ClusterMine: A knowledge-integrated clustering approach based on expression profiles of gene sets.

Journal of Bioinformatics and Computational Biology
Hong-Dong LiJianxin Wang

Abstract

Clustering analysis of gene expression data is essential for understanding complex biological data, and is widely used in important biological applications such as the identification of cell subpopulations and disease subtypes. In commonly used methods such as hierarchical clustering (HC) and consensus clustering (CC), holistic expression profiles of all genes are often used to assess the similarity between samples for clustering. While these methods have been proven successful in identifying sample clusters in many areas, they do not provide information about which gene sets (functions) contribute most to the clustering, thus limiting the interpretability of the resulting cluster. We hypothesize that integrating prior knowledge of annotated gene sets would not only achieve satisfactory clustering performance but also, more importantly, enable potential biological interpretation of clusters. Here we report ClusterMine, an approach that identifies clusters by assessing functional similarity between samples through integrating known annotated gene sets in functional annotation databases such as Gene Ontology. In addition to the cluster membership of each sample as provided by conventional approaches, it also outputs gene sets tha...Continue Reading

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Citations

Jul 24, 2020·Journal of Bioinformatics and Computational Biology·Shuigeng Zhou
Mar 2, 2021·Genomics, Proteomics & Bioinformatics·Zhenlan LiangJianxin Wang

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