Introduction

When doing single cell multiome analysis (ATAC + RNA) in R using packages like Seurat or ArchR, it is useful to load both the scRNA-seq and scATAC-seq datasets in the same object under the same framework.

This however brings some problems, as ArchR doesn’t have built-in functions or tutorials on how to filter out bad RNA reads from the ArchR object, and vice versa from the Seurat object.

This is an easy problem to fix, however I haven’t found any easy resources online that explain the very simple process, and chose to write my own.

Loading the data

For this tutorial, I’m going to use ArchR as the main framework. I first load in the data like this:

inputFiles <- "data/<your-file-name>"

ArrowFiles <- createArrowFiles(
  inputFiles = inputFiles,
  minTSS = 0,
  minFrags = 500, 
  addTileMat = TRUE,
  addGeneScoreMat = TRUE
)

proj <- ArchRProject(ArrowFiles)

seRNA <- import10xFeatureMatrix(
  input = c("data/pbmc_unsorted_10k_filtered_feature_bc_matrix.h5"),
  names = c("PBMC_10k")
)

proj <- addGeneExpressionMatrix(input = proj, seRNA = seRNA, force = TRUE)

# Filter out NA Reads
proj <- proj[!is.na(proj$Gex_nGenes) & 
               !is.na(proj$Gex_MitoRatio)]

Quality Control

Then, I start my quality control pipeline. In this case, I want to set the minTSS to 12, minFrags to 1200, minFeatures to 600, maxFeatures to 4000 and maxMT to 0.019.

minTSS <- 12
minFrags <- 1200

minFeatures <- 600
maxFeatures <- 4000
maxMT <- 0.019

ATAC Quality Control

In order to filter out ATAC cells that don’t pass these thresholds, I can index into the ArchR object like so:

proj <- proj[proj$TSSEnrichment > minTSS & proj$nFrags > minFrags]

RNA Quality Control

I can then repeat the same process for the RNA data.

proj <- proj[proj$Gex_nGenes > minFeatures
             & proj$Gex_nGenes < maxFeatures
             & proj$Gex_MitoRatio < maxMT]

Conclusions