Erschienen:
Springer Science and Business Media LLC, 2020
Erschienen in:
Genome Biology, 21 (2020) 1
Sprache:
Englisch
DOI:
10.1186/s13059-020-02084-2
ISSN:
1474-760X
Entstehung:
Anmerkungen:
Beschreibung:
AbstractIdentifying and removing multiplets are essential to improving the scalability and the reliability of single cell RNA sequencing (scRNA-seq). Multiplets create artificial cell types in the dataset. We propose a Gaussian mixture model-based multiplet identification method, GMM-Demux. GMM-Demux accurately identifies and removes multiplets through sample barcoding, including cell hashing and MULTI-seq. GMM-Demux uses a droplet formation model to authenticate putative cell types discovered from a scRNA-seq dataset. We generate two in-house cell-hashing datasets and compared GMM-Demux against three state-of-the-art sample barcoding classifiers. We show that GMM-Demux is stable and highly accurate and recognizes 9 multiplet-induced fake cell types in a PBMC dataset.