Abstract
Abstract
Barcode-based multiplexing strategies can significantly increase sample throughput and decrease costs, while mitigating batch effects of single-cell RNA sequencing experiments. However, these approaches can be limited by inaccurate or inefficient demultiplexing, resulting in cell loss and reduced statistical power. Here, we present BEACON, a novel sample demultiplexing method that efficiently learns the background count distribution from data and removes it from individual cells, thereby improving classification accuracy. BEACON outperforms other state-of-the-art methods on multiple human data sets. We apply it to cancer cell line time course experiments in vitro, enabling the identification of genes associated with aggressive tumors in vivo. Finally, we adapt BEACON to multimodal protein-transcriptome profiling, enhancing protein signal recovery to identify a CD161-positive effector memory CD4 T-cell population with a Th17-like phenotype, which we prospectively validate. BEACON can therefore be applied to other droplet-based single-cell sequencing methodologies.