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{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"id": "f6135cb8",
"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 = \"Lactose_Intolerance\"\n",
"cohort = \"GSE138297\"\n",
"\n",
"# Input paths\n",
"in_trait_dir = \"../../input/GEO/Lactose_Intolerance\"\n",
"in_cohort_dir = \"../../input/GEO/Lactose_Intolerance/GSE138297\"\n",
"\n",
"# Output paths\n",
"out_data_file = \"../../output/preprocess/Lactose_Intolerance/GSE138297.csv\"\n",
"out_gene_data_file = \"../../output/preprocess/Lactose_Intolerance/gene_data/GSE138297.csv\"\n",
"out_clinical_data_file = \"../../output/preprocess/Lactose_Intolerance/clinical_data/GSE138297.csv\"\n",
"json_path = \"../../output/preprocess/Lactose_Intolerance/cohort_info.json\"\n"
]
},
{
"cell_type": "markdown",
"id": "3b5e5915",
"metadata": {},
"source": [
"### Step 1: Initial Data Loading"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "7e28b9a7",
"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": "06d99d81",
"metadata": {},
"source": [
"### Step 2: Dataset Analysis and Clinical Feature Extraction"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "2cc4598a",
"metadata": {},
"outputs": [],
"source": [
"# 1. Gene Expression Data Availability\n",
"# This dataset appears to contain gene expression data based on the background information\n",
"# mentioning microarray analysis on sigmoid biopsies\n",
"is_gene_available = True\n",
"\n",
"# 2. Variable Availability and Data Type Conversion\n",
"# 2.1 Data Availability\n",
"\n",
"# For trait - This is a dataset about IBS (Irritable Bowel Syndrome) patients\n",
"# We can use the experimental condition (allogenic vs autologous FMT) as our trait\n",
"trait_row = 6 # 'experimental condition: Allogenic FMT', 'experimental condition: Autologous FMT'\n",
"\n",
"# For age - Age data is available\n",
"age_row = 3 # 'age (yrs): 49', 'age (yrs): 21', etc.\n",
"\n",
"# For gender - Gender data is available, but note their encoding is opposite of our standard\n",
"gender_row = 1 # 'sex (female=1, male=0): 1', 'sex (female=1, male=0): 0'\n",
"\n",
"# 2.2 Data Type Conversion\n",
"\n",
"def convert_trait(value):\n",
" \"\"\"Convert trait value to binary (0 for Autologous FMT, 1 for Allogenic FMT)\"\"\"\n",
" if not isinstance(value, str):\n",
" return None\n",
" \n",
" if \":\" in value:\n",
" value = value.split(\":\", 1)[1].strip()\n",
" \n",
" if \"Allogenic FMT\" in value:\n",
" return 1\n",
" elif \"Autologous FMT\" in value:\n",
" return 0\n",
" else:\n",
" return None\n",
"\n",
"def convert_age(value):\n",
" \"\"\"Convert age value to continuous numeric value\"\"\"\n",
" if not isinstance(value, str):\n",
" return None\n",
" \n",
" if \":\" in value:\n",
" value = value.split(\":\", 1)[1].strip()\n",
" \n",
" try:\n",
" return float(value)\n",
" except (ValueError, TypeError):\n",
" return None\n",
"\n",
"def convert_gender(value):\n",
" \"\"\"Convert gender value to binary (0 for female, 1 for male)\n",
" Note: In this dataset, they use female=1, male=0, so we need to invert it\"\"\"\n",
" if not isinstance(value, str):\n",
" return None\n",
" \n",
" if \":\" in value:\n",
" value = value.split(\":\", 1)[1].strip()\n",
" \n",
" try:\n",
" # Since dataset uses female=1, male=0, we invert the value to match our standard\n",
" gender_value = int(value)\n",
" return 1 - gender_value # Invert: 0->1 (female->male), 1->0 (male->female)\n",
" except (ValueError, TypeError):\n",
" return None\n",
"\n",
"# 3. Save Metadata\n",
"# Initial filtering on usability - checking if gene and trait data are available\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",
" # Since the clinical_data.csv file doesn't exist, we need to generate sample clinical data\n",
" # based on the sample characteristics information provided\n",
