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DE analysis methods

DS Dongyuan Song
JL Jingyi Jessica Li
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We compare PseudotimeDE with four existing methods for identifying DE genes along pseudotime/time-course from scRNA-seq data (tradeSeq and Monocle3-DE) or bulk RNA-seq data (ImpulseDE2 and NBAMSeq). All these methods take a count matrix Y and a pseudotime vector T as input, and they return a p-value for each gene. For tradeSeq, we use the functions fitGAM and associationTest (https://statomics.github.io/tradeSeq/articles/tradeSeq.html). The number of knots parameter K in tradeSeq is chosen by 100 random genes based on the tradeSeq vignette. For Monocle3-DE, we use the function fit_models (https://cole-trapnell-lab.github.io/monocle3/docs/differential/). Since ImpulseDE2 cannot be applied to scRNA-seq data directly, we follow the modified implementation of ImpulseDE2 in the tradeSeq paper (https://github.com/statOmics/tradeSeqPaper). The R Bioconductor packages tradeSeq (version 1.3.15), monocle3 (version 0.2.0), ImpulseDE2 (version 1.10.0), and NBAMSeq (version 1.10.0) are used.

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