PONE-D-22-03667An evaluation of RNA-seq differential analysis methodsPLOS ONE

Dear Dr. Li,

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“This work is supported by the University of Rochester's Clinical and Translational Science Award (CTSA) number UL1 TR000042, UL1 TR002001, and U24TR002260 from the National Center for Advancing Translational Sciences of the National Institutes of Health (Drs. Li and Zand). Dr. Zand is also supported by the National Institute of Allergy and Infectious Diseases and the National Institute of Immunology, grant numbers AI098112 and AI069351. This study was supported by the National Institute of Environmental Health Sciences with grant number NIH 1R21ES032159-01A1 and National Institute on Aging with grant number NIH 1U54AG075931-01 (Drs. Li and Rahman). This study was supported by the grants from the WNY Center for Research on Flavored Tobacco Products (CRoFT) under cooperative agreement U54CA228110 which is supported by the National Cancer Institute of the National Institutes of Health (NIH) and the Food and Drug Administration (FDA) Center for Tobacco Products (Drs. Li, Goniewicz, Rahman, Xie). The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH and FDA.”

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“This work is supported by the University of Rochester’s Clinical and Translational 496 Science Award (CTSA) number UL1 TR000042, UL1 TR002001, and U24TR002260 497 from the National Center for Advancing Translational Sciences of the National Institutes 498 of Health (Drs. Li and Zand). Dr. Zand is also supported by the National Institute of 499 Allergy and Infectious Diseases and the National Institute of Immunology, grant 500 numbers AI098112 and AI069351. This study was supported by the National Institute 501 of Environmental Health Sciences with grant number NIH 1R21ES032159-01A1 and 502 National Institute on Aging with grant number NIH 1U54AG075931-01 (Drs. Li and 503 Rahman). This study was supported by the grants from the WNY Center for Research 504 on Flavored Tobacco Products (CRoFT) under cooperative agreement U54CA228110 505 which is supported by the National Cancer Institute of the National Institutes of Health 506 (NIH) and the Food and Drug Administration (FDA) Center for Tobacco Products (Drs. 507 Li, Goniewicz, Rahman, Xie). The content is solely the responsibility of the authors and 508 does not necessarily represent the official views of the NIH and FDA.”

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“This work is supported by the University of Rochester's Clinical and Translational Science Award (CTSA) number UL1 TR000042, UL1 TR002001, and U24TR002260 from the National Center for Advancing Translational Sciences of the National Institutes of Health (Drs. Li and Zand). Dr. Zand is also supported by the National Institute of Allergy and Infectious Diseases and the National Institute of Immunology, grant numbers AI098112 and AI069351. This study was supported by the National Institute of Environmental Health Sciences with grant number NIH 1R21ES032159-01A1 and National Institute on Aging with grant number NIH 1U54AG075931-01 (Drs. Li and Rahman). This study was supported by the grants from the WNY Center for Research on Flavored Tobacco Products (CRoFT) under cooperative agreement U54CA228110 which is supported by the National Cancer Institute of the National Institutes of Health (NIH) and the Food and Drug Administration (FDA) Center for Tobacco Products (Drs. Li, Goniewicz, Rahman, Xie). The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH and FDA.”

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Reviewer #1: Yes

Reviewer #2: Yes

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2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: Yes

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Reviewer #1: Yes

Reviewer #2: No

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Reviewer #1: Yes

Reviewer #2: Yes

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5. Review Comments to the Author

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Reviewer #1: Li et al have produced a robust and thorough review of differential expression analysis tools. The article is very well written, included all technical descriptions of the tools and methods they employ and gives a well reasoned discussion and conclusion for each.

I have a few very minor things which I wold like to see addressed:

On line 42 you describe DeSeq2 as recent, I would disagree as it was published in 2014. I think this needs rewording.

I think in the introduction more emphasis could be made on previous published comparisons being 8-10 years old and it being time for an update.

I think a layman's description of Power and Stability would be of benefit to newer bioinformaticians and would widen the scope of the readership.

Figure 3 axis titles are a bit squashed compared to others, would be nice to see them consistent (however this may be an artefact of the submission).

Figure 4 legend needs to include A and B descriptions o match the figure.

Finally why has Sleuth been committed? Rationale needs adding to discussion on choice of tools. As "popular" depends on the audience.

Reviewer #2: The authors have conducted an interesting study comparing popular tools for gene differential expression analysis. Using a combination of simulations and RNA-seq data from mice they compare the power, FDR and stability of results for each tool.

The results are clearly presented and address relevant questions for RNA-seq analysts.

The discussion would benefit from some comments on why the number of DE genes vary so much among the negative-binomial models in the mouse data. Why does EdgeR rank amongst the lowest for power in the simulations at n=6 and n=3 samples, but ranks highly for number of significant genes identified in the real data?

The discussion also needs to mention the limitations of the simulations used. In particular, random sampling from negative-binomial and log-normal distributions assumes independence between samples, which is unrealistic in the case of gene expression. This approach does not take the structural correlation among genes into account.

It would also be appreciated if the code and simulation data could be made available on a public repository, such as Zenodo or Github. Having it available upon request by email introduces an unnecessary hurdle, especially in light of the recent paper by Gabelica et al. https://doi.org/10.1016/j.jclinepi.2022.05.019

Some minor points to address:

Line 240 — "have very little power and reject very few genes" doesn't make sense here, please clarify. Does this refer to rejecting hypotheses?

Line 341 — "the sample size is 12 in each group" doesn't fit with Figure 4a containing n=21 total. For consistency with Figures 1-3, it would be clearer to stick to the "n=samples per group" format rather than "n=total samples".

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Reviewer #1: Yes: Adam Mark Blanchard

Reviewer #2: No

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An evaluation of RNA-seq differential analysis methods

PONE-D-22-03667R1

Dear Dr. Li,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

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Kind regards,

Dov Joseph Stekel

Academic Editor

PLOS ONE

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