.png){kind=icon}
Return
Trajectory-based differential expression analysis for single-cell sequencing data
.png){kind=icon}
OA
AI
DOI：10.1038/s41467-020-14766-3.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
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 within-lineage and between-lineage differential expression. By incorporating observation-level weights, the model additionally allows to account for zero inflation. We evaluate the method on simulated datasets and on real datasets from droplet-based and full-length protocols, and show that it yields biological insights through a clear interpretation of the data. Downstream of trajectory inference for cell lineages based on scRNA-seq data, differential expression analysis yields insight into biological processes. Here, Van den Berge et al. develop tradeSeq, a framework for the inference of within and between-lineage differential expression, based on negative binomial generalized additive models.
Journal
IF:
15.7
Papers:
9.4W
Citations:
91.2W
Organization
View more organizations
Cited Papers
Differential expression analysis of multifactor RNA-Seq experiments with respect to biological variation.png)
NUCLEIC ACIDS RESEARCHOAAI
NUCLEIC ACIDS RESEARCHIF13.1
OAAIShareSaveSlingshot: cell lineage and pseudotime inference for single-cell transcriptomicsBMC GENOMICSOAAI
BMC GENOMICSIF3.7
OAAIShareSaveThe dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cellsNATURE BIOTECHNOLOGYOAAI
NATURE BIOTECHNOLOGYIF41.7
OAAIShareSaveShareSaveShareSaveEvaluation of drilling performances of nanocomposites reinforced with graphene and graphene oxide
PREAI
ShareSaveTSCAN: Pseudo-time reconstruction and evaluation in single-cell RNA-seq analysisNUCLEIC ACIDS RESEARCHOAAI
NUCLEIC ACIDS RESEARCHIF13.1
OAAIShareSaveInfluence of Na+ and Mg2+ ions on RNA structures studied with molecular dynamics simulationsNUCLEIC ACIDS RESEARCHOAAI
NUCLEIC ACIDS RESEARCHIF13.1
OAAIShareSave View more