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An integrative clustering and modeling algorithm for dynamical gene expression data.

Sivriver J, Habib N, Friedman N - Bioinformatics (2011)

Bottom Line: Moreover, our approach provides an easy way to compare between responses to different stimuli at the dynamical level.We use our approach to analyze the dynamical transcriptional responses to inflammation and anti-viral stimuli in mice primary dendritic cells, and extract a concise representation of the different dynamical response types.We analyze the similarities and differences between the two stimuli and identify potential regulators of this complex transcriptional response.

View Article: PubMed Central - PubMed

Affiliation: School of Computer Science and Engineering, The Hebrew University of Jerusalem, Jerusalem 91904, Israel.

ABSTRACT

Motivation: The precise dynamics of gene expression is often crucial for proper response to stimuli. Time-course gene-expression profiles can provide insights about the dynamics of many cellular responses, but are often noisy and measured at arbitrary intervals, posing a major analysis challenge.

Results: We developed an algorithm that interleaves clustering time-course gene-expression data with estimation of dynamic models of their response by biologically meaningful parameters. In combining these two tasks we overcome obstacles posed in each one. Moreover, our approach provides an easy way to compare between responses to different stimuli at the dynamical level. We use our approach to analyze the dynamical transcriptional responses to inflammation and anti-viral stimuli in mice primary dendritic cells, and extract a concise representation of the different dynamical response types. We analyze the similarities and differences between the two stimuli and identify potential regulators of this complex transcriptional response.

Availability: The code to our method is freely available http://www.compbio.cs.huji.ac.il/DynaMiteC.

Contact: nir@cs.huji.ac.il.

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An integrative clustering-modeling algorithm. (A) The impulse model capturing a two-phase temporal response by a product of two sigmoids, with parameters: onset time (t1), offset time (t2), the original baseline height (h0), peak response height (h1), new baseline height (h2), onset rate (β1) and offset rate (β2). (B) Fitting the model to the data with mixture priors on the parameters (bottom), which are distinct prototypes of responses (top). (C) A scheme describing our integrative clustering and modeling algorithm DynaMiteC: (i) Choosing initial clusters. (ii) Iterating between optimizing the fit to the genes and optimizing the prototypes. (iii) The resulting models per gene and clusters.
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Figure 1: An integrative clustering-modeling algorithm. (A) The impulse model capturing a two-phase temporal response by a product of two sigmoids, with parameters: onset time (t1), offset time (t2), the original baseline height (h0), peak response height (h1), new baseline height (h2), onset rate (β1) and offset rate (β2). (B) Fitting the model to the data with mixture priors on the parameters (bottom), which are distinct prototypes of responses (top). (C) A scheme describing our integrative clustering and modeling algorithm DynaMiteC: (i) Choosing initial clusters. (ii) Iterating between optimizing the fit to the genes and optimizing the prototypes. (iii) The resulting models per gene and clusters.

Mentions: Here, we present DynaMiteC (Dynamic Modeling and Clustering), an integrative approach that simultaneously models time course gene-expression profiles with biologically meaningful parameters, and assigns them to clusters of different dynamical responses. Our approach is to model gene responses using an impulse model (Fig. 1a). Our premise is that many genes have a similar response. Thus, instead of estimating the profile of each gene separately, and thus risk overfitting, our method estimates the profile with a prior that prefers to match one out of a set of ‘prototype’ responses (Fig. 1b). We thus pose the parameter estimation task as jointly learning the prototypes and the parameters of individual genes. When learning these prototypes we, in fact, assign genes to clusters. These two problems are solved by iterating between them (Fig. 1c).Fig. 1.


An integrative clustering and modeling algorithm for dynamical gene expression data.

Sivriver J, Habib N, Friedman N - Bioinformatics (2011)

An integrative clustering-modeling algorithm. (A) The impulse model capturing a two-phase temporal response by a product of two sigmoids, with parameters: onset time (t1), offset time (t2), the original baseline height (h0), peak response height (h1), new baseline height (h2), onset rate (β1) and offset rate (β2). (B) Fitting the model to the data with mixture priors on the parameters (bottom), which are distinct prototypes of responses (top). (C) A scheme describing our integrative clustering and modeling algorithm DynaMiteC: (i) Choosing initial clusters. (ii) Iterating between optimizing the fit to the genes and optimizing the prototypes. (iii) The resulting models per gene and clusters.
© Copyright Policy - creative-commons
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3117368&req=5

Figure 1: An integrative clustering-modeling algorithm. (A) The impulse model capturing a two-phase temporal response by a product of two sigmoids, with parameters: onset time (t1), offset time (t2), the original baseline height (h0), peak response height (h1), new baseline height (h2), onset rate (β1) and offset rate (β2). (B) Fitting the model to the data with mixture priors on the parameters (bottom), which are distinct prototypes of responses (top). (C) A scheme describing our integrative clustering and modeling algorithm DynaMiteC: (i) Choosing initial clusters. (ii) Iterating between optimizing the fit to the genes and optimizing the prototypes. (iii) The resulting models per gene and clusters.
Mentions: Here, we present DynaMiteC (Dynamic Modeling and Clustering), an integrative approach that simultaneously models time course gene-expression profiles with biologically meaningful parameters, and assigns them to clusters of different dynamical responses. Our approach is to model gene responses using an impulse model (Fig. 1a). Our premise is that many genes have a similar response. Thus, instead of estimating the profile of each gene separately, and thus risk overfitting, our method estimates the profile with a prior that prefers to match one out of a set of ‘prototype’ responses (Fig. 1b). We thus pose the parameter estimation task as jointly learning the prototypes and the parameters of individual genes. When learning these prototypes we, in fact, assign genes to clusters. These two problems are solved by iterating between them (Fig. 1c).Fig. 1.

Bottom Line: Moreover, our approach provides an easy way to compare between responses to different stimuli at the dynamical level.We use our approach to analyze the dynamical transcriptional responses to inflammation and anti-viral stimuli in mice primary dendritic cells, and extract a concise representation of the different dynamical response types.We analyze the similarities and differences between the two stimuli and identify potential regulators of this complex transcriptional response.

View Article: PubMed Central - PubMed

Affiliation: School of Computer Science and Engineering, The Hebrew University of Jerusalem, Jerusalem 91904, Israel.

ABSTRACT

Motivation: The precise dynamics of gene expression is often crucial for proper response to stimuli. Time-course gene-expression profiles can provide insights about the dynamics of many cellular responses, but are often noisy and measured at arbitrary intervals, posing a major analysis challenge.

Results: We developed an algorithm that interleaves clustering time-course gene-expression data with estimation of dynamic models of their response by biologically meaningful parameters. In combining these two tasks we overcome obstacles posed in each one. Moreover, our approach provides an easy way to compare between responses to different stimuli at the dynamical level. We use our approach to analyze the dynamical transcriptional responses to inflammation and anti-viral stimuli in mice primary dendritic cells, and extract a concise representation of the different dynamical response types. We analyze the similarities and differences between the two stimuli and identify potential regulators of this complex transcriptional response.

Availability: The code to our method is freely available http://www.compbio.cs.huji.ac.il/DynaMiteC.

Contact: nir@cs.huji.ac.il.

Show MeSH