Mimosa: Mixture model of co-expression to detect modulators of regulatory interaction
<p>Abstract</p> <p>Background</p> <p>Functionally related genes tend to be correlated in their expression patterns across multiple conditions and/or tissue-types. Thus co-expression networks are often used to investigate functional groups of genes. In particular, when o...
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doaj-f542f286ea534f7180c5625de8662e9f2020-11-24T22:07:54ZengBMCAlgorithms for Molecular Biology1748-71882010-01-0151410.1186/1748-7188-5-4Mimosa: Mixture model of co-expression to detect modulators of regulatory interactionSingh LarryEverett LoganHansen MatthewHannenhalli Sridhar<p>Abstract</p> <p>Background</p> <p>Functionally related genes tend to be correlated in their expression patterns across multiple conditions and/or tissue-types. Thus co-expression networks are often used to investigate functional groups of genes. In particular, when one of the genes is a transcription factor (TF), the co-expression-based interaction is interpreted, with caution, as a direct regulatory interaction. However, any particular TF, and more importantly, any particular regulatory interaction, is likely to be active only in a subset of experimental conditions. Moreover, the subset of expression samples where the regulatory interaction holds may be marked by presence or absence of a modifier gene, such as an enzyme that post-translationally modifies the TF. Such subtlety of regulatory interactions is overlooked when one computes an overall expression correlation.</p> <p>Results</p> <p>Here we present a novel mixture modeling approach where a TF-Gene pair is presumed to be significantly correlated (with unknown coefficient) in an (unknown) subset of expression samples. The parameters of the model are estimated using a Maximum Likelihood approach. The estimated mixture of expression samples is then mined to identify genes potentially modulating the TF-Gene interaction. We have validated our approach using synthetic data and on four biological cases in cow, yeast, and humans.</p> <p>Conclusions</p> <p>While limited in some ways, as discussed, the work represents a novel approach to mine expression data and detect potential modulators of regulatory interactions.</p> http://www.almob.org/content/5/1/4 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Singh Larry Everett Logan Hansen Matthew Hannenhalli Sridhar |
spellingShingle |
Singh Larry Everett Logan Hansen Matthew Hannenhalli Sridhar Mimosa: Mixture model of co-expression to detect modulators of regulatory interaction Algorithms for Molecular Biology |
author_facet |
Singh Larry Everett Logan Hansen Matthew Hannenhalli Sridhar |
author_sort |
Singh Larry |
title |
Mimosa: Mixture model of co-expression to detect modulators of regulatory interaction |
title_short |
Mimosa: Mixture model of co-expression to detect modulators of regulatory interaction |
title_full |
Mimosa: Mixture model of co-expression to detect modulators of regulatory interaction |
title_fullStr |
Mimosa: Mixture model of co-expression to detect modulators of regulatory interaction |
title_full_unstemmed |
Mimosa: Mixture model of co-expression to detect modulators of regulatory interaction |
title_sort |
mimosa: mixture model of co-expression to detect modulators of regulatory interaction |
publisher |
BMC |
series |
Algorithms for Molecular Biology |
issn |
1748-7188 |
publishDate |
2010-01-01 |
description |
<p>Abstract</p> <p>Background</p> <p>Functionally related genes tend to be correlated in their expression patterns across multiple conditions and/or tissue-types. Thus co-expression networks are often used to investigate functional groups of genes. In particular, when one of the genes is a transcription factor (TF), the co-expression-based interaction is interpreted, with caution, as a direct regulatory interaction. However, any particular TF, and more importantly, any particular regulatory interaction, is likely to be active only in a subset of experimental conditions. Moreover, the subset of expression samples where the regulatory interaction holds may be marked by presence or absence of a modifier gene, such as an enzyme that post-translationally modifies the TF. Such subtlety of regulatory interactions is overlooked when one computes an overall expression correlation.</p> <p>Results</p> <p>Here we present a novel mixture modeling approach where a TF-Gene pair is presumed to be significantly correlated (with unknown coefficient) in an (unknown) subset of expression samples. The parameters of the model are estimated using a Maximum Likelihood approach. The estimated mixture of expression samples is then mined to identify genes potentially modulating the TF-Gene interaction. We have validated our approach using synthetic data and on four biological cases in cow, yeast, and humans.</p> <p>Conclusions</p> <p>While limited in some ways, as discussed, the work represents a novel approach to mine expression data and detect potential modulators of regulatory interactions.</p> |
url |
http://www.almob.org/content/5/1/4 |
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