On June 25th, 2018, Huang et al. published a computational method SAVER on
Nature Methods for imputing dropout gene expression levels in single cell RNA
sequencing (scRNA-seq) data. Huang et al. performed a set of comprehensive
benchmarking analyses, including comparison with the data from RNA fluorescence
in situ hybridization, to demonstrate that SAVER outperformed two existing
scRNA-seq imputation methods, scImpute and MAGIC. However, their computational
analyses were based on semi-synthetic data that the authors had generated
following the Poisson-Gamma model used in the SAVER method. We have therefore
re-examined Huang et al.'s study. We find that the semi-synthetic data have
very different properties from those of real scRNA-seq data and that the cell
clusters used for benchmarking are inconsistent with the cell types labeled by
biologists. We show that a reanalysis based on real scRNA-seq data and grounded
on biological knowledge of cell types leads to different results and
conclusions from those of Huang et al.Comment: 5 page
