Abstract
Single cell RNA-seq (scRNA-seq) measurements of gene expression enable an unprecedented high-resolution 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.
Footnotes
nicholas{at}calicolabs.com, nicole{at}calicolabs.com, irene{at}calicolabs.com, mroy{at}calicolabs.com, dgh{at}calicolabs.com, drk{at}calicolabs.com