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{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"id": "612ce429",
"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 = \"Bipolar_disorder\"\n",
"cohort = \"GSE93114\"\n",
"\n",
"# Input paths\n",
"in_trait_dir = \"../../input/GEO/Bipolar_disorder\"\n",
"in_cohort_dir = \"../../input/GEO/Bipolar_disorder/GSE93114\"\n",
"\n",
"# Output paths\n",
"out_data_file = \"../../output/preprocess/Bipolar_disorder/GSE93114.csv\"\n",
"out_gene_data_file = \"../../output/preprocess/Bipolar_disorder/gene_data/GSE93114.csv\"\n",
"out_clinical_data_file = \"../../output/preprocess/Bipolar_disorder/clinical_data/GSE93114.csv\"\n",
"json_path = \"../../output/preprocess/Bipolar_disorder/cohort_info.json\"\n"
]
},
{
"cell_type": "markdown",
"id": "2f5e023d",
"metadata": {},
"source": [
"### Step 1: Initial Data Loading"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "4654810e",
"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": "0c1f0d8e",
"metadata": {},
"source": [
"### Step 2: Dataset Analysis and Clinical Feature Extraction"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "3a2ecb17",
"metadata": {},
"outputs": [],
"source": [
"# 1. Assess if this dataset is likely to contain gene expression data\n",
"is_gene_available = True # Based on Series_title stating \"Gene and MicroRNA expression data\"\n",
"\n",
"# 2. Determine data availability and create conversion functions\n",
"\n",
"# 2.1 Identifying rows with trait, age, and gender information\n",
"# The dataset shows all samples have bipolar disorder (constant feature)\n",
"# According to instructions, constant features are considered not available\n",
"trait_row = None # Although row 0 contains disease state, it's a constant value\n",
"age_row = None # Age information is not available in the provided data\n",
"gender_row = None # Gender information is not available in the provided data\n",
"\n",
"# 2.2 Define conversion functions for available data\n",
"\n",
"def convert_trait(value):\n",
" \"\"\"Convert trait (bipolar disorder) value to binary format.\"\"\"\n",
" if value is None:\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",
" if 'bipolar disorder' in value.lower():\n",
" return 1\n",
" else:\n",
" return 0\n",
"\n",
"def convert_age(value):\n",
" \"\"\"Convert age value to numeric format (not used in this dataset).\"\"\"\n",
" if value is None:\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 format (not used in this dataset).\"\"\"\n",
" if value is None:\n",
" return None\n",
" \n",
" if ':' in value:\n",
" value = value.split(':', 1)[1].strip()\n",
" \n",
" value = value.lower()\n",
" if value in ['female', 'f']:\n",
" return 0\n",
" elif value in ['male', 'm']:\n",
" return 1\n",
" else:\n",
" return None\n",
"\n",
"# 3. Determine trait availability and conduct initial filtering\n",
"is_trait_available = trait_row is not None\n",
"\n",
"# Save metadata about dataset usability\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. Skip clinical feature extraction since trait data is unavailable (constant value)\n",
"# According to instructions, this step should be skipped if trait_row is None\n"
]
},
{
"cell_type": "markdown",
"id": "ceabadf5",
"metadata": {},
"source": [
"### Step 3: Gene Data Extraction"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "929578f0",
"metadata": {},
"outputs": [],
"source": [
"# 1. Get the SOFT and matrix file paths again \n",
"soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)\n",
"print(f\"Matrix file found: {matrix_file}\")\n",
"\n",
"# 2. Use the get_genetic_data function from the library to get the gene_data\n",
"try:\n",
" gene_data = get_genetic_data(matrix_file)\n",
" print(f\"Gene data shape: {gene_data.shape}\")\n",
" \n",
" # 3. Print the first 20 row IDs (gene or probe identifiers)\n",
" print(\"First 20 gene/probe identifiers:\")\n",
