Switchde: Inference of switch-like differential expression along single-cell trajectories

Bioinformatics

Volumn 33, Issue 8, 2017, Pages 1241-1242

  Campbell, Kieran R ^a,b   Yau, Christopher ^b,c  

^a UNIVERSITY OF OXFORD   (United Kingdom)

^b UNIVERSITY OF OXFORD   (United Kingdom)

^c UNIVERSITY OF OXFORD   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

BIOLOGICAL MODEL; GENE EXPRESSION PROFILING; PROCEDURES; SEQUENCE ANALYSIS; SINGLE CELL ANALYSIS; SOFTWARE; STATISTICAL MODEL;

GENE EXPRESSION PROFILING; MODELS, GENETIC; MODELS, STATISTICAL; SEQUENCE ANALYSIS, RNA; SINGLE-CELL ANALYSIS; SOFTWARE;

EID: 85019119633     PISSN: 13674803     EISSN: 14602059     Source Type: Journal    
DOI: 10.1093/bioinformatics/btw798     Document Type: Article

References (8)

1. Campbell, K. and Yau, C. (2016) Order under uncertainty: robust differential expression analysis using probabilistic models for pseudotime inference. PLoS. Comput. Biol.., 12, e1005212.
2. Ji, Z. and Ji, H. (2016) TSCAN: Pseudo-time reconstruction and evaluation in single-cell RNA-seq analysis. Nucleic Acids Res., 44, e117.
3. Kharchenko, P.V. et al. (2014) Bayesian approach to single-cell differential expression analysis. Nat. Methods, 11, 740-742.
4. Pierson, E. and Yau, C. (2015) ZIFA: Dimensionality reduction for zeroinflated single-cell gene expression analysis. Genome Biol., 16, 1.
5. Reid, J.E. and Wernisch, L. (2016) Pseudotime estimation: deconfounding single cell time series. Bioinformatics, 32, 2973-2980.
6. Shin, J. et al. (2015) Single-cell RNA-seq with waterfall reveals molecular cascades underlying adult neurogenesis. Cell Stem Cell, 17, 360-372.
7. Trapnell, C. (2015) Defining cell types and states with single-cell genomics. Genome Res., 25, 1491-1498.
8. Trapnell, C. et al. (2014) The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells. Nat. Biotechnol., 32, 381-386.