Matching of flow-cytometry histograms using information theory in feature space.
Flow cytometry is a widely available technique for analyzing cell-surface protein expression. Data obtained from flow cytometry is frequently used to produce fluorescence intensity histograms. Comparison of histograms can be useful in the identification of unknown molecules and in the analysis of protein expression. In this study, we examined the combination of a new smoothing technique called SiZer with information theory to measure the difference between cytometry histograms. SiZer provides cross-bandwidth smoothing and allowed analysis in feature space. The new methods were tested on a panel of monoclonal antibodies raised against proteins expressed on peripheral blood lymphocytes and compared with previous methods. The findings suggest that comparing information content of histograms in feature space is effective and efficient for identifying antibodies with similar cell-surface binding patterns.
Zeng
Qing
Q
Decision System Group, Brigham & Women's Hospital, Harvard Medical School, Boston, MA, USA.
Wand
Matthew
M
Young
Alan J
AJ
Rawn
James
J
Milford
Edgar L
EL
Mentzer
Steven J
SJ
Greenes
Robert A
RA
D000818:Animals; D000911:Antibodies, Monoclonal / Q000378:metabolism*; D000954:Antigens, Surface / Q000276:immunology / Q000378:metabolism; D019540:Area Under Curve; D005434:Flow Cytometry; D005453:Fluorescence; D006825:Hybridomas / Q000276:immunology; D007257:Information Theory; D008214:Lymphocytes / Q000276:immunology / Q000378:metabolism; D051379:Mice; D012372:ROC Curve; D016008:Statistical Distributions
D000911:Antibodies, Monoclonal; D000954:Antigens, Surface