A MARKOV RANDOM FIELD-BASED APPROACH TO CHARACTERIZING HUMAN BRAIN DEVELOPMENT USING SPATIAL-TEMPORAL TRANSCRIPTOME DATA.
A MARKOV RANDOM FIELD-BASED APPROACH TO CHARACTERIZING HUMAN BRAIN DEVELOPMENT USING SPATIAL-TEMPORAL TRANSCRIPTOME DATA.
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DOI：
10.1214/14-aoas802
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发表时间：
2015-03
期刊：
影响因子：
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通讯作者：
Zhao H
中科院分区：
文献类型：
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作者：
Lin Z;Sanders SJ;Li M;Sestan N;State MW;Zhao H
人类神经发育是一个高度受调控的生物过程。本文通过对人脑微阵列数据的分析，研究了神经发育15个时期内16个脑区的动态变化。我们开发了一个两步推理程序来识别表达和未表达的基因，并检测相邻时间段之间的差异表达基因。在该方法中，马尔可夫随机场(MRF)模型被用来有效地利用嵌入在大脑区域相似性和时间依赖性中的信息。我们开发并实现了一种蒙特卡罗期望最大化(MCEM)算法来估计模型参数。仿真研究表明，与没有考虑空间相似性和时间相关性的模型相比，我们的方法获得了更低的误分类误差和潜在的功率增益。
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.