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Maximal information component analysis: a novel non-linear network analysis method
Background: Network construction and analysis algorithms provide scientists with the ability to sift through high-throughput biological outputs, such as transcription microarrays, for small groups of genes (modules) that are relevant for further research. Most of these algorithms ignore the importan...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2013
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3594742/ https://www.ncbi.nlm.nih.gov/pubmed/23487572 http://dx.doi.org/10.3389/fgene.2013.00028 |
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author | Rau, Christoph D. Wisniewski, Nicholas Orozco, Luz D. Bennett, Brian Weiss, James Lusis, Aldons J. |
author_facet | Rau, Christoph D. Wisniewski, Nicholas Orozco, Luz D. Bennett, Brian Weiss, James Lusis, Aldons J. |
author_sort | Rau, Christoph D. |
collection | PubMed |
description | Background: Network construction and analysis algorithms provide scientists with the ability to sift through high-throughput biological outputs, such as transcription microarrays, for small groups of genes (modules) that are relevant for further research. Most of these algorithms ignore the important role of non-linear interactions in the data, and the ability for genes to operate in multiple functional groups at once, despite clear evidence for both of these phenomena in observed biological systems. Results: We have created a novel co-expression network analysis algorithm that incorporates both of these principles by combining the information-theoretic association measure of the maximal information coefficient (MIC) with an Interaction Component Model. We evaluate the performance of this approach on two datasets collected from a large panel of mice, one from macrophages and the other from liver by comparing the two measures based on a measure of module entropy, Gene Ontology (GO) enrichment, and scale-free topology (SFT) fit. Our algorithm outperforms a widely used co-expression analysis method, weighted gene co-expression network analysis (WGCNA), in the macrophage data, while returning comparable results in the liver dataset when using these criteria. We demonstrate that the macrophage data has more non-linear interactions than the liver dataset, which may explain the increased performance of our method, termed Maximal Information Component Analysis (MICA) in that case. Conclusions: In making our network algorithm more accurately reflect known biological principles, we are able to generate modules with improved relevance, particularly in networks with confounding factors such as gene by environment interactions. |
format | Online Article Text |
id | pubmed-3594742 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2013 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-35947422013-03-13 Maximal information component analysis: a novel non-linear network analysis method Rau, Christoph D. Wisniewski, Nicholas Orozco, Luz D. Bennett, Brian Weiss, James Lusis, Aldons J. Front Genet Genetics Background: Network construction and analysis algorithms provide scientists with the ability to sift through high-throughput biological outputs, such as transcription microarrays, for small groups of genes (modules) that are relevant for further research. Most of these algorithms ignore the important role of non-linear interactions in the data, and the ability for genes to operate in multiple functional groups at once, despite clear evidence for both of these phenomena in observed biological systems. Results: We have created a novel co-expression network analysis algorithm that incorporates both of these principles by combining the information-theoretic association measure of the maximal information coefficient (MIC) with an Interaction Component Model. We evaluate the performance of this approach on two datasets collected from a large panel of mice, one from macrophages and the other from liver by comparing the two measures based on a measure of module entropy, Gene Ontology (GO) enrichment, and scale-free topology (SFT) fit. Our algorithm outperforms a widely used co-expression analysis method, weighted gene co-expression network analysis (WGCNA), in the macrophage data, while returning comparable results in the liver dataset when using these criteria. We demonstrate that the macrophage data has more non-linear interactions than the liver dataset, which may explain the increased performance of our method, termed Maximal Information Component Analysis (MICA) in that case. Conclusions: In making our network algorithm more accurately reflect known biological principles, we are able to generate modules with improved relevance, particularly in networks with confounding factors such as gene by environment interactions. Frontiers Media S.A. 2013-03-12 /pmc/articles/PMC3594742/ /pubmed/23487572 http://dx.doi.org/10.3389/fgene.2013.00028 Text en Copyright © 2013 Rau, Wisniewski, Orozco, Bennett, Weiss and Lusis. http://creativecommons.org/licenses/by/3.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in other forums, provided the original authors and source are credited and subject to any copyright notices concerning any third-party graphics etc. |
spellingShingle | Genetics Rau, Christoph D. Wisniewski, Nicholas Orozco, Luz D. Bennett, Brian Weiss, James Lusis, Aldons J. Maximal information component analysis: a novel non-linear network analysis method |
title | Maximal information component analysis: a novel non-linear network analysis method |
title_full | Maximal information component analysis: a novel non-linear network analysis method |
title_fullStr | Maximal information component analysis: a novel non-linear network analysis method |
title_full_unstemmed | Maximal information component analysis: a novel non-linear network analysis method |
title_short | Maximal information component analysis: a novel non-linear network analysis method |
title_sort | maximal information component analysis: a novel non-linear network analysis method |
topic | Genetics |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3594742/ https://www.ncbi.nlm.nih.gov/pubmed/23487572 http://dx.doi.org/10.3389/fgene.2013.00028 |
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