Molecular identification using flow cytometry histograms and information theory.
Flow cytometry is a common technique for quantitatively measuring the expression of individual molecules on cells. The molecular expression is represented by a frequency histogram of fluorescence intensity. For flow cytometry to be used as a knowledge discovery tool to identify unknown molecules, histogram comparison is a major limitation. Many traditional comparison methods do not provide adequate assessment of histogram similarity and molecular relatedness. We have explored a new approach   applying information theory to histogram comparison, and tested it with histograms from 14 antibodies over 3 cell types. The information theory approach was able to improve over traditional methods by recognizing various non-random correlations between histograms in addition to similarity and providing a quantitative assessment of similarity beyond hypothesis testing of identity.
Zeng
Q
Q
Decision System Group, Brigham & Women's Hospital, Harvard Medical School, Boston, MA, USA.
Young
A J
AJ
Boxwala
A
A
Rawn
J
J
Long
W
W
Wand
M
M
Salganik
M
M
Milford
E L
EL
Mentzer
S J
SJ
Greenes
R A
RA
D000818:Animals; D005434:Flow Cytometry / Q000379:methods*; D006825:Hybridomas / Q000276:immunology; D007257:Information Theory; D051379:Mice; D008967:Molecular Biology; D016011:Normal Distribution; D016008:Statistical Distributions