PeerJGenomicsPeer-reviewed journal article
Estimating the frequency of multiplets in single-cell RNA sequencing from cell-mixing experiments
Abstract
In single-cell RNA-sequencing, it is important to know the frequency at which the sequenced transcriptomes actually derive from multiple cells. A common method to estimate this multiplet frequency is to mix two different types of cells (e.g., human and mouse), and then determine how often the transcriptomes contain transcripts from both cell types. When the two cell types are mixed in equal proportion, the calculation of the multiplet frequency from the frequency of mixed transcriptomes is straightforward. But surprisingly, there are no published descriptions of how to calculate the multiplet frequency in the general case when the cell types are mixed unequally. Here I derive equations to analytically calculate the multiplet frequency from the numbers of observed pure and mixed transcriptomes when two cell types are mixed in arbitrary proportions, under the assumption that the loading of cells into droplets or wells is Poisson.
- Citations
- 46
- FWCI
- 1.16
- Field percentile
- top 22%
Reproducibility ledger
Nobody has reported trying to reproduce this paper yet. Did you replicate an experiment or run the code? Your report saves the next lab a week.
Sign in to file a reproducibility report.
Related papers
- Multiplets in scRNA-seq data: extent of the problem and efficacy of methods for removal
Dimitris Ttoouli, Daniel E. Hoffmann·PLoS ONEPeer-reviewed·Oct 30, 2025
- Probability of stealth multiplets in sample-multiplexing for droplet-based single-cell analysis
Fumio Nakaki, James A. Sharpe·BMC GenomicsPeer-reviewed·Jul 23, 2025
- Scrublet: computational identification of cell doublets in single-cell transcriptomic data
Samuel L. Wolock, Romain Lopez, Allon Moshe Klein·Cell SystemsPeer-reviewed·Apr 1, 2019
- Highly Multiplexed Single-Cell RNA-seq for Defining Cell Population and Transcriptional Spaces
Jase Gehring, Jong Hwee Park, … Lior S. Pachter·bioRxiv·May 5, 2018
- A probabilistic model-based bi-clustering method for single-cell transcriptomic data analysis
- Sample demultiplexing, multiplet detection, experiment planning and novel cell type verification in single cell sequencing
Hongyi Xin, Qiuyu Lian, … Wei Chen·Genome biologyPeer-reviewed·Jul 30, 2020