| Popis: |
Analysis 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, promising novel biological insights at single-cell resolution. |
