p3/preprocess/Fibromyalgia/code/GSE67311.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Fibromyalgia"
cohort = "GSE67311"

# Input paths
in_trait_dir = "../DATA/GEO/Fibromyalgia"
in_cohort_dir = "../DATA/GEO/Fibromyalgia/GSE67311"

# Output paths
out_data_file = "./output/preprocess/3/Fibromyalgia/GSE67311.csv"
out_gene_data_file = "./output/preprocess/3/Fibromyalgia/gene_data/GSE67311.csv"
out_clinical_data_file = "./output/preprocess/3/Fibromyalgia/clinical_data/GSE67311.csv"
json_path = "./output/preprocess/3/Fibromyalgia/cohort_info.json"

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

# Extract background info and clinical data from the matrix file
background_info, clinical_data = get_background_and_clinical_data(matrix_file_path)

# Get dictionary of unique values per row in clinical data
unique_values_dict = get_unique_values_by_row(clinical_data)

# Print background info
print("Background Information:")
print("-" * 50)
print(background_info)
print("\n")

# Print clinical data unique values
print("Sample Characteristics:")
print("-" * 50)
for row, values in unique_values_dict.items():
    print(f"{row}:")
    print(f"  {values}")
    print()
# 1. Gene Expression Data Availability
# Yes, this dataset contains gene expression data from Affymetrix Human Gene arrays
is_gene_available = True

# 2.1 Data Availability

# trait_row = 0 - diagnosis information is in row 0
trait_row = 0

# Age information is not available in the sample characteristics
age_row = None

# Gender information is not available in the sample characteristics
gender_row = None

# 2.2 Data Type Conversion Functions

def convert_trait(x):
    if pd.isna(x):
        return None
    # Extract value after colon and strip whitespace
    value = x.split(':')[1].strip().lower()
    # Convert to binary: fibromyalgia = 1, healthy control = 0
    if 'fibromyalgia' in value:
        return 1
    elif 'healthy control' in value:
        return 0
    return None

# Age conversion function not needed since data not available
convert_age = None

# Gender conversion function not needed since data not available
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 need to extract clinical features
clinical_features = 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 features
print("Preview of clinical features:")
print(preview_df(clinical_features))

# Save clinical features
clinical_features.to_csv(out_clinical_data_file)
# Extract gene expression data
genetic_data = get_genetic_data(matrix_file_path)

# Print first 20 probe IDs
print("First 20 probe IDs:")
print(genetic_data.index[:20])
# Looking at the probe IDs (e.g. '7892501'), these are Illumina probe IDs, not human gene symbols.
# They need to be mapped to HGNC gene symbols for consistent analysis.

requires_gene_mapping = True
# Extract gene annotation from SOFT file
gene_annotation = get_gene_annotation(soft_file_path)

# Preview column names and first few values
preview_dict = preview_df(gene_annotation)
print("Column names and preview values:")
for col, values in preview_dict.items():
    print(f"\n{col}:")
    print(values)
# Identify columns for mapping: ID for probes and gene_assignment for gene symbols
prob_col = 'ID'
gene_col = 'gene_assignment'

# Filter out rows where gene_assignment is '---' as they won't map to genes
filtered_annotation = gene_annotation[gene_annotation['gene_assignment'] != '---']

# Get the mapping between probe IDs and gene symbols
mapping_data = get_gene_mapping(filtered_annotation, prob_col, gene_col)

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

# Normalize gene symbols using the NCBI Gene database
gene_data = normalize_gene_symbols_in_index(gene_data)

# Preview results
print("\nFirst 5 rows of the gene mapping:")
print(mapping_data.head())

print("\nFirst 5 rows of the gene expression data:")
print(gene_data.head())
# 1. Normalize gene symbols and save normalized gene data
normalized_gene_data = normalize_gene_symbols_in_index(gene_data) 
normalized_gene_data.to_csv(out_gene_data_file)

# Read the processed clinical data file
clinical_df = pd.read_csv(out_clinical_data_file, index_col=0)

# Link clinical and genetic data using the normalized gene data
linked_data = geo_link_clinical_genetic_data(clinical_df, normalized_gene_data)

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

# Detect bias in trait and demographic features, remove biased demographic features
is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)

# Validate data quality and save cohort info
note = "Expression data comparing patients with Essential Thrombocythemia to controls with other myeloproliferative disorders (PMF, PV). No age or gender data available."
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=note
)

# Save linked data if usable
if is_usable:
    linked_data.to_csv(out_data_file)
else:
    print(f"Dataset {cohort} did not pass quality validation and will not be saved.")