DropletQC – improved identification of empty droplets and damaged cells in single-cell RNA-seq data

Advances in droplet-based single cell RNA-sequencing (scRNA-seq) have dramatically increased throughput, allowing tens of thousands of cells to be routinely sequenced in a single experiment. In addition to cells, droplets capture cell-free ‘ambient’ RNA predominately caused by lysis of cells during sample preparation. Samples with high ambient RNA concentration can create challenges in accurately distinguishing cell containing droplets and droplets containing ambient RNA. Current methods to separate these groups often retain a significant number of droplets that do not contain cells, so called empty droplets. Additional to the challenge of identifying empty drops, there are currently no methods available to detect droplets containing damaged cells, which comprise of partially lysed cells, the original source of the ambient RNA.

Researchers from the Garvan Institute of Medical Research have developed  DropletQC, a new method that is able to detect empty droplets, damaged, and intact cells, and accurately distinguish from one another. This approach is based on a novel quality control metric, the nuclear fraction, which quantifies for each droplet the fraction of RNA originating from unspliced, nuclear pre-mRNA. The researchers demonstrate how DropletQC provides a powerful extension to existing computational methods for identifying empty droplets such as EmptyDrops. They have implemented DropletQC as an R package, which can be easily integrated into existing single cell analysis workflows.

rna-seq

Illustration of how the nuclear fraction, in combination with the library size of each droplet, can be used to separate the populations of empty droplets, intact cells and damaged cells.

Availability – DropletQC is available as an R package at https://github.com/powellgenomicslab/DropletQC.

Muskovic W, Powell J. (2021) DropletQC: improved identification of empty droplets and damaged cells in single-cell RNA-seq data. bioRXiv [online preprint]. [abstract]

Leave a Reply

Your email address will not be published. Required fields are marked *

*

Time limit is exhausted. Please reload CAPTCHA.