RT Journal Article SR Electronic T1 distinct: a novel approach to differential distribution analyses JF bioRxiv FD Cold Spring Harbor Laboratory SP 2020.11.24.394213 DO 10.1101/2020.11.24.394213 A1 Simone Tiberi A1 Helena L Crowell A1 Lukas M Weber A1 Pantelis Samartsidis A1 Mark D Robinson YR 2021 UL http://biorxiv.org/content/early/2021/05/06/2020.11.24.394213.abstract AB We present distinct, a general method for differential analysis of full distributions that is well suited to applications on single-cell data, such as single-cell RNA sequencing and high-dimensional flow or mass cytometry data. High-throughput single-cell data reveal an unprecedented view of cell identity and allow complex variations between conditions to be discovered; nonetheless, most methods for differential expression target differences in the mean and struggle to identify changes where the mean is only marginally affected. distinct is based on a hierarchical non-parametric permutation approach and, by comparing empirical cumulative distribution functions, identifies both differential patterns involving changes in the mean, as well as more subtle variations that do not involve the mean. We performed extensive benchmarks across both simulated and experimental datasets from single-cell RNA sequencing and mass cytometry data, where distinct shows favourable performance, identifies more differential patterns than competitors, and displays good control of false positive and false discovery rates. distinct is available as a Bioconductor R package.Competing Interest StatementThe authors have declared no competing interest.