Seurat/Signac multiome analysis filtering on nCounts instead of nFeatures

Question

[yura.grabovska]

I'm fairly OK with working with gene expression data in Seurat, but it's been a while since I've done anything with Signac and ATAC/multiome single-cell data.

One thing I've noticed recently is that multiome guides for Seurat/Signac all suggest doing QC for nCount_RNA and nCount_ATAC rather than nFeature_RNA and nFeature_ATAC. Following the tutorial for Seurat PBMC expression you see that the advice is to filter on number of features:

pbmc <- subset(pbmc, subset = nFeature_RNA > 200 & nFeature_RNA < 2500 & percent.mt < 5)

However, if you follow the Weighted Nearest Neighbour tutorial for multiome, the advice is to filter on counts:

pbmc <- subset(x = pbmc, subset = nCount_ATAC < 7e4 & nCount_ATAC > 5e3 & nCount_RNA < 25000 & nCount_RNA > 1000 & percent.mt < 20)

Can anyone clarity the discrepancy here?