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flowCore: a Bioconductor package for high throughput flow cytometry.

Hahne F, LeMeur N, Brinkman RR, Ellis B, Haaland P, Sarkar D, Spidlen J, Strain E, Gentleman R - BMC Bioinformatics (2009)

Bottom Line: In addition, our software constitutes a shared and extensible research platform that enables collaboration between bioinformaticians, computer scientists, statisticians, biologists and clinicians.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.

View Article: PubMed Central - HTML - PubMed

Affiliation: Life Sciences Department, Computational Biology Program, Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, Washington 98109-1024, USA. fhahne@fhcrc.org

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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Related in: MedlinePlus

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.
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Related In: Results  -  Collection

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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.

Mentions: Finally, we can chose one of the many visualization options from the flowViz package to plot the results of the recent filtering operation. A very basic matrix of density plots is shown in Figure 3, where each panel in the matrix represents the fluorescent measurements of two channels for one individual patient.


flowCore: a Bioconductor package for high throughput flow cytometry.

Hahne F, LeMeur N, Brinkman RR, Ellis B, Haaland P, Sarkar D, Spidlen J, Strain E, Gentleman R - BMC Bioinformatics (2009)

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.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2684747&req=5

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.
Mentions: Finally, we can chose one of the many visualization options from the flowViz package to plot the results of the recent filtering operation. A very basic matrix of density plots is shown in Figure 3, where each panel in the matrix represents the fluorescent measurements of two channels for one individual patient.

Bottom Line: In addition, our software constitutes a shared and extensible research platform that enables collaboration between bioinformaticians, computer scientists, statisticians, biologists and clinicians.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.

View Article: PubMed Central - HTML - PubMed

Affiliation: Life Sciences Department, Computational Biology Program, Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, Washington 98109-1024, USA. fhahne@fhcrc.org

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.

Show MeSH
Related in: MedlinePlus