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This is the development version of tradeSeq; for the stable release version, see tradeSeq.

trajectory-based differential expression analysis for sequencing data

Bioconductor version: Development (3.19)

tradeSeq provides a flexible method for fitting regression models that can be used to find genes that are differentially expressed along one or multiple lineages in a trajectory. Based on the fitted models, it uses a variety of tests suited to answer different questions of interest, e.g. the discovery of genes for which expression is associated with pseudotime, or which are differentially expressed (in a specific region) along the trajectory. It fits a negative binomial generalized additive model (GAM) for each gene, and performs inference on the parameters of the GAM.

Author: Koen Van den Berge [aut], Hector Roux de Bezieux [aut, cre] , Kelly Street [aut, ctb], Lieven Clement [aut, ctb], Sandrine Dudoit [ctb]

Maintainer: Hector Roux de Bezieux <hector.rouxdebezieux at>

Citation (from within R, enter citation("tradeSeq")):


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Reference Manual PDF


biocViews Clustering, DifferentialExpression, GeneExpression, MultipleComparison, RNASeq, Regression, Sequencing, SingleCell, Software, TimeCourse, Transcriptomics, Visualization
Version 1.17.0
In Bioconductor since BioC 3.10 (R-3.6) (4.5 years)
License MIT + file LICENSE
Depends R (>= 3.6)
Imports mgcv, edgeR, SingleCellExperiment, SummarizedExperiment, slingshot, magrittr, RColorBrewer, BiocParallel, Biobase, pbapply, igraph, ggplot2, princurve, methods, S4Vectors, tibble, Matrix, TrajectoryUtils, viridis, matrixStats, MASS
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