demuxlet

demuxlet assigns sample identities and detects multiplets in droplet single-cell RNA sequencing (dscRNA-seq) data by leveraging natural genetic variation (SNPs) to deconvolute pooled samples and identify doublets.

Key Features:

• Sample Identity Deconvolution: Uses per-cell single-nucleotide polymorphism (SNP) data to assign each cell to its originating individual, supporting pooling of up to 64 individuals.
• Multiplet Detection: Identifies doublets (droplets containing two cells) to flag multiplets that can confound single-cell analyses.
• High Accuracy: Demonstrated >99% correct singlet assignment in simulations and doublet identification consistent with benchmarks when genotyping data from eight pooled samples are available.
• Scalability for Large-Scale Studies: Enables multiplexed dscRNA-seq workflows to increase throughput for experiments involving multiple individuals.

Scientific Applications:

• Differential Expression Analysis: Enables accurate assignment of cells to individuals to support differential gene expression comparisons across samples while reducing pooling-related confounders.
• eQTL Analysis: Facilitates expression quantitative trait loci studies on pooled samples, as applied in analyses of 23 pooled samples.
• Disease-Specific Research: Applied to investigate cell-type-specific expression changes in lupus patient samples treated with interferon-beta (IFN-β).

Methodology:

Analyzes SNP data from each cell and processes genotyping data for pooled samples; requires a predefined set of SNPs per cell (simulations indicate ~50 SNPs are sufficient) to assign sample origin and detect multiplets.