Destiny: Diffusion maps for large-scale single-cell data in R - Angerer, Philipp; Haghverdi, Laleh; Buettner, Maren; Theis, Fabian J.; Marr, Carsten; Buettner, Florian (Bioinformatics, 2016)
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.
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