bioRxivBioinformaticsPreprint
One read per cell per gene is optimal for single-cell RNA-Seq
Abstract
An underlying question for virtually all single-cell RNA sequencing experiments is how to allocate the limited sequencing budget: deep sequencing of a few cells or shallow sequencing of many cells? A mathematical framework reveals that, for estimating many important gene properties, the optimal allocation is to sequence at the depth of one read per cell per gene. Interestingly, the corresponding optimal estimator is not the widely-used plugin estimator but one developed via empirical Bayes.
- Citations
- 11
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
- Quantifying the tradeoff between sequencing depth and cell number in single-cell RNA-seq
Valentine Svensson, Eduardo da Veiga Beltrame, Lior S. Pachter·bioRxiv·Sep 9, 2019
- Near-optimal probabilistic RNA-seq quantification
Nicolas Bray, Harold Pimentel, … Lior S. Pachter·Nature BiotechnologyPeer-reviewed·Apr 4, 2016
- GTestimate: Improving relative gene expression estimation in scRNA-seq using the Good-Turing estimator
Martin Fahrenberger, Christopher Esk, … Arndt von Haeseler·GigaSciencePeer-reviewed·Jan 1, 2025
- Reproducibility across single-cell RNA-seq protocols for spatial ordering analysis
Morten Seirup, Li‐Fang Chu, … Rhonda L. Bacher·PLoS ONEPeer-reviewed·Sep 28, 2020
- GT-TS: Experimental design for maximizing cell type discovery in single-cell data
Bianca Dumitrascu, Kun-Jiang Feng, Barbara Elizabeth Engelhardt·bioRxiv·Aug 7, 2018
- Assessing the measurement transfer function of single-cell RNA sequencing
Hannah R. Dueck, Rizi Ai, … Junhyoung Kim·bioRxiv·Mar 24, 2016