GenePattern flow cytometry suite

Špidlen, Josef; Barsky, Aaron; Breuer, Karin; Carr, Peter A.; Nazaire, Marc‐Danie; Hill, Barbara; Yu, Qian; Liefeld, Ted; Reich, Michael; Wilkinson, Peter; Scheuermann, Richard H.; Sékaly, Rafick‐Pierre; Brinkman, Ryan R.

doi:10.1186/1751-0473-8-14

Cited by 18 publications

(15 citation statements)

References 37 publications

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“…These data are typically parsed into single cell events and converted to a flow cytometry standard (FCS) file for analysis [35]. Many software programs can handle FCS files, including Cytobank (www.cytobank.org), FlowJo (www.FlowJo.com), R/Bioconductor, MATLAB, Cytoscape, and GenePattern (http://genepattern.broadinstitute.org/) [36]. Text files containing the expression matrix (where rows are cells and features are columns, and there is a median intensity value for each cell) can also be extracted directly from the IMD file from the cytometer or from the FCS file for manual analysis outside of flow cytometry analysis software.…”

Section: Data Collection Processing and Initial Population Identmentioning

confidence: 99%

Methods for discovery and characterization of cell subsets in high dimensional mass cytometry data

Diggins

Ferrell

Irish

2015

Methods

125

149

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The flood of high-dimensional data resulting from mass cytometry experiments that measure more than 40 features of individual cells has stimulated creation of new single cell computational biology tools. These tools draw on advances in the field of machine learning to capture multi-parametric relationships and reveal cells that are easily overlooked in traditional analysis. Here, we introduce a workflow for high dimensional mass cytometry data that emphasizes unsupervised approaches and visualizes data in both single cell and population level views. This workflow includes three central components that are common across mass cytometry analysis approaches: 1) distinguishing initial populations, 2) revealing cell subsets, and 3) characterizing subset features. In the implementation described here, viSNE, SPADE, and heatmaps were used sequentially to comprehensively characterize and compare healthy and malignant human tissue samples. The use of multiple methods helps provide a comprehensive view of results, and the largely unsupervised workflow facilitates automation and helps researchers avoid missing cell populations with unusual or unexpected phenotypes. Together, these methods develop a framework for future machine learning of cell identity.

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Section: Data Collection Processing and Initial Population Identmentioning

confidence: 99%

Methods for discovery and characterization of cell subsets in high dimensional mass cytometry data

Diggins

Ferrell

Irish

2015

Methods

125

149

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“…A free open source Gating-ML 2.0 reader is available in MATLAB from MATLAB CENTRAL [10]. GenePattern module ApplyGatingML2 [11] can also read Gating-ML 2.0 files and apply these on FCS data in order to extract all cell populations defined in the Gating-ML file. While FlowJo's Gating-ML 2.0 support is not well documented, users can drag and drop a Gating-ML 2.0 file onto their samples, and FlowJo X will extract and apply the gates.…”

Section: Resultsmentioning

confidence: 99%

ISAC's Gating‐ML 2.0 data exchange standard for gating description

et al. 2015

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The lack of software interoperability with respect to gating has traditionally been a bottleneck preventing the use of multiple analytical tools and reproducibility of flow cytometry data analysis by independent parties. To address this issue, ISAC developed Gating-ML, a computer file format to encode and interchange gates. Gating-ML 1.5 was adopted and published as an ISAC Candidate Recommendation in 2008. Feedback during the probationary period from implementors, including major commercial software companies, instrument vendors and the wider community, has led to a streamlined Gating-ML 2.0. Gating-ML has been significantly simplified and therefore easier to support by software tools. To aid developers, free, open source reference implementations, compliance tests and detailed examples are provided to stimulate further commercial adoption. ISAC has approved Gating-ML as a standard ready for deployment in the public domain and encourages its support within the community as it is at a mature stage of development having undergone extensive review and testing, under both theoretical and practical conditions.

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“…We adapted flowClean for use on GenePattern (an online suite of data analysis tools, Broad Institute) (8,9), so that individuals with no programming experience or with no access to computing clusters could use the tool. GenePattern also allows flowClean to be used within data analysis pipelines that incorporate other tools (such as clustering algorithms).…”

Section: Flowclean Performance On Dataset From Biological Studymentioning

confidence: 99%

“…This algorithm is available under the Bioconductor package flowClean, within GenePattern under the FCSClean module, and as a plugin for FlowJo X. Our method operates on one flow file at a time, and is hence sample‐specific; it can be applied prior to any data compensation or gating schemes, and thereby serves as the first step in any analysis pipeline.…”

mentioning

confidence: 99%

flowClean: Automated identification and removal of fluorescence anomalies in flow cytometry data

et al. 2016

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Modern flow cytometry systems can be coupled to plate readers for high-throughput acquisition. These systems allow hundreds of samples to be analyzed in a single day. Quality control of the data remains challenging, however, and is further complicated when a large number of parameters is measured in an experiment. Our examination of 29.228 publicly available FCS files from laboratories worldwide indicates 13.7% have a fluorescence anomaly. In particular, fluorescence measurements for a sample over the collection time may not remain stable due to fluctuations in fluid dynamics; the impact of instabilities may differ between samples and among parameters. Therefore, we hypothesized that tracking cell populations (which represent a summary of all parameters) in centered log ratio (CLR) space would provide a sensitive and consistent method of quality control. Here, we present flowClean, an algorithm to track subset frequency changes within a sample during acquisition, and flag time periods with fluorescence perturbations leading to the emergence of false populations. Aberrant time periods are reported as a new parameter and added to a revised data file, allowing users to easily review and exclude those events from further analysis. We apply this method to proof-of-concept datasets and also to a subset of data from a recent vaccine trial. The algorithm flags events that are suspicious by visual inspection, as well as those showing more subtle effects that might not be consistently flagged by investigators reviewing the data manually, and out-performs the current state-of-the-art. flowClean is available as an R package on Bioconductor, as a module on the free-to-use GenePattern web server, and as a plugin for FlowJo X.

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GenePattern flow cytometry suite

Cited by 18 publications

References 37 publications

Methods for discovery and characterization of cell subsets in high dimensional mass cytometry data

Methods for discovery and characterization of cell subsets in high dimensional mass cytometry data

ISAC's Gating‐ML 2.0 data exchange standard for gating description

flowClean: Automated identification and removal of fluorescence anomalies in flow cytometry data

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