p1/preprocess/Crohns_Disease/code/GSE66407.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Crohns_Disease"
cohort = "GSE66407"

# Input paths
in_trait_dir = "../DATA/GEO/Crohns_Disease"
in_cohort_dir = "../DATA/GEO/Crohns_Disease/GSE66407"

# Output paths
out_data_file = "./output/preprocess/1/Crohns_Disease/GSE66407.csv"
out_gene_data_file = "./output/preprocess/1/Crohns_Disease/gene_data/GSE66407.csv"
out_clinical_data_file = "./output/preprocess/1/Crohns_Disease/clinical_data/GSE66407.csv"
json_path = "./output/preprocess/1/Crohns_Disease/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  # Based on the background info: "transcriptome analysis" implies gene expression data

# 2) Variable Availability
trait_row = 3    # Row with 'diagnosis: CD', 'diagnosis: Control', 'diagnosis: UC'
age_row = 2      # Row with 'age: <value>' entries
gender_row = None  # No mention of gender in the sample characteristics

# 2.2 Data Type Conversion Functions
def convert_trait(value: Any) -> Optional[int]:
    if not isinstance(value, str):
        return None
    # typical format: "diagnosis: CD"
    # extract the part after the colon
    val = value.split(":", 1)[-1].strip().lower()
    
    # Binary: 1 for Crohn's (CD), 0 for not Crohn's (Control or UC)
    if 'cd' in val:
        return 1
    elif 'control' in val:
        return 0
    elif 'uc' in val:
        return 0
    return None

def convert_age(value: Any) -> Optional[float]:
    if not isinstance(value, str):
        return None
    # typical format: "age: 37"
    val = value.split(":", 1)[-1].strip()
    try:
        return float(val)
    except ValueError:
        return None

def convert_gender(value: Any) -> Optional[int]:
    # No gender info available, so default to None
    return None

# 3) Save Metadata (initial filtering)
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 (only if trait data is available)
if trait_row is not None:
    # 'clinical_data' is assumed to be the DataFrame with sample characteristics
    selected_clinical = 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
    )

    # Observe the extracted clinical features
    preview_clinical = preview_df(selected_clinical)
    print("Preview of selected clinical features:", preview_clinical)

    # Save the clinical data
    selected_clinical.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])
# These identifiers appear to be Ensembl gene IDs (e.g., ENSG00000000003_at), not human gene symbols.
# Therefore, they need to be mapped to gene symbols.
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 & 2. Decide which columns correspond to the probe IDs and the gene symbol info, then create the mapping dataframe.
mapping_df = get_gene_mapping(
    annotation=gene_annotation,
    prob_col="ID",         # Matches the IDs in the gene expression data
    gene_col="Description" # Contains gene symbol text to be parsed
)

# 3. Apply the mapping to convert probe-level measurements to gene-level expression.
gene_data = apply_gene_mapping(gene_data, mapping_df)
# STEP7

import pandas as pd

# 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)

# 2. Link the clinical and genetic data on sample IDs
#    Read the clinical CSV without forcing any column as index,
#    then manually set the row labels to [trait, "Age"] because
#    we have two rows: one for the trait, one for age.
clinical_temp = pd.read_csv(out_clinical_data_file, header=0)
clinical_temp.index = [trait, "Age"]  # We found no gender info, so only two rows exist

# Link clinical and genetic data
linked_data = geo_link_clinical_genetic_data(clinical_temp, normalized_gene_data)

# 3. Handle missing values (drop missing trait rows, remove genes with >20% missing, etc.)
linked_data = handle_missing_values(linked_data, trait)

# 4. Determine whether the trait or demographic features are severely biased
trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)

# 5. Conduct final quality validation and save metadata
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=linked_data,
    note="Successfully processed and trait data is available."
)

# 6. If the data is usable, save the final linked data
if is_usable:
    linked_data.to_csv(out_data_file)