Package: tradeSeq 1.13.13

tradeSeq: trajectory-based differential expression analysis for sequencing data

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.

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

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manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
tradeSeq/json (API)

# Install 'tradeSeq' in R:
install.packages('tradeSeq', repos = c('https://zaoqu-liu.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/zaoqu-liu/tradeseq/issues

Datasets:
  • celltype - A vector defining cell types, used in the package vignette.
  • countMatrix - A count matrix, used in the package vignette.
  • crv - A SlingshotDataset object, used in the package vignette.
  • gamList - A list of GAM models, used to demonstrate the various tests.
  • sds - A SlingshotDataset object, used in the package unit tests.

On BioConductor:tradeSeq-1.27.0(bioc 3.24)tradeSeq-1.26.0(bioc 3.23)

clusteringregressiontimecoursedifferentialexpressiongeneexpressionrnaseqsequencingsoftwaresinglecelltranscriptomicsmultiplecomparisonvisualization

7.28 score 904 scripts 18 exports 63 dependencies

Last updated from:b8b64d76d1 (on main). Checks:7 NOTE, 2 OK. Indexed: no.

TargetResultTimeFilesSyslog
linux-devel-x86_64NOTE766
source / vignettesOK621
linux-release-x86_64NOTE761
macos-release-arm64NOTE750
macos-oldrel-arm64NOTE460
windows-develNOTE923
windows-releaseNOTE948
windows-oldrelNOTE936
wasm-releaseOK248

Exports:associationTestcascadeclusterExpressionPatternsconditionTestdiffEndTestearlyDETestevaluateKfitGAMgetSmootherPvaluesgetSmootherTestStatsnknotspatternTestplot_evalutateK_resultsplotGeneCountplotSmootherspredictCellspredictSmoothstartVsEndTest

Dependencies:abindBHBiobaseBiocGenericsBiocParallelclicodetoolscpp11DelayedArrayedgeRfarverformatRfutile.loggerfutile.optionsgenericsGenomicRangesggplot2gluegridExtragtableigraphIRangesisobandlabelinglambda.rlatticelifecyclelimmalocfitmagrittrMASSMatrixMatrixGenericsmatrixStatsmgcvnlmepbapplypillarpkgconfigprincurveR6RColorBrewerRcpprlangS4ArraysS4VectorsS7scalesSeqinfoSingleCellExperimentslingshotsnowSparseArraystatmodSummarizedExperimenttibbleTrajectoryUtilsutf8vctrsviridisviridisLitewithrXVector

Quick Start Guide
Introduction | Installation | Quick Workflow | Step 1: Load Required Packages | Step 2: Load Example Data | Step 3: Fit GAM Models | Step 4: Run Differential Expression Tests | Association Test | Differential End Points Test | Pattern Test | Step 5: Visualize Results | Plot Gene Expression Smoothers | Plot Gene Count | Summary | Session Info
Rendered fromquickstart.Rmdusingknitr::rmarkdown

Last update: 2026-01-24
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Statistical Framework and Algorithm
Overview | The Statistical Model | Negative Binomial GAM | Cubic Regression Splines | Knot Selection | Statistical Tests | Association Test | Differential End Test | Pattern Test | Early DE Test | Wald Test Framework | Fold Change Thresholds | Multiple Conditions | Computational Considerations | Parallelization | Memory Efficiency | References | Session Info
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Visualization Gallery
Introduction | Setup | Fit GAM Models | Gene Expression Smoothers | Basic Smoother Plot | Customized Smoother Plot | Custom Color Schemes | Plot Specific Lineages | Gene Count Plots | Basic Gene Count Plot | With Model Knots | Multiple Gene Comparison | Comparing Expression Patterns | Heatmap Visualization | Expression Heatmap Along Trajectory | Knot Evaluation Plot | Volcano Plot of Results | Ranked Gene Plot | Publication-Ready Figures | Combined Figure | Tips for Effective Visualization | Session Info
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Last update: 2026-01-24
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Working with multiple conditions
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Last update: 2026-01-23
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Using Monocle as input to tradeSeq
Introduction | Load data | Monocle3 | Constructing the trajectory | Extracting the pseudotimes and cell weights for tradeSeq | Session | References
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Last update: 2026-01-23
Started: 2026-01-23

