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Thanks, that's helpful. It's interesting how much the sign flip flops across tissues, even though the quantile score is similarly high. The only thing that makes me less worried is that the signs from unrelated samples in the same tissue/cell-type seem to be in the same direction (at least from...

Good question, I assume randomly because it was the next closest in the credible set outside of the island around the strongest hit. The TF binding data explains the switch in signs for some genes, but might not be relevant to ME/CFS

Oh duh, it's amazing the things you miss when you're just scrolling quickly on your phone. chr20:48914387:T>TA Most significant hit In lncRNA ENSG00000294533, described as anti-sense to ARFGEF2. Meaning that it's a complementary sequence to part of the actual sequence in the gene--when the...

@forestglip if you share the variant locations from your examples above I can check the overlap with TF binding sites when I have a chance to verify if that’s what causes the sign change

Nice work! When I looked at that locus a while ago I remember that the region of SNPs with the highest significance overlapped an area with a lot of different transcription factor binding motifs. It seems to be an important regulatory region, so theoretically these findings are biologically...

If you can use a raw sequence of DNA as input it should be possible to just manually change all the base pairs to match the SNPs from DecodeME in a certain region and then run that string as your input. Might be too labor intensive, though

Judging from some of the example plots posted on the AlphaGenome forum it looks the expression differences from the variant are just small here. I see some very small instances where the grey line slightly peaks out behind the red--you might be able to see some slight variation if you zoom in a...

Not sure I have managed the right balance, but thank you :) to be clear my my conservative approach here is moreso towards trusting the results more than other methods and whether it’s worth incorporating the tool into genomics pipelines for publishable projects. For just testing the results on...

Yeah quantity, quality, and context-specificity of training data is really going to make or break a tool like this. I tend to reserve judgement until after several teams have used it and validated experimentally since the methods paper introducing the tool is always going to highlight the few...

You expect that specific metabolites would be significant in one analysis and not the other. But the kidneys exist to keep blood concentrations within specific ranges by filtering out excess, so one kinda has to be an echo of the other unless kidney function is just breaking down (in which case...

Yep pretty much. The reason I keep harping on the distinction is because its the only way I can see to reconcile these results with the plasma findings—i.e. there are changes after exercise, but what comes out in the urine has more layers of complication for the analysis, making it look like...

For individual metabolites, yes. Though the question of “no change after exertion” is about all the comparisons, relying on the assumption that no-significant-metabolites is actually what you would expect to see if the only thing driving change between time points is random fluctuation. We’ve...

It could be an additional issue, but the main problem would be the degree of “cancel-out-ness”, the centeredness around 0. It doesn’t need to be a larger spread so long as things cancel out TLDR it is a intra group variability issue but not one that translates to variance necessarily (and not...

It would be because in the ME/CFS group you have more “evenness” between people whose level increases and decreases between time point, basically negating the mean difference. So variability in directionally rather than variance was what I’ve been trying to describe—that’s what I’m trying to...

Sorry, something just occurred to me--that's not necessarily true. This is what the plot is showing: It's plotting the distribution of standard deviations of the per sample per metabolite logFCs. It doesn't actually address the issue I was talking about Let's say we had 10 participants in...

Yup it's mentioned as a speculation for slightly elevated blood glucose, not as proof of insulin resistance. The armstrong (2015) paper and the same groups re-analysis of the same UK biobank data also found slightly elevated blood glucose compared to HC. In all cases the fold change is less than...

That part too but they don’t specify the test used for just the volcano plot comparisons, which makes me think they just used the default everywhere. Pretty sure that's Tukey's for calling pairs() from emmeans but they also specify using BH correction so maybe something else was done for the...

Tukey’s post-hoc from emmeans also assumes independence of samples—someone correct me if I’m wrong, but I thought it wouldn’t be appropriate for comparing timepoints in the same group (though it is oft-used for that purpose).

Quick skim of the plasma CPET metabolomics study: definitely many significant metabolites between D1 vs. D2 in ME/CFS. Also this caught my eye: referring to day 1 vs. day 2 pathway analysis in the female cohort And this indicates that there isn’t just a simple kidney problem leading to buildup...

Either way if we’re not seeing the same phenomenon in plasma it means that something weird is happening when analyzing urine specifically. It wouldn’t necessarily reflect a biological kidney problem since that would be expected to reflect in the plasma as well