Droplet-based scRNA-seq assays are known to produce a significant amount of background RNA counts, the hallmark of which is non-zero transcript counts in presumably empty droplets. The presence of background RNA can lead to systematic biases and batch effects in various downstream analyses such as differential expression and marker gene discovery. This talk will take a detailed look at the 10x Genomics droplet-based scRNA-seq protocol, and examine sources of technical background noise in count matrix data. An algorithm, part of the CellBender suite of tools, will be presented for learning the background RNA profile, distinguishing cell-containing droplets from empty ones, and retrieving background-free gene expression profiles. Simulations and investigations of several scRNA-seq datasets will be used to show that processing raw data using "CellBender remove-background" significantly boosts the magnitude and specificity of differential expression across different cell types.
Primer: CellBender remove-background: A deep generative model for unsupervised removal of background noise from scRNA-seq datasets
November 20, 2019