A Markov random field-based approach to characterizing human brain development using spatial-temporal transcriptome data

TL;DR

This paper introduces a Markov Random Field-based method to analyze spatial-temporal transcriptome data, revealing gene expression dynamics during human brain development with improved accuracy over traditional models.

Contribution

The study develops a novel two-step inferential procedure combined with MRF models and MCEM algorithm to better identify gene expression changes across brain regions and time points.

Findings

Lower misclassification error in gene expression detection

Enhanced power in identifying differentially expressed genes

Effective utilization of spatial and temporal dependencies

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.