Extracting a cellular hierarchy from high-dimensional cytometry data with SPADE

被引:696
作者
Qiu, Peng [1 ,2 ]
Simonds, Erin F. [3 ]
Bendall, Sean C. [3 ]
Gibbs, Kenneth D., Jr. [3 ]
Bruggner, Robert V. [3 ]
Linderman, Michael D. [4 ]
Sachs, Karen [3 ]
Nolan, Garry P. [3 ]
Plevritis, Sylvia K. [1 ]
机构
[1] Stanford Univ, Dept Radiol, Stanford, CA 94305 USA
[2] Univ Texas MD Anderson Canc Ctr, Dept Bioinformat & Computat Biol, Houston, TX 77030 USA
[3] Stanford Univ, Dept Microbiol & Immunol, Stanford, CA 94305 USA
[4] Stanford Univ, Comp Syst Lab, Stanford, CA 94305 USA
基金
美国国家科学基金会; 美国国家卫生研究院;
关键词
FLOW-CYTOMETRY; MASS CYTOMETRY; STEM-CELL; IDENTIFICATION; IMMUNE;
D O I
10.1038/nbt.1991
中图分类号
Q81 [生物工程学(生物技术)]; Q93 [微生物学];
学科分类号
071005 ; 0836 ; 090102 ; 100705 ;
摘要
The ability to analyze multiple single-cell parameters is critical for understanding cellular heterogeneity. Despite recent advances in measurement technology, methods for analyzing high-dimensional single-cell data are often subjective, labor intensive and require prior knowledge of the biological system. To objectively uncover cellular heterogeneity from single-cell measurements, we present a versatile computational approach, spanning-tree progression analysis of density-normalized events (SPADE). We applied SPADE to flow cytometry data of mouse bone marrow and to mass cytometry data of human bone marrow. In both cases, SPADE organized cells in a hierarchy of related phenotypes that partially recapitulated well-described patterns of hematopoiesis. We demonstrate that SPADE is robust to measurement noise and to the choice of cellular markers. SPADE facilitates the analysis of cellular heterogeneity, the identification of cell types and comparison of functional markers in response to perturbations.
引用
收藏
页码:886 / U181
页数:8
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