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Thank you for addressing the reviewer concerns in your revision of this interesting work. Congratulations again and thanks for your patience.
[# PeerJ Staff Note - this decision was reviewed and approved by Paula Soares, a PeerJ Section Editor covering this Section #]
The authors have responded to all of my concerns and I see no problem with publication. I'll second the cover letter comment that negative results should be a welcome part of the publication record. Sometimes it's more interesting to see that an idea that should work doesn't in the context of a given paper, but can hopefully inspire others to see if it can in new situations.
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Thank you for your submission. While the reviewers found the manuscript to be clear and well constructed, there were a small number of concerns around the experimental design. Please try and address these concerns as best as possible.
[# PeerJ Staff Note: Please ensure that all review and editorial comments are addressed in a response letter and any edits or clarifications mentioned in the letter are also inserted into the revised manuscript where appropriate. It is a common mistake to address reviewer questions in the response letter but not in the revised manuscript. If a reviewer raised a question then your readers will probably have the same question so you should ensure that the manuscript can stand alone without the response letter. Directions on how to prepare a response letter can be found at: https://peerj.com/benefits/academic-rebuttal-letters/ #]
This is an interesting study that worth publication. The writing in clear, logical and easy to understand. Research questions were well defined. Methods and data descriptions had sufficient details. Overall, the analyses are technically and statistically sound.
Major concerns:
1) The authors calculated the average methylation for each gene (300 bp around TSS) first, then did the differential analysis. It makes more sense to do differential analysis at probe/CpG level first, then mapped the differential CpGs to genes.
2) During differential analyses (for example, comparing tumor to normal, or comparing different tumors), how the author handles confounding factors such as gender?
This is a meta-analysis project. All analyzed data (accession numbers) were provided. Conclusion are well stated.
Other minor problems:
• Line 9 -10: “Methylation is an epigenetic process of regulating gene expression in which methyl groups are attached to DNA molecules, often in promoter regions”. Such definition of “DNA methylation” is not accurate, and have these problems: 1) in the context of this manuscript, the audience know “Methylation” was referring to “DNA methylation”(5mC), but there are other type of methylations including RNA methylation (m6A) and histone methylation. 2) the methyl groups are attached to the 5th C of the cytosine ring. In somatic cells, 5mC most occurs in the CpG dinucleotides, and CpG CpG dinucleotides form CpG islands (CGIs). Many CGIs are located in promoters, but not all of them.
• Line 99-100: “We anticipated that many genes (such as tumor suppressor genes) would have consistently low methylation levels because those genes must be consistently expressed to properly regulate cellular division, growth, and other proliferation activities”. This is not always true, TP53 is a well-known tumor suppressor gene, but this gene is inactive or expressed at very low level in normal conditions.
• Line 238-240: “Because oncogenes are typically expressed at relatively high levels and tumor suppressor genes are typically expressed at relatively low levels, we expected that oncogenes would have higher methylation levels than tumor suppressor genes.” I don’t’ understand the logic here, if you assume oncogenes are “HIGH” expressed, you should expect “LOW” methylation at the promoter.
• Line 61: gene symbols should be in italics
This paper describes the process of collecting methylation data related to pediatric tumors and a set of normal samples. These samples were then analyzed to attempt to find genes which varied in methylation between the tumor and normal samples. The authors have also deposited all of their source code and the code appears to be well written and annotated. The paper is also well written and clear.
For the data presented in figure one, I’m wondering what the overall distribution of the gene-level methylation value looks like. I think you have two plotting options here, assuming you think this is a good recommendation. Option one would be two histograms showing the average methylation and another showing the CV values. Your other option would be a scatter plot of the average value vs CV (although you will probably have to use geom_hex with ~20000 data points). It would also be helpful to add a few lines to these plots to show where the categorization thresholds are.
Minor:
Typo line 38: “one type of genes” -> “one type of gene”
Typo line 40: “type of genes” -> “type of gene”
I suppose my largest problem with the entire design of this study is I don’t really think the samples you’ve gathered can be compared. Why would methylation data from fetuses through to 18 year olds from diverse tissues be comparable to unmatched tumor samples? I of course realize that there probably isn’t an ironclad great answer to this and you did somewhat address this by indicating that there weren’t dramatic differences in baseline data sets, but I’d like to see this examined more. Are all the genes in the high variance categories in Figure 1 showing significant differences between the normal data sets?
I’m also worried about using batch correction with the study ID as the batch correction factor. As far as I understand, the batch correction makes assumptions about the structure of the data that are only met when you have genuine repeats of an experimental setup, which you expect to show the same result. I think the diversity of these studies breaks that assumption. What does the methylation variance picture look like in the absence of batch correction? Specifically, if you do an analysis that is comparable to Figure 2 between the normal data sets, how many differences are you seeing?
If the other reviewer or editor don’t have a problem with combining these data sets without a deeper dive into differences in the normal data sets, then I’ll defer to their opinions.
No additional comments beyond those made in the experimental design section.
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