README.md
In scDblFinder: scDblFinder

scDblFinder

The scDblFinder package gathers various methods for the detection and handling of doublets/multiplets in single-cell sequencing data (i.e. multiple cells captured within the same droplet or reaction volume). The methods included here are complementary to doublets detection 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.

For a brief overview of the methods, see the introductory vignette (vignette("introduction", package="scDblFinder")). Here, we will showcase doublet detection using the fast and comprehensive scDblFinder method.

You may install the latest version of the package with:

BiocManager::install("plger/scDblFinder")

Given an object sce of class SingleCellExperiment (which does not contain any empty drops, but hasn't been further filtered), you can launch the doublet detection with:

library(scDblFinder)
sce <- scDblFinder(sce)

This will add a number of columns to the colData of sce, the most important of which are:

sce$scDblFinder.score : the final doublet score (the higher the more likely that the cell is a doublet)
sce$scDblFinder.ratio : the ratio of artificial doublets in the cell's neighborhood
sce$scDblFinder.class : the classification (doublet or singlet)

There are several additional columns containing further information (e.g. the most likely origin of the putative doublet), an overview of which is available in the vignette (vignette("scDblFinder")).

If you have multiple samples (understood as different cell captures, i.e. for multiplexed samples with cell hashes, rather use the well/batch), then it is preferable to provide scDblFinder with this information. The generation of artificial doublets and the characterization of the nearest neighbors will be performed separately for each capture, the final scoring will be performed globally, and the thresholding will take into consideration batch/sample-specific doublet rates. You can do this by simply providing a vector of the sample ids to the samples parameter of scDblFinder or, if these are stored in a column of colData, the name of the column. In this case, you might also consider multithreading it using the BPPARAM parameter. For example:

library(BiocParallel)
sce <- scDblFinder(sce, samples="sample_id", BPPARAM=MulticoreParam(3))
table(sce$scDblFinder.class)

The expected proportion of doublets has no impact on the score, but a very strong impact on where the threshold will be placed (the thresholding procedure simultaneously minimizes classification error and departure from the expected doublet rate). It is specified through the dbr parameter and the dbr.sd parameter (the latter specifies the standard deviation of dbr, i.e. the uncertainty in the expected doublet rate). For 10x data, the more cells you capture the higher the chance of creating a doublet, and Chromium documentation indicates a doublet rate of roughly 1\% per 1000 cells captures (so with 5000 cells, (0.01*5)*5000 = 250 doublets), and the default expected doublet rate will be set to this value (with a default standard deviation of 0.015). Note however that different protocols may create considerably more doublets, and that this should be updated accordingly.

Contrarily to other methods also based on the generation of artificial doublets, scDblFinder does not generate them in an entirely random fashion, but specifically generates inter-cluster doublets. This also means that, for putative doublets among the real cells, scDblFinder can guess from what clusters their originate. To make this information easier to interpret, you may provide your own clusters (though the clusters argument) rather than use the fast internal procedure to determine them. It is important that subpopulations are not misrepresented as belonging to the same cluster, and for this reason, we favor over-clustering for this purpose. For more detail, or for application to datsets containing continuous gradients (e.g. trajectories) rather than distinct subpopulations, see vignette("scDblFinder").

If you already know of some doublets in the data (e.g. identified via cell hashes and SNPs in multiplexed samples), providing this information through the knownDoublets argument will improve the scDblFinder scoring and calling of new doublets.

For more detail, please see vignette("scDblFinder").

We have not yet thoroughly tested the scDblFinder paremeters in the context of scATACseq data, however preliminary results on a couple of datasets (applied on peak-level counts) suggest that it works decently there (consider increasing the nfeatures).

To benchmark scDblFinder against alternatives we used two datasets in which cells from multiple individuals were mixed and their identity deconvoluted using SNPs (via demuxlet), which also enables the identification of doublets from different individuals. scDblFinder is compared to two other excellent methods, DoubletFinder and scds's hybrid method, in terms of accuracy and speed:

Comparison with other tools