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aPEAR: an R package for autonomous visualization of pathway enrichment networks

SUMMARY: The interpretation of pathway enrichment analysis results is frequently complicated by an overwhelming and redundant list of significantly affected pathways. Here, we present an R package aPEAR (Advanced Pathway Enrichment Analysis Representation) which leverages similarities between the pa...

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Detalles Bibliográficos
Autores principales: Kerseviciute, Ieva, Gordevicius, Juozas
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10641035/
https://www.ncbi.nlm.nih.gov/pubmed/37935424
http://dx.doi.org/10.1093/bioinformatics/btad672
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author Kerseviciute, Ieva
Gordevicius, Juozas
author_facet Kerseviciute, Ieva
Gordevicius, Juozas
author_sort Kerseviciute, Ieva
collection PubMed
description SUMMARY: The interpretation of pathway enrichment analysis results is frequently complicated by an overwhelming and redundant list of significantly affected pathways. Here, we present an R package aPEAR (Advanced Pathway Enrichment Analysis Representation) which leverages similarities between the pathway gene sets and represents them as a network of interconnected clusters. Each cluster is assigned a meaningful name that highlights the main biological themes in the experiment. Our approach enables an automated and objective overview of the data without manual and time-consuming parameter tweaking. AVAILABILITY AND IMPLEMENTATION: The package aPEAR is implemented in R, published under the MIT open-source licence. The source code, documentation, and usage instructions are available on https://gitlab.com/vugene/aPEAR as well as on CRAN (https://CRAN.R-project.org/package=aPEAR).
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spelling pubmed-106410352023-11-14 aPEAR: an R package for autonomous visualization of pathway enrichment networks Kerseviciute, Ieva Gordevicius, Juozas Bioinformatics Applications Note SUMMARY: The interpretation of pathway enrichment analysis results is frequently complicated by an overwhelming and redundant list of significantly affected pathways. Here, we present an R package aPEAR (Advanced Pathway Enrichment Analysis Representation) which leverages similarities between the pathway gene sets and represents them as a network of interconnected clusters. Each cluster is assigned a meaningful name that highlights the main biological themes in the experiment. Our approach enables an automated and objective overview of the data without manual and time-consuming parameter tweaking. AVAILABILITY AND IMPLEMENTATION: The package aPEAR is implemented in R, published under the MIT open-source licence. The source code, documentation, and usage instructions are available on https://gitlab.com/vugene/aPEAR as well as on CRAN (https://CRAN.R-project.org/package=aPEAR). Oxford University Press 2023-11-03 /pmc/articles/PMC10641035/ /pubmed/37935424 http://dx.doi.org/10.1093/bioinformatics/btad672 Text en © The Author(s) 2023. Published by Oxford University Press. https://creativecommons.org/licenses/by/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Applications Note
Kerseviciute, Ieva
Gordevicius, Juozas
aPEAR: an R package for autonomous visualization of pathway enrichment networks
title aPEAR: an R package for autonomous visualization of pathway enrichment networks
title_full aPEAR: an R package for autonomous visualization of pathway enrichment networks
title_fullStr aPEAR: an R package for autonomous visualization of pathway enrichment networks
title_full_unstemmed aPEAR: an R package for autonomous visualization of pathway enrichment networks
title_short aPEAR: an R package for autonomous visualization of pathway enrichment networks
title_sort apear: an r package for autonomous visualization of pathway enrichment networks
topic Applications Note
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10641035/
https://www.ncbi.nlm.nih.gov/pubmed/37935424
http://dx.doi.org/10.1093/bioinformatics/btad672
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