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Level 2 Level 2 has 565 cell types. I've tried to deduce some info form the markers they have. For example: Gad1Gad2' = GABAergic, while Slc17a7 or Slc17a6 = Glutamatergic. There were significant hits for both. Here are the ID's of the 13 cell types that reached bonferonni-significance...

Level 1 Here's what I got for the level 1 classification which has 88 cell types. The results show again that only the neuron type reaches statistical significance. It's also clear that the signal isn't confined to one brain region. The signals in the cerebellum and substantia nigra look a...

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...

The paper writes: Has anyone been able to find this appendix?

ImmGen data The Finucane 2018 paper also included an older version of the dataset on immune cells from the Immunological Genome (ImmGen) project. Immunological Genome Project Using LDSC, I got similar results to trafalmadorian97, with the top hit reaching a p-value around 0.005 for 292 cell...

Linnarson Mouse Brain Atlas This is an older Mouse Brain Atlas from the Linnarson lab at the Karolinska Institute (the same group that published the human brain atlas). The data from different dissections was spread out over multiple publications. Skene et al. 2018 and later Olislagers et al...

Yes, you reported it correctly, and thanks for clarifying. I just wanted to add that the Seeker white matter cells aren't seen as independent from the top Siletti eMSN signal if you follow the Watanabe 2019 approach (EDIT: ignoring the HEAL2 replication).

Background Had a closer look at the conditional analysis as specified by Watanabe et al. 2019. Step 3 is about checking if cell types from different datasets are independent of each other. The basic idea is that for each pair of cell types, you first do a MAGMA regression with one cell type...

A recent and useful lecture on the limitations of cell-type enrichment analysis with GWAS data. It's from Rachel Brouwer, a member of the Posthuma group that produced FUMA, MAGMA, FLAMES, etc. The conclusion is a bit sobering. These analyses work well for broad cell types (like neurons, glia...

These analyses were a bit above my level as an amateur so don't take them too seriously.

There is one caveat to this analysis, the olisalgers et al. 2021 paper found that this linear MAGMA analysis with quantiles performed worse (did not align with their other methods). They prefer LDSC or MAGMA with the top 10% of genes as binary annotation. They also restrict their GWAS to the...

The data is available in the R package MAGMA.celltyping by the authors. neurogenomics/MAGMA_Celltyping: Find causal cell-types underlying complex trait genetics Mouse gene have been mapped to human equivalents by them. They also changed the gene expression specificity into 40 quantile (I...

Another cell type study that I wanted to test the DecodeME data to. They used an older Mouse Brain Atlas from the Linnarson lab at the Karolinska Institute (the same group that published the human brain atlas). The data from different dissections was spread out over multiple publications such...

Abstract With few exceptions, the marked advances in knowledge about the genetic basis of schizophrenia have not converged on findings that can be confidently used for precise experimental modeling. By applying knowledge of the cellular taxonomy of the brain from single-cell RNA sequencing, we...

A note on the FUMA platform datasets The FUMA platform contains many of these dataset and makes it easy to test for them by uploading your data. Their approach is explained in this preprint...

DESCARTES This is also a major dataset with a lot of strong points: multiple tissues, in humans, 4 million cells. The main limitation is that it is from foetuses so may not directly apply to the cell types in adults. The main paper is Cao et al. 2020...

Human Brain Cell Atlas v1.0 This is currently the most extensive dataset on brain cells in humans. Published in 2023 by the lab of Sten Linnarsson at the Karolinska Institute, one of the important teams that have been making multiple RNA datasets over the years. The relevant paper is Siletti...

GTEx6 + Franke Lab An older version of the GTEx (version 6) was combined with RNA data from Franke's lab in the study by Hilary Finucane et al. 2018. This is the paper where they introduced the LDSC-SEG approach to testing cell type enrichment and where they tested multiple diseases and traits...

Genotype-Tissue Expression (GTEx) This analysis was done in the DecodeME preprint using the MAGMA tool on the FUMA website. The preprint only posted some of the most significant regions, but the entire plot looks something like this if you upload the DecodeME data. All significant tissue types...

I thought it would be useful to make an overview of RNA datasets that we can match to the DecodeME DNA results. This could give us clues to which tissues or cell types are potentially involved in ME/CFS. I've made an overview of datasets below and plan to update it as we find new ones. The Chan...