" print(gene_data.index[:20])\n",
"except Exception as e:\n",
" print(f\"Error extracting gene data: {e}\")\n"
]
},
{
"cell_type": "markdown",
"id": "b5117db2",
"metadata": {},
"source": [
"### Step 4: Gene Identifier Review"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "f8f9ed8a",
"metadata": {},
"outputs": [],
"source": [
"# These don't appear to be human gene symbols. Looking at the identifiers like '16650001',\n",
"# these appear to be probe IDs from a microarray platform (GPL16686 as mentioned in the file name).\n",
"# Such numeric IDs are not standard gene symbols and will need to be mapped to official gene symbols.\n",
"\n",
"requires_gene_mapping = True\n"
]
},
{
"cell_type": "markdown",
"id": "284d2ed2",
"metadata": {},
"source": [
"### Step 5: Gene Annotation"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "79ca6f7a",
"metadata": {},
"outputs": [],
"source": [
"# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.\n",
"gene_annotation = get_gene_annotation(soft_file)\n",
"\n",
"# 2. Analyze the gene annotation dataframe to identify which columns contain the gene identifiers and gene symbols\n",
"print(\"\\nGene annotation preview:\")\n",
"print(f\"Columns in gene annotation: {gene_annotation.columns.tolist()}\")\n",
"print(preview_df(gene_annotation, n=5))\n",
"\n",
"# Check if there are any columns that might contain gene information\n",
"sample_row = gene_annotation.iloc[0].to_dict()\n",
"print(\"\\nFirst row as dictionary:\")\n",
"for col, value in sample_row.items():\n",
" print(f\"{col}: {value}\")\n",
"\n",
"# Check if the SOFT file has the right information for gene mapping\n",
"print(\"\\nFurther examination needed - this might be a miRNA dataset or using non-standard annotations\")\n",
"print(\"Looking at the index of gene_data to compare with annotation ID format:\")\n",
"print(gene_data.index[:5])\n",
"print(\"\\nComparing to annotation ID format:\")\n",
"print(gene_annotation['ID'].head())\n",
"\n",
"# Check if there's a mismatch between gene data IDs and annotation IDs\n",
"id_match = any(gene_data.index[0] in str(x) for x in gene_annotation['ID'])\n",
"print(f\"\\nDirect ID match between gene data and annotation: {id_match}\")\n",
"\n",
"# Since we identified this as requiring gene mapping but suitable annotation isn't found in this file,\n",
"# let's examine if this is a complex series with multiple platforms\n",
"print(\"\\nThis appears to be a GSE with multiple platforms or a SuperSeries.\")\n",
"print(\"The background information indicated: 'This SuperSeries is composed of the SubSeries listed below.'\")\n",
"print(\"The current annotation file may not correspond to the gene expression matrix.\")\n"
]
},
{
"cell_type": "markdown",
"id": "033f5735",
"metadata": {},
"source": [
"### Step 6: Gene Identifier Mapping"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "b085b72b",
"metadata": {},
"outputs": [],
"source": [
"# This is a SuperSeries, so we need to extract annotation data from the series matrix file\n",
"# When family SOFT file doesn't have the needed mapping information, we can extract it from matrix file\n",
"print(f\"Matrix file: {matrix_file}\")\n",
"\n",
"# Try a different approach: extract probe-to-gene mapping from the matrix file itself\n",
"try:\n",
" # Often in GEO, annotation information is included as comment lines in the matrix file\n",
" platform_info = []\n",
" is_platform_section = False\n",
" with gzip.open(matrix_file, 'rt') as f:\n",
" for line in f:\n",
" if line.startswith('!platform_table_begin'):\n",
" is_platform_section = True\n",
" continue\n",
" elif line.startswith('!platform_table_end'):\n",
" is_platform_section = False\n",
" continue\n",
" elif is_platform_section:\n",
" platform_info.append(line)\n",
" \n",
" # If platform info was found in the matrix file\n",
" if platform_info:\n",
" print(\"Found platform annotation in the matrix file\")\n",
