ME/CFS Science Blog
Senior Member (Voting Rights)
I had a go at matching genetic data on ME/CFS with the gene expression data from Dropviz.
This is the paper that discovered the eccentric medium spiny neuron (discussed here). It's a mouse brain atlas with RNA data from 0.69 million cells. The main publication is Saunders et al. 2018. but there's also a website with visualization and an option to download the data.
Paolo Maccalini already tested this dataset in his paper using the meta-analysis of DecodeME and the Million Veterans Program (MVP) data. Using the level 2 classification of Dropviz he found 6 cell types that passed the bonferonni-correction as shown in Table A1.

https://s4me.info/threads/biologica...encing-of-me-cfs-2026-maccallini-et-al.50225/
This uses the FUMA website approach of grouping the data per dissection and controlling for the average gene expression of that dissection. That's fine but it also means that the results for a cell type are influenced by what else is in the dissection.
As a supplementary to this, I want to do analysis that ignores the dissection groupings and controls for the average of the entire dataset, similar to what we did with the Human Brain Atlas. Luckily the FUMA website provides a dataset that groups all dissections and has the data already in the right format. You can download these here:
This is the paper that discovered the eccentric medium spiny neuron (discussed here). It's a mouse brain atlas with RNA data from 0.69 million cells. The main publication is Saunders et al. 2018. but there's also a website with visualization and an option to download the data.
Paolo Maccalini already tested this dataset in his paper using the meta-analysis of DecodeME and the Million Veterans Program (MVP) data. Using the level 2 classification of Dropviz he found 6 cell types that passed the bonferonni-correction as shown in Table A1.

https://s4me.info/threads/biologica...encing-of-me-cfs-2026-maccallini-et-al.50225/
This uses the FUMA website approach of grouping the data per dissection and controlling for the average gene expression of that dissection. That's fine but it also means that the results for a cell type are influenced by what else is in the dissection.
As a supplementary to this, I want to do analysis that ignores the dissection groupings and controls for the average of the entire dataset, similar to what we did with the Human Brain Atlas. Luckily the FUMA website provides a dataset that groups all dissections and has the data already in the right format. You can download these here:






