Reviewer response for version 1
In this article, the authors Perraudeau, Risso, Street, Purdom and Dudoit present a nice workflow for normalization, dimensionality reduction, clustering, and lineage inference of single-cell RNA-seq data (scRNA-seq) using R packages from the open-source Bioconductor project. 
I enthusiastically agree with the authors on an “increasing need for workflows that integrate these tools to yield a seamless scRNA-seq data analysis pipeline” and this workflow is a great step in the right direction. 
However, I have some constructive suggestions that will better integrate other previously developed work and improve this workflow.
- In this workflow, the authors start with a count table. 
However, the majority of researchers will start with raw reads (e.g. a FASTQ file). 
It would be great if the author discussed current best practices for the quantification step of scRNA-seq data. 
Alternatively, the authors could point to other references that have already been developed.
- I would like to see the authors take advantage of the rich functionality and data exploration tools for cell- and gene-specific quality control (QC) introduced in low-level analysis workflows such as the one from Lun et al. (2016) 1 . 
Also, in this workflow, the authors create multiple SummarizedExperiment objects (e.g. one with only the top 1000 highly variable genes (HVGs), one with all genes, etc). 
This doesn’t seem efficient, especially with large single cell data sets such as the 1.3 million cells from embryonic mouse brains. 
I think both of these concerns can now be addressed with efforts such as the recently developed SingleCellExperiment Bioconductor object ( https://github.com/drisso/SingleCellExperiment ). 
For example, the authors could add a “USE” column in the gene- or cell-specific meta table to represent whether or not a particular gene in a particular cell met the filtering criteria applied. 
The authors could store W in the reduceDim assay of the SingleCellExperiment object.
- In ZINB-WaVE, the authors specify the number of dimensions for the low-dimensional space (K) to be K=50. 
Could the authors add more details for the reader explaining why they picked K=50 and describe situations in which a user would want to specify a higher or lower K? 
In particular, it would be useful to discuss computational time in terms of number of genes and cells. 
Also, it would be useful to note that if you only wanted to use ZINB-WaVE to remove known covariates for normalization, you can use K=0.
Minor comments:
- When selecting the top 1000 HVGs, why do the authors not take into account the overall mean-variance relationship and only select genes based on the variance?
- It would be great if the authors referenced other tools available for similar analyses currently available. 
For example there are several available packages for normalization of scRNA-seq data, such as calculating global scaling factors can be done with scran ( https://bioconductor.org/packages/release/bioc/html/scran.html ) or gene and cell-specific scaling factors using SCnorm ( https://github.com/rhondabacher/SCnorm ). 
Alternatively, users might want to try using relative transcript counts using Census ( https://bioconductor.org/packages/release/bioc/html/monocle.html ).
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