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Reviewer 1: Holden Maecker

Posted by PLOS_CompBiol on 06 Dec 2013 at 14:56 GMT

[This is a review of the original version. See Text S1 for the version history. The authors’ responses are included in line and are reflected in the published version.]

I find this to be a good and wide-ranging summary of topics associated with flow cytometry analysis and bioinformatics. It spans the territory from basic flow cytometry concepts and gating, to newer bioinformatics approaches like SPADE and PCA, and routines for data processing such as those in Bioconductor. Few people's expertise spans all of these areas, but this page provides a good synthesis for folks who work in one or more of these areas, and want to learn more. I would suggest expanding the section on Gating, to make some basic but missing or merely implied points, e.g.: -Gating is hierarchical, usually focusing in on specific subsets by sequential selection of populations, usually in two dimensions at a time (e.g., Lymphocytes->T cells->CD4+ T cells->naive CD4+ T cells). -This approach suffers from the inability to visualize all other relevant dimensions when gating on only two dimensions at a time; it may even make it difficult to distinguish closely spaced populations that could be better separated in >2-dimensional space. And it suffers from "tunnel vision", in that an overview of the entire dataset is virtually impossible. -Boolean gates can be created (to some extent, automatically in software such as FlowJo) that divide a population of cells into all logical combinations of markers. This is a complementary approach to automated gating algorithms that find "where the cell clusters are"; in a Boolean approach, one asks "what are all the possible cell phenotypes" and then monitors those compartments to see which ones are populated and to what extent. It is, however, a deterministic approach, assuming that cells are either positive or negative for a given marker, and the user decides the positive/negative boundary. The number of compartments can also become staggering with increasing dimensions. Clustering algorithms are, by contrast, unsupervised, in that they do not require any user input about what is positive or negative; they simply find regions of cell density, inflection points, etc.

Response: These are some excellent suggestions. As there is some overlap between these comments on gating and the comments of reviewer 3, we have addressed both reviewers' comments in our response there.

No competing interests declared.