Just came across the following thread in Github , I've analyzed all of my single-cell RNA-seq data using Seurat V3.0 and recently came across Monocle. Looking for opinions if I should move to Monocle or functions available in Seurat is enough for single-cell RNA-seq data exploration.
I think that the reviewer comment may not have been optimally worded. Many people have focused on the "could be done in Monocle" part, but the reviewer does not say that it should be done in Monocle. It's probably more important to focus on "deeper investigations could yield interesting findings".
I don't think that the reviewer argues that Seurat is better or worse than Monocle. They are looking for more in-depth analysis. Popular scRNA-seq packages like Seurat or Monocle will generate a t-SNE/UMAP, identify a set of clusters, and calculate cluster markers. The packages will not tell you which of those clusters are important or uncover some novel biological insights. You often need some additional custom analysis for that.
You can look at some of the Satija Lab publications that aren't new computational methods, such as Developmental diversification of cortical inhibitory interneurons. Although the authors use Seurat extensively and have an interest in promoting it, if you look at the figures, they do not look like they are copied and pasted from the Seurat tutorials (unlike many other publications). I highly doubt this reviewer would ask them to use a different package (even if the names were hidden).