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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

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

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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.

Mentions: Data analysis for most experiments usually begins with a quality assurance step. In a FCS analysis work flow, we can use functionality from the flowQ package, build upon flowCore, to create an HTML report that highlights potential quality issues. Assuming that the data has already been imported as the flowSet object "dat", using for instance the read. flowSet method, the following simple lines of code produce the output shown in Figure 2:


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)

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

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

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.
Mentions: Data analysis for most experiments usually begins with a quality assurance step. In a FCS analysis work flow, we can use functionality from the flowQ package, build upon flowCore, to create an HTML report that highlights potential quality issues. Assuming that the data has already been imported as the flowSet object "dat", using for instance the read. flowSet method, the following simple lines of code produce the output shown in Figure 2:

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