destiny: diffusion maps for large-scale single cell data in R
destiny: diffusion maps for large-scale single cell data in R
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DOI：
10.1093/bioinformatics/btv715
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发表时间：
2016-04-15
期刊：
影响因子：
5.8
通讯作者：
Buettner, Florian
中科院分区：
文献类型：
--
作者：
Angerer, Philipp;Haghverdi, Laleh;Buettner, Florian
扩散图是一种非线性降维的光谱方法，最近已适用于单细胞表达数据的可视化。在这里，我们提出了命运，一个有效的R实现的扩散地图算法。我们的软件包包括一个单细胞特定的噪声模型，允许缺失和删失值。与以前的实现相比，我们进一步提出了一种有效的最近邻近似，允许处理数十万个细胞和现有扩散图上投影新数据的功能。我们示范性地将命运应用于最近的细胞重编程的时间分辨质谱细胞术数据集。
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.