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# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Retinoblastoma"
cohort = "GSE25307"

# Input paths
in_trait_dir = "../DATA/GEO/Retinoblastoma"
in_cohort_dir = "../DATA/GEO/Retinoblastoma/GSE25307"

# Output paths
out_data_file = "./output/preprocess/3/Retinoblastoma/GSE25307.csv"
out_gene_data_file = "./output/preprocess/3/Retinoblastoma/gene_data/GSE25307.csv"
out_clinical_data_file = "./output/preprocess/3/Retinoblastoma/clinical_data/GSE25307.csv"
json_path = "./output/preprocess/3/Retinoblastoma/cohort_info.json"

# Get file paths
soft_file_path, matrix_file_path = geo_get_relevant_filepaths(in_cohort_dir)

# Get background info and clinical data
background_info, clinical_data = get_background_and_clinical_data(matrix_file_path)
print("Background Information:")
print(background_info)
print("\nSample Characteristics:")

# Get dictionary of unique values per row 
unique_values_dict = get_unique_values_by_row(clinical_data)
for row, values in unique_values_dict.items():
    print(f"\n{row}:")
    print(values)
# 1. Gene expression data availability
# Yes - this is a gene expression profiling study using oligonucleotide microarrays
is_gene_available = True

# 2.1 Data locations
trait_row = 2  # familial status indicates BRCA mutation status
age_row = None  # age not available
gender_row = None  # gender not available

# 2.2 Data type conversion functions
def convert_trait(x):
    if not x or pd.isna(x):
        return None
    value = x.split(': ')[1].strip().lower()
    if value == 'brca1':
        return 1  # BRCA1 mutation carrier
    elif value in ['sporadic', 'brca2', 'familial', 'non malignant']:
        return 0  # Not BRCA1 mutation carrier
    return None

def convert_age(x):
    return None  # Not used since age data unavailable

def convert_gender(x):
    return None  # Not used since gender data unavailable

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

# 4. Extract clinical features
selected_clinical_df = geo_select_clinical_features(clinical_df=clinical_data,
                                                  trait=trait,
                                                  trait_row=trait_row,
                                                  convert_trait=convert_trait)

# Preview and save
print("Preview of clinical data:")
print(preview_df(selected_clinical_df))

os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
selected_clinical_df.to_csv(out_clinical_data_file)
# Get gene expression data from matrix file
genetic_data = get_genetic_data(matrix_file_path)

# Examine data structure
print("Data structure and head:")
print(genetic_data.head())

print("\nShape:", genetic_data.shape)

print("\nFirst 20 row IDs (gene/probe identifiers):")
print(list(genetic_data.index)[:20])

# Get a few column names to verify sample IDs
print("\nFirst 5 column names:")
print(list(genetic_data.columns)[:5])
requires_gene_mapping = True
# Try reading gene annotation with different encodings
encodings = ['utf-8', 'latin-1', 'iso-8859-1', 'cp1252']

def try_read_gene_annotation(file_path, encoding):
    try:
        with gzip.open(file_path, 'rt', encoding=encoding) as f:
            lines = [line.strip() for line in f if not line.startswith(('^', '!', '#'))]
            data = pd.read_csv(io.StringIO('\n'.join(lines)), sep='\t')
            return data
    except Exception:
        return None

gene_annotation = None
for enc in encodings:
    gene_annotation = try_read_gene_annotation(soft_file_path, enc)
    if gene_annotation is not None:
        break

if gene_annotation is None:
    raise RuntimeError("Failed to read gene annotation with any encoding")

# Display column names and preview data
print("Column names:")
print(gene_annotation.columns)

print("\nPreview of gene annotation data:")
print(preview_df(gene_annotation))
# Get gene mapping from probe IDs to gene symbols
mapping_df = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='OligoSet_geneSymbol')

# Convert probe-level measurements to gene expression data
gene_data = apply_gene_mapping(genetic_data, mapping_df)

# Preview results
print("\nShape of gene expression data after mapping:", gene_data.shape)
print("\nFirst 5 gene symbols:", list(gene_data.index)[:5])
# Reload clinical data that was processed earlier
selected_clinical_df = pd.read_csv(out_clinical_data_file, index_col=0)

# 1. Normalize gene symbols 
gene_data = normalize_gene_symbols_in_index(gene_data)
gene_data.to_csv(out_gene_data_file)

# 2. Link clinical and genetic data
linked_data = geo_link_clinical_genetic_data(selected_clinical_df, gene_data)

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

# 4. Check for bias in trait and demographic features
trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)

# 5. Final validation and information saving
note = "Dataset contains gene expression data from primary human retinoblastoma samples profiled with Affymetrix microarray."
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=trait_biased,
    df=linked_data,
    note=note
)

# 6. Save linked data only if usable 
if is_usable:
    os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
    linked_data.to_csv(out_data_file)