title: A MARKOV RANDOM FIELD-BASED APPROACH TO CHARACTERIZING HUMAN BRAIN DEVELOPMENT USING SPATIAL-TEMPORAL TRANSCRIPTOME DATA.
abstract: Human neurodevelopment is a highly regulated biological process. In this article, we study the dynamic changes of neurodevelopment through the analysis of human brain microarray data, sampled from 16 brain regions in 15 time periods of neurodevelopment. We develop a two-step inferential procedure to identify expressed and unexpressed genes and to detect differentially expressed genes between adjacent time periods. Markov Random Field (MRF) models are used to efficiently utilize the information embedded in brain region similarity and temporal dependency in our approach. We develop and implement a Monte Carlo expectation-maximization (MCEM) algorithm to estimate the model parameters. Simulation studies suggest that our approach achieves lower misclassification error and potential gain in power compared with models not incorporating spatial similarity and temporal dependency.
authors.affiliation: Yale University.
authors.forename: Zhixiang
authors.initials: Z
authors.lastname: Lin
authors.affiliation: University of California, San Francisco.
authors.forename: Stephan J
authors.initials: SJ
authors.lastname: Sanders
authors.affiliation: Yale University.
authors.forename: Mingfeng
authors.initials: M
authors.lastname: Li
authors.affiliation: Yale University.
authors.forename: Nenad
authors.initials: N
authors.lastname: Sestan
authors.affiliation: University of California, San Francisco.
authors.forename: Matthew W
authors.initials: MW
authors.lastname: State
authors.affiliation: Yale University.
authors.forename: Hongyu
authors.initials: H
authors.lastname: Zhao
keywords: Markov Random Field model; Monte Carlo expectation–maximization algorithm; differential expression; gene expression; microarray; neurodevelopment; spatial and temporal data