Insights into Proteomic Immune Cell Signaling and Communication via Data-Driven Modeling
Over the past decade, studies applying data-driven modeling approaches have demonstrated significant contributions toward the integrative understanding of multivariate cell regulatory system operation. Here we review applications of several of these approaches, including principal component analysis, partial least squares regression, partial least squares discriminant analysis, decision trees, and Bayesian networks, and describe the advances they have offered in systems-level understanding of immune cell signaling and communication. We show how these approaches generate novel insights from high-throughput proteomic data, from classification to association to influence to mechanisms. Looking forward, new experimental technologies involving single-cell measurements of cytokine expression beckon extension of these modeling techniques to inference of immune cell–cell communication networks, with a goal of aiding development of improved vaccine therapeutics.
KeywordsPartial Little Square Regression Mean Fluorescent Intensity Partial Little Square Discriminant Analysis Signaling Node Decision Tree Algorithm
Many thanks to Gregory Szeto, Brian Joughin, and Alexandra Hill for assistance in editing the manuscript. This work was partially supported by the Ragon Institute of MGH, MIT, and Harvard, NIH grant U19 AI 089992, and NIH grant TR01- EB010246.
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