Abstract
The explosion of next-generation sequencing technologies has allowed researchers to move from studying single genes to thousands of genes, and thereby to also consider the relationships within gene networks. Like others, we are interested in understanding how developmental and evolutionary forces shape the expression of individual genes, as well as the interactions among genes. In pursuing these questions, we confronted the central challenge that standard approaches fail to control the Type I error and/or have low power in the presence of high dimensionality (i.e., large number of genes) and small sample size, as in many gene expression studies. To overcome these challenges, we used random projection tests and correlation network comparisons to characterize differences in network connectivity and density. We detail central challenges, discuss sample size guidelines, and provide rigorous statistical approaches for exploring coexpression differences with small sample sizes. We apply these approaches in a species known for rapid adaptation - the Trinidadian guppy (Poecilia reticulata). Our findings provide evidence for coexpression network differences at developmental and evolutionary timescales and suggest that flexibility in gene coexpression relationships could promote evolvability.