[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.]

This page provides an informative overview of the type of multidimensional data generated by flow cytometry and the role of bioinformatics in analyzing increasingly complex data sets.
The introductory section on the basics of fluorescence based flow cytometry is missing a description of spectral cross-over compensation.This would seem an oversight, as compensation and compensation matrices are mentioned in other sections of the page.
Manual gating in the analysis of flow data should be described earlier in the page, certainly before describing Gating-ML, and with a bit more detail. The authors describing the process as "error prone" and "non-reproducible".Given the same data set, two investigators may use different hierarchical manual gating strategies to define a cell population, but this does not imply intrinsic non-reproducibility in the process. Indeed, clinical flow cytometry laboratories are certified based on their ability to reproduce results while testing a defined sample, and this testing involves manual gating. As for "error prone", the inference is that there is a correct way to gate flow data, and that when this process is done manually, it is likely to be done incorrectly. This statement is then ignored in the discussion of combinatorial gating approaches, like flow type/RchyOptimyx, that use manual gating. On the other hand, the discussion of automated gating using clustering algorithms fails to mention that repeated analysis of data sets with large number of clusters may report different cluster partitions (http://www.biomedcentral....). I would invite the authors to present a balanced characterization of manual gating that recognizes its limitations in the analysis of increasingly complex flow data; it is a time consuming hierarchical approach that is limited to two dimensional analysis at each step.

Response:
Re. manual gating:
We have made several changes to address this comment:
We have re-organized the content to discuss manual gating earlier as requested. We have also clarified that manual analysis can indeed be reproducible specially in controlled clinical settings and have better described the cases in which it can cause inaccuracies. We have also clarified that despite the recent advances in computational analysis, manual gating still is the main solution for identification of specific rare cell populations (e.g., for gating rare populations for the combinatorial gating algorithms). Finally, we have explained that the computational gating algorithms we have discussed here can automatically select the number of cell populations using different methods and that this choice can affect the sensitivity and specificity of the results.

Lastly, I'd emphasize the need for informative representation of cell populations identified through automated gating of complex multidimensional flow data. It is not informative to show all cell populations defined through multidimensional analysis on two dimensional dot plots. The SPADE software does a great job as it organizes defined cell populations in hierarchies of related phenotypes and it also allows for the comparison of individual markers across all the cell populations. This facilitates the identification of cell lineages, identification of rare cell types and comparison of different samples.

Response:
Re. visualization:
First, we would like to clarify that the SPADE algorithm is not always suitable for identification of lineages (as spanning trees are not necessarily representing lineages) or rare cell populations (due to the down sampling). Several approaches are being considered for addressing these limitations. This being said, we agree with the reviewer that SPADE is a fantastic algorithm for visualization of an entire sample to identify major cell populations and have discussed it in the "gating guided by dimension reduction" section.
Re. compensation:
Initially we had thought to exclude compensation, as the methods for performing it, while computational and automated, are standard and have not advanced significantly since the development of multicolour flow. However, on re-reading, it did indeed feel missing, and we have consequently included a section discussing the computational aspects of compensation.