Bats are able to employ an astonishin- gly complex vocal repertoire for navigating their environment and conveying social information. A handful of species also show evidence for vocal learning, an extremely rare ability shared only with humans and few other animals. However, despite their obvious potential for the study of vocal communication, bats remain severely understudied at a molecular level. To address this fundamental gap we performed the first transcriptome profiling and genetic interrogation of molecular networks in the brain of a highly vo- cal bat species, P. discolor. To identify functional, biologically relevant gene networks, we utilized two contrasting co-expression network analysis methods with distinct underlying algorithms; WGCNA and MCLUST. These methods typically need large sample sizes for correct clustering, which can be prohibitive where samples are limited, such as in this study. To overcome this, we built on the WGCNA and MCLUST methods to develop a novel approach for identifying robust co-expression gene networks using few samples (≤6). Using this approach, we were able to ge- nerate tissue-specific functional gene networks from the bat PAG, a brain region fundamental for mammalian vocalization. The most highly connected of the networks identified in our study represented a cluster of genes involved in glu- tamatergic synaptic transmission. Glutamatergic signaling plays an essential role in vocalizations elicited from the PAG, suggesting that the gene network uncovered here is mechanistically impor - tant for vocal-motor control in mammals. These findings show that our innovative gene clustering approach can reveal robust biologically relevant gene co-expression networks with limited sample sizes. Moreover, this work reports the first gene network analysis performed in a bat brain and establishes P. discolor as a novel, tractable model system for understanding the genetics of vocal communication.