ClusterMine: a Knowledge-integrated Clustering Approach based on Expression Profiles of Gene Sets

BioRxiv : the Preprint Server for Biology
Hongdong LiJianxin Wang


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. 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 di...Continue Reading

Related Concepts

Sequence Determinations, RNA
Statistical Cluster
Cell Differentiation Process
Cell Cycle
Gene Clusters
Malignant Neoplasms
Silo (Dataset)
Gene Ontology

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