Author:
Song Dongyuan,Li Jingyi Jessica
Abstract
AbstractTo investigate molecular mechanisms underlying cell state changes, a crucial analysis is to identify differentially expressed (DE) genes along the pseudotime inferred from single-cell RNA-sequencing data. However, existing methods do not account for pseudotime inference uncertainty, and they have either ill-posed p-values or restrictive models. Here we propose PseudotimeDE, a DE gene identification method that adapts to various pseudotime inference methods, accounts for pseudotime inference uncertainty, and outputs well-calibrated p-values. Comprehensive simulations and real-data applications verify that PseudotimeDE outperforms existing methods in false discovery rate control and power.
Funder
Directorate for Biological Sciences
National Institute of General Medical Sciences
Johnson and Johnson
Alfred P. Sloan Foundation
W. M. Keck Foundation
Publisher
Springer Science and Business Media LLC
Cited by
41 articles.
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