![]() ![]() R/Bioconductor infrastructure to optimize parameter choice for different transformations R/Bioconductor package to support Gating-ML specification to exchange gate coordinates between softwareĮstimate parameters for data transformation Import gates, transformation and compensation R/Biconductor software that collects several algorithms together for normalization and gating R/Bioconductor package that provides preprocessing, automated gating, and meta-analysis of cytometry dataĪutomatically calculates detector efficiency (Q), optical background (B), and instrinsic CV of the beadsĪdvanced statistical methods and functions, specialized and general gating algorithms R/Bioconductor package that includes normalization, single-cell deconvolution and compensation for Mass cytometry data Pipeline for preprocessing of mass cytometry data R/Bioconductor package that combines flow cytometry data multiplexed into tubes by common markers R/Bioconductor package that provides gating and normalization specific to bead dataĬombining multitube flow cytometry data by binning 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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