Model-based branching point detection in single-cell data by K-branches clustering

Bioinformatics

Volumn 33, Issue 20, 2017, Pages 3211-3219

  Chlis, Nikolaos K ^a,b   Wolf, F Alexander ^a   Theis, Fabian J ^a,b  

^a INSTITUTE OF EPIDEMIOLOGY   (Germany)

^b TECHNICAL UNIVERSITY OF MUNICH   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHM; ANIMAL; BIOLOGICAL MODEL; CELL DIFFERENTIATION; CLUSTER ANALYSIS; GENE EXPRESSION PROFILING; HUMAN; MOUSE; PROCEDURES; SEQUENCE ANALYSIS; SINGLE CELL ANALYSIS; SOFTWARE;

ALGORITHMS; ANIMALS; CELL DIFFERENTIATION; CLUSTER ANALYSIS; GENE EXPRESSION PROFILING; HUMANS; MICE; MODELS, BIOLOGICAL; SEQUENCE ANALYSIS, RNA; SINGLE-CELL ANALYSIS; SOFTWARE;

EID: 85031825743     PISSN: 13674803     EISSN: 14602059     Source Type: Journal    
DOI: 10.1093/bioinformatics/btx325     Document Type: Article

References (26)

1. Bendall, S. et al. (2014) Single-cell trajectory detection uncovers progression and regulatory coordination in human b cell development. Cell, 157, 714-725.
2. Coifman, R.R. et al. (2005) Geometric diffusions as a tool for harmonic analysis and structure definition of data: diffusion maps. Proc. Natl. Acad. Sci. USA, 102, 7426-7431.
3. de Vargas Roditi, L., and Claassen, M. (2015) Computational and experimental single cell biology techniques for the definition of cell type heterogeneity, interplay and intracellular dynamics. Curr. Opin. Biotechnol., 34, 9-15. Systems biology Nanobiotechnology.
4. Grun, D., and van Oudenaarden, A. (2015) Design and analysis of single-cell sequencing experiments. Cell, 163, 799-810.
5. Guo, G. et al. (2010) Resolution of cell fate decisions revealed by single-cell gene expression analysis from zygote to blastocyst. Develop. Cell, 18, 675-685.
6. Haghverdi, L. et al. (2015) Diffusion maps for high-dimensional single-cell analysis of differentiation data. Bioinformatics, 31, 2989-2998.
7. Haghverdi, L. et al. (2016) Diffusion pseudotime robustly reconstructs lineage branching. Nat. Methods, 13, 845-848.
8. Hastie, T.J. et al. (2009) The Elements of Statistical Learning: data Mining, Inference, and Prediction. Springer series in statistics, Springer, New York. Autres impressions: 2011 (corr), 2013 (7e corr).
9. Jaitin, D.A. et al. (2014) Massively parallel single-cell rna-seq for marker-free decomposition of tissues into cell types. Science, 343, 776-779.
10. Ji, Z., and Ji, H. (2016) Tscan: pseudo-time reconstruction and evaluation in single-cell rna-seq analysis. Nucleic Acids Res., 44, e117.
11. Kiselev, V.Y. et al. (2017) Sc3-consensus clustering of single-cell rna-seq data. Nat. Meth., 14, 483-486.
12. Kouno, T. et al. (2013) Temporal dynamics and transcriptional control using single-cell gene expression analysis. Genome Biol., 14, R118.
13. Mahata, B. et al. (2014) Single-cell rna sequencing reveals t helper cells synthesizing steroids de novo to contribute to immune homeostasis. Cell Rep., 7, 1130-1142.
14. Mohajer, M. et al. (2010) A comparison of gap statistic definitions with and without logarithm function. https://epub.ub.uni-muenchen.de/11920/.
15. Moignard, V. et al. (2015) Decoding the regulatory network of early blood development from single-cell gene expression measurements. Nat. Biotech., 33, 269-276.
16. Paul, F. et al. (2015) Transcriptional heterogeneity and lineage commitment in myeloid progenitors. Cell, 163, 1663-1677.
17. Proserpio, V. et al. (2016) Single-cell analysis of cd4+ t-cell differentiation reveals three major cell states and progressive acceleration of proliferation. Genome Biol., 17, 1-15.
18. Roweis, S.T., and Saul, L.K. (2000) Nonlinear dimensionality reduction by locally linear embedding. Science, 290, 2323-2326.
19. Setty, M. et al. (2016) Wishbone identifies bifurcating developmental trajectories from single-cell data. Nat. Biotech., 34, 637-645.
20. Stegle, O. et al. (2015) Computational and analytical challenges in single-cell transcriptomics. Nat. Rev. Genet., 16, 133-145.
21. Theodoridis, S., and Koutroumbas, K. (2008) Pattern Recognition, 4th edn., Academic Press, Burlington, MA.
22. Tibshirani, R. et al. (2001) Estimating the number of clusters in a data set via the gap statistic. J. R Stat. Soc. Ser. B (Stat. Methodol.), 63, 411-423.
23. Trapnell, C. et al. (2014) The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells. Nat. Biotech., 32, 381-386. Research.
24. Waddington, C.H. (1942) Canalization of development and the inheritance of acquired characters. Nature, 150, 563-565.
25. Waddington, C.H. (1957) The Strategy of the Genes. A Discussion of Some Aspects of Theoretical Biology. With an appendix by Kacser H. Allen &Unwin, London.
26. Welch, J.D. et al. (2016) Slicer: inferring branched, nonlinear cellular trajectories from single cell rna-seq data. Genome Biol., 17, 106.