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# Path Configuration
from tools.preprocess import *
# Processing context
trait = "Psoriasis"
cohort = "GSE158448"
# Input paths
in_trait_dir = "../DATA/GEO/Psoriasis"
in_cohort_dir = "../DATA/GEO/Psoriasis/GSE158448"
# Output paths
out_data_file = "./output/preprocess/3/Psoriasis/GSE158448.csv"
out_gene_data_file = "./output/preprocess/3/Psoriasis/gene_data/GSE158448.csv"
out_clinical_data_file = "./output/preprocess/3/Psoriasis/clinical_data/GSE158448.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
# Based on the series title and design description, this is a gene expression study
# examining IL-17 family cytokines signaling in psoriasis
is_gene_available = True
# 2. Feature Analysis
# From sample characteristics, we can see treatment groups compared at the molecular level
# The untreated samples can serve as controls while treated samples represent cases
trait_row = 4 # treatment feature
age_row = None # age not available
gender_row = None # gender not available
def convert_trait(value: str) -> int:
"""Convert treatment status to binary trait"""
if not value or not isinstance(value, str):
return None
value = value.split(': ')[-1].lower()
# untreated samples are controls (0), treated samples are cases (1)
return 0 if 'untreated' in value else 1
def convert_age(value: str) -> float:
"""Placeholder function since age is not available"""
return None
def convert_gender(value: str) -> int:
"""Placeholder function since gender is not available"""
return None
# 3. Save initial 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. Extract clinical features
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)
print("Preview of processed clinical data:")
print(preview)
# Save clinical data
selected_clinical_df.to_csv(out_clinical_data_file)
# Get file paths
soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
# Extract gene expression data from matrix file
gene_data = get_genetic_data(matrix_file)
# Print first 20 row IDs and shape of data to help debug
print("Shape 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])
# Inspect a snippet of raw file to verify identifier format
import gzip
with gzip.open(matrix_file, 'rt', encoding='utf-8') as f:
lines = []
for i, line in enumerate(f):
if "!series_matrix_table_begin" in line:
# Get the next 5 lines after the marker
for _ in range(5):
lines.append(next(f).strip())
break
print("\nFirst few lines after matrix marker in raw file:")
for line in lines:
print(line)
# Looking at the identifiers, they seem to be Illumina probe IDs starting with "16650"
# These need to be mapped to standard human gene symbols
requires_gene_mapping = True
# Extract gene annotation data
gene_metadata = get_gene_annotation(soft_file)
# Preview the annotation data
print("Column names:", gene_metadata.columns.tolist())
print("\nFirst few rows preview:")
print(preview_df(gene_metadata))
# 1. ID column in annotation data matches probe IDs in expression data
# gene_assignment column contains gene symbol info in format "SYMBOL // DESCRIPTION"
prob_col = 'ID'
gene_col = 'gene_assignment'
# 2. Extract mapping data from annotation
mapping_data = get_gene_mapping(gene_metadata, prob_col, gene_col)
# 3. Apply mapping to convert probe level data to gene level
gene_data = apply_gene_mapping(gene_data, mapping_data)
# Preview output
print("Shape of gene expression data after mapping:", gene_data.shape)
print("\nFirst few rows of mapped data:")
print(gene_data.head())
# 1. Normalize gene symbols
gene_data = normalize_gene_symbols_in_index(gene_data)
gene_data.to_csv(out_gene_data_file)
# Clinical data is not available (trait_row was None), so skip remaining steps and mark dataset as not usable
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=gene_data,
note="Contains gene expression data but lacks clinical information needed for trait association studies."
) |