I see your point. Usually on my own plots I would have two different colors denoting p<0.05 and adj. p<0.05. I think the line is most often there because the first thing that someone in my field would want to know is whether we're looking at a situation of just a few weak hits that need to be investigated further with skepticism or a lot of strong signals that might not all get covered thoroughly in the text (and you wouldn't expect them to remember offhandedly what -log10(0.05) is).
That seems like the kind of bad practice that is bound to end up causing some confusion down the line. I don’t doubt that you or @DMissa might think that way because you are very rigorous, but judging by the papers we read here every day most people tend to make a muddle out of things.
It was already in the paper before that other thread existed. So, I thought, okay, this debate is extending beyond my confident knowledge of statistics and I don't want to do something wrong, so let's consult a colleague (an Accredited Statistician of the Statistical Society of Australia) who we have analysed data with before, and who has used PCA similarly in the past. They confirmed to me that it was a valid application of it, so I kept it there.
I can only present things as I observe them, as neutrally and comprehensively and systematically as I can endeavour to. If people choose to misappropriate things put forward in such a way I am not responsible for that. I have put appropriate qualifiers in the immediately adjacent text. If people don't read the text in good faith what can I do?
Which is why right next to the PCA, in the text itself, I wrote "this is not to suggest these lipids as potential biomarkers". Nobody who understands any biology or wields any significant influence is going to see two lipids from transformed cell lines in 15 people and determine it to be a prospective biomarker, especially if it blows up somehow on twitter and I, the author, step in and say: sorry, no.
It's not a matter of "so set" - I am not feeling particularly impassioned by this (I am open to what you are saying). At the time of its inclusion the rationale was to visually illustrate the group to group clustering produced by the most significant lipids moving in either direction (because they can be interconverted, and stand-out depletions or accumulations might be related, whether directly or indirectly by other intermediates that can have either altered or unaltered steady state levels according to flux through the relevant reactions). Abnormal interconversion is actually one of the most likely drivers of accumulation (see discussion for potential explanations being narrowed down). I got the idea from an application of PCA by a statistician in another paper where two functionally related transcripts were used to produce clusters. I later saw that thread where it was brought into question so I sought expert advice and then proceeded accordingly, in good faith. Maybe it's an arguably redundant application of PCA where another scatter may have sufficed. I am happy to accept that and to approach similar issues in future analyses differently.
For the record I require no explanation and understand the reasons.
Maybe, maybe not. I guess coming from environments where perfectionism was expected to the point of paralysis, I have just learned the hard way that if I try to compensate for the many ways that someone barely giving my work the time of day and with little context-specific knowledge might possibly misinterpret things, my work will very much suffer in the other direction.
That figure is really quite striking. From the stats in the supplementary files, it doesn't look like any other lipid came close. q=.0017 and FC=0.32 for this lipid versus q=0.79 and FC=0.64 for the next most significant one (PE(O-40:5)).
So yeah this is why in the last response to Hutan I'd mentioned lipids that clearly stood out as opposed to the whole dataset for the PCA - it was clear that the top end, particularly this one, were behaving far more dramatically different than the rest, so this behaviour formed part of the rationale for taking the most elevated and most reduced.
Just to cover all the bases..were the groups mixed up for all the measurement steps? So the order of testing would be something like HC, HC, ME, HC, ME, ME, HC, ME, ME, HC.
Weighted standing time (WST) = Time standing (mins) x (1-(Difficulty/14)).
I really appreciated your clear posts summarising the study. My brain is not working at the moment, so I cannot read the paper (and have learned not to read pre-prints because the line spacing, line numbers and diagonal stamps obliterate my ability to understand text!) So your posts are very helpful.
I really appreciate that approach to writing up your results.
Oh that's horrible. Hope it will ease for you as soon as possible.
Really appreciate your explanations, @jnmaciuch, very helpful.
I found this really helpful too.
Thank you for that explanation about whether potential differences between groups in BMI could have confounded your results.
I have a lot of sympathy with your experience here, but I don’t think this is asking for perfection. Clearly telling the reader if the values are corrected or not is need to have, not nice to have.
It's a necessity only if you're trying to prove the relevance of your finding--which doesn't matter here when it's already been made clear that the enrichment was only driven by one single metabolite (and therefore isn't that relevant). It's misleading only for people who don't fully read the paper or understand what that detail means. In which case, we should be much more concerned about people mistakenly interpreting that these findings in immortalized B cells are inherently representative of a global lipid problem in ME/CFS, since that is the most likely and materially consequential source of confusion. But for good reason, we typically don't expect authors to write their entire paper based on potential misinterpretations of that caliber.
The figure text says there are three not one. So it’s inconsistent with what’s said in the main text of the paper.