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Reviewer 2: Nolwenn Le Meur

Posted by PLOS_CompBiol on 06 Dec 2013 at 14:54 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.]

This topic page gives a good review of the field of flow cytometry bioinformatics. It covers the fundamentals of data handling and analysis for flow cytometry. It also highlights new approaches and ongoing developments, notably for cell population identification where room for improvements remains.

My main comment is on the lead paragraph. The sentence “Flow cytometry bioinformatics is the application of bioinformatics, computational statistics and machine learning to analyze flow cytometry data” is confusing. As mentioned in the Wikipedia page for Bioinformatics, this interdisciplinary field uses many areas of computer science, mathematics and engineering and therefore includes the concept of data analysis with notably machine learning technics. I would rather say: “Flow cytometry bioinformatics is the application of bioinformatics to flow cytometry, which involves storing, retrieving, organizing and analyzing flow cytometry data using extensive computational resources and tools." Maybe it could be added that flow cytometry bioinformatics requires and contributes to the development of computational statistics and machine learning methods. In addition, the introduction could be developed with examples of application fields. Indeed flow cytometry is used in wide range of domains from medicine and environment for human health to the analysis of the microbiome in seawater (e.g. Wang, Y et al. (2010). Past, present and future applications of flow cytometry in aquatic microbiology. Trends in Biotechnology, 28(8), 416–424. doi:10.1016/j.tibtech.2010.04.006.)

A minor comment is on the description of the different steps in computational flow cytometry analysis. This description is well done although the concept of workflow could be emphasized. Some software allows storing analysis workflows, which are notably useful for qualitative and reproducible research. For instance, for gating which is a hierarchical process, it is especially required to keep track of the process used for population selection. It is also essential when flow cytometry is used as a diagnostic tool to automate population selection. Finally, workflows saved in standard file format such as XML can be played by different software, which can be useful in terms of reproducible research.

Response: The comments on the lead section were extremely helpful, and have been taken into account in the expansion of that section.

We have added a listing of some of the applications of flow cytometry to the introduction section.

We have added a paragraph to the section overviewing the steps in flow cytometry analysis to emphasise the importance of workflows and their interchange for reproducibility.

No competing interests declared.