doubletCluster {scran}R Documentation

Detect doublet clusters

Description

Identify potential clusters of doublet cells based on intermediate expression profiles.

Usage

## S4 method for signature 'ANY'
doubletCluster(x, clusters, subset.row=NULL, threshold=0.05, ...) 

## S4 method for signature 'SingleCellExperiment'
doubletCluster(x, ..., subset.row=NULL, assay.type="counts", 
    get.spikes=FALSE)

Arguments

x

A numeric matrix-like object of count values, where each column corresponds to a cell and each row corresponds to an endogenous gene. Alternatively, a SingleCellExperiment object containing such a matrix.

clusters

A vector of cluster identities for all cells.

subset.row

See ?"scran-gene-selection".

threshold

A numeric scalar specifying the FDR threshold with which to identify significant genes.

...

For the ANY method, additional arguments to pass to findMarkers.

For the SingleCellExperiment method, additional arguments to pass to the ANY method.

assay.type

A string specifying which assay values to use, e.g., "counts" or "logcounts".

get.spikes

See ?"scran-gene-selection".

Details

This function detects clusters of doublet cells in a manner similar to the method used by Bach et al. (2017). For each “query” cluster, we examine all possible pairs of “source” clusters, hypothesizing that the query consists of doublets formed from the two sources. If so, gene expression in the query cluster should be strictly intermediate between the two sources after library size normalization.

We apply pairwise t-tests to the normalized log-expression profiles (see normalize) to reject this null hypothesis. This is done by identifying genes that are consistently up- or down-regulated in the query compared to both of the sources. We count the number of genes that reject the null hypothesis at the specified FDR threshold. For each query cluster, the most likely pair of source clusters is that which minimizes the number of significant genes.

Potential doublet clusters are identified using the following characteristics:

For each query cluster, the function will only report the pair of source clusters with the lowest N. It is possible that a source pair with slightly higher (but still low) value of N may have more appropriate lib.size* values. Thus, it may be valuable to examine all.pairs in the output, especially in over-clustered data sets with closely neighbouring clusters.

The reported p.value is of little use in a statistical sense, and is only provided for inspection. Technically, it could be treated as the Simes combined p-value against the doublet hypothesis for the query cluster. However, this does not account for the multiple testing across all pairs of clusters for each chosen cluster, especially as we are chosing the pair that is most concordant with the doublet null hypothesis.

We use library size normalization (via librarySizeFactors) even if existing size factors are present. This is because intermediate expression of the doublet cluster is not guaranteed for arbitrary size factors. For example, expression in the doublet cluster will be higher than that in the source clusters if normalization was performed with spike-in size factors.

Value

A DataFrame containing one row per query cluster with the following fields:

source1:

String specifying the identity of the first source cluster.

source2:

String specifying the identity of the second source cluster.

N:

Integer, number of genes that are significantly non-intermediate in the query cluster compared to the two putative source clusters.

best:

String specifying the identify of the top gene with the lowest p-value against the doublet hypothesis for this combination of query and source clusters.

p.value:

Numeric, containing the adjusted p-value for the best gene.

lib.size1:

Numeric, ratio of the median library sizes for the first source cluster to the query cluster.

lib.size2:

Numeric, ratio of the median library sizes for the second source cluster to the query cluster.

prop:

Numeric, proportion of cells in the query cluster.

all.pairs:

A SimpleList object containing the above statistics for every pair of potential source clusters.

Each row is named according to its query cluster.

Author(s)

Aaron Lun

References

Bach K, Pensa S, Grzelak M, Hadfield J, Adams DJ, Marioni JC and Khaled WT (2017). Differentiation dynamics of mammary epithelial cells revealed by single-cell RNA sequencing. Nat Commun. 8, 1:2128.

Examples

# Mocking up an example.
ngenes <- 100
mu1 <- 2^rexp(ngenes)
mu2 <- 2^rnorm(ngenes)

counts.1 <- matrix(rpois(ngenes*100, mu1), nrow=ngenes)
counts.2 <- matrix(rpois(ngenes*100, mu2), nrow=ngenes)
counts.m <- matrix(rpois(ngenes*20, mu1+mu2), nrow=ngenes)

counts <- cbind(counts.1, counts.2, counts.m)
clusters <- rep(1:3, c(ncol(counts.1), ncol(counts.2), ncol(counts.m)))

# Find potential doublets...
dbl <- doubletCluster(counts, clusters)
dbl

library(scater)
isOutlier(dbl$N, log=TRUE, type="lower") # based on "N"...

dbl$lib.size1 < 1 & dbl$lib.size2 < 1 # with help from "lib.size"

[Package scran version 1.10.2 Index]