README.md

biclustlib

The package is an extension of biclustlib Python library by Victor Alexandre Padilha.
It is highly recommended to see the original repository first.
The goal of this package is to create a unified biclustering framework for performing research on gene expression data and comparing different biclustering algorithms and measures.

Distributed under GPLv3 license.

Installation

pip install biclustlib
You must also install R and the following R packages:

• biclust
• isa2

Benchmarking example

import multiprocessing

import pandas as pd
from sklearn.preprocessing import KBinsDiscretizer

from biclustlib.algorithms import *
from biclustlib.algorithms.wrappers import *
from biclustlib.benchmark import GeneExpressionBenchmark, Algorithm
from biclustlib.benchmark.data import load_tavazoie, load_prelic


def discretize_data(raw_data: pd.DataFrame, n_bins: int = 2) -> pd.DataFrame:
    return pd.DataFrame(KBinsDiscretizer(n_bins, encode='ordinal', strategy='kmeans').fit_transform(raw_data),
                        index=raw_data.index).astype(int if n_bins > 2 else bool)


if __name__ == '__main__':
    
    data = load_tavazoie()
    n_biclusters = 10
    reduction_level = 10
    discretion_level = 30

    data_dis = discretize_data(data, discretion_level)
    data_bin = discretize_data(data)

    setup = [
        Algorithm('CCA', ChengChurchAlgorithm(n_biclusters), data),
        Algorithm('xMotifs', RConservedGeneExpressionMotifs(n_biclusters), data_dis),
        Algorithm('BiBit', BitPatternBiclusteringAlgorithm(), data_bin),
        Algorithm('Bimax', RBinaryInclusionMaximalBiclusteringAlgorithm(n_biclusters), data_bin),
        Algorithm('LAS', LargeAverageSubmatrices(n_biclusters), data),
        Algorithm('Plaid', RPlaid(n_biclusters), data),
        Algorithm('Spectral', Spectral(n_clusters=data.shape[1] // 2), data + abs(data.min().min()) + 1),
    ]

    with multiprocessing.Pool() as pool:
        tavazoie_benchmark = GeneExpressionBenchmark(algorithms=setup,
                                                     raw_data=data,
                                                     reduction_level=reduction_level).run(pool)
    tavazoie_benchmark.generate_report()

    tavazoie_benchmark.perform_goea()
    tavazoie_benchmark.generate_goea_report()