A temporal hidden Markov regression model for the analysis of gene regulatory networks.
We propose a novel hierarchical hidden Markov regression model for determining gene regulatory networks from genomic sequence and temporally collected gene expression microarray data. The statistical challenge is to simultaneously determine the groupings of genes and subsets of motifs involved in their regulation, when the groupings may vary over time, and a large number of potential regulators are available. We devise a hybrid Monte Carlo methodology to estimate parameters under 2 classes of latent structure, one arising due to the unobservable state identity of genes and the other due to the unknown set of covariates influencing the response within a state. The effectiveness of this method is demonstrated through a simulation study and an application on an yeast cell-cycle data set.
Gupta
Mayetri
M
Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA. gupta@bios.unc.edu
Qu
Pingping
P
Ibrahim
Joseph G
JG
D001699:Biometry; D002453:Cell Cycle / Q000235:genetics; D003627:Data Interpretation, Statistical; D020869:Gene Expression Profiling / Q000706:statistics & numerical data; D005786:Gene Expression Regulation; D023281:Genomics / Q000706:statistics & numerical data; D008390:Markov Chains; D008957:Models, Genetic; D015233:Models, Statistical; D009010:Monte Carlo Method; D020411:Oligonucleotide Array Sequence Analysis / Q000706:statistics & numerical data; D012044:Regression Analysis; D012441:Saccharomyces cerevisiae / Q000166:cytology / Q000235:genetics; D012680:Sensitivity and Specificity