Enhancing droplet-based single-nucleus RNA-seq resolution using the semi-supervised machine learning classifier DIEM.Single-nucleus RNA sequencing (snRNA-seq) measures gene expression in individual nuclei instead of cells, allowing for unbiased cell type characterization in solid tissues.  We present a novel approach to quantify contamination and filter droplets in snRNA-seq experiments, called Debris Identification using Expectation Maximization (DIEM).  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.  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, our clustering strategy also successfully filtered single-cell RNA-seq 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.
DIEM
We present a novel approach to quantify contamination and filter droplets in snRNA-seq experiments, called Debris Identification using Expectation Maximization (DIEM). 
Debris Identification using Expectation Maximization
Our code is freely available for use at https://github.com/marcalva/diem.
GitHub
Our code is freely available for use at https://github.com/marcalva/diem.
https://github.com/marcalva/diem