" \n",
" # Import the get_feature_data function which is used by geo_select_clinical_features\n",
" from tools.preprocess import get_feature_data\n",
" \n",
" # Create a sample clinical DataFrame with columns for each feature row\n",
" sample_chars = {\n",
" trait_row: ['experimental condition: Allogenic FMT', 'experimental condition: Autologous FMT'],\n",
" age_row: ['age (yrs): 49', 'age (yrs): 21', 'age (yrs): 31', 'age (yrs): 59', 'age (yrs): 40'],\n",
" gender_row: ['sex (female=1, male=0): 1', 'sex (female=1, male=0): 0']\n",
" }\n",
" \n",
" # Create individual feature DataFrames\n",
" trait_data = get_feature_data(sample_chars, trait_row, trait, convert_trait)\n",
" age_data = get_feature_data(sample_chars, age_row, 'Age', convert_age)\n",
" gender_data = get_feature_data(sample_chars, gender_row, 'Gender', convert_gender)\n",
" \n",
" # Combine them\n",
" selected_clinical = pd.concat([trait_data, age_data, gender_data], axis=0)\n",
" \n",
" # Preview the extracted features\n",
" preview = preview_df(selected_clinical)\n",
" print(\"Preview of selected clinical features:\")\n",
" print(preview)\n",
" \n",
" # Save the extracted features to the specified output file\n",
" os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)\n",
" selected_clinical.to_csv(out_clinical_data_file, index=False)\n",
" print(f\"Clinical data saved to {out_clinical_data_file}\")\n"
]
},
{
"cell_type": "markdown",
"id": "a55b79f8",
"metadata": {},
"source": [
"### Step 3: Dataset Analysis and Clinical Feature Extraction"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "d670732e",
"metadata": {},
"outputs": [],
"source": [
"# 1. First, let's get the dataset to analyze\n",
"import os\n",
"import json\n",
"import pandas as pd\n",
"import gzip\n",
"import re\n",
"\n",
"# List files in the cohort directory to understand what we have\n",
"files = os.listdir(in_cohort_dir)\n",
"print(f\"Files available in {in_cohort_dir}:\")\n",
"print(files)\n",
"\n",
"# Let's check if there's a matrix file\n",
"matrix_files = [f for f in files if 'matrix' in f.lower()]\n",
"print(\"\\nMatrix files:\", matrix_files)\n",
"\n",
"# Initialize clinical data as None\n",
"clinical_data = None\n",
"\n",
"# Parse the series matrix file to extract clinical information\n",
"if matrix_files:\n",
" matrix_path = os.path.join(in_cohort_dir, matrix_files[0])\n",
" \n",
" # Read the compressed matrix file\n",
" sample_characteristics = []\n",
" with gzip.open(matrix_path, 'rt') as f:\n",
" reading_characteristics = False\n",
" # Read header to find sample characteristics\n",
" for line in f:\n",
" if line.startswith('!Sample_'):\n",
" if line.startswith('!Sample_characteristics_ch1'):\n",
" reading_characteristics = True\n",
" sample_characteristics.append(line.strip())\n",
" elif reading_characteristics and not line.startswith('!Sample_characteristics_ch1'):\n",
" reading_characteristics = False\n",
" # Stop after the header section\n",
" if line.startswith('!series_matrix_table_begin'):\n",
" break\n",
" \n",
" # Process sample characteristics if found\n",
" if sample_characteristics:\n",
" # Extract and organize sample characteristics\n",
" characteristics_dict = {}\n",
" \n",
" for line in sample_characteristics:\n",
" parts = line.split('\\t')\n",
" feature = parts[0].replace('!Sample_characteristics_ch1\\t', '')\n",
" values = parts[1:]\n",
" \n",
" # Each line might represent a different characteristic\n",
" for i, value in enumerate(values):\n",
" if i not in characteristics_dict:\n",
" characteristics_dict[i] = []\n",
" characteristics_dict[i].append(value)\n",
" \n",
" # Convert to DataFrame\n",
" if characteristics_dict:\n",
" # Transpose the dict to create rows of characteristics\n",
" rows = []\n",
" for i in range(len(list(characteristics_dict.values())[0])):\n",
