Reviewer response for version 1
Major comments:
- There is something wrong with the data download link in the F1000 version so that I am unable to download these files and actually reproduce the workflow. 
I experimented a bit to see if I could figure out how to download the data anyways, but will reserve further evaluation of this submission until this issue can be resolved by the authors. 
```{r} urls = c("https://www.ncbi.nlm.nih.gov/geo/download/?acc=GSE95601&format=file&file=GSE95601%5FoeHBCdiff% "https://raw.githubusercontent.com/rufletch/p63-HBC-diff/master/ref/oeHBCdiff_clusterLabels.) ```
- a. 
This workflow will likely be out-of-date when the underlying packages transition to use SingleCellExperiment. 
This is actually a positive thing because many of the more opaque lines of code (involving subsetting ERCC genes, etc) will be more streamlined. 
b. 
It requires installation of the development branch of bioconductor, which impacts the usefulness of the workflow to the average user. 
I expect the authors will revise this tutorial when Bioconductor 3.6 is released and use of the devel branch is no longer necessary. 
Additionally `slingshot` is an requirement, but currently only exists on github and no SHA1 provided. 
I hope that `slingshot` will be added as a bioconductor package shortly. 
In the meantime, a tag must be added to the git repo for the release being used in this workflow and instructions provided for how to install this tag. 
Additionally, the authors may wish to note that installation instructions for the packages will be provided at the end of the workflow so that someone proceeding sequentially will not be tripped up. 
c.O
Opaque code is presented in order to generate plots, e.g. ```{r} palDF <- ceObj@clusterLegend[[1]] 
pal <- palDF[, "color"] names(pal) <- palDF[, "name"] pal["-1"] = "transparent" plot(fit$points, col = pal[primaryClusterNamed(ceObj)], main = "", pch = 20, xlab = "Component1", ylab = "Component2") legend(x = "topleft", legend = names(pal), cex = .5, fill = pal, title = "Sample") ``` 
While this complexity may be necessary, perhaps some of it could be encapsulated as accessor functions in the package? 
Too much complexity here may cause users to miss the forest for the trees.
- The authors could better motivate (or at least explain the impact of) some of the default parameters and procedures.
- Why do we set a zcut threshold of 3 for the `scone` filtering?
- Why K=50 for zinbwave?
- RSEC parameters How should a user decide on a value for these parameters?
Structure
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Todo
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