title: A cluster pattern algorithm for the analysis of multiparametric cell assays.
abstract: The issue of multiparametric analysis of complex single cell assays of both static and flow cytometry (SC and FC, respectively) has become common in recent years. In such assays, the analysis of changes, applying common statistical parameters and tests, often fails to detect significant differences between the investigated samples. The cluster pattern similarity (CPS) measure between two sets of gated clusters is based on computing the difference between their density distribution functions' set points. The CPS was applied for the discrimination between two observations in a four-dimensional parameter space. The similarity coefficient (r) ranges between 0 (perfect similarity) to 1 (dissimilar). Three CPS validation tests were carried out: on the same stock samples of fluorescent beads, yielding very low r's (0, 0.066); and on two cell models: mitogenic stimulation of peripheral blood mononuclear cells (PBMC), and apoptosis induction in Jurkat T cell line by H2O2. In both latter cases, r indicated similarity (r < 0.23) within the same group, and dissimilarity (r > 0.48) otherwise. This classification and algorithm approach offers a measure of similarity between samples. It relies on the multidimensional pattern of the sample parameters. The algorithm compensates for environmental drifts in this apparatus and assay; it also may be applied to more than four dimensions.
authors.affiliation: The Biophysical Interdisciplinary Schottenstein Center for the Research and Technology of the Cellome, Physics Department, Bar-Ilan University, Ramat-Gan, Israel.
authors.forename: Menachem
authors.initials: M
authors.lastname: Kaufman
authors.forename: David
authors.initials: D
authors.lastname: Bloch
authors.forename: Naomi
authors.initials: N
authors.lastname: Zurgil
authors.forename: Yana
authors.initials: Y
authors.lastname: Shafran
authors.forename: Mordechai
authors.initials: M
authors.lastname: Deutsch
mesh_terms: D000465:Algorithms; D016000:Cluster Analysis; D019295:Computational Biology / Q000379:methods* / Q000706:statistics & numerical data*; D005434:Flow Cytometry / Q000706:statistics & numerical data; D005454:Fluorescence Polarization / Q000706:statistics & numerical data; D006801:Humans; D019169:Jurkat Cells; D008213:Lymphocyte Activation; D013601:T-Lymphocytes / Q000276:immunology