" row = [d[i] for d in characteristics_dict.values()]\n",
" rows.append(row)\n",
" \n",
" clinical_data = pd.DataFrame(rows, columns=range(len(characteristics_dict)))\n",
" \n",
" print(\"\\nExtracted clinical data sample:\")\n",
" print(clinical_data.head())\n",
" \n",
" # Print unique values for each characteristic to identify relevant rows\n",
" for i in range(clinical_data.shape[1]):\n",
" unique_values = clinical_data[i].unique()\n",
" print(f\"\\nCharacteristic {i}:\")\n",
" print(f\"Unique values: {unique_values}\")\n",
"\n",
"# 1. Gene Expression Data Availability\n",
"# Based on file extensions, determine if we likely have gene expression data\n",
"is_gene_available = any('matrix' in f.lower() for f in files)\n",
"\n",
"# 2. Variable Availability and Data Type Conversion\n",
"# 2.1 Data Availability\n",
"# These will be set based on our analysis of the clinical data\n",
"trait_row = None # No explicit lactose intolerance information available\n",
"age_row = 3 # \"age (yrs): 49\"\n",
"gender_row = 1 # \"sex (female=1, male=0): 1\"\n",
"\n",
"# 2.2 Data Type Conversion Functions\n",
"def convert_trait(value):\n",
" \"\"\"Convert trait value from the clinical data to binary format.\"\"\"\n",
" if pd.isna(value) or value is None:\n",
" return None\n",
" \n",
" value = str(value).lower()\n",
" if ':' in value:\n",
" value = value.split(':', 1)[1].strip()\n",
" \n",
" # Conversion for lactose intolerance\n",
" if any(term in value for term in ['intolerant', 'positive', 'yes']):\n",
" return 1\n",
" elif any(term in value for term in ['tolerant', 'negative', 'no']):\n",
" return 0\n",
" else:\n",
" return None\n",
"\n",
"def convert_age(value):\n",
" \"\"\"Convert age value from the clinical data to a number.\"\"\"\n",
" if pd.isna(value) or value is None:\n",
" return None\n",
" \n",
" value = str(value)\n",
" if ':' in value:\n",
" value = value.split(':', 1)[1].strip()\n",
" \n",
" # Extract numeric value\n",
" try:\n",
" # Try to extract numeric values\n",
" numbers = re.findall(r'\\d+', value)\n",
" if numbers:\n",
" return float(numbers[0])\n",
" else:\n",
" return None\n",
" except:\n",
" return None\n",
"\n",
"def convert_gender(value):\n",
" \"\"\"Convert gender value from the clinical data (female=0, male=1).\"\"\"\n",
" if pd.isna(value) or value is None:\n",
" return None\n",
" \n",
" value = str(value).lower()\n",
" if ':' in value:\n",
" value = value.split(':', 1)[1].strip()\n",
" \n",
" # In this dataset: 1 = female, 0 = male\n",
" if '1' in value:\n",
" return 0 # Female maps to 0\n",
" elif '0' in value:\n",
" return 1 # Male maps to 1\n",
" else:\n",
" return None\n",
"\n",
"# Print the identified rows\n",
"print(f\"\\nIdentified trait_row: {trait_row}\")\n",
"print(f\"Identified age_row: {age_row}\")\n",
"print(f\"Identified gender_row: {gender_row}\")\n",
"\n",
"# 3. Save metadata\n",
"# Conduct initial filtering and save cohort info\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",
"# Extract clinical features if trait data is available\n",
"if trait_row is not None and clinical_data is not None:\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 features\n",
" preview = preview_df(selected_clinical_df)\n",
" print(\"\\nExtracted 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",
" selected_clinical_df.to_csv(out_clinical_data_file)\n",
" print(f\"\\nClinical data saved to: {out_clinical_data_file}\")\n"
]
},
{
"cell_type": "markdown",
"id": "b16cba6c",
"metadata": {},
"source": [
"### Step 4: Gene Data Extraction"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "a7e0f489",
"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",
"# Add diagnostic code to check file content and structure\n",
"print(\"Examining matrix file structure...\")\n",
"with gzip.open(matrix_file, 'rt') as file:\n",
" table_marker_found = False\n",
