dc.contributor.author | Ehsani, Rezvan | |
dc.contributor.author | Drabløs, Finn | |
dc.date.accessioned | 2020-04-16T12:33:40Z | |
dc.date.available | 2020-04-16T12:33:40Z | |
dc.date.created | 2020-04-15T11:22:46Z | |
dc.date.issued | 2020 | |
dc.identifier.citation | BMC Bioinformatics. 2020, 21 134-?. | en_US |
dc.identifier.issn | 1471-2105 | |
dc.identifier.uri | https://hdl.handle.net/11250/2651328 | |
dc.description.abstract | Background
Diseases like cancer will lead to changes in gene expression, and it is relevant to identify key regulatory genes that can be linked directly to these changes. This can be done by computing a Regulatory Impact Factor (RIF) score for relevant regulators. However, this computation is based on estimating correlated patterns of gene expression, often Pearson correlation, and an assumption about a set of specific regulators, normally transcription factors. This study explores alternative measures of correlation, using the Fisher and Sobolev metrics, and an extended set of regulators, including epigenetic regulators and long non-coding RNAs (lncRNAs). Data on prostate cancer have been used to explore the effect of these modifications.
Results
A tool for computation of RIF scores with alternative correlation measures and extended sets of regulators was developed and tested on gene expression data for prostate cancer. The study showed that the Fisher and Sobolev metrics lead to improved identification of well-documented regulators of gene expression in prostate cancer, and the sets of identified key regulators showed improved overlap with previously defined gene sets of relevance to cancer. The extended set of regulators lead to identification of several interesting candidates for further studies, including lncRNAs. Several key processes were identified as important, including spindle assembly and the epithelial-mesenchymal transition (EMT).
Conclusions
The study has shown that using alternative metrics of correlation can improve the performance of tools based on correlation of gene expression in genomic data. The Fisher and Sobolev metrics should be considered also in other correlation-based applications. | en_US |
dc.language.iso | eng | en_US |
dc.publisher | BioMed Central | en_US |
dc.rights | Navngivelse 4.0 Internasjonal | * |
dc.rights.uri | http://creativecommons.org/licenses/by/4.0/deed.no | * |
dc.title | Enhanced identification of significant regulators of gene expression | en_US |
dc.type | Peer reviewed | en_US |
dc.type | Journal article | en_US |
dc.description.version | publishedVersion | en_US |
dc.source.pagenumber | 134-? | en_US |
dc.source.volume | 21 | en_US |
dc.source.journal | BMC Bioinformatics | en_US |
dc.identifier.doi | 10.1186/s12859-020-3468-z | |
dc.identifier.cristin | 1806309 | |
dc.description.localcode | Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. | en_US |
cristin.ispublished | true | |
cristin.fulltext | original | |
cristin.qualitycode | 2 | |