Systematic benchmark of single-cell hashtag demultiplexing approaches reveals robust performance of a clustering-based method.

Single-cell technology opened up a new avenue to delineate cellular status at a single-cell resolution and has become an essential tool for studying human diseases. Multiplexing allows cost-effective experiments by combining multiple samples and effectively mitigates batch effects. It starts by giving each sample a unique tag and then pooling them together for library preparation and sequencing. After sequencing, sample demultiplexing is performed based on tag detection, where cells belonging to one sample are expected to have a higher amount of the corresponding tag than cells from other samples. However, in reality, demultiplexing is not straightforward due to the noise and contamination from various sources. Successful demultiplexing depends on the efficient removal of such contamination. Here, we perform a systematic benchmark combining different normalization methods and demultiplexing approaches using real-world data and simulated datasets. We show that accounting for sequencing depth variability increases the separability between tagged and untagged cells, and the clustering-based approach outperforms existing tools. The clustering-based workflow is available as an R package from https://github.com/hwlim/hashDemux.