Trajectory-based differential expression analysis for single-cell sequencing data
Ghent University · University of California, Berkeley · +6 more institutions
Abstract
Trajectory inference has radically enhanced single-cell RNA-seq research by enabling the study of dynamic changes in gene expression. Downstream of trajectory inference, it is vital to discover genes that are (i) associated with the lineages in the trajectory, or (ii) differentially expressed between lineages, to illuminate the underlying biological processes. Current data analysis procedures, however, either fail to exploit the continuous resolution provided by trajectory inference, or fail to pinpoint the exact types of differential expression. We introduce tradeSeq, a powerful generalized additive model framework based on the negative binomial distribution that allows flexible inference of both…
Citation impact
- FWCI
- 32.42
- Percentile
- 100%
- References
- 43
Authors
8- KVKoen Van den BergeCorresponding
Ghent University, University of California, Berkeley
- HRHector Roux de Bézieux
University of California, Berkeley
- KSKelly Street
Harvard University, Dana-Farber Cancer Institute
- WSWouter Saelens
Ghent University, VIB-UGent Center for Inflammation Research
- RCRobrecht Cannoodt
Ghent University Hospital, Ghent University, VIB-UGent Center for Inflammation Research
Topics & keywords
- Inference
- Trajectory
- Computer science
- Expression (computer science)
- Negative binomial distribution
- Computational biology
- Gene expression profiling
- Lineage (genetic)
- Decent work and economic growth