# Souporcell3: robust demultiplexing for high-donor single-cell RNA-seq datasets

## TL;DR

This paper introduces Souporcell3, a new method for accurately separating single-cell RNA-seq data from many donors, improving accuracy and scalability.

## Contribution

The novel contribution is an enhanced demultiplexing method that handles up to 64 donors with improved clustering and accuracy.

## Key findings

- Souporcell3 eliminates incorrectly merged clusters and achieves high Adjusted Rand Index scores.
- The method outperforms existing tools like vireo in high-donor scenarios with overlapping genotypes.
- It uses robust clustering techniques like K-Harmonic Means and iterative refinement for better accuracy.

## Abstract

Accurate demultiplexing of pooled single-cell RNA-seq (scRNA-seq) 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.

We present an enhanced version of souporcell capable of demultiplexing up to 64 donors. The method uses 10× 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, overclustered vireo, and the original souporcell, our approach completely eliminates incorrectly merged clusters and achieves consistently high Adjusted Rand Index (ARI) scores across various doublet rates, demonstrating improved accuracy and scalability.

Souporcell3 is freely available under the MIT open-source license at https://github.com/wheaton5/souporcell.