Statistical classification of multivariate flow cytometry data analyzed by manual gating: stem, progenitor, and epithelial marker expression in nonsmall cell lung cancer and normal lung.

ABSTRACT: The use of supervised classification to extract markers from primary flow cytometry data is an emerging field that has made significant progress, spurred by the growing complexity of multidimensional flow cytometry. Whether the markers are extracted without supervision or by conventional gate and region methods, the number of candidate variables identified is typically larger than the number of specimens (p > n) and many variables are highly intercorrelated. Thus, comparison across groups or treatments to determine which markers are significant is challenging. Here, we utilized a data set in which 86 variables were created by conventional manual analysis of individual listmode data files, and compared the application of five multivariate classification methods to discern subtle differences

SUBMITTER: Normolle DP 

PROVIDER: S-EPMC4149906 | biostudies-literature | 2013 Jan

REPOSITORIES: biostudies-literature