Comparison of sparse biclustering algorithms for gene expression datasets.

ABSTRACT:

Motivation

Gene clustering and sample clustering are commonly used to find patterns in gene expression datasets. However, genes may cluster differently in heterogeneous samples (e.g. different tissues or disease states), whilst traditional methods assume that clusters are consistent across samples. Biclustering algorithms aim to solve this issue by performing sample clustering and gene clustering simultaneously. Existing reviews of biclustering algorithms have yet to include a number of more recent algorithms and have based comparisons on simplistic simulated datasets without specific evaluation of biclusters in real datasets, using less robust metrics.

Results

We compared four classes of sparse biclustering algorithms on a range of simulated and real datasets. All algorithm

SUBMITTER: Nicholls K 

PROVIDER: S-EPMC8574648 | biostudies-literature | 2021 Nov

REPOSITORIES: biostudies-literature