RT Journal Article
SR Electronic
T1 ClusterMine: a Knowledge-integrated Clustering Approach based on Expression Profiles of Gene Sets
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 255711
DO 10.1101/255711
A1 Hong-Dong Li
A1 Yunpei Xu
A1 Xiaoshu Zhu
A1 Quan Liu
A1 Gilbert S. Omenn
A1 Jianxin Wang
YR 2018
UL http://biorxiv.org/content/early/2018/01/29/255711.abstract
AB Motivation Clustering analysis is essential for understanding complex biological data. In widely used methods such as hierarchical clustering (HC) and consensus clustering (CC), expression profiles of all genes are often used to assess similarity between samples for clustering. These methods output sample clusters, but are not able to provide information about which gene sets (functions) contribute most to the clustering. So interpretability of their results is limited. We hypothesized that integrating prior knowledge of annotated biological processes would not only achieve satisfying clustering performance but also, more importantly, enable potential biological interpretation of clusters.Results Here we report ClusterMine, a novel approach that identifies clusters by assessing functional similarity between samples through integrating known annotated gene sets, e.g., in Gene Ontology. In addition to outputting cluster membership of each sample as conventional approaches do, it outputs gene sets that are most likely to contribute to the clustering, a feature facilitating biological interpretation. Using three cancer datasets, two single cell RNA-sequencing based cell differentiation datasets, one cell cycle dataset and two datasets of cells of different tissue origins, we found that ClusterMine achieved similar or better clustering performance and that top-scored gene sets prioritized by ClusterMine are biologically relevant.Implementation and availability ClusterMine is implemented as an R package and is freely available at: www.genemine.org/clustermine.phpContact jxwang@csu.edu.cnSupplementary Information Supplementary data are available at Bioinformatics online.