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

Philipp Angerer, Laleh Haghverdi, Maren Büttner, Fabian J. Theis, Carsten Marr, Florian Buettner

Research output: Contribution to journalArticlepeer-review

362 Scopus citations

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.

Original languageEnglish
Pages (from-to)1241-1243
Number of pages3
JournalBioinformatics
Volume32
Issue number8
DOIs
StatePublished - 15 Apr 2016

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