Circuit Mining in Transcriptomics Data.

Abstract

A central goal in neuropharmacological research is to alter brain function by targeting genes whose expression is specific to the corresponding brain circuit. Identifying such genes in large spatially resolved transcriptomics data requires the expertise of bioinformaticians for handling data complexity and to perform statistical tests. This time-consuming process is often decoupled from the routine workflow of neuroscientists, inhibiting fast target discovery. Here, we present a visual analytics approach to mining expression data in the context of meso-scale brain circuits for potential target genes tailored to domain experts with limited technical background. We support several workflows for interactive definition and refinement of circuits in the human or mouse brain, and combine spatial indexing with an alternative formulation of sample variance to enable differential gene expression analysis in arbitrary brain circuits at runtime. A user study highlights the usefulness, benefits, and future potential of our work.