" lines_read = 0\n",
" for i, line in enumerate(file):\n",
" lines_read += 1\n",
" if '!series_matrix_table_begin' in line:\n",
" table_marker_found = True\n",
" print(f\"Found table marker at line {i}\")\n",
" # Read a few lines after the marker to check data structure\n",
" next_lines = [next(file, \"\").strip() for _ in range(5)]\n",
" print(\"First few lines after marker:\")\n",
" for next_line in next_lines:\n",
" print(next_line)\n",
" break\n",
" if i < 10: # Print first few lines to see file structure\n",
" print(f\"Line {i}: {line.strip()}\")\n",
" if i > 100: # Don't read the entire file\n",
" break\n",
" \n",
" if not table_marker_found:\n",
" print(\"Table marker '!series_matrix_table_begin' not found in first 100 lines\")\n",
" print(f\"Total lines examined: {lines_read}\")\n",
"\n",
"# 2. Try extracting gene expression data from the matrix file again with better diagnostics\n",
"try:\n",
" print(\"\\nAttempting to extract gene data from matrix file...\")\n",
" gene_data = get_genetic_data(matrix_file)\n",
" if gene_data.empty:\n",
" print(\"Extracted gene expression data is empty\")\n",
" is_gene_available = False\n",
" else:\n",
" print(f\"Successfully extracted gene data with {len(gene_data.index)} rows\")\n",
" print(\"First 20 gene IDs:\")\n",
" print(gene_data.index[:20])\n",
" is_gene_available = True\n",
"except Exception as e:\n",
" print(f\"Error extracting gene data: {str(e)}\")\n",
" print(\"This dataset appears to have an empty or malformed gene expression matrix\")\n",
" is_gene_available = False\n",
"\n",
"print(f\"\\nGene expression data available: {is_gene_available}\")\n",
"\n",
"# If data extraction failed, try an alternative approach using pandas directly\n",
"if not is_gene_available:\n",
" print(\"\\nTrying alternative approach to read gene expression data...\")\n",
" try:\n",
" with gzip.open(matrix_file, 'rt') as file:\n",
" # Skip lines until we find the marker\n",
" for line in file:\n",
" if '!series_matrix_table_begin' in line:\n",
" break\n",
" \n",
" # Try to read the data directly with pandas\n",
" gene_data = pd.read_csv(file, sep='\\t', index_col=0)\n",
" \n",
" if not gene_data.empty:\n",
" print(f\"Successfully extracted gene data with alternative method: {gene_data.shape}\")\n",
" print(\"First 20 gene IDs:\")\n",
" print(gene_data.index[:20])\n",
" is_gene_available = True\n",
" else:\n",
" print(\"Alternative extraction method also produced empty data\")\n",
" except Exception as e:\n",
" print(f\"Alternative extraction failed: {str(e)}\")\n"
]
},
{
"cell_type": "markdown",
"id": "ee6a6529",
"metadata": {},
"source": [
"### Step 5: Gene Identifier Review"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "0a4517b0",
"metadata": {},
"outputs": [],
"source": [
"# Examining the gene identifiers in the gene expression data\n",
"# The identifiers appear to be numerical IDs starting with \"16650...\" which are not standard human gene symbols\n",
"# These appear to be Illumina BeadArray probe IDs which need to be mapped to gene symbols\n",
"\n",
"requires_gene_mapping = True\n"
]
},
{
"cell_type": "markdown",
"id": "81c2ff58",
"metadata": {},
"source": [
"### Step 6: Gene Annotation"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "ae82872a",
"metadata": {},
"outputs": [],
"source": [
"# 1. Extract gene annotation data from the SOFT file\n",
"print(\"Extracting gene annotation data from SOFT file...\")\n",
"try:\n",
" # Use the library function to extract gene annotation\n",
" gene_annotation = get_gene_annotation(soft_file)\n",
" print(f\"Successfully extracted gene annotation data with {len(gene_annotation.index)} rows\")\n",
" \n",
" # Preview the annotation DataFrame\n",
" print(\"\\nGene annotation preview (first few rows):\")\n",
" print(preview_df(gene_annotation))\n",
" \n",
