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destiny: diffusion maps for large-scale single-cell data in R

Item Type:Article
Title:destiny: diffusion maps for large-scale single-cell data in R
Creators Name:Angerer, P. and Haghverdi, L. and Büttner, M. and Theis, F.J. and Marr, C. and Buettner, F.
Abstract: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 “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.
Keywords:Algorithms, Cluster Analysis, Diffusion, Single-Cell Analysis, Software
Publisher:Oxford University Press
Page Range:1241-1243
Date:15 April 2016
Official Publication:https://doi.org/10.1093/bioinformatics/btv715
PubMed:View item in PubMed
Related to:
https://edoc.mdc-berlin.de/19030/Preprint version

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