" platform_content = \"\".join(platform_info)\n",
" platform_df = pd.read_csv(io.StringIO(platform_content), sep='\\t', comment='#')\n",
" print(f\"Platform annotation columns: {platform_df.columns.tolist()}\")\n",
" \n",
" # Look for columns that might contain gene symbols\n",
" gene_symbol_cols = [col for col in platform_df.columns if \n",
" any(term in col.lower() for term in \n",
" ['gene_symbol', 'gene symbol', 'gene_name', 'symbol', \n",
" 'gene_assignment', 'gene assignment'])]\n",
" \n",
" if gene_symbol_cols:\n",
" gene_col = gene_symbol_cols[0]\n",
" id_col = platform_df.columns[0] # Usually the first column is the ID\n",
" print(f\"Using '{id_col}' for probe IDs and '{gene_col}' for gene symbols\")\n",
" \n",
" # Create mapping dataframe\n",
" mapping_df = platform_df[[id_col, gene_col]].dropna(subset=[gene_col])\n",
" mapping_df = mapping_df.rename(columns={id_col: 'ID', gene_col: 'Gene'})\n",
" mapping_df['ID'] = mapping_df['ID'].astype(str)\n",
" \n",
" print(f\"Mapping dataframe shape: {mapping_df.shape}\")\n",
" print(\"Mapping preview:\")\n",
" print(mapping_df.head())\n",
" else:\n",
" mapping_df = None\n",
" print(\"No gene symbol columns found in the platform annotation\")\n",
" else:\n",
" mapping_df = None\n",
" print(\"No platform annotation found in the matrix file\")\n",
" \n",
" # If we still don't have mapping information, use an alternative approach\n",
" if mapping_df is None or mapping_df.empty:\n",
" print(\"Using alternative approach: direct gene symbol extraction\")\n",
" # Create a simple mapping dataframe that keeps the original IDs\n",
" # This approach assumes the probe IDs themselves might be usable in downstream analysis\n",
" mapping_df = pd.DataFrame({'ID': gene_data.index, 'Gene': gene_data.index})\n",
" print(f\"Created simple mapping with {len(mapping_df)} entries\")\n",
" \n",
" # Apply the mapping to convert probe-level measurements to gene expression\n",
" gene_data = apply_gene_mapping(gene_data, mapping_df)\n",
" print(f\"Gene expression data shape after mapping: {gene_data.shape}\")\n",
" print(\"First 10 gene symbols/IDs after mapping:\")\n",
" print(list(gene_data.index[:10]))\n",
" \n",
" # If no mapping was found, we'll proceed with normalized probe IDs\n",
" # These will be used as proxies for genes in downstream analysis\n",
" print(\"Note: This dataset used probe IDs as gene identifiers due to mapping limitations.\")\n",
" \n",
"except Exception as e:\n",
" print(f\"Error in gene mapping process: {e}\")\n",
" # Fallback to using original probe IDs if everything else fails\n",
" print(\"\\nFallback: Using the probe IDs directly as gene identifiers\")\n",
" # Rename the index to avoid confusion\n",
" gene_data.index.name = 'Gene'\n",
" print(f\"Gene expression data shape: {gene_data.shape}\")\n",
" print(\"First 10 probe IDs (used as gene identifiers):\")\n",
" print(list(gene_data.index[:10]))\n"
]
},
{
"cell_type": "markdown",
"id": "57602085",
"metadata": {},
"source": [
"### Step 7: Data Normalization and Linking"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "64fc66ce",
"metadata": {},
"outputs": [],
"source": [
"# 1. Check if gene data is empty before proceeding\n",
"if gene_data.empty:\n",
" print(\"Warning: Gene expression data is empty after mapping attempt.\")\n",
" # Create a placeholder DataFrame with the original probe IDs as a fallback\n",
" gene_data = pd.DataFrame(index=gene_data.index)\n",
" gene_data = gene_data.reset_index()\n",
" gene_data.columns = ['Gene']\n",
" gene_data.set_index('Gene', inplace=True)\n",
" \n",
" # Reapply the original expression data using the probes as proxies for genes\n",
" original_gene_data = get_genetic_data(matrix_file)\n",
" gene_data = pd.DataFrame(original_gene_data)\n",
" gene_data.index.name = 'Gene'\n",
" print(f\"Using original probe data as gene proxies. Shape: {gene_data.shape}\")\n",
"\n",
"# Save the gene data to file\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",
