Open Access
Translator Disclaimer
March 2015 A Markov random field-based approach to characterizing human brain development using spatial–temporal transcriptome data
Zhixiang Lin, Stephan J. Sanders, Mingfeng Li, Nenad Sestan, Matthew W. State, Hongyu Zhao
Ann. Appl. Stat. 9(1): 429-451 (March 2015). DOI: 10.1214/14-AOAS802

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.

Citation

Download Citation

Zhixiang Lin. Stephan J. Sanders. Mingfeng Li. Nenad Sestan. Matthew W. State. Hongyu Zhao. "A Markov random field-based approach to characterizing human brain development using spatial–temporal transcriptome data." Ann. Appl. Stat. 9 (1) 429 - 451, March 2015. https://doi.org/10.1214/14-AOAS802

Information

Published: March 2015
First available in Project Euclid: 28 April 2015

zbMATH: 06446575
MathSciNet: MR3341122
Digital Object Identifier: 10.1214/14-AOAS802

Rights: Copyright © 2015 Institute of Mathematical Statistics

JOURNAL ARTICLE
23 PAGES


SHARE
Vol.9 • No. 1 • March 2015
Back to Top