Abstract
Identifying co-expressed gene clusters can provide evidence for genetic or physical interactions. Thus, co-expression clustering is a routine step in large-scale analyses of gene expression data. We show that commonly used clustering methods produce results that substantially disagree and that do not match the biological expectations of co-expressed gene clusters. We present clust, a method that solves these problems by extracting clusters matching the biological expectations of co-expressed genes and outperforms widely used methods. Additionally, clust can simultaneously cluster multiple datasets, enabling users to leverage the large quantity of public expression data for novel comparative analysis. Clust is available at https://github.com/BaselAbujamous/clust.
Highlights
Gene transcription is dynamically and coordinately regulated in all living organisms
We show that clust satisfies these expectations by extracting co-expressed clusters with lower levels of dispersion than data partitioning methods and partial clustering methods
This inclusion results in clusters with high levels of dispersion and high levels of inter-cluster similarity, violating the expectations of co-expressed gene clusters, and producing clusters whose gene assignment is unreliable
Summary
Gene transcription is dynamically and coordinately regulated in all living organisms. Such coordinate regulation is manifest as concordant changes in the transcript abundance of genes in time series and perturbation-response datasets. Transcription factors comprise ~ 10% of the total number of genes in a genome, and complex spatio-temporal patterns of transcription are achieved through the combinatorial action of these genes in regulatory networks [1]. A corollary of this is that within any one experimental context (e.g., time series spanning some biological process or perturbation-response experiment), not all genes will be behaving coordinately. Subsets of genes will have the right combination of regulators to behave coordinately during the experimental context while others are following patterns of regulation that are independent of the experimental design. Within a given observation window (i.e., experimental context), it is not expected that all genes can be assigned to a limited set of coordinate behavior [2, 3]
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