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

Angerer P, Haghverdi L, Buettner M, Theis FJ, Marr C, Buettner F (2016)

Publication Type: Journal article

Publication year: 2016

Journal

Bioinformatics Oxford University Press (OUP): Policy B - Oxford Open Option B

Book Volume: 32

Pages Range: 1241-1243

Journal Issue: 8

DOI: 10.1093/bioinformatics/btv715

Abstract

Diffusion maps are a spectral method for non-linear dimension reduction and have recently been adapted for the visualization of single-cell expression data. Here we present destiny, an efficient R implementation of the diffusion map algorithm. Our package includes a single-cell specific noise model allowing for missing and censored values. In contrast to previous implementations, we further present an efficient nearest-neighbour approximation that allows for the processing of hundreds of thousands of cells and a functionality for projecting new data on existing diffusion maps. We exemplarily apply destiny to a recent time-resolved mass cytometry dataset of cellular reprogramming.

Involved external institutions

Helmholtz Zentrum München - Deutsches Forschungszentrum für Gesundheit und Umwelt (HMGU) / Helmholtz Munich DE Germany (DE)

How to cite

APA:

Angerer, P., Haghverdi, L., Buettner, M., Theis, F.J., Marr, C., & Buettner, F. (2016). Destiny: Diffusion maps for large-scale single-cell data in R. Bioinformatics, 32(8), 1241-1243. https://doi.org/10.1093/bioinformatics/btv715

MLA:

Angerer, Philipp, et al. "Destiny: Diffusion maps for large-scale single-cell data in R." Bioinformatics 32.8 (2016): 1241-1243.

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