p1/preprocess/Anxiety_disorder/code/GSE60491.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Anxiety_disorder"
cohort = "GSE60491"

# Input paths
in_trait_dir = "../DATA/GEO/Anxiety_disorder"
in_cohort_dir = "../DATA/GEO/Anxiety_disorder/GSE60491"

# Output paths
out_data_file = "./output/preprocess/1/Anxiety_disorder/GSE60491.csv"
out_gene_data_file = "./output/preprocess/1/Anxiety_disorder/gene_data/GSE60491.csv"
out_clinical_data_file = "./output/preprocess/1/Anxiety_disorder/clinical_data/GSE60491.csv"
json_path = "./output/preprocess/1/Anxiety_disorder/cohort_info.json"

# STEP 1

from tools.preprocess import *

# 1. Identify the paths to the SOFT file and the matrix file
soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)

# 2. Read the matrix file to obtain background information and sample characteristics data
background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
background_info, clinical_data = get_background_and_clinical_data(
    matrix_file, 
    background_prefixes, 
    clinical_prefixes
)

# 3. Obtain the sample characteristics dictionary from the clinical dataframe
sample_characteristics_dict = get_unique_values_by_row(clinical_data)

# 4. Explicitly print out all the background information and the sample characteristics dictionary
print("Background Information:")
print(background_info)
print("\nSample Characteristics Dictionary:")
print(sample_characteristics_dict)
# 1. Gene Expression Data Availability
is_gene_available = True  # The series explicitly mentions "gene expression profiling", so we consider it available.

# 2. Variable Availability and Data Type Conversion
# After examining the sample characteristics,
# we do not see any key referencing "anxiety" or "anxiety_disorder." 
# Therefore, we conclude that the trait data is not available in this dataset.
trait_row = None

# The 'age' information appears in row 0.
age_row = 0

# The 'gender' information appears in row 1 (it's labeled 'male: 0' or 'male: 1').
gender_row = 1

# Define data-type conversion functions.
def convert_trait(value: str):
    # Trait data is unavailable, so this function won't be used.
    # We'll just return None as a placeholder.
    return None

def convert_age(value: str):
    # Extract the numeric part after the colon. Convert to float if possible, else None.
    try:
        val = value.split(':', 1)[1].strip()
        return float(val)
    except:
        return None

def convert_gender(value: str):
    # The field is "male: 0" for female, "male: 1" for male.
    # We'll parse it and convert to 0 (female) or 1 (male).
    try:
        val = value.split(':', 1)[1].strip()
        return 1 if val == '1' else 0
    except:
        return None

# Determine whether trait data is available
is_trait_available = trait_row is not None

# 3. Save Metadata (initial filtering)
_ = 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. Clinical Feature Extraction
# Since trait_row is None, we skip this step.
# STEP3
# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.
gene_data = get_genetic_data(matrix_file)

# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
print(gene_data.index[:20])
# Based on the identifiers such as "7A5" (likely SLC7A5) and "A2BP1" (also known as RBFOX1),
# it appears that these gene symbols are not standardized. Therefore, mapping is required.
requires_gene_mapping = True
# STEP5
# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
gene_annotation = get_gene_annotation(soft_file)

# 2. Use the 'preview_df' function from the library to preview the data and print out the results.
print("Gene annotation preview:")
print(preview_df(gene_annotation))
# STEP: Gene Identifier Mapping

# 1. Identify which columns match the gene expression data
#    Based on the previous previews, 'ID' matches the expression data identifiers,
#    and 'ORF' contains the gene symbols.

mapping_df = get_gene_mapping(annotation=gene_annotation, prob_col='ID', gene_col='ORF')

# 2. Convert probe-level measurements to gene expression data by applying the gene mapping
gene_data = apply_gene_mapping(expression_df=gene_data, mapping_df=mapping_df)

# Print a small preview of the resulting mapped gene data
print("Mapped gene data shape:", gene_data.shape)
print("First 5 mapped gene symbols:", gene_data.index[:5])
# STEP 7: Data Normalization and Linking

# 1. Normalize gene symbols in the obtained gene expression data
normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
normalized_gene_data.to_csv(out_gene_data_file)
print(f"Saved normalized gene data to {out_gene_data_file}")

# 2. Link the clinical and genetic data on sample IDs
linked_data = geo_link_clinical_genetic_data(clinical_data, normalized_gene_data)

# Since trait_row is None, trait data is unavailable, we cannot perform final trait-based validation.

# 3. We skip any trait-based missing value handling and bias checking because there's no trait.

# 4. Perform only partial metadata validation (is_final=False) since no trait data is available.
is_usable = validate_and_save_cohort_info(
    is_final=False,
    cohort=cohort,
    info_path=json_path,
    is_gene_available=True,
    is_trait_available=False
)

# 5. We do not save a final linked data file because trait-based analysis is not possible.
print("Dataset does not have the specified trait data; no final data output generated.")