DESCEND - Gene expression distribution deconvolution in scRNA-seq
Microarray and bulk RNA-seq data measure.
Background knowledge
Goal of this paper
Methods behind DESCEND
Results
Model evaluation
Case studies
Effect of cell size on differential non-zero mean and fraction analysis
DESCEND improves accuracy of cell type identification, by selecting better highly variable genes
Take-home messages
- When all across-cell differences are accounted for, Poisson distribution is sufficient to capture technical noise in UMI-based scRNA-seq counts.
References
- Wang, J., Huang, M., Torre, E., Dueck, H., Shaffer, S., Murray, J., … & Zhang, N. R. (2018). Gene expression distribution deconvolution in single-cell RNA sequencing. Proceedings of the National Academy of Sciences, 115(28), E6437-E6446.