# Path Configuration from tools.preprocess import * # Processing context trait = "Epilepsy" cohort = "GSE63808" # Input paths in_trait_dir = "../DATA/GEO/Epilepsy" in_cohort_dir = "../DATA/GEO/Epilepsy/GSE63808" # Output paths out_data_file = "./output/preprocess/1/Epilepsy/GSE63808.csv" out_gene_data_file = "./output/preprocess/1/Epilepsy/gene_data/GSE63808.csv" out_clinical_data_file = "./output/preprocess/1/Epilepsy/clinical_data/GSE63808.csv" json_path = "./output/preprocess/1/Epilepsy/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 # Based on the background info ("analysis of biopsy hippocampal tissue ... provide insight into molecular mechanisms") # it is likely that this dataset contains gene expression data. is_gene_available = True # 2. Variable Availability and Data Type Conversion # From the sample characteristics dictionary: # {0: ['tissue: hippocampal formation'], 1: ['phenotype: epilepsy']} # We see that "phenotype: epilepsy" is constant for all samples and provides no variation. # Therefore, for this study, treat trait, age, and gender as unavailable. trait_row = None # no variability found for "epilepsy" age_row = None # no age data gender_row = None # no gender data # Define conversion functions (though these will not be used here). def convert_trait(value: str): return None # not available def convert_age(value: str): return None # not available def convert_gender(value: str): return None # not available # 3. Save Metadata (initial filtering) # If trait_row is None, then trait data isn't available is_trait_available = (trait_row is not None) is_usable = 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 (no clinical data extraction). # 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]) # The gene identifiers displayed (e.g., 'ILMN_1343291') are Illumina probe IDs, # not standard human gene symbols. They require mapping to gene symbols. print("requires_gene_mapping = True") # STEP5 import pandas as pd import io # 1. Extract the lines that do NOT start with '^', '!', or '#', but do NOT parse them into a DataFrame yet. annotation_text, _ = filter_content_by_prefix( source=soft_file, prefixes_a=['^', '!', '#'], unselect=True, source_type='file', return_df_a=False, return_df_b=False ) # 2. Manually parse the filtered text into a DataFrame, specifying engine="python" to avoid buffer overflow issues. gene_annotation = pd.read_csv( io.StringIO(annotation_text), delimiter='\t', on_bad_lines='skip', engine='python' ) print("Gene annotation preview:") print(preview_df(gene_annotation)) # STEP: Gene Identifier Mapping # 1. Decide which columns in gene_annotation store the probe identifiers (matching the gene_data index) # and which store the gene symbols. From the preview, "ID" matches the probe IDs like "ILMN_####", # and "Symbol" corresponds to the gene symbol. # 2. Get a gene mapping dataframe by extracting these two columns. mapping_df = get_gene_mapping(gene_annotation, prob_col="ID", gene_col="Symbol") # 3. Convert probe-level measurements to gene-level expression data using the mapping_df. gene_data = apply_gene_mapping(gene_data, mapping_df) import os import pandas as pd # STEP7 # 1) Normalize gene symbols and save normalized_gene_data = normalize_gene_symbols_in_index(gene_data) normalized_gene_data.to_csv(out_gene_data_file) # Check whether we actually have a clinical CSV file (i.e., trait data) from Step 2 if os.path.exists(out_clinical_data_file): # 2) Link the clinical and gene expression data selected_clinical_df = pd.read_csv(out_clinical_data_file, index_col=0) linked_data = geo_link_clinical_genetic_data(selected_clinical_df, normalized_gene_data) # 3) Handle missing values final_data = handle_missing_values(linked_data, trait_col=trait) # 4) Evaluate bias in the trait trait_biased, final_data = judge_and_remove_biased_features(final_data, trait) # 5) Final validation (trait is available) 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=final_data, note="Trait data successfully extracted in Step 2." ) # 6) If the dataset is usable, save if is_usable: final_data.to_csv(out_data_file) else: # If the clinical file does not exist, the trait is unavailable # Perform final validation indicating that we lack trait data empty_df = pd.DataFrame() validate_and_save_cohort_info( is_final=True, cohort=cohort, info_path=json_path, is_gene_available=True, is_trait_available=False, is_biased=True, # Arbitrary non-None to skip usage df=empty_df, note="No trait data was found; linking and final dataset output are skipped." )