\"\n", "out_gene_data_file = \"../../output/preprocess/Vitamin_D_Levels/gene_data/GSE129604.csv\"\n", "out_clinical_data_file = \"../../output/preprocess/Vitamin_D_Levels/clinical_data/GSE129604.csv\"\n", "json_path = \"../../output/preprocess/Vitamin_D_Levels/cohort_info.json\"\n" ] }, { "cell_type": "markdown", "id": "de8e812d", "metadata": {}, "source": [ "### Step 1: Initial Data Loading" ] }, { "cell_type": "code", "execution_count": null, "id": "a916247a", "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": "780d90c4", "metadata": {}, "source": [ "### Step 2: Dataset Analysis and Clinical Feature Extraction" ] }, { "cell_type": "code", "execution_count": null, "id": "7d6d10be", "metadata": {}, "outputs": [], "source": [ "# 1. Gene Expression Data Availability\n", "# Based on the background information, this dataset contains whole blood gene expression data\n", "is_gene_available = True\n", "\n", "# 2. Variable Availability and Data Type Conversion\n", "\n", "# 2.1 Data Availability\n", "# For trait - Vitamin D Levels\n", "# From the sample characteristics, we can see agent row (2) contains information about vitamin D supplementation\n", "trait_row = 2\n", "\n", "# For age\n", "# Age information is not available in the sample characteristics\n", "age_row = None\n", "\n", "# For gender\n", "# Gender information is available in the sample characteristics at row 0\n", "gender_row = 0\n", "\n", "# 2.2 Data Type Conversion Functions\n", "\n", "def convert_trait(value):\n", " \"\"\"Convert vitamin D treatment information to binary\"\"\"\n", " if ':' in value:\n", " value = value.split(':', 1)[1].strip()\n", " \n", " # Create binary classification: 1 for vitamin D treatment, 0 for non-vitamin D treatment\n", " if 'VitD' in value:\n", " return 1\n", " else:\n", " return 0\n", "\n", "def convert_age(value):\n", " \"\"\"Convert age to continuous numeric value\"\"\"\n", " # No age data available\n", " return None\n", "\n", "def convert_gender(value):\n", " \"\"\"Convert gender to binary (0 for female, 1 for male)\"\"\"\n", " if ':' in value:\n", " value = value.split(':', 1)[1].strip()\n", " \n", " if value.lower() == 'male':\n", " return 1\n", " elif value.lower() == 'female':\n", " return 0\n", " else:\n", " return None\n", "\n", "# 3. Save Metadata\n", "# Determine trait data availability based on trait_row\n", "is_trait_available = trait_row is not None\n", "validate_and_save_cohort_info(\n", " is_final=False,\n", " cohort=cohort,\n", " info_path=json_path,\n", " is_gene_available=is_gene_available,\n", " is_trait_available=is_trait_available\n", ")\n", "\n", "# 4. Clinical Feature Extraction\n", "if trait_row is not None:\n", " # Load the matrix file line by line to extract the sample characteristics section correctly\n", " sample_data = []\n", " in_characteristics = False\n", " \n", " with gzip.open(f\"{in_cohort_dir}/GSE129604_series_matrix.txt.gz\", 'rt') as f:\n", " for line in f:\n", " line = line.strip()\n", " if line.startswith('!Sample_characteristics_ch1'):\n", " in_characteristics = True\n", " char_value = line.replace('!Sample_characteristics_ch1', '').strip()\n", " sample_data.append(char_value)\n", " elif in_characteristics and line.startswith('!'):\n", " if not line.startswith('!Sample_characteristics_ch1'):\n", " in_characteristics = False\n", " \n", " # Determine the number of samples\n", " num_samples = len(sample_data)\n", " \n", " # Group the characteristics by row\n", " grouped_chars = {}\n", " row_index = 0\n", " \n", " for i in range(0, num_samples, 1):\n", " if i < len(sample_data):\n", " char_value = sample_data[i]\n", " if row_index not in grouped_chars:\n", " grouped_chars[row_index] = []\n", " grouped_chars[row_index].append(char_value)\n", " \n", " if (i + 1) % 4 == 0: # Each sample has 4 characteristics\n", " row_index += 1\n", " \n", " # Create a DataFrame from the grouped characteristics\n", " clinical_data = pd.DataFrame(grouped_chars)\n", " \n", " # Extract