Demuxafy: Improvement in droplet assignment by integrating multiple single-cell demultiplexing and doublet detection methods
Recent innovations in droplet-based single-cell RNA-sequencing (scRNA-seq) have provided the technology necessary to investigate biological questions at cellular resolution. With the ability to assay thousands of cells in a single capture, pooling cells from multiple individuals has become a common strategy. Droplets can subsequently be assigned to a specific individual by leveraging their inherent genetic differences, and numerous computational methods have been developed to address this problem. However, another challenge implicit with droplet-based scRNA-seq is the occurrence of doublets - droplets containing two or more cells. The inaccurate assignment of cells to individuals or failure to remove doublets contribute unwanted noise to the data and result in erroneous scientific conclusions. Therefore, it is essential to assign cells to individuals and remove doublets accurately. We present a new framework to improve individual singlet classification and doublet removal through a multi-method intersectional approach. We developed a framework to evaluate the enhancement in donor assignment and doublet removal through the consensus intersection of multiple demultiplexing and doublet detecting methods. The accuracy was assessed using scRNA-seq data of [~]1.4 million peripheral blood mononucleated cells from 1,034 unrelated individuals and [~]90,000 fibroblast cells from 81 unrelated individuals. We show that our approach significantly improves droplet assignment by separating singlets from doublets and classifying the correct individual compared to any single method. We show that the best combination of techniques varies under different biological and experimental conditions, and we present a framework to optimise cell assignment for a given experiment. We offer Demuxafy (https://demultiplexing-doublet-detecting-docs.readthedocs.io/en/latest/index.html) - a framework built-in Singularity to provide clear, consistent documentation of each method and additional tools to simplify and improve demultiplexing and doublet removal. Our results indicate that leveraging multiple demultiplexing and doublet detecting methods improves accuracy and, consequently, downstream analyses in multiplexed scRNA-seq experiments.