p3/preprocess/Obesity/code/GSE123086.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Obesity"
cohort = "GSE123086"

# Input paths
in_trait_dir = "../DATA/GEO/Obesity"
in_cohort_dir = "../DATA/GEO/Obesity/GSE123086"

# Output paths
out_data_file = "./output/preprocess/3/Obesity/GSE123086.csv"
out_gene_data_file = "./output/preprocess/3/Obesity/gene_data/GSE123086.csv"
out_clinical_data_file = "./output/preprocess/3/Obesity/clinical_data/GSE123086.csv"
json_path = "./output/preprocess/3/Obesity/cohort_info.json"

# Get file paths
soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)

# Extract background info and clinical data 
background_info, clinical_data = get_background_and_clinical_data(matrix_file)

# Get unique values per clinical feature
sample_characteristics = get_unique_values_by_row(clinical_data)

# Print background info
print("Dataset Background Information:")
print(f"{background_info}\n")

# Print sample characteristics
print("Sample Characteristics:")
for feature, values in sample_characteristics.items():
    print(f"Feature: {feature}")
    print(f"Values: {values}\n")
# 1. Gene Expression Data Availability
# Yes, from series design we can see RNA microarray was used
is_gene_available = True

# 2.1 Data Availability
# Trait data is in Feature 1 under "primary diagnosis"
trait_row = 1

# Age data is spread across Features 3 and 4
age_row = 3

# Gender data is in Feature 2 under "Sex"
gender_row = 2

# 2.2 Data Type Conversion Functions
def convert_trait(x):
    # Extract value after colon and convert to binary
    if pd.isna(x):
        return None
    value = x.split(': ')[1]
    if value == 'OBESITY':
        return 1
    elif value == 'HEALTHY_CONTROL':
        return 0
    return None

def convert_age(x):
    # Extract age value as continuous
    if pd.isna(x):
        return None
    if not x.startswith('age:'):
        return None
    try:
        return float(x.split(': ')[1])
    except:
        return None

def convert_gender(x):
    # Convert gender to binary (female=0, male=1)
    if pd.isna(x):
        return None
    if not x.startswith('Sex:'):
        return None
    value = x.split(': ')[1]
    if value == 'Female':
        return 0
    elif value == 'Male':
        return 1
    return None

# 3. Initial Filtering and Save Metadata
is_trait_available = trait_row is not None
validate_and_save_cohort_info(is_final=False, 
                             cohort=cohort,
                             info_path=json_path,
                             is_gene_available=is_gene_available,
                             is_trait_available=is_trait_available)

# 4. Clinical Feature Extraction
if trait_row is not None:
    clinical_features = geo_select_clinical_features(
        clinical_data,
        trait=trait,
        trait_row=trait_row,
        convert_trait=convert_trait,
        age_row=age_row,
        convert_age=convert_age,
        gender_row=gender_row,
        convert_gender=convert_gender
    )
    print("Preview of extracted clinical features:")
    print(preview_df(clinical_features))
    clinical_features.to_csv(out_clinical_data_file)
# Extract gene expression data from matrix file
gene_data = get_genetic_data(matrix_file)

# Print first 20 row IDs and shape of data to help debug
print("Shape of gene expression data:", gene_data.shape)
print("\nFirst few rows of data:")
print(gene_data.head())
print("\nFirst 20 gene/probe identifiers:")
print(gene_data.index[:20])

# Inspect a snippet of raw file to verify identifier format
import gzip
with gzip.open(matrix_file, 'rt', encoding='utf-8') as f:
    lines = []
    for i, line in enumerate(f):
        if "!series_matrix_table_begin" in line:
            # Get the next 5 lines after the marker
            for _ in range(5):
                lines.append(next(f).strip())
            break
print("\nFirst few lines after matrix marker in raw file:")
for line in lines:
    print(line)
# Looking at the identifiers - they are numeric values (1,2,3,9,10 etc)
# These appear to be probe/feature IDs rather than gene symbols
# Therefore mapping to gene symbols will be required
requires_gene_mapping = True
# Get file paths using library function
soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)

# For SOFT files with platform annotations, we need to get rows not starting with '!'
# but also not containing just numeric IDs and entrez genes
gene_annotation = pd.read_csv(soft_file, compression='gzip', sep='\t', header=None,
                            comment='!', on_bad_lines='skip')

# Find rows with detailed platform annotations (containing gene symbols)
annotation_starts = False
annotation_lines = []
with gzip.open(soft_file, 'rt', encoding='utf-8') as f:
    for line in f:
        if "!platform_table_begin" in line:
            annotation_starts = True
            # Get the header line
            header = next(f).strip()
            annotation_lines.append(header)
            continue
        if annotation_starts:
            if "!platform_table_end" in line:
                break
            annotation_lines.append(line.strip())

# Convert annotation lines to dataframe
annotation_content = '\n'.join(annotation_lines)
gene_annotation = pd.read_csv(io.StringIO(annotation_content), sep='\t')

# Preview annotation data
print("Gene annotation shape:", gene_annotation.shape)
print("\nAll column names:")
print(gene_annotation.columns.tolist())

print("\nFirst few rows:") 
print(gene_annotation.head().to_string())

print("\nNumber of non-null values in each column:")
print(gene_annotation.count())

