Community detection describes the organization of a network in terms of patterns of connection. A wide variety of methods for community detection have been proposed, with a number of available software packages. The past decade has included significant interest in communities in multilayer networks, a general framework that can be used to describe networks with different types of relationships, that change in time, or that network together multiple kinds of networks. We describe various generalizations of community detection to multilayer networks, with special attention on a new post-processing procedure to explore the parameter space of multilayer modularity. We emphasize throughout the use of community detection in biological applications. We close with a brief presentation of new results using network representations for supervised classification.