Open Access Highly Accessed Software

flowCore: a Bioconductor package for high throughput flow cytometry

Florian Hahne1*, Nolwenn LeMeur1,2*, Ryan R Brinkman3, Byron Ellis4, Perry Haaland5, Deepayan Sarkar1, Josef Spidlen3, Errol Strain5 and Robert Gentleman1

Author Affiliations

1 Life Sciences Department, Computational Biology Program, Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, 1100 Fairview Ave N, M2-B876, PO Box 19024, Seattle, Washington 98109-1024, USA

2 EA SeRAIC INSERM, IRISA – Symbiose, Campus Beaulieu, Université de Rennes I, 35042 Rennes Cedex, France

3 Terry Fox Laboratory, British Columbia Cancer Agency Research Centre, 675 West 10th Avenue, Vancouver, BC V5Z 1L3, Canada

4 AdBrite Inc, 731 Market St, 5th Floor, San Francisco, California 94103, USA

5 BD Biosciences, Research Triangle Park, North Carolina 27709, USA

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BMC Bioinformatics 2009, 10:106 doi:10.1186/1471-2105-10-106

Published: 9 April 2009

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.