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# Path Configuration
from tools.preprocess import *
# Processing context
trait = "Kidney_Chromophobe"
cohort = "GSE68606"
# Input paths
in_trait_dir = "../DATA/GEO/Kidney_Chromophobe"
in_cohort_dir = "../DATA/GEO/Kidney_Chromophobe/GSE68606"
# Output paths
out_data_file = "./output/preprocess/3/Kidney_Chromophobe/GSE68606.csv"
out_gene_data_file = "./output/preprocess/3/Kidney_Chromophobe/gene_data/GSE68606.csv"
out_clinical_data_file = "./output/preprocess/3/Kidney_Chromophobe/clinical_data/GSE68606.csv"
json_path = "./output/preprocess/3/Kidney_Chromophobe/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)
# Get unique values for each clinical feature
unique_values_dict = get_unique_values_by_row(clinical_data)
# Print background information
print("Background Information:")
print(background_info)
print("\nSample Characteristics:")
print(json.dumps(unique_values_dict, indent=2))
# 1. Gene Expression Data Availability
# Yes, it uses Affymetrix Human Genome U133A arrays for gene expression profiling
is_gene_available = True
# 2. Variable Availability and Data Type Conversion
# For trait data
trait_row = 7 # histology field contains chromophobe info
def convert_trait(x):
if not x or x == '--':
return None
value = x.split(': ')[-1].lower()
if 'chromophobe' in value:
return 1
return 0
# For age data
age_row = 6 # age field
def convert_age(x):
if not x or x == '--':
return None
try:
return float(x.split(': ')[-1])
except:
return None
# For gender data
gender_row = 5 # Sex field
def convert_gender(x):
if not x or x == '--':
return None
value = x.split(': ')[-1].lower()
if value == 'female':
return 0
elif value == 'male':
return 1
return None
# 3. Save Metadata
is_trait_available = trait_row is not None
is_usable = 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:
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 processed clinical data
preview = preview_df(selected_clinical_df)
# Save to CSV
os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
selected_clinical_df.to_csv(out_clinical_data_file)
# Extract gene expression data from the matrix file
genetic_data = get_genetic_data(matrix_file_path)
# Print first 20 row IDs
print("First 20 row IDs:")
print(genetic_data.index[:20].tolist())
# These are Affymetrix probe IDs rather than gene symbols.
# Need to map from probe IDs to human gene symbols.
requires_gene_mapping = True
# Extract gene annotation data from SOFT file
gene_metadata = get_gene_annotation(soft_file_path)
# Display information about the annotation data
print("Column names:")
print(gene_metadata.columns.tolist())
# Look at general data statistics
print("\nData shape:", gene_metadata.shape)
# Preview the first few rows
print("\nPreview of the annotation data:")
print(json.dumps(preview_df(gene_metadata), indent=2))
# Extract gene mapping information
prob_col = 'ID' # The column containing probe IDs in annotation
gene_col = 'Gene Symbol' # The column containing gene symbols in annotation
mapping_data = get_gene_mapping(gene_metadata, prob_col, gene_col)
# Apply gene mapping to convert probe-level data to gene-level data
gene_data = apply_gene_mapping(genetic_data, mapping_data)
# Preview results
print("Gene data shape:", gene_data.shape)
print("\nFirst few gene symbols:")
print(gene_data.index[:10].tolist())
# 1. Normalize gene symbols
gene_data = normalize_gene_symbols_in_index(gene_data)
gene_data.to_csv(out_gene_data_file)
# Get clinical features
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
)
# 2. Link clinical and genetic data
linked_data = geo_link_clinical_genetic_data(clinical_features, gene_data)
# 3. Handle missing values
linked_data = handle_missing_values(linked_data, trait)
# Early exit if trait values are all NaN
if linked_data[trait].isna().all():
is_biased = True
linked_data = None
else:
# 4. Judge whether features are biased and remove biased demographic features
is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
# 5. Final validation and save metadata
note = "Dataset from a cancer gene expression study using oligonucleotide microarrays, containing samples from various tissue types including kidney, lung, stomach and other organs, with both tumor and normal tissues."
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
)
# 6. Save the linked data only if it's usable
if is_usable:
os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
linked_data.to_csv(out_data_file) |