# Path Configuration from tools.preprocess import * # Processing context trait = "Schizophrenia" cohort = "GSE120342" # Input paths in_trait_dir = "../DATA/GEO/Schizophrenia" in_cohort_dir = "../DATA/GEO/Schizophrenia/GSE120342" # Output paths out_data_file = "./output/preprocess/1/Schizophrenia/GSE120342.csv" out_gene_data_file = "./output/preprocess/1/Schizophrenia/gene_data/GSE120342.csv" out_clinical_data_file = "./output/preprocess/1/Schizophrenia/clinical_data/GSE120342.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) Gene Expression Data Availability is_gene_available = True # "Aberrant transcriptomes" suggests expression data is included # 2) Variable Availability trait_row = 0 # Key 0 -> disease state has multiple unique values including "SCZ" age_row = None # No age information found gender_row = None # No gender information found # 2) Data Type Conversion def convert_trait(value: str): parts = value.split(':', 1) if len(parts) < 2: return None val = parts[1].strip().lower() if val == 'scz': return 1 elif val in ['control', 'bd(-)', 'bd(+)']: return 0 return None def convert_age(value: str): # Not available, return None return None def convert_gender(value: str): # Not available, return None return None # 3) Save Metadata (initial filtering) is_trait_available = (trait_row is not None) filter_passed = 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 if trait_row is not None: selected_clinical_df = geo_select_clinical_features( clinical_df=clinical_data, 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 ) print(preview_df(selected_clinical_df, n=5)) selected_clinical_df.to_csv(out_clinical_data_file, index=False) # 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]) # The provided identifiers (cgXXXXX) are typically CpG probe IDs from an Illumina methylation array, not human gene symbols. print("requires_gene_mapping = True") # STEP5 # 1 & 2. Only extract and preview gene annotation data if the SOFT file exists, otherwise skip. if soft_file is None: print("No SOFT file found. Skipping gene annotation extraction.") gene_annotation = pd.DataFrame() else: try: # Attempt to extract gene annotation with the default method gene_annotation = get_gene_annotation(soft_file) except UnicodeDecodeError: # Fallback if UTF-8 decoding fails: read with a more lenient encoding and pass the content as a string import gzip with gzip.open(soft_file, 'rt', encoding='latin-1', errors='replace') as f: content = f.read() gene_annotation = filter_content_by_prefix( content, prefixes_a=['^','!','#'], unselect=True, source_type='string', return_df_a=True )[0] print("Gene annotation preview:") print(preview_df(gene_annotation)) # STEP: Gene Identifier Mapping # 1) Identify which columns correspond to the identifiers used in gene_data and the gene symbols # From the previews, 'ID' matches the probe IDs (cgXXXX...), and 'Symbol' stores the corresponding gene symbols. # 2) Create a gene mapping dataframe mapping_df = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='Symbol') # 3) Apply the mapping to convert probe-level measurements into gene-level expression data gene_data = apply_gene_mapping(gene_data, mapping_df) # (Optional) Quick check: print a small portion of the resulting gene_data print("Mapped gene_data shape:", gene_data.shape) print("Sample of gene_data index (gene symbols):", gene_data.index[:10].tolist()) 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)