Dawnn: single-cell differential abundance with neural networks

Abstract

AbstractAnalysis of single-cell transcriptomes can identify cell populations more abundant in one sample or condition than another. However, existing methods to discover them suffer from either low discovery rates or high rates of false positives. We introduce Dawnn, a deep neural network able to find differential abundance with higher accuracy than current tools, both on simulated and biological datasets. Further, we demonstrate that Dawnn recovers published findings and discovers more cells in regions of differential abundance than existing methods, both in abundant and rare cell types, promising novel biological insights at single-cell resolution.