Destiny: Diffusion maps for large-scale single-cell data in R

Bioinformatics

Volumn 32, Issue 8, 2016, Pages 1241-1243

  Angerer, Philipp ^a   Haghverdi, Laleh ^a   Büttner, Maren ^a   Theis, Fabian J ^a,b   Marr, Carsten ^a   Buettner, Florian ^a,c  

^a INSTITUTE OF EPIDEMIOLOGY   (Germany)

^b TECHNICAL UNIVERSITY OF MUNICH   (Germany)

^c WELLCOME TRUST SANGER INSTITUTE   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHM; CLUSTER ANALYSIS; DIFFUSION; PROCEDURES; SINGLE CELL ANALYSIS; SOFTWARE;

ALGORITHMS; CLUSTER ANALYSIS; DIFFUSION; SINGLE-CELL ANALYSIS; SOFTWARE;

EID: 84966667709     PISSN: 13674803     EISSN: 14602059     Source Type: Journal    
DOI: 10.1093/bioinformatics/btv715     Document Type: Article

References (7)

1. Buettner, F., Theis, F.J. (2012) A novel approach for resolving differences in single-cell gene expression patterns from zygote to blastocyst. Bioinformatics, 28, 1626-1632.
2. Coifman, R.R. et al. (2008) Diffusion maps, reduction coordinates, and low dimensional representation of stochastic systems. Multisc. Model. Simul., 7, 842-864.
3. 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., 102, 7426-7431.
4. Haghverdi, L.F et al. (2015) Diffusion maps for high-dimensional singlecell analysis of differentiation data. Bioinformatics., 31, 2989-2998.
5. Richards, J. (2014). diffusionMap: Diffusion Map. CRAN. http://cran.r-pro ject.org/web/packages/diffusionMap/.
6. Roditi, V. et al. (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.
7. Zunder, E.R. et al. (2015) A continuous molecular roadmap to iPSC reprogramming through progression analysis of single-cell mass cytometry. Cell Stem Cell, 16, 323-337.