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. 2009 Apr 9:10:106.
doi: 10.1186/1471-2105-10-106.

flowCore: a Bioconductor package for high throughput flow cytometry

Florian Hahne  1 , Nolwenn LeMeur, Ryan R Brinkman, Byron Ellis, Perry Haaland, Deepayan Sarkar, Josef Spidlen, Errol Strain, Robert Gentleman
Affiliations

flowCore: a Bioconductor package for high throughput flow cytometry

Florian Hahne et al. BMC Bioinformatics. .

Abstract

Background: Recent advances in automation technologies have enabled the use of flow cytometry for high throughput screening, generating large complex data sets often in clinical trials or drug discovery settings. However, data management and data analysis methods have not advanced sufficiently far from the initial small-scale studies to support modeling in the presence of multiple covariates.

Results: We developed a set of flexible open source computational tools in the R package flowCore to facilitate the analysis of these complex data. A key component of which is having suitable data structures that support the application of similar operations to a collection of samples or a clinical cohort. In addition, our software constitutes a shared and extensible research platform that enables collaboration between bioinformaticians, computer scientists, statisticians, biologists and clinicians. This platform will foster the development of novel analytic methods for flow cytometry.

Conclusion: The software has been applied in the analysis of various data sets and its data structures have proven to be highly efficient in capturing and organizing the analytic work flow. Finally, a number of additional Bioconductor packages successfully build on the infrastructure provided by flowCore, open new avenues for flow data analysis.

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Figures

Figure 1
Figure 1
flowCore framework. For each experiment, the content of the FCS files, phenotypic and metadata are stored in a flowSet. Each flowFrame in a flowSet corresponds to one FCS file. All basic operations (e.g., compensation, transformation, gating) can be applied to either single flowFrames or a flowSet simultaneously.
Figure 2
Figure 2
Quality assessment. HTML quality assessment report generated by the flowQ package for a subset of data from an experiment focusing on Graft-Versus-Host Disease [1]. Rows correspond to the samples in the set, columns to different quality checks.
Figure 3
Figure 3
Batch gating. Scatterplot matrix of a single flowSet from an experiment focusing on immune tolerance following kidney transplantation. Outlines of the gating regions identified by a curve2Filter automated gating operation are added on top of the density representation of the data.
See this image and copyright information in PMC

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