# Path Configuration from tools.preprocess import * # Processing context trait = "Adrenocortical_Cancer" cohort = "GSE75415" # Input paths in_trait_dir = "../DATA/GEO/Adrenocortical_Cancer" in_cohort_dir = "../DATA/GEO/Adrenocortical_Cancer/GSE75415" # Output paths out_data_file = "./output/preprocess/1/Adrenocortical_Cancer/GSE75415.csv" out_gene_data_file = "./output/preprocess/1/Adrenocortical_Cancer/gene_data/GSE75415.csv" out_clinical_data_file = "./output/preprocess/1/Adrenocortical_Cancer/clinical_data/GSE75415.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. Determine if gene expression data is available is_gene_available = True # Based on the series title/summary indicating gene expression microarray data. # 2. Identify rows and define conversion functions for trait, age, and gender. trait_row = 1 # "histologic type: ..." key age_row = None # No age info found gender_row = 0 # "gender: ..." key def convert_trait(value: str): """ Convert histologic type to a binary variable: 1 => adrenocortical carcinoma 0 => adenoma or normal None => unknown """ parts = value.split(':', 1) if len(parts) == 2: val = parts[1].strip().lower() if 'carcinoma' in val: return 1 elif 'adenoma' in val or 'normal' in val: return 0 elif 'unknown' in val: return None return None def convert_age(value: str): """ Age data is not available, so return None. """ return None def convert_gender(value: str): """ Convert gender to binary: 0 => female 1 => male None => unknown """ parts = value.split(':', 1) if len(parts) == 2: val = parts[1].strip().lower() if val == 'female': return 0 elif val == 'male': return 1 return None # 3. Conduct initial filtering and save metadata 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. Extract clinical features if trait data is available if trait_row is not None: selected_clinical_df = geo_select_clinical_features( clinical_data, # Assuming clinical_data is the DataFrame with sample characteristics trait=trait, trait_row=trait_row, convert_trait=convert_trait, age_row=age_row, convert_age=convert_age, gender_row=gender_row, convert_gender=convert_gender ) # Preview and save previewed = preview_df(selected_clinical_df) print("Selected Clinical Features Preview:", previewed) selected_clinical_df.to_csv(out_clinical_data_file, index=False) # 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]) # Observing the provided list of identifiers (e.g., "1007_s_at", "1053_at"), they are Affymetrix probe set IDs. # These are not standard human gene symbols; hence they do require mapping. 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)) # STEP: Gene Identifier Mapping # 1. Based on the annotation preview, the column "ID" in 'gene_annotation' matches the probe identifiers # in the gene expression data (also labeled "ID"). The column "Gene Symbol" contains the actual gene symbols. # 2. Extract the two columns from the gene annotation dataframe, "ID" (probe ID) and "Gene Symbol" (gene symbol), # to create the mapping dataframe. mapping_df = get_gene_mapping(gene_annotation, "ID", "Gene Symbol") # 3. Convert probe-level measurements to gene-level expression data using the mapping. gene_data = apply_gene_mapping(gene_data, mapping_df) # (Optional) Print the resulting dataframe shape and some rows to verify. print("Gene expression data shape after mapping:", gene_data.shape) print(gene_data.head()) # STEP 7: Data Normalization and Linking # 1. Normalize gene symbols and save the normalized gene data normalized_gene_data = normalize_gene_symbols_in_index(gene_data) normalized_gene_data.to_csv(out_gene_data_file, index=True) # 2. Link clinical and genetic data on sample IDs # "selected_clinical_df" was defined in a previous step, so we can use it directly. linked_data = geo_link_clinical_genetic_data(selected_clinical_df, normalized_gene_data) # 3. Handle missing values systematically processed_data = handle_missing_values(linked_data, trait) # 4. Determine whether the trait or demographic features are severely biased trait_biased, processed_data = judge_and_remove_biased_features(processed_data, trait) # 5. Final quality validation 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=trait_biased, df=processed_data, note="Trait data present and mapped from step 2." ) # 6. Save the final linked data only if usable if is_usable: processed_data.to_csv(out_data_file, index=True)