destiny: diffusion maps for large-scale single-cell data in R
Angerer, P., Haghverdi, L., Büttner, M. et al.
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.
Angerer, P., Haghverdi, L., Büttner, M. et al. "destiny: diffusion maps for large-scale single-cell data in R" Bioinformatics (2016): 1,241–3