Matching phosphorylation response patterns of antigen-receptor-stimulated T cells via flow cytometry

BMC Bioinformatics

Proceedings

Ariful Azad¹  Alex Pothen¹  Saumyadipta Pyne² 

[1] Department of Computer Science, Purdue University, 47906, West Lafayette, IN, USA;Department of Medical Oncology, Dana-Farber Cancer Institute, Harvard Medical School, 02115, Boston, MA, USA;Broad Institute of MIT and Harvard University, 02142, Cambridge, MA, USA;

关键词: Bipartite Graph;    Flow Cytometric Data;    Dirichlet Process Mixture;    Class Template;    Cross Class;   

DOI  :  10.1186/1471-2105-13-S2-S10

来源: Springer

【 摘 要 】

BackgroundWhen flow cytometric data on mixtures of cell populations are collected from samples under different experimental conditions, computational methods are needed (a) to classify the samples into similar groups, and (b) to characterize the changes within the corresponding populations due to the different conditions. Manual inspection has been used in the past to study such changes, but high-dimensional experiments necessitate developing new computational approaches to this problem. A robust solution to this problem is to construct distinct templates to summarize all samples from a class, and then to compare these templates to study the changes across classes or conditions.ResultsWe designed a hierarchical algorithm, flowMatch, to first match the corresponding clusters across samples for producing robust meta-clusters, and to then construct a high-dimensional template as a collection of meta-clusters for each class of samples. We applied the algorithm on flow cytometry data obtained from human blood cells before and after stimulation with anti-CD3 monoclonal antibody, which is reported to change phosphorylation responses of memory and naive T cells. The flowMatch algorithm is able to construct representative templates from the samples before and after stimulation, and to match corresponding meta-clusters across templates. The templates of the pre-stimulation and post-stimulation data corresponding to memory and naive T cell populations clearly show, at the level of the meta-clusters, the overall phosphorylation shift due to the stimulation.ConclusionsWe concisely represent each class of samples by a template consisting of a collection of meta-clusters (representative abstract populations). Using flowMatch, the meta-clusters across samples can be matched to assess overall differences among the samples of various phenotypes or time-points.

【 授权许可】

Unknown   
© Azad et al.; licensee BioMed Central Ltd. 2012. This article is published under license to BioMed Central Ltd. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.