title: Improved compensation in flow cytometry by multivariable optimization.
abstract: Conventional compensation of flow cytometry (FMC) data of an N-stained sample requires additional data sets, of N single-stained control samples, to estimate the spillover coefficients. Single-stained controls however are the least rigorous controls because any of the multi-stained controls are closer to the N-stained sample. In this article, a new, optimization based, compensation method has been developed that is able to use not only single- but also multi-stained controls to improve estimates of the spillover coefficients. The method is demonstrated on a data set from five-stained dentritic cells (DCs) with five single-stained and eight multi-stained controls. This approach is practical and leads to significant improvements in FCM compensation.
authors.affiliation: Department of Neurology and Center for Translational Systems Biology, Mount Sinai School of Medicine, New York, New York 10029, USA. istvan.sugar@mssm.edu
authors.forename: István P
authors.initials: IP
authors.lastname: Sugár
authors.forename: Joanna
authors.initials: J
authors.lastname: González-Lergier
authors.forename: Stuart C
authors.initials: SC
authors.lastname: Sealfon
mesh_terms: D000465:Algorithms; D003713:Dendritic Cells / Q000166:cytology; D005434:Flow Cytometry / Q000379:methods* / Q000639:trends; D015999:Multivariate Analysis; D013194:Staining and Labeling