Solo: doublet identification via semi-supervised deep learning

Bernstein, N.; Fong, N.; Lam, I.; Roy, M.; Hendrickson, D. G.; Kelley, D. R.

doi:10.1101/841981

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bioRxiv · 10.1101/841981

Solo: doublet identification via semi-supervised deep learning

Bernstein, N.·Fong, N.·Lam, I.·Roy, M.·Hendrickson, D. G.·Kelley, D. R.

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Abstract

AO_SCPLOWBSTRACTC_SCPLOWSingle 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.

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Bernstein, N., Fong, N., Lam, I., Roy, M., Hendrickson, D. G., Kelley, D. R.. 2019-11-14. Solo: doublet identification via semi-supervised deep learning. https://doi.org/10.1101/841981

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