Simultaneous inference and clustering of transcriptional dynamics in gene regulatory networks

H.m. Shahzad Asif, Guido Sanguinetti

Research output: Contribution to journalArticlepeer-review

Abstract / Description of output

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.
Original languageEnglish
Pages (from-to)545–557
Number of pages13
JournalStatistical applications in genetics and molecular biology
Issue number5
Publication statusPublished - Oct 2013


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