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Automation technologies developed during the last several years have enabled the use of flow cytometry (FCM) to generate large, complex data sets in both basic and clinical research applications (Brinkman et al., 2007). A serious bottleneck in the interpretation of existing studies and the application of high throughput FCM to even larger, more complex problems is that data management and data analysis methods have not advanced sufficiently far from the methods developed for applications of FCM to small-scale, tube-based studies . In particular, the data often need to be organized into groups of samples based on combinations of additional covariates and similar operations need to be applied to these groups in a transparent and reproducible manner. Furthermore, the growing depth of knowledge in the field of immunology, for instance the characterization of distinct human T-cell sub-population , clearly argues for more systematic approaches.
(Hahne et al., 2009) flowCore: a Bioconductor package for high throughput flow cytometry. BMC Bioinformatics.
(Brinkman et al., 2007) High-Content Flow Cytometry and Temporal Data Analysis for Defining a Cellular Signature of Graft-Versus-Host Disease. Biology of Blood and Marrow Transplantation.
(Mahnke et al., 2007) Optimizing a Multicolor Immunophenotyping Assay. Clinics in Laboratory Medicine.