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# Path Configuration
from tools.preprocess import *
# Processing context
trait = "Sjögrens_Syndrome"
cohort = "GSE143153"
# Input paths
in_trait_dir = "../DATA/GEO/Sjögrens_Syndrome"
in_cohort_dir = "../DATA/GEO/Sjögrens_Syndrome/GSE143153"
# Output paths
out_data_file = "./output/preprocess/3/Sjögrens_Syndrome/GSE143153.csv"
out_gene_data_file = "./output/preprocess/3/Sjögrens_Syndrome/gene_data/GSE143153.csv"
out_clinical_data_file = "./output/preprocess/3/Sjögrens_Syndrome/clinical_data/GSE143153.csv"
json_path = "./output/preprocess/3/Sjögrens_Syndrome/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
# From background info, it's Agilent Whole Human Genome microarray data
is_gene_available = True
# 2.1 Identify rows containing trait, age and gender data
trait_row = 1 # aecg disease classification, categorical values
age_row = 2 # age values
gender_row = 3 # Sex values
# 2.2 Data type conversion functions
def convert_trait(value):
"""Convert trait data (Primary SS vs non-SS) to binary."""
if value is None:
return None
# Extract value after colon and strip whitespace
val = value.split(':')[-1].strip()
if val == 'Primary SS':
return 1
elif val == 'non-SS':
return 0
return None
def convert_age(value):
"""Convert age to continuous numeric."""
if value is None:
return None
try:
# Extract number after colon
age = int(value.split(':')[-1].strip())
return age
except:
return None
def convert_gender(value):
"""Convert gender to binary (F:0, M:1)."""
if value is None:
return None
# Extract value after colon and strip whitespace
val = value.split(':')[-1].strip()
if val == 'F':
return 0
elif val == 'M':
return 1
return None
# 3. Save metadata - initial filtering
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. Extract clinical features since trait_row is not None
if trait_row is not None:
selected_clinical = 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 data
print(preview_df(selected_clinical))
# Save to CSV
selected_clinical.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])
# Review gene identifiers
# The identifiers appear to be row numbers rather than human gene symbols
# This suggests these are probe IDs and need mapping to gene symbols
requires_gene_mapping = True
# Extract gene annotation data
gene_annotation = get_gene_annotation(soft_file_path)
# Preview column names and values from annotation dataframe
print("Gene annotation DataFrame preview:")
print(preview_df(gene_annotation))
# 1. Identify relevant columns for mapping (ID -> Gene Symbol)
# From gene annotation preview, 'ID' matches probe identifiers and 'GeneName' has gene symbols
prob_col = 'ID'
gene_col = 'GeneName'
# 2. Get mapping dataframe
mapping_data = get_gene_mapping(gene_annotation, prob_col, gene_col)
# 3. Apply mapping to convert probe-level data to gene expression
gene_data = apply_gene_mapping(genetic_data, mapping_data)
# Preview result
print("\nGene expression data preview:")
print(preview_df(gene_data))
print("\nShape:", gene_data.shape)
# 1. Normalize gene symbols in gene expression data
gene_data = normalize_gene_symbols_in_index(gene_data)
os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
gene_data.to_csv(out_gene_data_file)
print("\nGene data shape (normalized gene-level):", gene_data.shape)
# Load clinical data previously processed
selected_clinical_df = pd.read_csv(out_clinical_data_file, index_col=0)
print("\nClinical data shape:", selected_clinical_df.shape)
# 2. Link clinical and genetic data using normalized gene-level data
linked_data = geo_link_clinical_genetic_data(selected_clinical_df, gene_data)
print("\nLinked data shape:", linked_data.shape)
# 3. Handle missing values systematically
if trait in linked_data.columns:
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 = "Data was successfully preprocessed from probe-level to gene-level expression using gene symbol normalization with NCBI Gene database."
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 and not biased
if is_usable and not trait_biased:
os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
linked_data.to_csv(out_data_file)