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

Qiu, Peng; Simonds, Erin F.; Bendall, Sean C.; Gibbs, Kenneth D.; Bruggner, Robert V.; Linderman, Michael D.; Sachs, Karen; Nolan, Garry P.; Plevritis, Sylvia K.

doi:10.1038/nbt.1991

Cited by 889 publications

(948 citation statements)

References 22 publications

Supporting

Mentioning

921

Contrasting

Unclassified

Order By: Relevance

“…In response to the increasing complexity and dimensionality of flow cytometry data, several algorithms have recently been developed to automate the identification and/or quantification of cell populations in flow cytometry data 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11. Some algorithms function by automating the traditional manual gating procedure for detecting and isolating specific subpopulations of interest, others attempt to completely resolve cells within a sample into clusters that ideally correspond to biologically meaningful subpopulations.…”

Section: Introductionmentioning

confidence: 99%

Competitive SWIFT cluster templates enhance detection of aging changes

Rebhahn

Roumanes

et al. 2015

Cytometry Pt A

View full text Add to dashboard Cite

Clustering‐based algorithms for automated analysis of flow cytometry datasets have achieved more efficient and objective analysis than manual processing. Clustering organizes flow cytometry data into subpopulations with substantially homogenous characteristics but does not directly address the important problem of identifying the salient differences in subpopulations between subjects and groups. Here, we address this problem by augmenting SWIFT—a mixture model based clustering algorithm reported previously. First, we show that SWIFT clustering using a “template” mixture model, in which all subpopulations are represented, identifies small differences in cell numbers per subpopulation between samples. Second, we demonstrate that resolution of inter‐sample differences is increased by “competition” wherein a joint model is formed by combining the mixture model templates obtained from different groups. In the joint model, clusters from individual groups compete for the assignment of cells, sharpening differences between samples, particularly differences representing subpopulation shifts that are masked under clustering with a single template model. The benefit of competition was demonstrated first with a semisynthetic dataset obtained by deliberately shifting a known subpopulation within an actual flow cytometry sample. Single templates correctly identified changes in the number of cells in the subpopulation, but only the competition method detected small changes in median fluorescence. In further validation studies, competition identified a larger number of significantly altered subpopulations between young and elderly subjects. This enrichment was specific, because competition between templates from consensus male and female samples did not improve the detection of age‐related differences. Several changes between the young and elderly identified by SWIFT template competition were consistent with known alterations in the elderly, and additional altered subpopulations were also identified. Alternative algorithms detected far fewer significantly altered clusters. Thus SWIFT template competition is a powerful approach to sharpen comparisons between selected groups in flow cytometry datasets. © 2015 The Authors. Published Wiley Periodicals Inc.

show abstract

Section: Introductionmentioning

confidence: 99%

Competitive SWIFT cluster templates enhance detection of aging changes

Rebhahn

Roumanes

et al. 2015

Cytometry Pt A

View full text Add to dashboard Cite

show abstract

“…Other analysis methods have been published that examine and visualize the phenotypic changes in cells as they differentiate and mature in the marrow and other tissues (1)(2)(3). Some of these methods have reported reproducibility issues and nonbiological branches (1,2).…”

Section: Introductionmentioning

confidence: 99%

“…Some of these methods have reported reproducibility issues and nonbiological branches (1,2). One recently published method (3) generates plots that resemble probability state modeling (PSM) expression profiles (EPs) but these plots are, for the most part, not consistent with the biology as shown in the companion B-cell manuscript (4).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Probability state modeling theory

Bagwell¹,

Hunsberger²,

Herbert³

et al. 2015

Cytometry Pt A

View full text Add to dashboard Cite

As the technology of cytometry matures, there is mounting pressure to address two major issues with data analyses. The first issue is to develop new analysis methods for high-dimensional data that can directly reveal and quantify important characteristics associated with complex cellular biology. The other issue is to replace subjective and inaccurate gating with automated methods that objectively define subpopulations and account for population overlap due to measurement uncertainty. Probability state modeling (PSM) is a technique that addresses both of these issues. The theory and important algorithms associated with PSM are presented along with simple examples and general strategies for autonomous analyses. PSM is leveraged to better understand B-cell ontogeny in bone marrow in a companion Cytometry Part B manuscript. Three short relevant videos are available in the online supporting information for both of these papers. PSM avoids the dimensionality barrier normally associated with high-dimensionality modeling by using broadened quantile functions instead of frequency functions to represent the modulation of cellular epitopes as cells differentiate. Since modeling programs ultimately minimize or maximize one or more objective functions, they are particularly amenable to automation and, therefore, represent a viable alternative to subjective and inaccurate gating approaches. V C 2015 International Society for Advancement of Cytometry

