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# Path Configuration
from tools.preprocess import *
# Processing context
trait = "Amyotrophic_Lateral_Sclerosis"
cohort = "GSE61322"
# Input paths
in_trait_dir = "../DATA/GEO/Amyotrophic_Lateral_Sclerosis"
in_cohort_dir = "../DATA/GEO/Amyotrophic_Lateral_Sclerosis/GSE61322"
# Output paths
out_data_file = "./output/preprocess/3/Amyotrophic_Lateral_Sclerosis/GSE61322.csv"
out_gene_data_file = "./output/preprocess/3/Amyotrophic_Lateral_Sclerosis/gene_data/GSE61322.csv"
out_clinical_data_file = "./output/preprocess/3/Amyotrophic_Lateral_Sclerosis/clinical_data/GSE61322.csv"
json_path = "./output/preprocess/3/Amyotrophic_Lateral_Sclerosis/cohort_info.json"
# Get file paths
soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
# Extract background info and clinical data
background_info, clinical_data = get_background_and_clinical_data(matrix_file)
# 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 # Microarray data is mentioned in Series_overall_design
# 2.1 Data Availability
trait_row = 0 # ALS status can be inferred from diagnosis
age_row = None # Age not available
gender_row = None # Gender not available
# 2.2 Data Type Conversion Functions
def convert_trait(value):
"""Convert diagnosis to binary trait (ALS)"""
if not isinstance(value, str):
return None
if ':' in value:
value = value.split(':')[1].strip().lower()
if value == 'affected':
return 1
elif value == 'carrier':
return 0
return None
def convert_age(value):
return None # Not used
def convert_gender(value):
return None # Not used
# 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. Clinical Feature Extraction
if trait_row is not None:
clinical_df = geo_select_clinical_features(clinical_df=clinical_data,
trait=trait,
trait_row=trait_row,
convert_trait=convert_trait)
print("Preview of processed clinical data:")
print(preview_df(clinical_df))
clinical_df.to_csv(out_clinical_data_file)
# 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)
# The gene identifiers start with "ILMN_", indicating they are Illumina probe IDs
# These need to be mapped to standard human gene symbols
requires_gene_mapping = True
# Extract gene annotation from SOFT file and get meaningful data
gene_annotation = get_gene_annotation(soft_file)
# Preview gene annotation data
print("Gene annotation shape:", gene_annotation.shape)
print("\nGene annotation preview:")
print(preview_df(gene_annotation))
print("\nNumber of non-null values in each column:")
print(gene_annotation.count())
print("\nNote: Gene mapping will use:")
print("'ID' column: Probe identifiers")
print("'Symbol' column: Gene name mapping")
# 1. Identify mapping columns from gene annotation
mapping_df = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='Symbol')
# 2 & 3. Apply gene mapping to convert probe data to gene expression data
gene_data = apply_gene_mapping(expression_df=gene_data, mapping_df=mapping_df)
# Print preview to verify mapping result
print("Shape of mapped gene expression data:", gene_data.shape)
print("\nPreview of mapped gene expression data:")
print(gene_data.head())
# Save gene expression data
gene_data.to_csv(out_gene_data_file)
# 1. Normalize gene symbols
gene_data = normalize_gene_symbols_in_index(gene_data)
# Save normalized gene data
gene_data.to_csv(out_gene_data_file)
# 2. Link clinical and genetic data
try:
clinical_data = pd.read_csv(out_clinical_data_file, index_col=0)
linked_data = geo_link_clinical_genetic_data(clinical_data, gene_data)
# 3. Handle missing values
linked_data = handle_missing_values(linked_data, trait)
# 4. Determine if features are biased
is_trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
# 5. Validate and save cohort info
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_trait_biased,
df=linked_data,
note="Gene expression data successfully mapped and linked with clinical features"
)
# 6. Save linked data only if usable AND trait is not biased
if is_usable and not is_trait_biased:
linked_data.to_csv(out_data_file)
except Exception as e:
print(f"Error in data linking and processing: {str(e)}")
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=True,
df=pd.DataFrame(),
note=f"Data processing failed: {str(e)}"
) |