p1/preprocess/Anxiety_disorder/code/GSE119995.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Anxiety_disorder"
cohort = "GSE119995"

# Input paths
in_trait_dir = "../DATA/GEO/Anxiety_disorder"
in_cohort_dir = "../DATA/GEO/Anxiety_disorder/GSE119995"

# Output paths
out_data_file = "./output/preprocess/1/Anxiety_disorder/GSE119995.csv"
out_gene_data_file = "./output/preprocess/1/Anxiety_disorder/gene_data/GSE119995.csv"
out_clinical_data_file = "./output/preprocess/1/Anxiety_disorder/clinical_data/GSE119995.csv"
json_path = "./output/preprocess/1/Anxiety_disorder/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  # Based on the description: "Exposure-induced changes of plasma mRNA expression levels."

# 2. Variable Availability and Data Type Conversion
#    The sample characteristics dictionary is:
#    {0: ['disease: panic disorder'],
#     1: ['tissue: blood plasma'],
#     2: ['Sex: female', 'Sex: male', 'Sex: not determined'],
#     3: ['medication: 0', 'medication: 1'],
#     4: ['timepoint: b1', 'timepoint: p24_1', 'timepoint: pe1'],
#     5: [ ... 'individual: #' ... ]}
#
#    We see no row for a varying 'trait' or 'age' field, but row 2 has 'Sex' with different values.
trait_row = None     # No row with 'trait' variation (only "disease: panic disorder").
age_row = None       # No age data found in the dictionary.
gender_row = 2       # Row with varying "Sex" values.

def convert_trait(x: str):
    return None  # Trait is not available in this dataset for variation.

def convert_age(x: str):
    return None  # Age is not available.

def convert_gender(x: str):
    # Example: "Sex: female" -> 'female'
    val = x.split(':')[-1].strip().lower()
    if val == 'female':
        return 0
    elif val == 'male':
        return 1
    else:
        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
#    Skip because trait_row is None.
# 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])
# Since the identifiers follow the pattern "ILMN_<id>", they appear to be Illumina probe IDs rather than human gene symbols.
# Therefore, they require mapping 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. Determine the appropriate columns for the probe IDs and gene symbols
prob_col = 'ID'       # Matches the "ILMN_xxxxx" identifiers seen in the gene expression data
gene_col = 'Symbol'   # Column containing gene symbols

# 2. Extract the gene mapping from the annotation dataframe
mapping_df = get_gene_mapping(gene_annotation, prob_col, gene_col)

# 3. Convert probe-level measurements to gene-level by applying the mapping
gene_data = apply_gene_mapping(gene_data, mapping_df)
# STEP 7: Data Normalization and Linking

import pandas as pd

# 1) Normalize the 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}")

# Because 'trait_row' was None in earlier steps, we have no varying trait data. Therefore, we cannot link
# clinical and genetic data in a meaningful way (no trait to analyze) nor perform trait-based processing.

# 2) As we are in the final step and must call 'validate_and_save_cohort_info' with is_final=True, we need
#    to provide both 'df' and 'is_biased'. Since there's no trait data, we create an empty DataFrame and
#    set is_biased=False (the trait is effectively unavailable, so "bias" is moot).

empty_df = pd.DataFrame()

is_usable = validate_and_save_cohort_info(
    is_final=True,
    cohort=cohort,
    info_path=json_path,
    is_gene_available=True,       # We do have gene expression data
    is_trait_available=False,     # No valid trait data
    is_biased=False,              # Arbitrary boolean because we must supply one; trait isn't actually present
    df=empty_df,
    note="Trait data not available; skipping linking and final dataset output."
)

# 3) If the dataset were usable, we'd save a final linked dataset, but it's not usable without a trait.
if is_usable:
    # This block won't run because is_trait_available=False => is_usable=False
    pass
else:
    print("No trait data found; the dataset is not usable for trait-based association. Skipping final output.")