A statistical analysis of memory CD8 T cell differentiation: An application of a hierarchical state space model to a short time course microarray experiment
Haiyan Wu, Ming Yuan, Susan M. Kaech, M. Elizabeth Halloran

TL;DR
This paper introduces a hierarchical state space model with hidden Markov components to analyze short time course microarray data, revealing temporal gene expression patterns during memory CD8 T cell differentiation.
Contribution
The paper develops a novel hierarchical state space model incorporating hidden Markov models for analyzing complex time series microarray data in immunology.
Findings
Identified temporally differentially expressed genes.
Mapped the direction of gene expression changes over time.
Demonstrated the model's effectiveness through simulation and real data analysis.
Abstract
CD8 T cells are specialized immune cells that play an important role in the regulation of antiviral immune response and the generation of protective immunity. In this paper we investigate the differentiation of memory CD8 T cells in the immune response using a short time course microarray experiment. Structurally, this experiment is similar to many in that it involves measurements taken on independent samples, in one biological group, at a small number of irregularly spaced time points, and exhibiting patterns of temporal nonstationarity. To analyze this CD8 T-cell experiment, we develop a hierarchical state space model so that we can: (1) detect temporally differentially expressed genes, (2) identify the direction of successive changes over time, and (3) assess the magnitude of successive changes over time. We incorporate hidden Markov models into our model to utilize the information…
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