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.
Cite
@article{arxiv.1506.00480,
title = {A Markov random field-based approach to characterizing human brain development using spatial-temporal transcriptome data},
author = {Zhixiang Lin and Stephan J. Sanders and Mingfeng Li and Nenad Sestan and Matthew W. State and Hongyu Zhao},
journal= {arXiv preprint arXiv:1506.00480},
year = {2015}
}
Comments
Published at http://dx.doi.org/10.1214/14-AOAS802 in the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org)