CB2 improves power of cell detection in droplet-based single-cell RNA sequencing data

Abstract

An important challenge in pre-processing data from droplet-based single-cell RNA sequencing protocols is distinguishing barcodes associated with real cells from those binding background reads. Existing methods test barcodes individually and consequently do not leverage the strong cell-to-cell correlation present in most datasets. To improve cell detection, we introduce CB2, a cluster-based approach for distinguishing real cells from background barcodes. As demonstrated in simulated and case study datasets, CB2 has increased power for identifying real cells which allows for the identification of novel subpopulations and improves the precision of downstream analyses.

Signatures of plasticity, metastasis, and immunosuppression in an atlas of human small cell lung cancer

“…The pipeline then performs the following initial cell filtering steps: 1) true cells are distinguished from empty droplets based on the cumulative distribution of total molecule counts; 2) cells with a high fraction of mitochondrial molecules are filtered (> 20%); and 3) cells with low library complexity are filtered (cells that express very few unique genes). In addition, we perform additional filtering of empty droplets using the CB2 package with parameter ''lower'' set at 100 to estimate the background distribution of ambient RNA and an FDR threshold of 0.01 for calling real cells (Ni et al, 2020). Putative doublets were removed using the DoubletDetection package (https://doi.org/10.5281/zenodo.2658729).…”

Comprehensive generation, visualization, and reporting of quality control metrics for single-cell RNA sequencing data

“…Often, these cells are excluded by setting a minimum threshold on the number of UMIs per cell and/or genes detected. (2) Another aspect unique to droplet-based microfluidic devices is that the majority of the droplets (>90%) will not contain an actual cell 7 , 8 . Despite the absence of a cell, these “empty droplets” may contain low levels of background ambient RNA that was present in the cell solution 9 .…”

Resolving the origins of secretory products and anthelmintic responses in a human parasitic nematode at single-cell resolution

“…The raw Cell Ranger barcode matrix output for each scRNA-seq dataset was filtered to remove empty droplets using the cluster-based R package scCB2 (Ni et al, 2020). Using EmptyDrops (ED) as a scaffold, scCB2 increases the power to distinguish real cells from background empty droplets as well as low-expressing, small cells by pooling low-count barcodes with similar gene expression patterns.…”