I am looking for opinions (hands-on based experience) towards your favourit feature selection (followed by dimensionality reduction) method for 10X-based 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.
scran or the choices provided by
Seurat. 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.