Fitting the models and additional control of fitGAM in tradeSeq
Introduction | Installation | Load data | Choosing K: a deeper dive into the output from evaluateK | Fit additive models | Adding covariates to the model | Parallel computing | Fitting only a subset of genes | Zero inflation | Convergence issues on small or zero-inflated datasets | tradeSeq list output | Session | References
Rendered fromfitGAM.Rmdusingknitr::rmarkdown

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The tradeSeq workflow
Installation | Load data | Fit negative binomial model | Within-lineage comparisons | Association of gene expression with pseudotime | Discovering progenitor marker genes | Comparing specific pseudotime values within a lineage | Between-lineage comparisons | Discovering differentiated cell type markers | Discovering genes with different expression patterns | Example on combining patternTest with diffEndTest results | Early drivers of differentiation | Differential expression in large datasets | Clustering of genes according to their expression pattern | Extracting fitted values to use with any clustering method | Clustering using RSEC, clusterExperiment | Contributing and requesting | Session | References
Rendered fromtradeSeq.Rmdusingknitr::rmarkdown

Last update: 2026-01-23
Started: 2026-01-23

Readme and manuals

Help Manual

Help pageTopics
Perform statistical test to check whether gene expression is constant across pseudotime within a lineageassociationTest associationTest,list-method associationTest,SingleCellExperiment-method
Get expression peak cascade of genes along a lineage.cascade cascade,SingleCellExperiment-method
A vector defining cell types, used in the package vignette.celltype
Cluster gene expression patterns.clusterExpressionPatterns clusterExpressionPatterns,list-method clusterExpressionPatterns,SingleCellExperiment-method
Assess differential expression patterns between conditions within a lineage.conditionTest conditionTest,SingleCellExperiment-method
A count matrix, used in the package vignette.countMatrix
A SlingshotDataset object, used in the package vignette.crv
Perform statistical test to check for DE between final stages of every lineage.diffEndTest diffEndTest,list-method diffEndTest,SingleCellExperiment-method
Perform test of early differences between lineagesearlyDETest earlyDETest,list-method earlyDETest,SingleCellExperiment-method
Evaluate the optimal number of knots required for fitGAM.evaluateK evaluateK,dgCMatrix-method evaluateK,matrix-method evaluateK,SingleCellExperiment-method
fitGAMfitGAM fitGAM,generalMatrix-method fitGAM,matrix-method fitGAM,SingleCellExperiment-method
A list of GAM models, used to demonstrate the various tests.gamList
Get smoother p-value as returned by 'mgcv'.getSmootherPvalues
Get smoother Chi-squared test statistics.getSmootherTestStats
knotsnknots nknots,list-method nknots,SingleCellExperiment-method
Assess differential expression pattern between lineages.patternTest patternTest,list-method patternTest,SingleCellExperiment-method
Evaluate an appropriate number of knots.plot_evalutateK_results
Plot gene expression in reduced dimension.plotGeneCount plotGeneCount,PseudotimeOrdering-method plotGeneCount,SingleCellExperiment-method plotGeneCount,SlingshotDataSet-method
Plot the log-transformed counts and the fitted values for a particular gene along all lineagesplotSmoothers plotSmoothers,gam-method plotSmoothers,SingleCellExperiment-method
predictCellspredictCells predictCells,list-method predictCells,SingleCellExperiment-method
predictSmoothpredictSmooth predictSmooth,list-method predictSmooth,SingleCellExperiment-method
A SlingshotDataset object, used in the package unit tests.sds
Perform statistical test to check for DE between starting point and the end stages of every lineagestartVsEndTest startVsEndTest,list-method startVsEndTest,SingleCellExperiment-method