Gene expression analysis most often involves the detection of genes that are either up or down regulated, based on their degree of differential expression when comparing conditions. Known disease genes, however, are often not differentially expressed because mutations in the coding region can affect the function of the gene without affecting its expression level. Also, post-translational modifications can affect regulatory activities of a gene product independently of its expression level. Lastly, since genes act in networks, a change in function or regulatory activity of a gene will result in perturbations of these networks, which underlie human disease. Differential co-expression and co-differential co-expression
identify groups of genes whose patterns of expression diverge between experimental groups, either because of a coding change in an important protein which changes its interaction with other proteins (differential co-expression), or a change in a regulatory genomic unit which has concerted changes downstream (co-differential co-expression). Our research, therefore, is focused on detecting these perturbations using RNA microarray and RNA sequencing data derived from relevant tissues, i.e.: in vitro and in vivo disease models, motor neurons derived from induced pluripotent stem cells, and fresh frozen post-mortem tissue from the central nervous system (CNS).