p3/preprocess/Psoriasis/code/GSE178228.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Psoriasis"
cohort = "GSE178228"

# Input paths
in_trait_dir = "../DATA/GEO/Psoriasis"
in_cohort_dir = "../DATA/GEO/Psoriasis/GSE178228"

# Output paths
out_data_file = "./output/preprocess/3/Psoriasis/GSE178228.csv"
out_gene_data_file = "./output/preprocess/3/Psoriasis/gene_data/GSE178228.csv"
out_clinical_data_file = "./output/preprocess/3/Psoriasis/clinical_data/GSE178228.csv"
json_path = "./output/preprocess/3/Psoriasis/cohort_info.json"

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

# Extract background info and clinical data using specified prefixes
background_info, clinical_data = get_background_and_clinical_data(
    matrix_file,
    prefixes_a=['!Series_title', '!Series_summary', '!Series_overall_design'],
    prefixes_b=['!Sample_geo_accession', '!Sample_characteristics_ch1']
)

# 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
# From the background info we can see this is a study measuring gene expression in skin samples at different time points
is_gene_available = True

# 2. Variable Availability and Data Type Conversion
# For trait, we can use pasi (Psoriasis Area and Severity Index) scores from Feature 2
trait_row = 2

def convert_trait(x):
    # Extract numeric PASI value after colon 
    try:
        return float(x.split(': ')[1])
    except:
        return None

# Age and gender not provided in sample characteristics
age_row = None
gender_row = None
convert_age = None  
convert_gender = None

# 3. 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
# Since trait_row is not None, we extract clinical features
selected_clinical_df = geo_select_clinical_features(clinical_df=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)

# Preview the extracted clinical data
preview_df(selected_clinical_df)

# Save clinical data
selected_clinical_df.to_csv(out_clinical_data_file)
# Get file paths
soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)

# 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)
# Based on the probe IDs like "2824546_st", this appears to be Affymetrix microarray data 
# that needs to be mapped to gene symbols
requires_gene_mapping = True
# Extract gene annotation data
gene_metadata = get_gene_annotation(soft_file)

# Preview the annotation data 
print("Column names:", gene_metadata.columns.tolist())
print("\nFirst few rows preview:")
print(preview_df(gene_metadata))
# Looking at the gene expression data and annotation data:
# Gene expression data uses IDs like "2824546_st"
# Gene symbols are embedded in the 'gene_assignment' field and need to be extracted using extract_human_gene_symbols

# Get mapping between probe IDs and gene symbols 
mapping_data = get_gene_mapping(annotation=gene_metadata, prob_col='ID', gene_col='gene_assignment')

# Apply the mapping to convert probe-level data to gene expression data
gene_data = apply_gene_mapping(gene_data, mapping_data)
gene_data = normalize_gene_symbols_in_index(gene_data)

# Save gene data
gene_data.to_csv(out_gene_data_file)

# Print info about the number of genes
print(f"Number of original probes: {len(gene_metadata)}")
print(f"Number of probe-gene mappings: {len(mapping_data)}")
print(f"Final number of unique genes: {len(gene_data)}")
# 1. Get raw genetic data
gene_data = get_genetic_data(matrix_file)
print(f"Initial probes: {len(gene_data)}")

# 2. Get mapping between probe IDs and gene symbols 
mapping_data = get_gene_mapping(annotation=gene_metadata, prob_col='probeset_id', gene_col='gene_assignment')
print(f"Mappings found: {len(mapping_data)}")

# 3. Apply mapping and normalize
gene_data = apply_gene_mapping(gene_data, mapping_data) 
print(f"Genes after mapping: {len(gene_data)}")
gene_data = normalize_gene_symbols_in_index(gene_data)
print(f"Genes after normalization: {len(gene_data)}")
gene_data.to_csv(out_gene_data_file)

# 2. Link clinical and genetic data
# Clinical data is available with PASI scores as trait values
clinical_data = selected_clinical_df
linked_data = geo_link_clinical_genetic_data(clinical_data, gene_data)

# 3. Handle missing values
linked_data = handle_missing_values(linked_data, trait)

# 4. Check for biased features
is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)

# 5. Validate and save metadata
is_usable = validate_and_save_cohort_info(
    is_final=True,
    cohort=cohort, 
    info_path=json_path,
    is_gene_available=True,
    is_trait_available=True,
    is_biased=is_biased,
    df=linked_data,
    note="Gene expression data with PASI scores as trait values from skin biopsies."
)

# 6. Save if usable
if is_usable:
    linked_data.to_csv(out_data_file)
# Looking at the gene expression data and gene annotation data:
# Gene expression data has IDs like "2824546_st"
# Gene annotation dictionary has column 'gene_assignment' that contains gene symbols

# Prepare annotation data with correct column names and no duplicates
gene_metadata = gene_metadata.rename(columns={'probeset_id': 'ID'})
gene_metadata = gene_metadata.drop_duplicates(subset=['ID'])

# Get gene mapping from annotation data
# Use 'ID' as identifier column and 'gene_assignment' as gene symbol column 
mapping_data = get_gene_mapping(annotation=gene_metadata, prob_col='ID', gene_col='gene_assignment')

# Apply the mapping to convert probe-level data to gene expression data
gene_data = apply_gene_mapping(gene_data, mapping_data)

# Normalize gene symbols
gene_data = normalize_gene_symbols_in_index(gene_data)

# Save gene data
gene_data.to_csv(out_gene_data_file)

# Print info about the number of genes
print(f"Number of original probes: {len(gene_metadata)}")
print(f"Number of probe-gene mappings: {len(mapping_data)}")
print(f"Final number of unique genes: {len(gene_data)}")