Classifying Antibodies Using Flow Cytometry Data: Class Prediction and Class Discovery

Abstract

Classifying monoclonal antibodies, based on the similarity of their binding to the proteins (antigens) on the surface of blood cells, is essential for progress in immunology, hematology and clinical medicine. The collaborative efforts of researchers from many countries have led to the classification of thousands of antibodies into 247 clusters of differentiation (CD). Classification is based on flow cytometry and biochemical data. In preliminary classifications of antibodies based on flow cytometry data, the object requiring classification (an antibody) is described by a set of random samples from unknown densities of fluorescence intensity. An individual sample is collected in the experiment, where a population of cells of a certain type is stained by the identical fluorescently marked replicates of the antibody of interest. Samples are collected for multiple cell types. The classification problems of interest include identifying new CDs (class discovery or unsupervised learning) and assigning new antibodies to the known CD clusters (class prediction or supervised learning). These problems have attracted limited attention from statisticians. We recommend a novel approach to the classification process in which a computer algorithm suggests to the analyst the subset of the "most appropriate" classifications of an antibody in class prediction problems or the "most similar" pairs/ groups of antibodies in class discovery problems. The suggested algorithm speeds up the analysis of a flow cytometry data by a factor 10-20. This allows the analyst to focus on the interpretation of the automatically suggested preliminary classification solutions and on planning the subsequent biochemical experiments.

CD molecules 2005: human cell differentiation molecules

“…[3][4][5][6] Although a number of pairs and groups of antibodies were found to have very similar reactivity with the panel of cells, we were not able to assign any new CD molecules on the basis of these studies. This was due in part to incomplete biochemical studies, and these are continuing after the completion of the HLDA8 meeting.…”

A framework for analytical characterization of monoclonal antibodies based on reactivity profiles in different tissues

“…Previous mAb classification studies (e.g. Pratt et al, 2009;Salganik et al, 2005) have used non-parametric kernel density estimation techniques for detection of significant features in cytometric histograms, typically followed by hierarchical clustering based on Euclidean distances between the features. While being practical, such approaches may not always be precise or robust.…”

“…In general, analytical characterization of mAb reactivity patterns has received limited attention in statistics and computer science (Gilks and Shaw, 1995;Kim et al, 2002;Pratt et al, 2009;Salganik et al, 2005;Spiegelhalter and Gilks, 1987;Zeng et al, 2002, 2007 andreferences therein). As shown in Pratt et al (2009), mAb classification faces technical challenges at multiple levels.…”

Hierarchical clustering of monoclonal antibody reactivity patterns in nonhuman species

“…Because features are identified relative to an absolute calibration standard, pairwise comparisons used in many approaches (32)(33) are unnecessary. As a result, the database is cumulative; that is, new antibody data can be added to the database without the necessity of testing the entire antibody “panel.” A cumulative database is a particular advantage in nonhuman species because international antibody workshops have become increasingly impractical.…”