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, (Formula presented.), of a larger data matrix, (Formula presented.), such that the features and observations in (Formula presented.) differ from those not contained in (Formula presented.). We present a novel two-step method, SC-Biclust, for identifying (Formula presented.). In the first step, the observations in the bicluster are identified to maximize the sum of the weighted between-cluster feature differences. In the second step, features in the bicluster are identified based on their contribution to the clustering of the observations. This versatile method can be used to identify biclusters that differ on the basis of feature means, feature variances, or more general differences. The bicluster identification accuracy of SC-Biclust is illustrated through several simulated studies. Application of SC-Biclust to pain research illustrates its ability to identify biologically meaningful subgroups.
Bibliographical noteFunding Information:
EH was supported by the NSF GRFP (Grant no. DGE‐1144081). MK received support from NCI (Grant no. P01 CA142538). EB was supported by NIH/NIDCR (Grant no. R03DE023592), NIH/NCATS (Grant no. UL1RR025747), and NIH/NIEHS (Grant no. P03ES010126).
EH was supported by the NSF GRFP (Grant no. DGE-1144081). MK received support from NCI (Grant no. P01 CA142538). EB was supported by NIH/NIDCR (Grant no. R03DE023592), NIH/NCATS (Grant no. UL1RR025747), and NIH/NIEHS (Grant no. P03ES010126).
© 2019 The International Biometric Society
- hierarchical clustering
- high-dimensional data
- k-means clustering
- sparse clustering
PubMed: MeSH publication types
- Journal Article
- Research Support, N.I.H., Extramural
- Research Support, U.S. Gov't, Non-P.H.S.