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? Here we present a mathematical framework which reveals that, for estimating many important gene properties, the optimal allocation is to sequence at a depth of around one read per cell per gene. Interestingly, the corresponding optimal estimator is not the widely-used plug-in estimator, but one developed via empirical Bayes.
Publication types
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Research Support, N.I.H., Extramural
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Research Support, Non-U.S. Gov't
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Research Support, U.S. Gov't, Non-P.H.S.
MeSH terms
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Computational Biology / methods*
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Computational Biology / statistics & numerical data
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Gene Expression
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Gene Regulatory Networks
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In Situ Hybridization, Fluorescence
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Models, Theoretical
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Reproducibility of Results
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S100 Calcium-Binding Protein A4 / genetics
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Sequence Analysis, RNA / methods*
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Sequence Analysis, RNA / statistics & numerical data*
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Single-Cell Analysis / methods*
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Single-Cell Analysis / statistics & numerical data*
Substances
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S100 Calcium-Binding Protein A4