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EpiVisR: exploratory data analysis and visualization in epigenome-wide association analyses
BACKGROUND: With the widespread availability of microarray technology for epigenetic research, methods for calling differentially methylated probes or differentially methylated regions have become effective tools to analyze this type of data. Furthermore, visualization is usually employed for qualit...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9308245/ https://www.ncbi.nlm.nih.gov/pubmed/35870905 http://dx.doi.org/10.1186/s12859-022-04836-2 |
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author | Röder, Stefan Herberth, Gunda Zenclussen, Ana C. Bauer, Mario |
author_facet | Röder, Stefan Herberth, Gunda Zenclussen, Ana C. Bauer, Mario |
author_sort | Röder, Stefan |
collection | PubMed |
description | BACKGROUND: With the widespread availability of microarray technology for epigenetic research, methods for calling differentially methylated probes or differentially methylated regions have become effective tools to analyze this type of data. Furthermore, visualization is usually employed for quality check of results and for further insights. Expert knowledge is required to leverage capabilities of these methods. To overcome this limitation and make visualization in epigenetic research available to the public, we designed EpiVisR. RESULTS: The EpiVisR tool allows to select and visualize combinations of traits (i.e., concentrations of chemical compounds) and differentially methylated probes/regions. It supports various modes of enriched presentation to get the most knowledge out of existing data: (1) enriched Manhattan plot and enriched volcano plot for selection of probes, (2) trait-methylation plot for visualization of selected trait values against methylation values, (3) methylation profile plot for visualization of a selected range of probes against selected trait values as well as, (4) correlation profile plot for selection and visualization of further probes that are correlated to the selected probe. EpiVisR additionally allows exporting selected data to external tools for tasks such as network analysis. CONCLUSION: The key advantage of EpiVisR is the annotation of data in the enriched plots (and tied tables) as well as linking to external data sources for further integrated data analysis. Using the EpiVisR approach will allow users to integrate data from traits with epigenetic analyses that are connected by belonging to the same individuals. Merging data from various data sources among the same cohort and visualizing them will enable users to gain more insights from existing data. |
format | Online Article Text |
id | pubmed-9308245 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-93082452022-07-24 EpiVisR: exploratory data analysis and visualization in epigenome-wide association analyses Röder, Stefan Herberth, Gunda Zenclussen, Ana C. Bauer, Mario BMC Bioinformatics Software BACKGROUND: With the widespread availability of microarray technology for epigenetic research, methods for calling differentially methylated probes or differentially methylated regions have become effective tools to analyze this type of data. Furthermore, visualization is usually employed for quality check of results and for further insights. Expert knowledge is required to leverage capabilities of these methods. To overcome this limitation and make visualization in epigenetic research available to the public, we designed EpiVisR. RESULTS: The EpiVisR tool allows to select and visualize combinations of traits (i.e., concentrations of chemical compounds) and differentially methylated probes/regions. It supports various modes of enriched presentation to get the most knowledge out of existing data: (1) enriched Manhattan plot and enriched volcano plot for selection of probes, (2) trait-methylation plot for visualization of selected trait values against methylation values, (3) methylation profile plot for visualization of a selected range of probes against selected trait values as well as, (4) correlation profile plot for selection and visualization of further probes that are correlated to the selected probe. EpiVisR additionally allows exporting selected data to external tools for tasks such as network analysis. CONCLUSION: The key advantage of EpiVisR is the annotation of data in the enriched plots (and tied tables) as well as linking to external data sources for further integrated data analysis. Using the EpiVisR approach will allow users to integrate data from traits with epigenetic analyses that are connected by belonging to the same individuals. Merging data from various data sources among the same cohort and visualizing them will enable users to gain more insights from existing data. BioMed Central 2022-07-23 /pmc/articles/PMC9308245/ /pubmed/35870905 http://dx.doi.org/10.1186/s12859-022-04836-2 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis 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. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Software Röder, Stefan Herberth, Gunda Zenclussen, Ana C. Bauer, Mario EpiVisR: exploratory data analysis and visualization in epigenome-wide association analyses |
title | EpiVisR: exploratory data analysis and visualization in epigenome-wide association analyses |
title_full | EpiVisR: exploratory data analysis and visualization in epigenome-wide association analyses |
title_fullStr | EpiVisR: exploratory data analysis and visualization in epigenome-wide association analyses |
title_full_unstemmed | EpiVisR: exploratory data analysis and visualization in epigenome-wide association analyses |
title_short | EpiVisR: exploratory data analysis and visualization in epigenome-wide association analyses |
title_sort | epivisr: exploratory data analysis and visualization in epigenome-wide association analyses |
topic | Software |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9308245/ https://www.ncbi.nlm.nih.gov/pubmed/35870905 http://dx.doi.org/10.1186/s12859-022-04836-2 |
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