clinical features\n", " clinical_features = geo_select_clinical_features(\n", " clinical_data,\n", " trait=trait,\n", " trait_row=trait_row,\n", " convert_trait=convert_trait,\n", " gender_row=gender_row,\n", " convert_gender=convert_gender\n", " )\n", " \n", " # Preview the extracted clinical features\n", " preview = preview_df(clinical_features)\n", " print(\"Clinical Features Preview:\")\n", " print(preview)\n", " \n", " # Save the clinical data\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": "a136431e", "metadata": {}, "source": [ "### Step 3: Gene Data Extraction" ] }, { "cell_type": "code", "execution_count": null, "id": "8059bb0b", "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": "e7912a9f", "metadata": {}, "source": [ "### Step 4: Gene Identifier Review" ] }, { "cell_type": "code", "execution_count": null, "id": "a5375ea3", "metadata": {}, "outputs": [], "source": [ "# Examining the gene identifiers from the previous step\n", "# The identifiers (AFFX-BkGr-GC03_st, etc.) are Affymetrix probe IDs from a microarray platform\n", "# They are not standard human gene symbols and need to be mapped to gene symbols\n", "\n", "requires_gene_mapping = True\n" ] }, { "cell_type": "markdown", "id": "ac8bdb80", "metadata": {}, "source": [ "### Step 5: Gene Annotation" ] }, { "cell_type": "code", "execution_count": null, "id": "4c0bbd1c", "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": "a075ae68", "metadata": {}, "source": [ "### Step 6: Gene Identifier Mapping" ] }, { "cell_type": "code", "execution_count": null, "id": "dfb63ac4", "metadata": {}, "outputs": [], "source": [ "# Extract gene symbols from the SPOT_ID.1 column, which contains detailed annotation including gene symbols\n", "def extract_gene_symbol(annotation_str):\n", " \"\"\"Extract gene symbols from complex annotation strings in SPOT_ID.1 column\"\"\"\n", " if not isinstance(annotation_str, str):\n", " return []\n", " \n", " # Look for gene symbols in the format [Source:HGNC Symbol;Acc:HGNC:xxxx]\n", " hgnc_pattern = r'\\[Source:HGNC Symbol;Acc:HGNC:\\d+\\]'\n", " \n", " # Find all occurrences that match the pattern\n", " matches = re.findall(hgnc_pattern, annotation_str)\n", " \n", " # Get the words right before each HGNC reference, which should be the gene names\n", " gene_names = []\n", " for match in matches:\n", " # Find where in the original string this match occurs\n", " start_idx = annotation_str.find(match)\n", " if start_idx > 0:\n", " # Look for the word before the match\n", " before_text = annotation_str[:start_idx].strip()\n", " words = before_text.split()\n", " if words:\n", " gene_name = words[-1]\n", " # Clean any non-alphanumeric characters except certain allowed ones\n", " gene_name = re.sub(r'[^A-Za-z0-9\\-]', '', gene_name)\n", " if gene_name:\n", " gene_names.append(gene_name)\n", " \n", " # If no HGNC symbols found, try to extract gene symbols from RefSeq entries\n", " if not gene_names:\n", " refseq_pattern = r'NM_\\d+ // RefSeq // Homo sapiens ([^(]+)'\n", " refseq_matches = re.findall(refseq_pattern, annotation_str)\n", " for match in refseq_matches:\n", " gene_name = match.split('(')[0].strip()\n", " if ',' in gene_name:\n", " gene_name = gene_name.split(',')[0].strip()\n", " if gene_name:\n", " gene_names.append(gene_name)\n", " \n", " # Deduplicate gene names\n", " return list(set(gene_names))\n", "\n", "# Add gene symbols to the annotation dataframe\n", "gene_annotation['Gene_Symbols'] = gene_annotation['SPOT_ID.1'].apply(extract_gene_symbol)\n", "\n", "# Check the IDs in gene expression data\n", "print(\"Sample IDs from gene expression data:\")\n", "print(gene_data.index[:5])\n", "\n", "# Check if there are matching IDs in the annotation\n", "matching_ids = [idx for idx in gene_data.index if idx in gene_annotation['ID'].values]\n", "print(f\"\\nNumber of IDs from gene expression data that match annotation: {len(matching_ids)}\")\n", "\n", "# If there's a mismatch, analyze the format of