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This article is part of the supplement: Proceedings from the Great Lakes Bioinformatics Conference 2011

Open Access Proceedings

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

Ariful Azad1, Saumyadipta Pyne23* and Alex Pothen1*

Author affiliations

1 Department of Computer Science, Purdue University, West Lafayette, IN 47906, USA

2 Department of Medical Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA 02115, USA

3 Broad Institute of MIT and Harvard University, Cambridge, MA 02142, USA

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Citation and License

BMC Bioinformatics 2012, 13(Suppl 2):S10  doi:10.1186/1471-2105-13-S2-S10

Published: 13 March 2012

Abstract

Background

When 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.

Results

We 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.

Conclusions

We 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.