tradeSeq

📖 Documentation: https://zaoqu-liu.github.io/tradeSeq/

TRAjectory Differential Expression analysis for SEQuencing data

tradeSeq is an R/Bioconductor package for trajectory-based differential expression analysis of single-cell RNA sequencing (scRNA-seq) data. It implements a flexible statistical framework based on generalized additive models (GAMs) to identify genes whose expression patterns change significantly along developmental trajectories or pseudotime.

Overview

Single-cell transcriptomics enables the study of continuous biological processes such as cell differentiation, development, and disease progression. tradeSeq provides a comprehensive statistical framework for:

Association testing: Identifying genes associated with pseudotime progression
Differential expression between lineages: Comparing expression patterns across branching trajectories
Pattern analysis: Detecting genes with distinct temporal expression profiles
Condition-specific effects: Analyzing differential expression between experimental conditions within trajectories

Methodology

The statistical framework is based on fitting negative binomial generalized additive models (GAMs) with cubic regression splines for each gene:

$$\log(\mu_{gc}) = \sum_{l=1}^{L} \mathbf{1}{cl} \cdot s_l(t{cl}) + O_c$$

Where $s_l(t)$ are smooth functions of pseudotime for each lineage $l$, fitted using penalized cubic regression splines.

For detailed methodology, see the Statistical Framework vignette.

Publication

Van den Berge K, Roux de Bézieux H, Street K, Saelens W, Cannoodt R, Saeys Y, Dudoit S, Clement L. (2020). Trajectory-based differential expression analysis for single-cell sequencing data. Nature Communications, 11:1201. DOI: 10.1038/s41467-020-14766-3

Installation

R-universe (Recommended for latest development version)

install.packages("tradeSeq", repos = "https://zaoqu-liu.r-universe.dev")

Bioconductor (Stable release)

if (!requireNamespace("BiocManager", quietly = TRUE)) {
    install.packages("BiocManager")
}
BiocManager::install("tradeSeq")

GitHub (Development version)

if (!requireNamespace("remotes", quietly = TRUE)) {
    install.packages("remotes")
}
remotes::install_github("Zaoqu-Liu/tradeSeq")

Quick Start

library(tradeSeq)
library(slingshot)

# Fit GAM models for trajectory analysis
sce <- fitGAM(counts = counts, sds = slingshot_object, nknots = 6)

# Test for association with pseudotime
assocRes <- associationTest(sce)

# Compare expression at trajectory endpoints
endRes <- diffEndTest(sce)

# Visualize gene expression along trajectory
plotSmoothers(sce, counts, gene = "GeneOfInterest")

For a complete tutorial, see the Quick Start Guide.

Key Functions

Function	Description
`fitGAM()`	Fit negative binomial GAMs for each gene
`associationTest()`	Test association between gene expression and pseudotime
`diffEndTest()`	Compare expression at lineage endpoints
`earlyDETest()`	Test for early differential expression between lineages
`patternTest()`	Compare expression patterns across lineages
`startVsEndTest()`	Compare expression at start vs end of trajectories
`conditionTest()`	Test for condition-specific differential expression
`plotSmoothers()`	Visualize fitted expression curves
`plotGeneCount()`	Plot gene counts along trajectories

Workflow

tradeSeq workflow cheatsheet

Vignettes

Quick Start Guide - Get started in 5 minutes
Main Workflow - Comprehensive tutorial
Statistical Framework - Methodology details
Visualization Gallery - Plotting examples
Multiple Conditions - Condition-specific analysis

Compatibility

tradeSeq integrates seamlessly with popular trajectory inference tools:

Slingshot (recommended)
Monocle 3
CellRank
Any method providing pseudotime and cell weights

System Requirements

R (≥ 4.0)
Bioconductor (≥ 3.14)
See DESCRIPTION for complete dependency list

Citation

If you use tradeSeq in your research, please cite:

@article{vandenberge2020trajectory,
  title={Trajectory-based differential expression analysis for single-cell sequencing data},
  author={Van den Berge, Koen and Roux de B{\'e}zieux, Hector and Street, Kelly and Saelens, Wouter and Cannoodt, Robrecht and Saeys, Yvan and Dudoit, Sandrine and Clement, Lieven},
  journal={Nature Communications},
  volume={11},
  number={1},
  pages={1201},
  year={2020},
  publisher={Nature Publishing Group},
  doi={10.1038/s41467-020-14766-3}
}

Contributing

We welcome contributions! Please submit issues, bug reports, and feature requests via GitHub Issues.

License

This project is licensed under the MIT License - see the LICENSE file for details.

Maintainer

Zaoqu Liu (@Zaoqu-Liu)

Developed for the single-cell genomics community