The curvHDR method for gating flow cytometry samples.
BACKGROUND
High-throughput flow cytometry experiments produce hundreds of large multivariate samples of cellular characteristics. These samples require specialized processing to obtain clinically meaningful measurements. A major component of this processing is a form of cell subsetting known as gating. Manual gating is time-consuming and subjective. Good automatic and semi-automatic gating algorithms are very beneficial to high-throughput flow cytometry.


RESULTS
We develop a statistical procedure, named curvHDR, for automatic and semi-automatic gating. The method combines the notions of significant high negative curvature regions and highest density regions and has the ability to adapt well to human-perceived gates. The underlying principles apply to dimension of arbitrary size, although we focus on dimensions up to three. Accompanying software, compatible with contemporary flow cytometry infor-matics, is developed.


CONCLUSIONS
The method is seen to adapt well to nuances in the data and, to a reasonable extent, match human perception of useful gates. It offers big savings in human labour when processing high-throughput flow cytometry data whilst retaining a good degree of efficacy.
Naumann
Ulrike
U
School of Mathematics and Applied Statistics, The University of New South Wales, Sydney, Australia.
Luta
George
G
Wand
Matthew P
MP
D016208:Databases, Factual; D005434:Flow Cytometry / Q000379:methods*; D015203:Reproducibility of Results; D012984:Software