title: Molecular identification using flow cytometry histograms and information theory.
abstract: 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.
authors.affiliation: Decision System Group, Brigham & Women's Hospital, Harvard Medical School, Boston, MA, USA.
authors.forename: Q
authors.initials: Q
authors.lastname: Zeng
authors.forename: A J
authors.initials: AJ
authors.lastname: Young
authors.forename: A
authors.initials: A
authors.lastname: Boxwala
authors.forename: J
authors.initials: J
authors.lastname: Rawn
authors.forename: W
authors.initials: W
authors.lastname: Long
authors.forename: M
authors.initials: M
authors.lastname: Wand
authors.forename: M
authors.initials: M
authors.lastname: Salganik
authors.forename: E L
authors.initials: EL
authors.lastname: Milford
authors.forename: S J
authors.initials: SJ
authors.lastname: Mentzer
authors.forename: R A
authors.initials: RA
authors.lastname: Greenes
mesh_terms: 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