MOTIVATION: Monoclonal antibodies (mAbs) are among the most powerful and important tools in biology and medicine. MAb development is of great significance to many research and clinical applications. Therefore, objective mAb classification is essential for categorizing and comparing mAb panels based on their reactivity patterns in different cellular species. However, typical flow cytometric mAb profiles present unique modeling challenges with their non-Gaussian features and intersample variations. It makes accurate mAb classification difficult to do with the currently used kernel-based or hierarchical clustering techniques. RESULTS: To address these challenges, in the present study we developed a formal two-step framework called mAbprofiler for systematic, parametric characterization of mAb profiles. Further, we measured the reactivity of hundreds of new antibodies in diverse tissues using flow cytometry, which we successfully classified using mAbprofiler. First, mAbprofiler fits a mAb's flow cytometric histogram with a finite mixture model of skew t distributions that is robust against non-Gaussian features, and constructs a precise, smooth and mathematically rigorous profile. Then it performs novel curve clustering of the fitted mAb profiles using a skew t mixture of non-linear regression model that can handle intersample variation. Thus, mAbprofiler provides a new framework for identifying robust mAb classes, all well defined by distinct parametric templates, which can be used for classifying new mAb samples. We validated our classification results both computationally and empirically using mAb profiles of known classification. AVAILABILITY AND IMPLEMENTATION: A demonstration code in R is available at the journal website. The R code implementing the full framework is available from the author website - http://amath.nchu.edu.tw/www/teacher/tilin/software CONTACT: [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Flow cytometry is a standard platform for studying intracellular and extracellular protein expression of different cell populations in a tissue sample. Using specific antibody profiles, surface protein expression may be found as different for certain cell populations in samples that belong to different classes such as disease and normal or to cohorts with different genotypes. Analysis of such statistically significant differential expression can yield important biomarkers. Here we describe a computational tool DVisE to identify and localize precisely the cell subpopulations with statistically significant differential expression across different cohorts and classes. We analyzed HLA-DQ surface expression in Lymphoblastic cell lines using 266 out of 270 samples from the HapMap project. The cohorts were subdivided into 3 genotypic classes according to an allelic variant within upstream of the HLA-DQ gene. With the help of the present tool we were able to identify a significantly distinctive cytomic signature that is well preserved among genotypes in all the populations. Because of its novel ability to locate distinct areas where immune cells differentially express proteins, DVisE can play a very useful role in our study of the immune system. Indeed the tool could be extended to multiple different applications in bioinformatics and pattern recognition such as data visualization and discriminant analysis.