Souporcell3: Robust Demultiplexing for High-Donor Single-Cell RNA-seq Datasets

Motivation Accurate demultiplexing of pooled single-cell RNA-seq (scRNAseq) data is critical for large-scale studies. However, existing methods like vireo, while effective up to ∼16 donors, often struggle with poor clustering due to local optima as donor numbers rise. In high-donor scenarios, overlapping genotypes, a dense genotype space, and increased doublet formation make demultiplexing challenging, requiring methods that are robust to sparse, high-dimensional data and maintain reliable accuracy even as sample complexity grows. Results We present an enhanced version of souporcell capable of demultiplexing up to 64 donors. The method uses 10x merge for initialization, K-Harmonic Means for robust clustering, and iterative refinement with reinitialization of low-quality clusters and locking of high-quality ones. Compared to vireo, vireo with overclustering, and the original souporcell, our approach completely eliminates duplicate clusters and achieves consistently high Adjusted Rand Index (ARI) scores across various doublet rates, demonstrating improved accuracy and scalability. Availability Souporcell3 source code and documentation are released on GitHub: https://github.com/wheaton5/souporcell