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Abstract
<p id="P3">Single-cell RNA sequencing (scRNA-seq) data are commonly affected by technical
artifacts
known as “doublets,” which limit cell throughput and lead to spurious biological conclusions.
Here, we present a computational doublet detection tool—Doublet-Finder—that identifies
doublets using only gene expression data. DoubletFinder predicts doublets according
to each real cell’s proximity in gene expression space to artificial doublets created
by averaging the transcriptional profile of randomly chosen cell pairs. We first use
scRNA-seq datasets where the identity of doublets is known to show that DoubletFinder
identifies doublets formed from transcriptionally distinct cells. When these doublets
are removed, the identification of differentially expressed genes is enhanced. Second,
we provide a method for estimating DoubletFinder input parameters, allowing its application
across scRNA-seq datasets with diverse distributions of cell types. Lastly, we present
“best practices” for DoubletFinder applications and illustrate that DoubletFinder
is insensitive to an experimentally validated kidney cell type with “hybrid” expression
features.
</p><p id="P5">
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</p><p id="P4">scRNA-seq data interpretation is confounded by technical artifacts
known as doublets—single-cell
transcriptome data representing more than one cell. Moreover, scRNA-seq cellular throughput
is purposefully limited to minimize doublet formation rates. By identifying cells
sharing expression features with simulated doublets, DoubletFinder detects many real
doublets and mitigates these two limitations.
</p>