Simultaneous inference and clustering of transcriptional dynamics in gene regulatory networks
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H.M. Shahzad Asif
Abstract
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.
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The network connectivity matrix is a binary matrix: an entry 1 in position ln denotes a (directed) edge between node l and node n.
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We attempted to cluster the inferred transition rates by fitting a mixture of Beta distributions, but the predictor obtained in terms of co-occurrence matrix was hardly better than random.
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©2013 by Walter de Gruyter Berlin Boston
Artikel in diesem Heft
- Masthead
- Masthead
- Research Articles
- Simultaneous inference and clustering of transcriptional dynamics in gene regulatory networks
- Markov chain Monte Carlo sampling of gene genealogies conditional on unphased SNP genotype data
- Performance and estimation of the true error rate of classification rules built with additional information. An application to a cancer trial
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Artikel in diesem Heft
- Masthead
- Masthead
- Research Articles
- Simultaneous inference and clustering of transcriptional dynamics in gene regulatory networks
- Markov chain Monte Carlo sampling of gene genealogies conditional on unphased SNP genotype data
- Performance and estimation of the true error rate of classification rules built with additional information. An application to a cancer trial
- Optimizing threshold-schedules for sequential approximate Bayesian computation: applications to molecular systems
- Model selection for prognostic time-to-event gene signature discovery with applications in early breast cancer data
- Identifying clusters in genomics data by recursive partitioning