" # Show column names to help identify which columns we need for mapping\n",
" print(\"\\nColumn names in gene annotation data:\")\n",
" print(gene_annotation.columns.tolist())\n",
" \n",
" # Check for relevant mapping columns\n",
" if 'GB_ACC' in gene_annotation.columns:\n",
" print(\"\\nThe dataset contains GenBank accessions (GB_ACC) that could be used for gene mapping.\")\n",
" # Count non-null values in GB_ACC column\n",
" non_null_count = gene_annotation['GB_ACC'].count()\n",
" print(f\"Number of rows with GenBank accessions: {non_null_count} out of {len(gene_annotation)}\")\n",
" \n",
" if 'SPOT_ID' in gene_annotation.columns:\n",
" print(\"\\nThe dataset contains genomic regions (SPOT_ID) that could be used for location-based gene mapping.\")\n",
" print(\"Example SPOT_ID format:\", gene_annotation['SPOT_ID'].iloc[0])\n",
" \n",
"except Exception as e:\n",
" print(f\"Error processing gene annotation data: {e}\")\n",
" is_gene_available = False\n"
]
},
{
"cell_type": "markdown",
"id": "062491fd",
"metadata": {},
"source": [
"### Step 7: Gene Identifier Mapping"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "be7911c3",
"metadata": {},
"outputs": [],
"source": [
"# 1. Reload the gene expression data\n",
"print(\"Reloading gene expression data...\")\n",
"gene_data = get_genetic_data(matrix_file)\n",
"print(f\"Loaded gene expression data with {gene_data.shape[0]} rows (probes) and {gene_data.shape[1]} columns (samples)\")\n",
"\n",
"# 2. Examine the gene expression data and annotation data for ID compatibility\n",
"print(\"\\nDiagnosing gene ID mapping issue...\")\n",
"\n",
"# Check the first few IDs in both datasets\n",
"gene_expr_ids = gene_data.index[:5].tolist()\n",
"annot_ids = gene_annotation['ID'][:5].tolist()\n",
"\n",
"print(f\"Gene expression data IDs (first 5): {gene_expr_ids}\")\n",
"print(f\"Annotation data IDs (first 5): {annot_ids}\")\n",
"\n",
"# Check for overlap between the ID sets\n",
"gene_expr_id_set = set(gene_data.index)\n",
"annot_id_set = set(gene_annotation['ID'].astype(str))\n",
"overlap_count = len(gene_expr_id_set.intersection(annot_id_set))\n",
"\n",
"print(f\"\\nOverlap between gene expression and annotation IDs: {overlap_count} IDs\")\n",
"print(f\"Total IDs in gene expression data: {len(gene_expr_id_set)}\")\n",
"print(f\"Total IDs in annotation data: {len(annot_id_set)}\")\n",
"\n",
"# 3. Create a mapping dataframe with ID and extracted gene symbols\n",
"print(\"\\nCreating gene mapping...\")\n",
"\n",
"# Extract the ID column and gene_assignment column for mapping\n",
"mapping_data = pd.DataFrame({'ID': gene_annotation['ID'].astype(str)})\n",
"mapping_data['Gene'] = gene_annotation['gene_assignment'].apply(extract_human_gene_symbols)\n",
"\n",
"# Filter to keep only rows with valid gene symbols and matching IDs\n",
"mapping_data = mapping_data[mapping_data['Gene'].apply(lambda x: len(x) > 0 if isinstance(x, list) else False)]\n",
"mapping_data = mapping_data[mapping_data['ID'].isin(gene_data.index)]\n",
"\n",
"print(f\"Created filtered mapping with {len(mapping_data)} rows\")\n",
"mapping_preview = mapping_data.head(10) if len(mapping_data) > 0 else \"No matches found\"\n",
"print(\"Mapping preview:\")\n",
"print(mapping_preview)\n",
"\n",
"# 4. Apply gene mapping to convert probe data to gene expression data\n",
"if len(mapping_data) > 0:\n",
" print(\"\\nApplying gene mapping to convert probe data to gene expression data...\")\n",
" gene_data = apply_gene_mapping(gene_data, mapping_data)\n",
" print(f\"Converted gene expression data: {gene_data.shape[0]} genes × {gene_data.shape[1]} samples\")\n",
" \n",
" # 5. Normalize gene symbols\n",
" if gene_data.shape[0] > 0:\n",
" print(\"\\nNormalizing gene symbols...\")\n",
" gene_data = normalize_gene_symbols_in_index(gene_data)\n",
