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{
"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
}
|