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# Path Configuration
from tools.preprocess import *
# Processing context
trait = "Sarcoma"
cohort = "GSE159848"
# Input paths
in_trait_dir = "../DATA/GEO/Sarcoma"
in_cohort_dir = "../DATA/GEO/Sarcoma/GSE159848"
# Output paths
out_data_file = "./output/preprocess/3/Sarcoma/GSE159848.csv"
out_gene_data_file = "./output/preprocess/3/Sarcoma/gene_data/GSE159848.csv"
out_clinical_data_file = "./output/preprocess/3/Sarcoma/clinical_data/GSE159848.csv"
json_path = "./output/preprocess/3/Sarcoma/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
# The dataset uses "Agilent-014850 Whole Human Genome Microarray", so it contains gene expression data
is_gene_available = True
# 2. Variable Availability and Row Identification
trait_row = 3 # metastasis data is available in row 3
age_row = 1 # age data is available in row 1
gender_row = 0 # gender data is available in row 0
# Define conversion functions
def convert_trait(value: str) -> Optional[float]:
"""Convert metastasis status to binary: 0 = no metastasis, 1 = has metastasis"""
if not value or 'metastasis:' not in value:
return None
try:
return float(value.split(': ')[1])
except:
return None
def convert_age(value: str) -> Optional[float]:
"""Convert age to continuous values"""
if not value or 'age:' not in value:
return None
try:
return float(value.split(': ')[1])
except:
return None
def convert_gender(value: str) -> Optional[float]:
"""Convert gender to binary: 0 = female, 1 = male"""
if not value or 'Sex:' not in value:
return None
gender = value.split(': ')[1].strip().upper()
if gender == 'F':
return 0.0
elif gender == 'M':
return 1.0
return None
# 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,
age_row=age_row,
convert_age=convert_age,
gender_row=gender_row,
convert_gender=convert_gender
)
# Preview the data
print(preview_df(selected_clinical_df))
# Save clinical data
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])
# The identifiers start with 'A_23_P', which appear to be Agilent probe IDs rather than standard human gene symbols
# These need to be mapped to their corresponding gene symbols for analysis
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. From the preview, we can see that 'ID' contains probe identifiers matching gene expression data,
# and 'GENE_SYMBOL' contains the gene symbols
probe_col = 'ID'
gene_col = 'GENE_SYMBOL'
# 2. Get mapping between probe IDs and gene symbols
gene_mapping = get_gene_mapping(gene_annotation, probe_col, gene_col)
# 3. Convert probe-level expression to gene-level expression
gene_data = apply_gene_mapping(genetic_data, gene_mapping)
# Save gene expression data to file
gene_data.to_csv(out_gene_data_file)
# Preview result
print("\nGene expression data preview:")
print(preview_df(gene_data))
print("\nShape:", gene_data.shape)
# 1. Normalize gene symbols
gene_data = normalize_gene_symbols_in_index(gene_data)
print("Gene data shape after normalization:", gene_data.shape)
os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
gene_data.to_csv(out_gene_data_file)
# 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
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 = "This dataset contains gene expression data from myxoid liposarcoma samples, with metastasis status as the trait."
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)
else:
# Handle case where clinical features were not properly extracted
note = "Failed to extract clinical trait information from sample characteristics."
validate_and_save_cohort_info(
is_final=True,
cohort=cohort,
info_path=json_path,
is_gene_available=True,
is_trait_available=False,
is_biased=None,
df=None,
note=note
) |