p1/preprocess/Schizophrenia/code/GSE273630.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Schizophrenia"
cohort = "GSE273630"

# Input paths
in_trait_dir = "../DATA/GEO/Schizophrenia"
in_cohort_dir = "../DATA/GEO/Schizophrenia/GSE273630"

# Output paths
out_data_file = "./output/preprocess/1/Schizophrenia/GSE273630.csv"
out_gene_data_file = "./output/preprocess/1/Schizophrenia/gene_data/GSE273630.csv"
out_clinical_data_file = "./output/preprocess/1/Schizophrenia/clinical_data/GSE273630.csv"
json_path = "./output/preprocess/1/Schizophrenia/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. Determine if the dataset likely contains gene expression data
is_gene_available = True  # Based on the custom Nanostring panel, we conclude gene expression data is present.

# 2. Check data availability for trait, age, and gender by looking at the sample characteristics dictionary.
#    According to the background information and the sample characteristics, there is only one record:
#       {0: ['tissue: Peripheral blood cells']}
#    This does not provide any variable of interest (trait, age, or gender).
#    Hence, all rows are None.

trait_row = None
age_row = None
gender_row = None

# 2.2 Define conversion functions for trait, age, and gender.
#     Even though data is not available, we provide them as placeholders.

def convert_trait(value: str) -> Optional[int]:
    # Because there's no trait info available, return None for any input
    return None

def convert_age(value: str) -> Optional[float]:
    # Because there's no age info available, return None for any input
    return None

def convert_gender(value: str) -> Optional[int]:
    # Because there's no gender info available, return None for any input
    return None

# 3. Save metadata by performing initial filtering
#    Trait data availability can be determined by whether trait_row is None.
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 is skipped, because trait_row is None.
# STEP3
import gzip
import pandas as pd

try:
    # 1. Attempt to extract gene expression data using the library function
    gene_data = get_genetic_data(matrix_file)
except KeyError:
    # Fallback: the expected "ID_REF" column may be absent, so manually parse the file
    # and rename the first column to "ID".
    marker = "!series_matrix_table_begin"
    skip_rows = None

    # Determine how many rows to skip before the matrix data begins
    with gzip.open(matrix_file, 'rt') as f:
        for i, line in enumerate(f):
            if marker in line:
                skip_rows = i + 1
                break
        else:
            raise ValueError(f"Marker '{marker}' not found in the file.")

    # Read the data from the determined position
    gene_data = pd.read_csv(
        matrix_file,
        compression='gzip',
        skiprows=skip_rows,
        comment='!',
        delimiter='\t',
        on_bad_lines='skip'
    )

    # If a different column name is used instead of 'ID_REF', rename appropriately
    if 'ID_REF' in gene_data.columns:
        gene_data.rename(columns={'ID_REF': 'ID'}, inplace=True)
    else:
        first_col = gene_data.columns[0]
        gene_data.rename(columns={first_col: 'ID'}, inplace=True)

    gene_data['ID'] = gene_data['ID'].astype(str)
    gene_data.set_index('ID', inplace=True)

# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
print(gene_data.index[:20])
# Based on the provided gene identifiers, they appear to be common human gene symbols.
# Therefore, no further mapping to human gene symbols is required.
print("requires_gene_mapping = False")
import os
import pandas as pd

# STEP 7: Data Normalization and Linking

# First, check if the clinical CSV file exists. If it does not, we cannot proceed with trait-based linking.
if not os.path.exists(out_clinical_data_file):
    # No trait data file => dataset is not usable for trait analysis
    df_null = pd.DataFrame()
    is_biased = True  # Arbitrary boolean to satisfy function requirement
    validate_and_save_cohort_info(
        is_final=True,
        cohort=cohort,
        info_path=json_path,
        is_gene_available=True,
        is_trait_available=False,
        is_biased=is_biased,
        df=df_null,
        note="No trait data file found; dataset not usable for trait analysis."
    )

else:
    # 1. Normalize the mapped gene expression data using known gene symbol synonyms, then save.
    normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
    normalized_gene_data.to_csv(out_gene_data_file)

    # 2. Load the previously extracted clinical CSV.
    selected_clinical_df = pd.read_csv(out_clinical_data_file)
    # If we had a single-row trait, rename row 0 to the trait name (example usage).
    selected_clinical_df = selected_clinical_df.rename(index={0: trait})

    # Combine these as our final clinical data; in this dataset, we only have trait info (if any).
    combined_clinical_df = selected_clinical_df

    # Link the clinical and genetic data by matching sample IDs in columns.
    linked_data = geo_link_clinical_genetic_data(combined_clinical_df, normalized_gene_data)

    # 3. Handle missing values in the linked data (drop incomplete rows/columns, then impute).
    processed_data = handle_missing_values(linked_data, trait)

    # 4. Check trait bias and remove any biased demographic features (if any).
    trait_biased, processed_data = judge_and_remove_biased_features(processed_data, trait)

    # 5. Final validation and metadata saving.
    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=trait_biased,
        df=processed_data,
        note="Completed trait-based preprocessing."
    )

    # 6. If final dataset is usable, save. Otherwise, skip.
    if is_usable:
        processed_data.to_csv(out_data_file)