Bipartite tight spectral clustering (BiTSC) algorithm for identifying conserved gene co-clusters in two species

Author:

Sun Yidan Eden1ORCID,Zhou Heather J1,Li Jingyi Jessica123

Affiliation:

1. Department of Statistics, University of California, Los Angeles, CA 90095-1554, USA

2. Department of Human Genetics, University of California, Los Angeles, CA 90095-7088, USA

3. Department of Computational Medicine, University of California, Los Angeles, CA 90095-1766, USA

Abstract

Abstract Motivation Gene clustering is a widely used technique that has enabled computational prediction of unknown gene functions within a species. However, it remains a challenge to refine gene function prediction by leveraging evolutionarily conserved genes in another species. This challenge calls for a new computational algorithm to identify gene co-clusters in two species, so that genes in each co-cluster exhibit similar expression levels in each species and strong conservation between the species. Results Here, we develop the bipartite tight spectral clustering (BiTSC) algorithm, which identifies gene co-clusters in two species based on gene orthology information and gene expression data. BiTSC novelly implements a formulation that encodes gene orthology as a bipartite network and gene expression data as node covariates. This formulation allows BiTSC to adopt and combine the advantages of multiple unsupervised learning techniques: kernel enhancement, bipartite spectral clustering, consensus clustering, tight clustering and hierarchical clustering. As a result, BiTSC is a flexible and robust algorithm capable of identifying informative gene co-clusters without forcing all genes into co-clusters. Another advantage of BiTSC is that it does not rely on any distributional assumptions. Beyond cross-species gene co-clustering, BiTSC also has wide applications as a general algorithm for identifying tight node co-clusters in any bipartite network with node covariates. We demonstrate the accuracy and robustness of BiTSC through comprehensive simulation studies. In a real data example, we use BiTSC to identify conserved gene co-clusters of Drosophila melanogaster and Caenorhabditis elegans, and we perform a series of downstream analysis to both validate BiTSC and verify the biological significance of the identified co-clusters. Availability and implementation The Python package BiTSC is open-access and available at https://github.com/edensunyidan/BiTSC. Supplementary information Supplementary data are available at Bioinformatics online.

Funder

National Science Foundation

National Institutes of Health

National Institute of General Medical Sciences

PhRMA Foundation Research Starter Grant in Informatics

Johnson and Johnson WiSTEM2D Award and Sloan Research Fellowship

Publisher

Oxford University Press (OUP)

Subject

Computational Mathematics,Computational Theory and Mathematics,Computer Science Applications,Molecular Biology,Biochemistry,Statistics and Probability

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