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# Path Configuration
from tools.preprocess import *
# Processing context
trait = "Psoriasis"
cohort = "GSE254707"
# Input paths
in_trait_dir = "../DATA/GEO/Psoriasis"
in_cohort_dir = "../DATA/GEO/Psoriasis/GSE254707"
# Output paths
out_data_file = "./output/preprocess/3/Psoriasis/GSE254707.csv"
out_gene_data_file = "./output/preprocess/3/Psoriasis/gene_data/GSE254707.csv"
out_clinical_data_file = "./output/preprocess/3/Psoriasis/clinical_data/GSE254707.csv"
json_path = "./output/preprocess/3/Psoriasis/cohort_info.json"
# Get file paths
soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
# Extract background info and clinical data using specified prefixes
background_info, clinical_data = get_background_and_clinical_data(
matrix_file,
prefixes_a=['!Series_title', '!Series_summary', '!Series_overall_design'],
prefixes_b=['!Sample_geo_accession', '!Sample_characteristics_ch1']
)
# Get unique values per clinical feature
sample_characteristics = get_unique_values_by_row(clinical_data)
# Print background info
print("Dataset Background Information:")
print(f"{background_info}\n")
# Print sample characteristics
print("Sample Characteristics:")
for feature, values in sample_characteristics.items():
print(f"Feature: {feature}")
print(f"Values: {values}\n")
# 1. Gene Expression Data Availability
is_gene_available = True # The title and background summary indicate this is transcriptomic data (RNA-seq)
# 2. Variable Availability and Data Type Conversion
# Trait - from Feature 5 (diagnosis)
trait_row = 5
def convert_trait(value):
if not value or ":" not in value:
return None
diagnosis = value.split(":")[1].strip()
if diagnosis == "Psoriasis":
return 1
elif diagnosis == "Healthy":
return 0
return None
# Age - not available
age_row = None
convert_age = None
# Gender - not available
gender_row = None
convert_gender = None
# 3. Save initial metadata
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=(trait_row is not None)
)
# 4. Extract clinical features if available
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
preview = preview_df(selected_clinical)
print("Clinical data preview:", preview)
# Save to file
os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
selected_clinical.to_csv(out_clinical_data_file)
# Get file paths
soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
# First inspect file structure more thoroughly
print("Scanning file for matrix data marker:")
marker_line_num = None
data_header = None
with gzip.open(matrix_file, 'rt', encoding='utf-8') as f:
for i, line in enumerate(f):
if i < 5: # Show first few lines
print(f"Line {i}: {line.strip()}")
if "!series_matrix_table_begin" in line.lower():
marker_line_num = i
print(f"\nFound matrix marker at line {i}")
# Get the next line which should be the header
data_header = next(f).strip()
print(f"Header line: {data_header}")
# Get a few data lines
print("\nFirst few data lines:")
for _ in range(3):
print(next(f).strip())
break
if marker_line_num is None:
print("\nWarning: Matrix data marker not found!")
# Try reading the gene expression data
if marker_line_num is not None:
try:
gene_data = get_genetic_data(matrix_file)
# Print first 20 row IDs and shape of data
print("\nShape of gene expression data:", gene_data.shape)
print("\nFirst few rows of data:")
print(gene_data.head())
print("\nFirst 20 gene/probe identifiers:")
print(gene_data.index[:20])
except Exception as e:
print(f"\nError reading gene data: {str(e)}")
else:
print("Cannot read gene data - matrix marker not found")
# Get file paths
soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
# First check the data structure
with gzip.open(matrix_file, 'rt', encoding='utf-8') as f:
# Skip to matrix line
for line in f:
if "!series_matrix_table_begin" in line:
# Read header and first few lines to inspect format
header = next(f).strip()
print("\nPeeking at matrix data structure:")
for _ in range(3): # Show first 3 data lines
print(next(f).strip())
break
# Modify read_csv parameters to handle the data format
def get_genetic_data_modified(file_path: str, marker: str = "!series_matrix_table_begin") -> pd.DataFrame:
# Determine rows to skip
with gzip.open(file_path, 'rt') as file:
for i, line in enumerate(file):
if marker in line:
skip_rows = i + 1
break
else:
raise ValueError(f"Marker '{marker}' not found")
# Read with modified parameters for quoted data
genetic_data = pd.read_csv(file_path,
compression='gzip',
skiprows=skip_rows,
comment='!',
delimiter='\t',
quotechar='"',
on_bad_lines='skip')
# Process column names to remove quotes
genetic_data.columns = genetic_data.columns.str.strip('"')
# Rename and set index
genetic_data = genetic_data.rename(columns={'ID_REF': 'ID'}).astype({'ID': 'str'})
genetic_data.set_index('ID', inplace=True)
return genetic_data
# Extract gene expression data using modified function
gene_data = get_genetic_data_modified(matrix_file)
# Print diagnostic information
print("\nShape of gene expression data:", gene_data.shape)
print("\nFirst few rows of data:")
print(gene_data.head())
print("\nFirst 20 gene/probe identifiers:")
print(gene_data.index[:20]) |