I am looking for opinions (hands-on based experience) towards your favourit feature selection method for 10x scRNA-seq data. The motivation for this is that I recently stumbled over the GLM-PCA approach from Rafael Irizarry's lab (links see on the bottom of the post) which made me dive into the literature. As expected there are plenty of methods out there, each claiming to perform superior. Since GLM-PCA operates on raw counts it frees the uses from choosing from one of the many normalization strategies such as the ones implemented in e.g.
scNorm or the choices provided by
Seurat, and is therefore attractive. This is admittedly not at all a precise question (therefore
Forum post), and I hope to initiate some chat here about your current best practices that users inexperienced in the single-cell world (including myself) can take inspiration from.
Edit: As suggested below one might have a look at the scry package https://bioconductor.org/packages/release/bioc/vignettes/scry/inst/doc/scry.html which makes use of glmpca for feature selection and dimensionality reduction.
Edit (09/20): Just to comment how it endedn up: I did not use GLMPCA/scry package eventually as in its current state it was unusably slow on normal-sized datasets (5k cells) datasets plus regularily caused errors related to poor model fits similar to https://github.com/kstreet13/scry/issues/15. That taken together made me abandon it. The concept is definitely interesting and I hope the package reaches a stable state soon to be used productively and with a reasonable runtime.
As an alternative I ended up using the GitHub version of
for feature selection.