RT Journal Article
SR Electronic
T1 Enhancing droplet-based single-nucleus RNA-seq resolution using the semi-supervised machine learning classifier DIEM
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 786285
DO 10.1101/786285
A1 Marcus Alvarez
A1 Elior Rahmani
A1 Brandon Jew
A1 Kristina M. Garske
A1 Zong Miao
A1 Jihane N. Benhammou
A1 Chun Jimmie Ye
A1 Joseph R. Pisegna
A1 Kirsi H. Pietiläinen
A1 Eran Halperin
A1 Päivi Pajukanta
YR 2019
UL http://biorxiv.org/content/early/2019/10/02/786285.abstract
AB Single-nucleus RNA sequencing (snRNA-seq) measures gene expression in individual nuclei instead of cells, allowing for unbiased cell type characterization in solid tissues. Contrary to single-cell RNA seq (scRNA-seq), we observe that snRNA-seq is commonly subject to contamination by high amounts of extranuclear background RNA, which can lead to identification of spurious cell types in downstream clustering analyses if overlooked. We present a novel approach to remove debris-contaminated droplets in snRNA-seq experiments, called Debris Identification using Expectation Maximization (DIEM). Our likelihood-based approach models the gene expression distribution of debris and cell types, which are estimated using EM. We evaluated DIEM using three snRNA-seq data sets: 1) human differentiating preadipocytes in vitro, 2) fresh mouse brain tissue, and 3) human frozen adipose tissue (AT) from six individuals. All three data sets showed various degrees of extranuclear RNA contamination. We observed that existing methods fail to account for contaminated droplets and led to spurious cell types. When compared to filtering using these state of the art methods, DIEM better removed droplets containing high levels of extranuclear RNA and led to higher quality clusters. Although DIEM was designed for snRNA-seq data, we also successfully applied DIEM to single-cell data. To conclude, our novel method DIEM removes debris-contaminated droplets from single-cell-based data fast and effectively, leading to cleaner downstream analysis. Our code is freely available for use at https://github.com/marcalva/diem.