![]() R/Bioconductor core infrastructure for representing cell populations and parent/child relationships among them Read/Write, process (transform, compensate) of flow data. R/Bioconductor package that removes mean variance correlations from cell populations R/Bioconductor software to adjust data to account for batch effects like laser drift ![]() This allows users to substitute new approaches to the same challenge as the field advances, an advantage over monolithic tools that attempt to solve a single or even multiple problems in isolation. Algorithms for data analysis are provided as packages that generally address a single step in the analysis pipelines, with interoperability enforced through Bioconductor. Many of the approaches have been released through the Bioconductor repository which enforces strict requirements on cross-platform compatibility and functional documentation. For example, the flowWorkspace package can export automated gating results in a format readable by FlowJo (FlowJo Inc., Ashland OR). However, these tools can be integrated into commercial tools familiar to users, facilitating adoption. These tools have been developed for high-throughput workflows, and are not generally amenable to graphical user interface manual interaction with individual files during the analysis process. ![]() The overwhelming majority have been developed and released as freely available, open-source tools using the R programming language. ![]() More than 50 approaches to automate flow cytometry (FCM) data analysis are available ( Table 1). ![]()
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