# Print example rows showing ID and gene symbol columns
print("\nSample rows showing the ID and gene symbol mapping:")
symbol_col = [col for col in gene_annotation.columns if 'symbol' in col.lower()][0]
print(gene_annotation[['ID', symbol_col]].head(10))
# Extract complete platform annotation table from SOFT file
platform_lines = []
annotation_starts = False
with gzip.open(soft_file, 'rt', encoding='utf-8') as f:
    for line in f:
        if line.startswith('^PLATFORM'):
            platform_lines.append(line)
        elif line.startswith('!Platform_data_row_count'):
            platform_lines.append(line)
        elif line.startswith('!Platform_table_begin'):
            annotation_starts = True
            header = next(f).strip()
            platform_lines.append(header)
            continue
        elif annotation_starts:
            if line.startswith('!Platform_table_end'):
                break
            platform_lines.append(line.strip())

# Parse platform annotation content
platform_content = '\n'.join(platform_lines)
gene_annotation = pd.read_csv(io.StringIO(platform_content), sep='\t', comment='!', skiprows=2)

# Create mapping DataFrame using ID and gene symbol
mapping_df = gene_annotation[['ID', 'GB_ACC']].copy() 
mapping_df = mapping_df.rename(columns={'GB_ACC': 'Gene'})

# Clean gene symbols - split on spaces/semicolons and take first value
mapping_df['Gene'] = mapping_df['Gene'].astype(str).apply(lambda x: x.split(';')[0].split()[0])
mapping_df = mapping_df.dropna()

# Apply gene mapping to convert probe expression to gene expression
gene_data = apply_gene_mapping(gene_data, mapping_df)

# Normalize gene symbols using the provided function
gene_data = normalize_gene_symbols_in_index(gene_data)

# Save gene expression data
gene_data.to_csv(out_gene_data_file)

# Preview the mapped gene data
print("Shape of gene expression data after mapping:", gene_data.shape)
print("\nFirst few mapped genes and their expression values:")
print(gene_data.head())
# Get file paths using library function
soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)

# Try to peek at the SOFT file contents first
with gzip.open(soft_file, 'rt', encoding='utf-8') as f:
    # Read first few lines to understand file structure
    print("First 20 lines of SOFT file:")
    for i, line in enumerate(f):
        if i < 20:
            print(line.strip())
        else:
            break

    # Reset file pointer
    f.seek(0)
    
    # Look for the platform table section
    print("\nPlatform table header:")
    for line in f:
        if "!Platform_table_begin" in line:
            # Print the next line which should be the header
            print(next(f).strip())
            break

# Now extract annotation data using a simpler method - get lines between table markers
annotation_lines = []
table_started = False
with gzip.open(soft_file, 'rt', encoding='utf-8') as f:
    for line in f:
        if "!Platform_table_begin" in line:
            header = next(f).strip()
            annotation_lines.append(header)
            table_started = True
            continue
        if table_started:
            if "!Platform_table_end" in line:
                break
            annotation_lines.append(line.strip())

# Convert to dataframe
annotation_text = '\n'.join(annotation_lines)
gene_annotation = pd.read_csv(io.StringIO(annotation_text), sep='\t')

# Preview annotation data
print("\nAnnotation data shape:", gene_annotation.shape)
print("\nColumn names:")
print(gene_annotation.columns.tolist())
print("\nFirst few rows:")
print(gene_annotation.head())
# 1. Let's examine SOFT file content first to identify correct gene identifier columns
with gzip.open(soft_file, 'rt', encoding='utf-8') as f:
    for line in f:
        if '!Platform_table_begin' in line:
            header = next(f).strip()
            print("Platform table header:")
            print(header)
            print("\nFirst few data rows:")
            for i in range(5):
                print(next(f).strip())
            break

# Extract gene metadata using library function
gene_metadata = get_gene_annotation(soft_file)

# Print available columns to identify gene symbol column
print("\nAvailable annotation columns:")
print(gene_metadata.columns.tolist())

print("\nPreview of gene metadata:")
print(gene_metadata.head())

# Get gene expression data
gene_data = get_genetic_data(matrix_file)

# Print shape and preview of expression data before mapping
print("\nShape of gene expression data before mapping:", gene_data.shape)
print("\nPreview of gene expression data before mapping:")
print(gene_data.head())
# Use library function to get gene annotation from SOFT file
gene_metadata = get_gene_annotation(soft_file)

# Create mapping DataFrame using ID and ENTREZ_GENE_ID 
mapping_df = gene_metadata[['ID', 'ENTREZ_GENE_ID']].copy()
mapping_df = mapping_df.rename(columns={'ENTREZ_GENE_ID': 'Gene'})

# Clean and prepare mapping data
mapping_df['ID'] = mapping_df['ID'].astype(str)
mapping_df['Gene'] = mapping_df['Gene'].astype(str)
mapping_df = mapping_df.dropna()

# Apply gene mapping to convert probe expression to gene expression
gene_data = apply_gene_mapping(gene_data, mapping_df)

# Normalize gene symbols
gene_data = normalize_gene_symbols_in_index(gene_data)

# Save processed gene expression data
gene_data.to_csv(out_gene_data_file)

print("\nShape of gene expression data after mapping:", gene_data.shape)
print("\nFirst few mapped genes and their expression values:")
print(gene_data.head())
# Get file paths using library function
soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)

# Extract gene annotation from SOFT file and get meaningful data 
gene_annotation = get_gene_annotation(soft_file)

# Preview gene annotation data
print("Gene annotation shape:", gene_annotation.shape)
print("\nGene annotation preview:")
print(preview_df(gene_annotation))

print("\nNumber of non-null values in each column:")
print(gene_annotation.count())

# Print example rows showing the mapping information columns
print("\nSample mapping columns ('ID' and 'GENE_SYMBOL'):")
print("\nFirst 5 rows:")
print(gene_annotation[['ID', 'GENE_SYMBOL']].head().to_string())

print("\nNote: Gene mapping will use:")
print("'ID' column: Probe identifiers") 
print("'GENE_SYMBOL' column: Contains gene symbol information")