HBMAP: Bayesian inference of neural circuits from barcoded projection mapping

Decoding how the brain routes information through its precise, long-range wiring remains a central chal-
lenge in neuroscience. Barcode-based mapping of axonal projections allows brain-wide, high-throughput
investigation of projections at single-neuron resolution offering a powerful solution. However, principled
methods for statistical analysis of barcode count data that can detect common projection rules and effec-
tively integrate datasets across subjects are lacking. To address these issues, here we present Hierarchical
Bayesian Mapping of Axonal Projections (HBMAP), a model-based clustering approach that directly
models barcode count data through hierarchical Bayesian mixtures. Applied to published datasets, we
show that HBMAP accurately reflects features of the data and allows simultaneous identification of
projection patterns and characterization of uncertainty that accounts for subject variability. Our study
presents a Bayesian approach to barcode-based projection mapping, offering a general solution for the
statistical analysis of this class of data.