Solo: Doublet Identification in Single-Cell RNA-Seq via Semi-Supervised Deep Learning
| Autor: | David R. Kelley, Irene Lam, Margaret Ann Roy, Nicholas Bernstein, David G. Hendrickson, Nicole L. Fong |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Histology
Computer science RNA-Seq Semi-supervised learning Barcode Pathology and Forensic Medicine law.invention 03 medical and health sciences 0302 clinical medicine Deep Learning law Humans 030304 developmental biology 0303 health sciences Artificial neural network business.industry Deep learning Pattern recognition Cell Biology Autoencoder Artificial intelligence Single-Cell Analysis business Encoder Classifier (UML) 030217 neurology & neurosurgery |
| Zdroj: | Cell systems. 11(1) |
| ISSN: | 2405-4720 |
| Popis: | Single-cell RNA sequencing (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 embeds cells unsupervised using a variational autoencoder and then appends a feed-forward neural network layer to the encoder to form a supervised classifier. We train this classifier to distinguish simulated doublets from the observed data. Solo can be applied in combination with experimental doublet detection methods to further purify scRNA-seq data to true single cells. It is freely available from https://github.com/calico/solo. A record of this paper's transparent peer review process is included in the Supplemental Information. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|