flowCore: a Bioconductor package for high throughput flow cytometry

By F. Hahne, N. LeMeur, Ryan Remy Brinkman, B. Ellis, P. Haaland, Deepayan Sarkar, Josef Spidlen, E. Strain, Robert Gentleman. BMC Bioinformatics, 2009

570 citations recorded by the source corpus.

BackgroundRecent 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.ResultsWe 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.ConclusionThe 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.

Subfields: Biomedical Engineering, Biophysics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/fd89d0969ed40a37e3de5dfae5c2a4c19170627b

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Citations in the corpus

Cytobank: providing an analytics platform for community cytometry data analysis and collaboration. (2014, methodology): “In the future, Cytobank will also enable the export of data and associated annotations from Cytobank into a user’s R/ﬂowCore.”
Web-Based Analysis and Publication of Flow Cytometry Experiments (2010, background): “Providing Cytobank as a platform to build new analyses or integrate with existing statistical frameworks (Frelinger et al., 2008; Hahne et al., 2009) can help in lowering this barrier by enabling biologists and clinicians to do a ﬁrst round of processing, and share with statisticians a data set…”
Computational flow cytometry: helping to make sense of high-dimensional immunology data (2016, methodology): “When using a Bioconductor pipeline, the flowCore package 33 can be used for these first steps.”