rbr1/MLtiplet_2.0: scRNA-seq doublet/multiplet detection using multi-omic profiling

The computational detection and exclusion of cellular doublets/multiplets is a cornerstone for the identification the true biological signals from single-cell RNAseq (scRNA-seq) data. Current methods do not sensitively identify both heterotypic and homotypic doublets/multiplets. Here, we describe a machine learning approach for doublet/multiplet detection utilising VDJ-seq and/or CITE-seq information to predict their presence based on transcriptional features associated with identified hybrid droplets. This approach highlights the utility of leveraging multi-omic single cell information for the generation of high-quality datasets. Our method has high sensitivity and specificity in inflammatory-cell dominant scRNA-seq samples, thus presenting a powerful approach to ensuring high quality scRNA-seq data.

install.packages("remotes")
remotes::install_github("rbr1/MLtiplet_2.0")