show abstract

“…As a consequence, several runs of SPADE will result in differently organized trees, which should be kept in mind when applying SPADE. While the organization of the tree at each individual run might appear distinct, the identified populations have been found to be comparable across several runs.SPADE has been used in several publications [25,[28][29][30], mostly to give an instant overview of different immune populations and their expression of surface markers or intracellular signaling molecules. SPADE is available as a Bioconductor package (CytoSPADE), which can be used through a standard command line interface or through an interactive graphical user interface available as a plugin for the Cytospace Network Visualization Platform (www.cytospace.org).…”

mentioning

confidence: 99%

The end of gating? An introduction to automated analysis of high dimensional cytometry data

et al. 2015

View full text Add to dashboard Cite

Ever since its invention half a century ago, flow cytometry has been a major tool for single-cell analysis, fueling advances in our understanding of a variety of complex cellular systems, in particular the immune system. The last decade has witnessed significant technical improvements in available cytometry platforms, such that more than 20 parameters can be analyzed on a single-cell level by fluorescence-based flow cytometry. The advent of mass cytometry has pushed this limit up to, currently, 50 parameters. However, traditional analysis approaches for the resulting high-dimensional datasets, such as gating on bivariate dot plots, have proven to be inefficient. Although a variety of novel computational analysis approaches to interpret these datasets are already available, they have not yet made it into the mainstream and remain largely unknown to many immunologists. Therefore, this review aims at providing a practical overview of novel analysis techniques for high-dimensional cytometry data including SPADE, t-SNE, Wanderlust, Citrus, and PhenoGraph, and how these applications can be used advantageously not only for the most complex datasets, but also for standard 14-parameter cytometry datasets. Keywords: Citrus CyTOF Data analysis Flow cytometry Mass cytometry PSM PhenoGraph SPADE t-SNE WanderlustYear 2015 marked the 50-year anniversary of the publication of the first cell sorter, a device that was able to separate cells in suspension based on their difference in volume [1], as well as the first cytometry-based cell analyzer [2]. Shortly after that, the first sorter that could discriminate cells based on fluorescence was developed [3,4], and this seminal work marked the advent of flow cytometry as a widely used, single-cell analysis technique driving the identification of all major immune cell subsets known today [3,5] (for an overview, see Fig. 1, top). These days, many laboratories are equipped with flow cytometers capable of detecting 10-20 parameters [6], revealing an ever-increasing diversity within established cellular subsets, such as CD4 + T-helper cells or professional APCs. Quite recently, an alternative cytometry-based technique has been developed that relies on antibodies labeled with heavyCorrespondence: Prof. Burkhard Becher e-mail: becher@immunology.uzh.ch metal isotopes instead of fluorophores, detecting the resulting signals using a time-of-flight detector as is done in atomic mass spectrometers [7][8][9]. This method has been termed "mass cytometry" and became commercially known as the "CyTOF" (cytometry by time-of-flight), allowing the theoretical detection of more than 100 parameters per cell.While both fluorescence-based flow cytometry as well as mass cytometry provide a technological platform to interrogate the immune system at a previously unprecedented level (for a comparison of the two techniques see [10]), scientific progress depends on our ability to analyze and comprehend the resulting data in a meaningful way. Historically, flow cytometric data were-and still is-analyzed using a se...

show abstract

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

Cited by 889 publications

References 22 publications

Competitive SWIFT cluster templates enhance detection of aging changes

Competitive SWIFT cluster templates enhance detection of aging changes

Probability state modeling theory

The end of gating? An introduction to automated analysis of high dimensional cytometry data

Contact Info

Product

Resources

About