scDblFinder package gathers various methods for the detection and handling of doublets/multiplets in single-cell RNA sequencing data (i.e. multiple cells captured within the same droplet or reaction volumne). This vignette provides a brief overview of the different approaches, which are covered in their own vignettes. For a more general introduction to the topic, refer to the OCSA book.
All methods require as an input either a matrix of counts or a
r Biocpkg("SingleCellExperiment") containing count data. With the exception of findDoubletClusters, which operates at the level of clusters (and consequently requires clustering information), all methods try to assign each cell a score indicating its likelihood (broadly understood) of being a doublet.
The approaches described here are complementary to doublets identified via cell hashes and SNPs in multiplexed samples: while hashing/genotypes can identify doublets formed by cells of the same type (homotypic doublets) from two samples, which are often nearly undistinguishable from real cells transcriptionally (and hence generally unidentifiable through the present package), it cannot identify doublets made by cells of the same sample, even if they are heterotypic (formed by different cell types). Instead, the methods presented here are primarily geared towards the identification of heterotypic doublets, which for most purposes are also the most critical ones.
computeDoubletDensity method (formerly
scran::doubletCells) generates random artificial doublets from the real cells, and tries to identify cells whose neighborhood has a high local density of articial doublets. See computeDoubletDensity for more information.
recoverDoublets method is meant to be used when some doublets are already known, for instance through genotype-based calls or cell hashing in multiplexed experiments. The function then tries to identify intra-sample doublets that are neighbors to the known inter-sample doublets. See recoverDoublets for more information.
scDblFinder method combines both known doublets (if available) and cluster-based artificial doublets to identify doublets. The approach builds and improves on a variety of earlier efforts, and in addition tries to identify the origins of the putative doublets. See scDblFinder for more information.
findDoubletClusters method identifies clusters that are likely to be composed of doublets by estimating whether their expression profile lies between two other clusters. See findDoubletClusters for more information.
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