Highlights d Semi-supervised learning improves doublet classification in single-cell RNA sequencing d Combining our method with experimental approaches further improves accuracy

Single cell RNA-seq (scRNA-seq) measurements of gene expression enable an unprecedented highresolution view into cellular state. However, current methods often result in two or more cells that share the same cell-identifying barcode; these "doublets" violate the fundamental premise of single cell technology and can lead to incorrect inferences. Here, we describe Solo, a semi-supervised deep learning approach that identifies doublets with greater accuracy than existing methods. Solo can be applied in combination with experimental doublet detection methods to further purify scRNA-seq data to true single cells beyond any previous approach.