Visualization and cellular hierarchy inference of single-cell data using SPADE
Visualization and cellular hierarchy inference of single-cell data using SPADE
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DOI：
10.1038/nprot.2016.066
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发表时间：
2016-07-01
期刊：
影响因子：
14.8
通讯作者：
Plevritis, Sylvia K.
中科院分区：
文献类型：
--
作者：
Anchang, Benedict;Hart, Tom D. P.;Plevritis, Sylvia K.
高通量单细胞技术为研究细胞异质性提供了前所未有的视角，但它们在数据分析和解释方面提出了新的挑战。在本协议中，我们描述了密度标准化事件的生成树进展分析（SPADE）的使用，这是一种基于密度的算法，用于可视化单细胞数据，并在相似细胞的亚群中实现细胞层次推断。它最初是为流式和细胞计数单细胞数据而开发的。我们使用一个运行在Mac OS X、Linux和Windows系统上的开源R包来描述SPADE的实现和应用。在2.27 ghz处理器的笔记本电脑上进行典型的SPADE分析大约需要5分钟。我们证明了SPADE对单细胞RNA-seq数据的适用性。我们将SPADE与最近开发的基于t分布随机邻域嵌入（t-SNE）算法的单细胞可视化方法进行了比较。我们对比了这些方法的实施和输出，对正常和恶性造血细胞进行了细胞计数分析，并提供了适当使用的建议。最后，我们提供了一种综合策略，结合t-SNE和SPADE的优势，从高维单细胞数据推断细胞层次结构。
High-throughput single-cell technologies provide an unprecedented view into cellular heterogeneity, yet they pose new challenges in data analysis and interpretation. In this protocol, we describe the use of Spanning-tree Progression Analysis of Density-normalized Events (SPADE), a density-based algorithm for visualizing single-cell data and enabling cellular hierarchy inference among subpopulations of similar cells. It was initially developed for flow and mass cytometry single-cell data. We describe SPADE's implementation and application using an open-source R package that runs on Mac OS X, Linux and Windows systems. A typical SPADE analysis on a 2.27-GHz processor laptop takes similar to 5 min. We demonstrate the applicability of SPADE to single-cell RNA-seq data. We compare SPADE with recently developed single-cell visualization approaches based on the t-distribution stochastic neighborhood embedding (t-SNE) algorithm. We contrast the implementation and outputs of these methods for normal and malignant hematopoietic cells analyzed by mass cytometry and provide recommendations for appropriate use. Finally, we provide an integrative strategy that combines the strengths of t-SNE and SPADE to infer cellular hierarchy from high-dimensional single-cell data.