| # Path Configuration | |
| from tools.preprocess import * | |
| # Processing context | |
| trait = "Aniridia" | |
| cohort = "GSE204791" | |
| # Input paths | |
| in_trait_dir = "../DATA/GEO/Aniridia" | |
| in_cohort_dir = "../DATA/GEO/Aniridia/GSE204791" | |
| # Output paths | |
| out_data_file = "./output/preprocess/1/Aniridia/GSE204791.csv" | |
| out_gene_data_file = "./output/preprocess/1/Aniridia/gene_data/GSE204791.csv" | |
| out_clinical_data_file = "./output/preprocess/1/Aniridia/clinical_data/GSE204791.csv" | |
| json_path = "./output/preprocess/1/Aniridia/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 includes mRNA expression, so we consider it gene expression data. | |
| # 2. Variable Availability and Data Type Conversion | |
| # 2.1 Determine row indices for 'trait', 'age', 'gender' | |
| # Based on the sample characteristics dictionary: | |
| # - 0: ['age: 59', 'age: 28', ... ] | |
| # - 1: ['gender: F', 'gender: M'] | |
| # - 2: ['disease: KC', 'disease: healthy control'] | |
| # - 3: [ ... staging info ... ] | |
| # We are looking for "Aniridia" but our dictionary lists "KC" or "healthy control" for disease. | |
| # Hence, we do not have data for "Aniridia." So trait_row = None. | |
| trait_row = None # No data on "Aniridia" found | |
| age_row = 0 # Multiple ages are present | |
| gender_row = 1 # "F" and "M" are present | |
| # 2.2 Write conversion functions | |
| def convert_trait(val: str): | |
| """ | |
| Attempt to parse 'Aniridia' or 'control' from the string after the colon. | |
| Since 'Aniridia' is not actually in our sample data, return None. | |
| """ | |
| return None | |
| def convert_age(val: str): | |
| """ | |
| Parse the value after the colon and convert to float. | |
| Non-numeric or invalid data is converted to None. | |
| """ | |
| parts = val.split(':') | |
| if len(parts) < 2: | |
| return None | |
| raw_value = parts[1].strip() | |
| try: | |
| return float(raw_value) | |
| except ValueError: | |
| return None | |
| def convert_gender(val: str): | |
| """ | |
| Parse the value after the colon and convert: | |
| F -> 0 | |
| M -> 1 | |
| Otherwise -> None | |
| """ | |
| parts = val.split(':') | |
| if len(parts) < 2: | |
| return None | |
| raw_value = parts[1].strip().upper() | |
| if raw_value == 'F': | |
| return 0 | |
| elif raw_value == 'M': | |
| return 1 | |
| else: | |
| return None | |
| # 3. Save Metadata | |
| # Trait data availability is determined by whether trait_row is None. | |
| is_trait_available = (trait_row is not None) | |
| # Perform initial filtering (is_final=False). | |
| # This will record metadata if data fails initial filtering. | |
| 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, clinical data extraction is skipped. | |
| # 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]) | |
| # These identifiers appear to be microarray probe IDs or custom probe identifiers rather than standard human gene symbols. | |
| # Therefore, they need to be mapped to gene symbols. | |
| 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)) | |
| # Gene Identifier Mapping | |
| # 1. Identify which columns correspond to the expression data's probe ID and to the gene symbols. | |
| # From the annotation preview, "ID" appears to match the probe identifiers (e.g., "A_19_P..."), | |
| # and "GENE_SYMBOL" appears to be the gene symbol column. | |
| probe_id_col = "ID" | |
| gene_symbol_col = "GENE_SYMBOL" | |
| # 2. Extract the mapping information between probe IDs and gene symbols. | |
| gene_mapping_df = get_gene_mapping(gene_annotation, probe_id_col, gene_symbol_col) | |
| # 3. Apply the mapping to convert probe-level data into gene-level data. | |
| gene_data = apply_gene_mapping(gene_data, gene_mapping_df) | |
| # STEP 7: Data Normalization and Linking | |
| import pandas as pd | |
| # Since in previous steps we determined trait_row = None (no available trait data), | |
| # we cannot link clinical data or perform trait-based filtering. Hence, we skip steps | |
| # that depend on clinical or trait information. | |
| # 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. No trait data found; skip clinical linking, missing-value handling, and bias assessment. | |
| print("No trait data found. Skipping clinical linking, missing-value handling, and bias assessment.") | |
| # 3. Conduct final quality validation and save metadata. | |
| # Since there's no trait data, we must pass some dummy DataFrame and a boolean for is_biased | |
| # to avoid the ValueError in final mode. | |
| dummy_df = pd.DataFrame() | |
| is_biased_dummy = False # Arbitrary placeholder since we can't assess bias | |
| 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=is_biased_dummy, | |
| df=dummy_df, | |
| note="Trait data not found; dataset cannot be used for trait-based analysis." | |
| ) | |
| # 4. Because we don't have usable trait data, skip saving the linked data | |
| if is_usable: | |
| # This case should not occur since there's no trait data | |
| pass | |
| else: | |
| print("The dataset is not usable for trait-based association. Skipping final output.") |