  With the recent availability of tissue-specific gene expression data, e.g.,
provided by the GTEx Consortium, there is interest in comparing gene
co-expression patterns across tissues. One promising approach to this problem
is to use a multilayer network analysis framework and perform multilayer
community detection. Communities in gene co-expression networks reveal groups
of genes similarly expressed across individuals, potentially involved in
related biological processes responding to specific environmental stimuli or
sharing common regulatory variations. We construct a multilayer network in
which each of the four layers is an exocrine gland tissue-specific gene
co-expression network. We develop methods for multilayer community detection
with correlation matrix input and an appropriate null model. Our correlation
matrix input method identifies five groups of genes that are similarly
co-expressed in multiple tissues (a community that spans multiple layers, which
we call a generalist community) and two groups of genes that are co-expressed
in just one tissue (a community that lies primarily within just one layer,
which we call a specialist community). We further found gene co-expression
communities where the genes physically cluster across the genome significantly
more than expected by chance (on chromosomes 1 and 11). This clustering hints
at underlying regulatory elements determining similar expression patterns
across individuals and cell types. We suggest that KRTAP3-1, KRTAP3-3, and
KRTAP3-5 share regulatory elements in skin and pancreas. Furthermore, we find
that CELA3A and CELA3B share associated expression quantitative trait loci in
the pancreas. The results indicate that our multilayer community detection
method for correlation matrix input extracts biologically interesting
communities of genes.
