Probability of stealth multiplets in sample-multiplexing for droplet-based single-cell analysis

Abstract

AbstractBackgroundRecent advances in sample-multiplexing droplet-based single-cell RNA sequencing (mx-scRNA-seq) enable us to evaluate large numbers of different samples or experiments simultaneously by reducing the occurrence of undetectable multiplets, that is, the droplets that capture multiple cells. However, the probability of potential multiplets in mx-scRNA-seq is yet to be quantitatively examined.ResultsWe developed a simple theoretical model to predict four classes of possible multiplets in mx-scRNA-seq: Homogeneous stealth, partial stealth, multilabelled, and unlabelled. We estimated the probability of each class and have found that the partial tsealth multiplet, which has been overlooked, may sub-stantially affect the whole data when the labelling process is suboptimal. Next, we illustrated their presence in actual mx-scRNA-seq datasets when the sample labelling was suboptimal. In addition, we found that choosing a suitable sample-demultiplexing algorithm was critical to circumvent the partial stealth multiplets.ConclusionOur results show the necessity of optimising the labelling procedure and offer a theoretical basis to estimate the probability of each type of multiplets to ensure the integrity of mx-scRNA-seq.