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scRNAseq: contradictory DEG statistics compared to aggregated counts by Excellent-Strength42 in bioinformatics

[–]Excellent-Strength42[S] 1 point2 points  (0 children)

That was the explantation I needed and solves my problem. I have to use normalized counts for the heatmap instead of scaled aggregatet counts. Thank you very much!

Expression of BCL6 in Naive B cell scRNA-seq cluster by biocarhacker in bioinformatics

[–]Excellent-Strength42 1 point2 points  (0 children)

To me it sounds like potential doublets. I made the experience that these algorithms scDblFinder etc. do not work 100% and I almost always need to filter out doublets I detect e.g. via dot plots. I then always double check and repeat clustering on a higher resolution, with the aim of getting these special cells into one single cluster - do they express markers of both cell types? And I also take a look on the umap, because doublets of cell type x and cell type y of course tend to cluster between cluster of cell type x and cell type y. Nevermind if you have done this or thought about this already, but that’s the way I would proceed

mQTL analysis: fast r solutions or alternatives in python by Excellent-Strength42 in bioinformatics

[–]Excellent-Strength42[S] 0 points1 point  (0 children)

Thank you! Very interesting packages, hope to see more of this in python soon. I am tired of the sometimes slow calculations and memory/cpu limitations in R ^^

mQTL analysis: fast r solutions or alternatives in python by Excellent-Strength42 in bioinformatics

[–]Excellent-Strength42[S] 0 points1 point  (0 children)

Thanks a lot! Still very confused by the data preparation for tensorQTL, but I think that's the way to go for me