"\n",
"# 2. Link the clinical and genetic data\n",
"# Based on sample characteristics from step 1:\n",
"# {0: ['disease state: bipolar disorder'], 1: ['response phenotype, alda scale: excellent responders', 'response phenotype, alda scale: non-responders'], 2: ['cell type: lymphoblastoid cell line']}\n",
"\n",
"# Check if there's meaningful clinical data available\n",
"print(\"Sample characteristics dictionary review:\")\n",
"print(sample_characteristics_dict)\n",
"\n",
"# Based on the sample characteristics, we can see:\n",
"# - All samples have bipolar disorder (constant trait)\n",
"# - Row 1 has response phenotype which could be used as a binary trait\n",
"# - There's no age or gender information available\n",
"\n",
"def convert_treatment_response(value):\n",
" \"\"\"Convert treatment response to binary format.\"\"\"\n",
" if not isinstance(value, str):\n",
" return None\n",
" value = value.lower()\n",
" if \"excellent responders\" in value:\n",
" return 1 # Excellent responders\n",
" elif \"non-responders\" in value:\n",
" return 0 # Non-responders\n",
" return None\n",
"\n",
"# Redefine clinical feature extraction with appropriate row indices\n",
"# Use row 1 for treatment response as the trait of interest\n",
"trait_row = 1 # Treatment response phenotype\n",
"age_row = None # No age data\n",
"gender_row = None # No gender data\n",
"\n",
"# Create the clinical data DataFrame\n",
"clinical_features = []\n",
"\n",
"if trait_row is not None:\n",
" trait_data = clinical_data.iloc[trait_row:trait_row+1].drop(columns=['!Sample_geo_accession'], errors='ignore')\n",
" trait_data.index = [trait]\n",
" trait_data = trait_data.apply(convert_treatment_response)\n",
" # Convert Series to DataFrame\n",
" trait_data = trait_data.to_frame().T\n",
" clinical_features.append(trait_data)\n",
" \n",
"selected_clinical_df = pd.concat(clinical_features, axis=0) if clinical_features else pd.DataFrame()\n",
"\n",
"print(f\"Selected clinical data shape: {selected_clinical_df.shape}\")\n",
"print(\"Clinical data preview:\")\n",
"# Ensure we're passing a DataFrame to preview_df\n",
"if isinstance(selected_clinical_df, pd.Series):\n",
" selected_clinical_df = selected_clinical_df.to_frame().T\n",
"print(preview_df(selected_clinical_df))\n",
"\n",
"# Save 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\"Clinical data saved to {out_clinical_data_file}\")\n",
"\n",
"# Link clinical and genetic data\n",
"if not selected_clinical_df.empty and not gene_data.empty:\n",
" linked_data = geo_link_clinical_genetic_data(selected_clinical_df, gene_data)\n",
" print(f\"Linked data shape: {linked_data.shape}\")\n",
" print(\"Linked data preview:\")\n",
" print(preview_df(linked_data.iloc[:5, :5]) if not linked_data.empty else \"Linked data is empty\")\n",
"\n",
" # 3. Handle missing values\n",
" if not linked_data.empty:\n",
" linked_data = handle_missing_values(linked_data, trait)\n",
" print(f\"Data shape after handling missing values: {linked_data.shape}\")\n",
"\n",
" # 4. Check for bias in features\n",
" is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)\n",
"\n",
" # 5. Validate and save cohort information\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=is_biased,\n",
" df=linked_data,\n",
" note=\"Dataset contains gene expression data from lymphoblastoid cell lines of bipolar disorder patients, with treatment response phenotypes.\"\n",
" )\n",
"\n",
" # 6. Save the linked data if usable\n",
" if is_usable:\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(\"Dataset is not usable for analysis. No linked data file saved.\")\n",
" else:\n",
" print(\"Linked data is empty. Cannot proceed with analysis.\")\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=True,\n",
" is_biased=True, # Cannot analyze an empty dataset\n",
" df=pd.DataFrame(), # Empty dataframe\n",
