TY - JOUR T1 - <em>destiny</em> – diffusion maps for large-scale single-cell data in R JF - bioRxiv DO - 10.1101/023309 SP - 023309 AU - Philipp Angerer AU - Laleh Haghverdi AU - Maren Büttner AU - Fabian J. Theis AU - Carsten Marr AU - Florian Buettner Y1 - 2015/01/01 UR - http://biorxiv.org/content/early/2015/08/03/023309.abstract N2 - Summary 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.Availability and implementation destiny is an open-source R/Bioconductor package http://bioconductor.org/packages/ destiny also available at https://www.helmholtz-muenchen.de/icb/destiny. A detailed vignette describing functions and workflows is provided with the package.Contact carsten.marr{at}helmholtz-muenchen.de, f.buettner{at}helmholtz-muenchen.de ER -