Benchmarking Computational Doublet-Detection Methods for Single-Cell RNA Sequencing Data

Abstract

In single-cell RNA sequencing (scRNA-seq), doublets form when two cells are encapsulated into one reaction volume by chance. The existence of doublets, which appear to be-but are not-real cells, is a key confounder in scRNA-seq data analysis. Computational methods have been developed to detect doublets in scRNA-seq data; however, the scRNA-seq field lacks a comprehensive benchmarking of these methods, making it difficult for researchers to choose an appropriate method for their specific analysis needs. Here, we conducted the first, systematic benchmark study of nine cutting-edge computational doublet-detection methods. In total, our study included 16 real datasets, which contain experimentally annotated doublets, and 112 realistic synthetic datasets. We compared doublet-detection methods in terms of their detection accuracy under various experimental settings, impacts on downstream analyses, and computational efficiency. Our results show that existing methods exhibited diverse performance and distinct advantages in different aspects. Overall, the DoubletFinder method has the best detection accuracy, and the cxds method has the highest computational efficiency.

A yeast-optimized single-cell transcriptomics platform elucidates how mycophenolic acid and guanine alter global mRNA levels

“…Surprisingly, DoubletDecon 28 has estimated an exceptionally high doublet rate with only 0.01% of the detected doublets is in agreement with the conserved doublets. This result could be explained by the benchmark experiment against DoubletFinder, Scrublet and solo; DoubletDecon tends to have higher sensitivity and lower specificity 28 , 29 . Therefore, we considered this inconsistent result as an outlier.…”

AMULET: a novel read count-based method for effective multiplet detection from single nucleus ATAC-seq data

“…However, the pace of computational method development for this assay lags behind the pace of data generation. An open computational problem is the detection of multiplets (i.e., two or more cells/nuclei captured and profiled together)—a common challenge for droplet-based single-cell technologies [ 5 ]. The presence of multiplets confounds downstream analyses, such as cell clustering, annotation, differential accessibility, and allelic accessibility analyses, by introducing combined epigenomic profiles that originate from two or more nuclei.…”

scDesign2: a transparent simulator that generates high-fidelity single-cell gene expression count data with gene correlations captured

“…In fact, all model-based simulators that learn a generative model from real data must ignore certain outlier cells that do not fit well to their model. Some outlier cells could either represent an extremely rare cell type or are just “doublets” [ 100 – 103 ], artifacts resulted from single-cell sequencing experiments. Hence, our stance is that ignorance of outlier cells is a sacrifice that every simulator has to make; the open question is the degree to which outlier cells should be ignored, and proper answers to this question must resort to statistical model selection principles.…”