Dear xxxx,
I hope this email finds you well.
I am forwarding you the authors' response. Can you check to see if all your concerns have been addressed?
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So first to explain why we used the PCA we think totally appropriately as a VISUAL TOOL to display the data we had generated and how that allowed us to highlight key questions to be further investigated in the study of how similar or different the ME/CFS and LC cohorts were in their methylomes.
Key steps.
1.We selected methylated fragments for study that were in all 15 participants.
2. We did a p<0.05 Anova to determine if there were many fragments with three way significant differences ie between ME/CFS and LC and CONTROLS
3. That gave a big reduction in fragments (73,239 down to 3,363), but 3,363 (or 4.6%) was a significant subfraction showing differences between ME/CFS and LC. For this reason we chose the PCA as a visual display of these differences.
4. We fully expected from the Anova data to see the three cohorts, Controls, ME/CFS , LC, separated in the PCA -but without pre-conceptions of how widely the ME/CFS and LC would be separated - the display showed wide separation. ( It should be noted we also did a PCA on the 73,239 fragments where there was no selection for significance difference selection and just got a random scatter of the 15 participants - -not shown in the paper)
5. An important additional purpose of the PCA on the selected subfraction was to see the contribution of individual patients and whether in each cohort there was a scatter of the patients or whether they were tightly grouped. This would suggest EACH patient of each cohort was making a similar contribution to the significant differences of that cohort from the other cohorts, whereas conversely a wide scatter would suggest much individual patient variation. We found tight grouping
6. The P<0.05 (Anova) and PCA suggested then two avenues to further investigate in the study
(a) what was the molecular explanation for the significant difference between ME/CFS and LC suggested. This further investrigatiion is shown Table 1 examining individual methylation sites - mostly it was the extent of change in methylation that was the difference (LC was greater) not different sites. Nevertheless we did find examples of sites where there was methylation change in one cohort but not the other and an example where the methylation levels had changed in opposite directions for there ME/CFS patients and LC patients as shown in the box plots of Figure 4)
(b) could we verify the suggested similar contributions of all patients in a cohort to the differences by examining individual genomic sites . This suggestion was validated in most cases in the box plots showing common individual patient responses at important genomic sites
7. Our specific conclusions came from these further investigations
So in conclusion
All authors felt that PCA had been used appropriately, and after further reflection after my contact from the complainant.
Specific responses to the comments of the complainant:
1. The paper claims that the Principal Components Analysis (PCA) shows that the Healthy Controls and Long Covid and ME/CFS cohorts are very distinct from each other, with a high level of similaritywithin each cohort, in terms of Differentially Methylated Fragments (DMFs). It goes on to suggest that the differences between the Long Covid and ME/CFS DMFs suggest that they may be different stages in the same disease.
No, It was the p<0.05 ANOVA showed there was a subfraction of the methylated fragments that were significantly different among the three cohorts and that was displayied in the PCA, with relatively tight clustering of the individual patients in each cohort . Interestingly, there were two outliers - in the healthy control cohort, the much older healthy control ( the methylome is know to change during ageing!) and the youngest ME/CFS patient whom has been shown to be somewhat of an outlier in most of our studies with her samples. The inference that stages of the disease might influence the methylome and explain why differences were found in the two cohorts was a suggestion that we recommended should be followed up when we have cohorts with the same disease duration.
2.It is therefore not surprising that the PCA chart showed the cohorts to be widely separated, with individuals tightly
We agree as we were wanting to display what the ANOVA was suggesting in the PCA ie there cohort with signicant differences - but not the comment about tightness of the clustering - and indeed as we indicated above there were outlier patients that di n to cluster so well
3. It is not clear if the authors selected DMFs based on a significant difference between the LC and ME/CFS cohorts combined versus the healthy controls, or between each of the LC and ME/CFS cohorts compared to the healthy controls.
We did a three way significance comparison with an ANOVA so our subfraction was fragments where each of the three cohorts were significantly different from the other two, as appropriate for such a three way analysis.
4. With that selection intensity, the differences could easily be the result of random variation. The paper does not specifically note this and instead draws firm conclusions about the diseases based on the identified “differences”
We wanted to select to see if there were many fragments where there were significant differences between all three cohorts - that was what we were potentially interested in for the study. If it were random variation among the 3363 fragments that allowed separation the three cohorts neatly into their 5 patients the subsequent analysis of individual methylation sites would not have shown the characteristics we derived. The trends and differences found in the detailed site studies were totally consistent with what the PCA had shown. Important to emphasis we were using the ANOVA and PCA as a guide to the further investigations.
5.The Discussion section comments on the PCA, incorrectly reporting that it shows that “the global DNA methylation patterns can separate the two disease groups from each other .. and both are well separated from the HC group”.
The ANOVA of the global methylation found a significant subfraction with significant differences between the ME/CFS and LC cohorts visually displayed in the PCA- that supports that conclusion.The subsequent data presented where an extra limit of a minimum of 10% methylation change is added showed the differences vs healthy controls at the specific sites and highlighted a few sites where there are major differences in methylation between the two ME/CFS and LC cohorts
I hope this is a sufficient explanation of our different points of view and why we think the way we used the PCA as a display tool of the significance ANOVA analysis was totally appropriate as a guide to the later more definitive analyses.
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Kind regards,
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Section Managing Editor
