Uncovering robust patterns of microRNA co-expression across cancers using Bayesian Relevance Networks.
Co-expression networks have long been used as a tool for investigating the molecular circuitry governing biological systems. However, most algorithms for constructing co-expression networks were developed in the microarray era, before high-throughput sequencing-with its unique statistical properties...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2017-01-01
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| Series: | PLoS ONE |
| Online Access: | http://europepmc.org/articles/PMC5560700?pdf=render |
| _version_ | 1818435179694260224 |
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| author | Parameswaran Ramachandran Daniel Sánchez-Taltavull Theodore J Perkins |
| author_facet | Parameswaran Ramachandran Daniel Sánchez-Taltavull Theodore J Perkins |
| author_sort | Parameswaran Ramachandran |
| collection | DOAJ |
| description | Co-expression networks have long been used as a tool for investigating the molecular circuitry governing biological systems. However, most algorithms for constructing co-expression networks were developed in the microarray era, before high-throughput sequencing-with its unique statistical properties-became the norm for expression measurement. Here we develop Bayesian Relevance Networks, an algorithm that uses Bayesian reasoning about expression levels to account for the differing levels of uncertainty in expression measurements between highly- and lowly-expressed entities, and between samples with different sequencing depths. It combines data from groups of samples (e.g., replicates) to estimate group expression levels and confidence ranges. It then computes uncertainty-moderated estimates of cross-group correlations between entities, and uses permutation testing to assess their statistical significance. Using large scale miRNA data from The Cancer Genome Atlas, we show that our Bayesian update of the classical Relevance Networks algorithm provides improved reproducibility in co-expression estimates and lower false discovery rates in the resulting co-expression networks. Software is available at www.perkinslab.ca. |
| first_indexed | 2024-12-14T16:48:46Z |
| format | Article |
| id | doaj.art-005eb80e69e74a6bbfe960babb291151 |
| institution | Directory Open Access Journal |
| issn | 1932-6203 |
| language | English |
| last_indexed | 2024-12-14T16:48:46Z |
| publishDate | 2017-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj.art-005eb80e69e74a6bbfe960babb2911512022-12-21T22:54:06ZengPublic Library of Science (PLoS)PLoS ONE1932-62032017-01-01128e018310310.1371/journal.pone.0183103Uncovering robust patterns of microRNA co-expression across cancers using Bayesian Relevance Networks.Parameswaran RamachandranDaniel Sánchez-TaltavullTheodore J PerkinsCo-expression networks have long been used as a tool for investigating the molecular circuitry governing biological systems. However, most algorithms for constructing co-expression networks were developed in the microarray era, before high-throughput sequencing-with its unique statistical properties-became the norm for expression measurement. Here we develop Bayesian Relevance Networks, an algorithm that uses Bayesian reasoning about expression levels to account for the differing levels of uncertainty in expression measurements between highly- and lowly-expressed entities, and between samples with different sequencing depths. It combines data from groups of samples (e.g., replicates) to estimate group expression levels and confidence ranges. It then computes uncertainty-moderated estimates of cross-group correlations between entities, and uses permutation testing to assess their statistical significance. Using large scale miRNA data from The Cancer Genome Atlas, we show that our Bayesian update of the classical Relevance Networks algorithm provides improved reproducibility in co-expression estimates and lower false discovery rates in the resulting co-expression networks. Software is available at www.perkinslab.ca.http://europepmc.org/articles/PMC5560700?pdf=render |
| spellingShingle | Parameswaran Ramachandran Daniel Sánchez-Taltavull Theodore J Perkins Uncovering robust patterns of microRNA co-expression across cancers using Bayesian Relevance Networks. PLoS ONE |
| title | Uncovering robust patterns of microRNA co-expression across cancers using Bayesian Relevance Networks. |
| title_full | Uncovering robust patterns of microRNA co-expression across cancers using Bayesian Relevance Networks. |
| title_fullStr | Uncovering robust patterns of microRNA co-expression across cancers using Bayesian Relevance Networks. |
| title_full_unstemmed | Uncovering robust patterns of microRNA co-expression across cancers using Bayesian Relevance Networks. |
| title_short | Uncovering robust patterns of microRNA co-expression across cancers using Bayesian Relevance Networks. |
| title_sort | uncovering robust patterns of microrna co expression across cancers using bayesian relevance networks |
| url | http://europepmc.org/articles/PMC5560700?pdf=render |
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