PCA of variable features vs all genes in integrated analysis

2 hours ago by

It is the variable features identified in this command:

ifnb.list <- lapply(X = ifnb.list, FUN = function(x) {
    x <- NormalizeData(x)
    x <- FindVariableFeatures(x, selection.method = "vst", nfeatures = 2000)
})

The idea is to first identify genes with large variance assuming that these will drive separation between cells, then reduce them into principle components and then perform integration in PCA space.