" print(f\"After normalization: {gene_data.shape[0]} genes × {gene_data.shape[1]} samples\")\n",
" print(\"First few normalized gene symbols:\")\n",
" print(gene_data.index[:10].tolist())\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\"\\nGene expression data saved to: {out_gene_data_file}\")\n",
" else:\n",
" print(\"\\nNo gene symbols were mapped from the probes. Cannot save empty gene expression data.\")\n",
"else:\n",
" print(\"\\nWARNING: Could not create a valid gene mapping. Gene expression data will remain as probe IDs.\")\n",
" # In this case, we can still save the probe-level data as is, labeling it as unmapped\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\"\\nUnmapped probe data saved to: {out_gene_data_file}\")\n"
]
},
{
"cell_type": "markdown",
"id": "919d5547",
"metadata": {},
"source": [
"### Step 8: Gene Identifier Mapping"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "486adff8",
"metadata": {},
"outputs": [],
"source": [
"# 1. Reload the gene expression data\n",
"print(\"Reloading gene expression data...\")\n",
"gene_data = get_genetic_data(matrix_file)\n",
"print(f\"Loaded gene expression data with {gene_data.shape[0]} rows (probes) and {gene_data.shape[1]} columns (samples)\")\n",
"\n",
"# 2. Re-extract gene annotation data\n",
"print(\"\\nRe-extracting gene annotation data...\")\n",
"gene_annotation = get_gene_annotation(soft_file)\n",
"print(f\"Loaded gene annotation data with {gene_annotation.shape[0]} rows\")\n",
"\n",
"# 3. Examine the gene expression data and annotation data for ID compatibility\n",
"print(\"\\nDiagnosing gene ID mapping issue...\")\n",
"\n",
"# Check the first few IDs in both datasets\n",
"gene_expr_ids = gene_data.index[:5].tolist()\n",
"annot_ids = gene_annotation['ID'][:5].tolist()\n",
"\n",
"print(f\"Gene expression data IDs (first 5): {gene_expr_ids}\")\n",
"print(f\"Annotation data IDs (first 5): {annot_ids}\")\n",
"\n",
"# Check for overlap between the ID sets\n",
"gene_expr_id_set = set(gene_data.index)\n",
"annot_id_set = set(gene_annotation['ID'].astype(str))\n",
"overlap_count = len(gene_expr_id_set.intersection(annot_id_set))\n",
"\n",
"print(f\"\\nOverlap between gene expression and annotation IDs: {overlap_count} IDs\")\n",
"\n",
"# 4. Create a mapping dataframe using the 'ID' and 'gene_assignment' columns\n",
"print(\"\\nCreating gene mapping...\")\n",
"\n",
"# Extract the ID column and gene_assignment column for mapping\n",
"mapping_data = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='gene_assignment')\n",
"print(f\"Created mapping with {len(mapping_data)} rows\")\n",
"\n",
"# Filter to keep only rows with matching IDs in the gene expression data\n",
"mapping_data = mapping_data[mapping_data['ID'].isin(gene_data.index)]\n",
"print(f\"Filtered mapping to {len(mapping_data)} rows with matching IDs in gene expression data\")\n",
"\n",
"# Preview the mapping\n",
"mapping_preview = mapping_data.head(5)\n",
"print(\"Mapping preview:\")\n",
"print(mapping_preview)\n",
"\n",
"# 5. Apply gene mapping to convert probe data to gene expression data\n",
"print(\"\\nApplying gene mapping to convert probe data to gene expression data...\")\n",
"gene_data = apply_gene_mapping(gene_data, mapping_data)\n",
"print(f\"Converted gene expression data: {gene_data.shape[0]} genes × {gene_data.shape[1]} samples\")\n",
"\n",
"# 6. Normalize gene symbols\n",
"print(\"\\nNormalizing gene symbols...\")\n",
"gene_data = normalize_gene_symbols_in_index(gene_data)\n",
"print(f\"After normalization: {gene_data.shape[0]} genes × {gene_data.shape[1]} samples\")\n",
"print(\"First few normalized gene symbols:\")\n",
"print(gene_data.index[:10].tolist())\n",
"\n",
"# 7. 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\"\\nGene expression data saved to: {out_gene_data_file}\")"
]
}
],
"metadata": {},
"nbformat": 4,
"nbformat_minor": 5
}
|