demuxmix

demuxmix demultiplexes single-cell sequencing experiments of pooled cells labeled with barcode oligonucleotides by fitting negative binomial regression mixture models for probabilistic classification of droplets, including multiplet detection.

Key Features:

• Probabilistic Classification Framework: Provides error probabilities for droplet assignments that can be used to discard uncertain droplets and inform about the quality of the hashtag oligonucleotide data and the success of the demultiplexing process.
• RNA-HTO Association Modeling: Utilizes the positive association between detected genes in the RNA library and hashtag oligonucleotide counts to explain parts of the variance in the hashtag oligonucleotide data, resulting in improved droplet assignments.
• Demultiplexing Error Rate Estimation: Methods are provided to estimate error rates of the demultiplexing process.
• Quality Control Methods: Methods for quality control of the demultiplexing results are included in the package.
• Non-labeled Cell Demultiplexing: Supports experimental designs where non-labeled cells are pooled with labeled cells.

Scientific Applications:

• Hashtag Oligonucleotide Demultiplexing: Assigns droplets to their sample of origin in droplet-based single-cell RNA sequencing experiments where cells are labeled with hashtag oligonucleotides.
• Multiplet Detection: Identifies droplets containing multiple cells from different samples in pooled single-cell sequencing experiments.
• Experimental Design with Non-labeled Cells: Enables accurate demultiplexing in experimental designs where non-labeled cells are pooled together with labeled cells.

Methodology:

demuxmix fits negative binomial regression mixture models to hashtag oligonucleotide count data, incorporating the positive association between detected genes in the RNA library and hashtag oligonucleotide counts to explain variance in the hashtag oligonucleotide data.