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I overlaid the plot from this study with the one from the other study. I changed the colors so all the people from this study (ME/CFS + POTS) are green and all the ME/CFS without POTS from the other study are red. Blue is healthy controls from the other study. It looks like the main clusters...

The %CO reduction was lower in the group with a lower %CO reduction? Again some interesting separation: Plot below from their study on people with ME/CFS and normal HR and BP responses, where I was intrigued about why the patients cleanly split into two distinct clusters: those whose...

Update from Dianna. Very moving video. She's doing a lot better, though still far from fully recovered. She describes the the suffering of another person with severe ME/CFS that she's gotten close to, Tobias in Germany. GoFundMe describing Tobias's situation.

Oh yeah, I see that now. Then it seems to me that the separation is to be expected.

Did they group the ME/CFS and LC cohorts together before doing the significance test to pick 3663 DMFs that were differentially methylated compared to HC? If so, I don't immediately see an issue. Separation from the HCs on the plot would be expected, but not between LC and ME/CFS, I think. If...

Merged thread Heart Rate Analysis for ME/CFS St. Denis, Catherine Abstract The article explores Catherine St. Denis's Heart Rate Analysis for ME/CFS, a hybrid work blending medical insight with poetic reflection to illuminate life with Myalgic Encephalomyelitis/Chronic Fatigue Syndrome. Topics...

Now on Youtube: 00:00 Amy Proal–An overview of PolyBio’s complex chronic illness research & clinical trials program 10:35 Resia Pretorius–Heterogenous fibrinaloid complexes (microclots): characterizing different phenotypes 19:46 Mark Painter–T cells as biosensors of viral persistence in Long...

Oh, I don't think this will tell us much. Most of the genes in those gene sets were not important, and the gene sets themselves might not be the best groupings of the genes that were important. Potentially, the overall gene sets/pathways themselves might provide clues, but I wouldn't do anything...

Just want to be sure I understand. You noted down all genes that are included in any of the top gene sets? And identified if they're duplicated in what way?

I have the same concern about the test set being so small. I wouldn't say their model definitely wouldn't replicate on UK BioBank cohort based on that. The comparison they and I did on the BB is much less sophisticated than if they had actually used their model for classification. Edit: But...

I thought it might be interesting, but when I go to Genebass and filter by any genes with "NOTCH" in the name, none of the six NOTCH genes are significant at all.

Final thing for now because I am exhausted. I ran GSEA with the cellular component collection since I already ran the same one on the Genebass data. Link to list of enriched component gene sets in Genebass I decided not to use collapsePathways here. Since I am comparing if any gene sets match...

Ok, I've run GSEA on the Zhang genes ranked by attention scores with the hallmark and canonical pathways collections: Hallmark: Canonical Pathways: I used collapsePathways to reduce the number of pathways, and it removes about half of them. Interestingly the first two, which seem very...

I'm not sure it's the strongest evidence, but if, as @Utsikt says, there is overlap in diagnosis, and it's not considered a totally unrelated condition, I think that supports the idea that it got number 1 most related because it actually does have genes in common with the ME/CFS group.

I think we can only go by the scores in the final model, and it'd be tough to get any specific individual protein interactions from that.

I have a basic familiarity. Currently working on getting acquainted with fgsea in R so that I can use the collapsePathways function, which seems useful, and the software I was using doesn't have it. I'd appreciate the help, but no rush!

Oh absolutely spend time on the important things. Maybe eventually I'll try to figure out how exactly they did it.

I got the impression they made 1261 modules using the entire STRING network, then tested each one against the 115 genes and got 4 significant modules.

Ok yes thank you that makes sense. Yes, that'd be ideal, and actually seems like what they did for their module enrichment of the top 115 genes. I don't understand a lot of the terms like Louvain, but it seems like they made discrete modules from STRING, then used Enrichr on the best matches. I...

Sure, but isn't that what's interesting? The networks of related genes, even if they include genes not actually very useful on their own in this cohort. Which networks did the model "pull higher up" based on its assessment that these networks are useful for classification. Maybe the divide is...