p1/preprocess/Adrenocortical_Cancer/code/GSE49278.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Adrenocortical_Cancer"
cohort = "GSE49278"

# Input paths
in_trait_dir = "../DATA/GEO/Adrenocortical_Cancer"
in_cohort_dir = "../DATA/GEO/Adrenocortical_Cancer/GSE49278"

# Output paths
out_data_file = "./output/preprocess/1/Adrenocortical_Cancer/GSE49278.csv"
out_gene_data_file = "./output/preprocess/1/Adrenocortical_Cancer/gene_data/GSE49278.csv"
out_clinical_data_file = "./output/preprocess/1/Adrenocortical_Cancer/clinical_data/GSE49278.csv"
json_path = "./output/preprocess/1/Adrenocortical_Cancer/cohort_info.json"

# STEP1
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("Sample Characteristics Dictionary:")
print(sample_characteristics_dict)
# 1. Gene Expression Data Availability
is_gene_available = True  # Based on the background info: "Expression profiling by array ..."

# 2. Variable Availability and Data Type Conversion
# Observing the sample characteristics, key=2 has only one unique value (Adrenocortical carcinoma),
# so that is constant and not useful for association analyses, thus trait_row = None.
trait_row = None

# key=0 shows multiple age values => available
age_row = 0

# key=1 shows two gender values => available
gender_row = 1

# Define conversion functions
def convert_trait(value: str):
    # Since trait data is effectively not available (constant),
    # this function returns None
    return None

def convert_age(value: str):
    # Typical format: "age (years): 70"
    # Convert the part after the colon to a numeric type
    try:
        val_str = value.split(':', 1)[1].strip()
        return float(val_str)
    except:
        return None

def convert_gender(value: str):
    # Typical format: "gender: F" or "gender: M"
    # Convert F -> 0, M -> 1
    try:
        val_str = value.split(':', 1)[1].strip().upper()
        if val_str == 'F':
            return 0
        elif val_str == 'M':
            return 1
        else:
            return None
    except:
        return None

# 3. Save Metadata (initial filtering)
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. Clinical Feature Extraction
# Skip this step because trait_row is None (no trait data available).
# 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])
print("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))
# STEP6: Gene Identifier Mapping

# After reviewing the annotation DataFrame columns:
#   ['ID', 'RANGE_STRAND', 'RANGE_START', 'RANGE_END', 'total_probes', 'GB_ACC', 'SPOT_ID', 'RANGE_GB']
# we see that 'GB_ACC' usually contains "NR_" transcripts and 'SPOT_ID' has genomic coordinates. Neither appear to provide
# valid gene symbols recognizable by extract_human_gene_symbols (which filters out NR_, XR_, LOC, etc.). 
# Therefore, mapping to standard gene symbols is not possible here. 
# We'll retain the original probe-level data without attempting gene-level aggregation.

print("No suitable gene symbol column found. Proceeding with probe-level data only.")
# The 'gene_data' DataFrame remains as probe-level data.
# No further action is required for mapping in this dataset.
# STEP 7: Data Normalization and Linking

# In this dataset, we determined in Step 2 that trait data is not available (trait_row = None).
# Therefore, we cannot link clinical and genetic data or perform trait-based processing.
# Nonetheless, we can still normalize probe-level data to standard gene symbols and finalize validation.

# 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, index=True)

# 2. Since trait data is missing, skip linking clinical and genetic data, 
#    skip missing-value handling and bias detection for the trait.

# 3. Conduct final validation and record info.
#    Since trait data is unavailable, set is_trait_available=False, 
#    pass a dummy/empty DataFrame and is_biased=False (it won't be used).
dummy_df = pd.DataFrame()
is_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=False,
    df=dummy_df,
    note="No trait data found; skipped clinical-linking steps."
)

# 4. If the dataset were usable, save. In this scenario, it's not usable due to missing trait data.
if is_usable:
    dummy_df.to_csv(out_data_file, index=True)