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 .