Single-cell RNA-sequencing has become a widely used, powerful approach for studying cell populations. However, these methods often generate multiplet artifacts, where two or more cells receive the same barcode, resulting in a hybrid transcriptome. In most experiments, multiplets account for several percent of transcriptomes and can confound downstream data analysis. Here, we present Single-Cell Remover of Doublets (Scrublet), a framework for predicting the impact of multiplets in a given analysis and identifying problematic multiplets. Scrublet avoids the need for expert knowledge or cell clustering by simulating multiplets from the data and building a nearest neighbor classifier. To demonstrate the utility of this approach, we test Scrublet on several datasets that include independent knowledge of cell multiplets. Scrublet is freely available for download at github.com/AllonKleinLab/scrublet. Display omitted •We define two multiplet errors in single-cell RNA-seq data: “embedded” and “neotypic”•Neotypic errors can lead to misidentification of cell types or transitional states•Scrublet code identifies neotypic doublets and predicts the overall doublet rate•The algorithm is tested against several experimental methods for labeling multiplets Single-cell RNA-sequencing experiments generate “multiplet errors” when multiple cells are labeled with the same barcode. Wolock et al. describe Scrublet, a method for predicting the effects of multiplets on downstream analyses and identifying problematic multiplets. They validate the method by applying Scrublet to several datasets with independent knowledge of multiplets.