Gene communities in co-expression networks across different tissues

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. Genes that are similarly expressed across individuals (i.e., co-expressed) are potentially involved in related biological processes. Therefore, the identification and biological analysis of co-expressed genes may be useful for revealing genes associated with specific diseases or other phenotypes. Because gene co-expression depends on the tissue in general, we compared co-expression patterns across four different exocrine gland tissues. This problem lends itself to multilayer network analysis in which each layer of the multilayer network is a tissue-specific gene co-expression network. The nodes in the network represent genes, and a pair of genes is directly connected by an edge if the two genes are co-expressed. We developed a method to detect groups of co-expressed genes in the multilayer gene co-expression network using correlational tissue-specific gene expression data. We found some groups of genes that are co-expressed in all four tissues and other groups of genes that are only co-expressed in one tissue. We also found that some of these groups of genes contain genes that are physically clustered across the genome. Our methods reveal groups of genes with potentially different mechanisms of gene co-expression.