{"doi":"10.1093/bioinformatics/btv715","title":"<i>destiny</i>\n                    : diffusion maps for large-scale single-cell data in R","abstract":"<h4>Unlabelled</h4>: 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.<h4>Availability and implementation</h4>destiny is an open-source R/Bioconductor package \"bioconductor.org/packages/destiny\" also available at www.helmholtz-muenchen.de/icb/destiny A detailed vignette describing functions and workflows is provided with the package.<h4>Contact</h4>carsten.marr@helmholtz-muenchen.de or f.buettner@helmholtz-muenchen.de<h4>Supplementary information</h4>Supplementary data are available at Bioinformatics 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