Biclustering via sparse clustering.

ABSTRACT: In identifying subgroups of a heterogeneous disease or condition, it is often desirable to identify both the observations and the features which differ between subgroups. For instance, it may be that there is a subgroup of individuals with a certain disease who differ from the rest of the population based on the expression profile for only a subset of genes. Identifying the subgroup of patients and subset of genes could lead to better-targeted therapy. We can represent the subgroup of individuals and genes as a bicluster, a submatrix, U , of a larger data matrix, X , such that the features and observations in U differ from those not contained in U . We present a novel two-step method, SC-Biclust, for identifying U . In the first step, the observations in the bicluster are identified to max

SUBMITTER: Helgeson ES 

PROVIDER: S-EPMC7028479 | biostudies-literature | 2020 Mar

REPOSITORIES: biostudies-literature