Thinking Outside the Gate: Single-Cell Assessments in Multiple Dimensions

Kvistborg, Pia; Gouttefangeas, Cécile; Aghaeepour, Nima; Cazaly, Angelica; Chattopadhyay, Pratip K.; Chan, Cliburn; Eckl, Judith; Finak, Greg; Hadrup, Sine Reker; Maecker, Holden T.; Maurer, Dominik; Mosmann, Tim R.; Qiu, Peng; Scheuermann, Richard H.; Welters, Marij J.P.; Ferrari, Guido; Brinkman, Ryan R.; Britten, Cedrik M.

doi:10.1016/j.immuni.2015.04.006

Cited by 61 publications

(70 citation statements)

References 8 publications

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“…Also boundary effects (data points at the margin of the data range) should be removed, and possibly a pre-gating step on a specific population of interest for further ana lysis can be performed (for example, using the flowQ 34 and flowStats 36 packages). Several of these Bioconductor tools are also available through the graphical interface of the GenePattern flow cytometry suite, and a more extensive list of pre-processing software can be found in Kvistborg et al 39 .…”

Section: Algorithmic Benchmarking and Software Availabilitymentioning

confidence: 99%

Computational flow cytometry: helping to make sense of high-dimensional immunology data

2016

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Recent advances in flow cytometry allow scientists to measure an increasing number of parameters per cell, generating huge and high-dimensional datasets. To analyse, visualize and interpret these data, newly available computational techniques should be adopted, evaluated and improved upon by the immunological community. Computational flow cytometry is emerging as an important new field at the intersection of immunology and computational biology; it allows new biological knowledge to be extracted from high-throughput single-cell data. This Review provides non-experts with a broad and practical overview of the many recent developments in computational flow cytometry.

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Section: Algorithmic Benchmarking and Software Availabilitymentioning

confidence: 99%

Computational flow cytometry: helping to make sense of high-dimensional immunology data

2016

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“…The first reports on automated cytometry data analysis date back as early as 2007 [12][13][14], and opened up the field for future developments (Fig. 1 [15,[18][19][20][21][22]). Nevertheless, few or none of these methods have reached the wider immunological community yet, probably due to the hesitation in adopting new technologies when existing approaches seemingly do the job adequately, a perceived difficulty in mastering the new methods, as well as the fact that the publication of many of these computational tools has been limited to bioinformatics journals.…”

Section: Setting the Stage For Alternativesmentioning

confidence: 99%

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

et al. 2015

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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...

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“…Complex FCM analysis code already arouses scepticism among the less programmatically inclined in the community, for whom such analyses may be difficult to verify (5). When details of algorithmic execution are provided only in textual format, reproducing the work often requires "forensic bioinformatics" (6).…”

Section: Consequences Of Irreproducibilitymentioning

confidence: 99%

“…A good idea to facilitate this is to ensure that the analysis uses free, open-source tools, such as the suite of packages for FCM analysis in Bioconductor that contains the vast majority of the software available for this purpose (5). Where analyses would take too long to run in a reasonable time to be part of that code, provide intermediate results of those high-throughput computations.…”

Section: Reproducible Flow Cytometry Bioinformatics: a Short Guidementioning

confidence: 99%

Publishing code is essential for reproducible flow cytometry bioinformatics

O’Neill

Brinkman

2016

Cytometry Pt A

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Thinking Outside the Gate: Single-Cell Assessments in Multiple Dimensions

Cited by 61 publications

References 8 publications

Computational flow cytometry: helping to make sense of high-dimensional immunology data

Computational flow cytometry: helping to make sense of high-dimensional immunology data

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

Publishing code is essential for reproducible flow cytometry bioinformatics

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