flowCore: a Bioconductor package for high throughput flow cytometry.
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.


CONCLUSIONS
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.
Life Sciences Department, Computational Biology Program, Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, Washington 98109-1024, USA. fhahne@fhcrc.org
Florian
F
Hahne
Nolwenn
N
LeMeur
Ryan R
RR
Brinkman
Byron
B
Ellis
Perry
P
Haaland
Deepayan
D
Sarkar
Josef
J
Spidlen
Errol
E
Strain
Robert
R
Gentleman
D019295:Computational Biology / Q000379:methods*; D003628:Database Management Systems; D055808:Drug Discovery; D005434:Flow Cytometry; D016247:Information Storage and Retrieval; D012984:Software; D014584:User-Computer Interface