demuxmix: Demultiplexing oligonucleotide-barcoded single-cell RNA sequencing data with regression mixture models

Area of Science:

• Genomics
• Bioinformatics
• Computational Biology

Background:

• Droplet-based single-cell RNA sequencing (scRNA-seq) enables large-scale transcriptome analysis.
• Pooling samples with hashtag oligonucleotides (HTOs) reduces costs and batch effects.
• Accurate demultiplexing of HTOs is critical but challenging due to noise and low-quality data.

Approach:

• Introduced demuxmix, a novel demultiplexing method utilizing negative binomial regression mixture models.
• Implemented a probabilistic classification framework to provide error probabilities for droplet assignments.
• Leveraged the association between RNA gene detection and HTO counts to improve assignment accuracy.

Key Points:

• demuxmix offers probabilistic assignments, allowing uncertain droplets to be discarded.
• The method accounts for HTO data variance by integrating RNA library information.
• Improved demultiplexing performance was validated on real and simulated datasets.
• Successful demultiplexing was demonstrated even when pooling labeled and unlabeled cells.

Conclusions:

• demuxmix provides a robust and accurate solution for HTO-based demultiplexing in scRNA-seq.
• The method enhances data quality assessment and experimental design flexibility.
• The demuxmix R/Bioconductor package is available for public use.