flowCore: a Bioconductor package for high throughput flow cytometry

TLDR

Recent advances in automation technologies have enabled high‑throughput flow cytometry, generating large complex datasets in clinical trials or drug discovery, but data management and analysis methods have not kept pace with the need to model multiple covariates. We developed the open‑source R package flowCore to provide flexible computational tools that facilitate analysis of complex flow cytometry data and foster the development of novel analytic methods. flowCore implements suitable data structures that allow applying consistent operations to collections of samples or clinical cohorts, and serves as a shared, extensible research platform that supports collaboration among bioinformaticians, computer scientists, statisticians, biologists, and clinicians. The software has been applied to various datasets, demonstrating efficient data structures for capturing and organizing analytic workflows, and several additional Bioconductor packages have built on flowCore’s infrastructure to open new avenues for flow data analysis.

Abstract

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. 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. 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.