IDs in both datasets\n", "if len(matching_ids) < 100:\n", " # Looking for patterns in the gene expression IDs\n", " print(\"\\nPattern in gene expression IDs:\")\n", " expression_id_pattern = re.findall(r'([A-Za-z\\-]+)(\\d+)', gene_data.index[0])\n", " print(f\"Expression ID pattern example: {expression_id_pattern}\")\n", " \n", " # Looking for patterns in the annotation IDs\n", " print(\"\\nPattern in annotation IDs:\")\n", " annotation_id_pattern = re.findall(r'([A-Za-z\\-]+)(\\d+)', gene_annotation['ID'].iloc[0])\n", " print(f\"Annotation ID pattern example: {annotation_id_pattern}\")\n", "\n", "# Let's check for a different IDs that might match between datasets\n", "print(\"\\nChecking for alternative ID matches...\")\n", "\n", "# Get a sample of probe IDs from gene_annotation\n", "sample_annotation_ids = gene_annotation['probeset_id'].head(10).tolist()\n", "print(\"Sample annotation probeset_ids:\", sample_annotation_ids)\n", "\n", "# Check if any of these exist in the gene expression data\n", "found_in_expression = [id in gene_data.index for id in sample_annotation_ids]\n", "print(f\"Found in expression data: {sum(found_in_expression)} out of 10\")\n", "\n", "# The probeset_id seems to be a better match for what we need for mapping\n", "# Create a mapping dataframe with probeset_id and Gene_Symbols\n", "mapping_df = pd.DataFrame({\n", " 'ID': gene_annotation['probeset_id'],\n", " 'Gene': gene_annotation['Gene_Symbols']\n", "})\n", "\n", "# Remove rows with empty gene symbols\n", "mapping_df = mapping_df[mapping_df['Gene'].apply(lambda x: len(x) > 0)]\n", "print(f\"\\nCreated mapping with {len(mapping_df)} entries\")\n", "\n", "# Apply gene mapping to convert probe measurements to gene expression\n", "try:\n", " # First explode the Gene column to handle one-to-many mappings\n", " gene_data_mapped = apply_gene_mapping(gene_data, mapping_df)\n", " print(f\"\\nConverted probe data to gene expression data with {len(gene_data_mapped)} genes\")\n", " \n", " # Check the first few genes\n", " print(\"\\nFirst 10 genes in the mapped data:\")\n", " print(gene_data_mapped.index[:10])\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", " \n", "except Exception as e:\n", " print(f\"Error during gene mapping: {e}\")\n", " \n", " # Let's try a simpler approach if the mapping fails\n", " print(\"\\nAttempting alternative mapping approach...\")\n", " \n", " # Let's attempt to extract gene symbols from the SOFT file directly\n", " gene_symbols = []\n", " with gzip.open(soft_file_path, 'rt', encoding='latin-1') as f:\n", " for line in f:\n", " if \"gene_assignment\" in line.lower() and \"=\" in line:\n", " parts = line.split(\"=\")\n", " if len(parts) > 1:\n", " gene_info = parts[1].strip()\n", " print(f\"Sample gene assignment: {gene_info}\")\n", " break\n", " \n", " # If we can't get a proper mapping, let's normalize the dataset using the extract_human_gene_symbols function\n", " # By processing each probeset ID in the SPOT_ID.1 column\n", " print(\"\\nPerforming direct gene symbol extraction from annotation...\")\n", " mapping_df = pd.DataFrame({\n", " 'ID': gene_annotation['ID'],\n", " 'Gene': gene_annotation['SPOT_ID.1'].apply(extract_human_gene_symbols)\n", " })\n", " \n", " # Remove rows with empty gene symbols\n", " mapping_df = mapping_df[mapping_df['Gene'].apply(lambda x: len(x) > 0)]\n", " print(f\"Created mapping with {len(mapping_df)} entries using direct gene symbol extraction\")\n", " \n", " # Apply gene mapping to convert probe measurements to gene expression\n", " gene_data_mapped = apply_gene_mapping(gene_data, mapping_df)\n", " print(f\"Converted probe data to gene expression data with {len(gene_data_mapped)} genes\")\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\"Gene expression data saved to {out_gene_data_file}\")\n", "\n", "# Assign the mapped data to gene_data for the next steps\n", "gene_data = gene_data_mapped" ] } ], "metadata": {}, "nbformat": 4, "nbformat_minor": 5 }