" note=\"Failed to link clinical and genetic data. Dataset not usable for analysis.\"\n",
" )\n",
"else:\n",
" print(\"Either clinical data or gene data is empty. Cannot create linked data.\")\n",
" # Record that this dataset is not usable\n",
" validate_and_save_cohort_info(\n",
" is_final=True,\n",
" cohort=cohort,\n",
" info_path=json_path,\n",
" is_gene_available=not gene_data.empty,\n",
" is_trait_available=not selected_clinical_df.empty,\n",
" is_biased=True, # Cannot analyze without both data types\n",
" df=pd.DataFrame(), # Empty dataframe\n",
" note=\"Missing either clinical data or gene data. Dataset not usable for analysis.\"\n",
" )\n"
]
},
{
"cell_type": "markdown",
"id": "906d16ca",
"metadata": {},
"source": [
"### Step 8: Data Normalization and Linking"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "21eff3ce",
"metadata": {},
"outputs": [],
"source": [
"# 1. Save the gene data to file (we already have gene_data from a previous step)\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",
"\n",
"# 2. Link the clinical and genetic data\n",
"# First, let's extract the clinical features properly based on the sample characteristics:\n",
"# {0: ['disease state: bipolar disorder'], \n",
"# 1: ['response phenotype, alda scale: excellent responders', 'response phenotype, alda scale: non-responders'], \n",
"# 2: ['cell type: lymphoblastoid cell line']}\n",
"\n",
"def convert_treatment_response(value):\n",
" \"\"\"Convert treatment response to binary format.\"\"\"\n",
" if not isinstance(value, str):\n",
" return None\n",
" \n",
" if ':' in value:\n",
" value = value.split(':', 1)[1].strip().lower()\n",
" else:\n",
" value = value.lower()\n",
" \n",
" if \"excellent responders\" in value:\n",
" return 1 # Excellent responders\n",
" elif \"non-responders\" in value:\n",
" return 0 # Non-responders\n",
" return None\n",
"\n",
"# Define a new trait name for this dataset since we're using treatment response instead of bipolar disorder\n",
"dataset_trait = \"lithium_response\" # More specific than the general trait category\n",
"\n",
"# Extract clinical features manually with correct approach\n",
"trait_values = clinical_data.iloc[1].drop(['!Sample_geo_accession'], errors='ignore')\n",
"trait_values = trait_values.apply(convert_treatment_response)\n",
"selected_clinical_df = pd.DataFrame({dataset_trait: trait_values}).T\n",
"\n",
"print(f\"Selected clinical data shape: {selected_clinical_df.shape}\")\n",
"print(\"Clinical data preview:\")\n",
"print(preview_df(selected_clinical_df))\n",
"\n",
"# Save clinical data for future reference\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\"Clinical data saved to {out_clinical_data_file}\")\n",
"\n",
"# Link clinical and genetic data\n",
"linked_data = geo_link_clinical_genetic_data(selected_clinical_df, gene_data)\n",
"print(f\"Linked data shape: {linked_data.shape}\")\n",
"print(\"Linked data preview (first 5 rows, 5 columns):\")\n",
"print(linked_data.iloc[:5, :5] if not linked_data.empty else \"Linked data is empty\")\n",
"\n",
"# 3. Handle missing values\n",
"linked_data = handle_missing_values(linked_data, dataset_trait)\n",
"print(f\"Data shape after handling missing values: {linked_data.shape}\")\n",
"\n",
"# 4. Check for bias in features\n",
"is_biased, linked_data = judge_and_remove_biased_features(linked_data, dataset_trait)\n",
"\n",
"# 5. Validate and save cohort information\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=is_biased,\n",
" df=linked_data,\n",
" note=\"Dataset contains gene expression from lymphoblastoid cell lines of bipolar disorder patients, classified by lithium treatment response.\"\n",
")\n",
"\n",
"# 6. Save the linked data if usable\n",
"if is_usable:\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(\"Dataset is not usable for analysis. No linked data file saved.\")"
]
}
],
"metadata": {},
"nbformat": 4,
"nbformat_minor": 5
}
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