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

被引：386

作者：

Angerer, Philipp ^{[1
]}

Haghverdi, Laleh ^{[1
]}

Buettner, Maren ^{[1
]}

Theis, Fabian J. ^{[1
,2
]}

Marr, Carsten ^{[1
]}

Buettner, Florian ^{[1
]}

机构：

[1] Helmholtz Zentrum Munchen, German Res Ctr Environm Hlth, Inst Computat Biol, Ingolstadter Landstr 1, D-85764 Neuherberg, Germany

[2] Tech Univ Munich, Ctr Math, Chair Math Modeling Biol Syst, Boltzmannstr 3, D-85748 Garching, Germany

来源：

BIOINFORMATICS | 2016年 / 32卷 / 08期

基金：

英国医学研究理事会;

关键词：

D O I：

10.1093/bioinformatics/btv715

中图分类号：

Q5 [生物化学];

学科分类号：

071010 ; 081704 ;

摘要：

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.

引用

页码：1241 / 1243

页数：3

共 7 条

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