Simultaneous inference and clustering of transcriptional dynamics in gene regulatory networks.
We present a novel method for simultaneous inference and nonparametric clustering of transcriptional dynamics from gene expression data. The proposed method uses gene expression data to infer time-varying TF profiles and cluster these temporal profiles according to the dynamics they exhibit. We use the latent structure of factorial hidden Markov model to model the transcription factor profiles as Markov chains and cluster these profiles using nonparametric mixture modeling. An efficient Gibbs sampling scheme is proposed for inference of latent variables and grouping of transcriptional dynamics into a priori unknown number of clusters. We test our model on simulated data and analyse its performance on two expression datasets; S. cerevisiae cell cycle data and E. coli oxygen starvation response data. Our results show the applicability of the method for genome wide analysis of expression data.
H M Shahzad
HM
Asif
Guido
G
Sanguinetti
D000465:Algorithms; D001499:Bayes Theorem; D016000:Cluster Analysis; D019295:Computational Biology; D003198:Computer Simulation; D029968:Escherichia coli Proteins / Q000235:genetics / Q000378:metabolism; D020869:Gene Expression Profiling; D015964:Gene Expression Regulation, Bacterial; D015966:Gene Expression Regulation, Fungal; D053263:Gene Regulatory Networks; D008390:Markov Chains; D008957:Models, Genetic; D029701:Saccharomyces cerevisiae Proteins / Q000235:genetics / Q000378:metabolism; D018709:Statistics, Nonparametric; D014158:Transcription, Genetic
D029968:Escherichia coli Proteins; D029701:Saccharomyces cerevisiae Proteins