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hacksig: a unified and tidy R framework to easily compute gene expression signature scores

SUMMARY: Hundreds of gene expression signatures have been developed during the last two decades. However, due to the multitude of development procedures and sometimes a lack of explanation for their implementation, it can become challenging to apply the original method on custom data. Moreover, at p...

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Detalles Bibliográficos
Autores principales: Carenzo, Andrea, Pistore, Federico, Serafini, Mara S, Lenoci, Deborah, Licata, Armando G, De Cecco, Loris
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9113261/
https://www.ncbi.nlm.nih.gov/pubmed/35561166
http://dx.doi.org/10.1093/bioinformatics/btac161
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author Carenzo, Andrea
Pistore, Federico
Serafini, Mara S
Lenoci, Deborah
Licata, Armando G
De Cecco, Loris
author_facet Carenzo, Andrea
Pistore, Federico
Serafini, Mara S
Lenoci, Deborah
Licata, Armando G
De Cecco, Loris
author_sort Carenzo, Andrea
collection PubMed
description SUMMARY: Hundreds of gene expression signatures have been developed during the last two decades. However, due to the multitude of development procedures and sometimes a lack of explanation for their implementation, it can become challenging to apply the original method on custom data. Moreover, at present, there is no unified and tidy interface to compute signature scores with different single sample enrichment methods. For these reasons, we developed hacksig, an R package intended as a unified framework to obtain single sample scores with a tidy output as well as a collection of manually curated gene signatures and methods from cancer transcriptomics literature. AVAILABILITY AND IMPLEMENTATION: The hacksig R package is freely available on CRAN (https://CRAN.R-project.org/package=hacksig) under the MIT license. The source code can be found on GitHub at https://github.com/Acare/hacksig. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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spelling pubmed-91132612022-05-18 hacksig: a unified and tidy R framework to easily compute gene expression signature scores Carenzo, Andrea Pistore, Federico Serafini, Mara S Lenoci, Deborah Licata, Armando G De Cecco, Loris Bioinformatics Applications Notes SUMMARY: Hundreds of gene expression signatures have been developed during the last two decades. However, due to the multitude of development procedures and sometimes a lack of explanation for their implementation, it can become challenging to apply the original method on custom data. Moreover, at present, there is no unified and tidy interface to compute signature scores with different single sample enrichment methods. For these reasons, we developed hacksig, an R package intended as a unified framework to obtain single sample scores with a tidy output as well as a collection of manually curated gene signatures and methods from cancer transcriptomics literature. AVAILABILITY AND IMPLEMENTATION: The hacksig R package is freely available on CRAN (https://CRAN.R-project.org/package=hacksig) under the MIT license. The source code can be found on GitHub at https://github.com/Acare/hacksig. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. Oxford University Press 2022-03-18 /pmc/articles/PMC9113261/ /pubmed/35561166 http://dx.doi.org/10.1093/bioinformatics/btac161 Text en © The Author(s) 2022. 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 Notes
Carenzo, Andrea
Pistore, Federico
Serafini, Mara S
Lenoci, Deborah
Licata, Armando G
De Cecco, Loris
hacksig: a unified and tidy R framework to easily compute gene expression signature scores
title hacksig: a unified and tidy R framework to easily compute gene expression signature scores
title_full hacksig: a unified and tidy R framework to easily compute gene expression signature scores
title_fullStr hacksig: a unified and tidy R framework to easily compute gene expression signature scores
title_full_unstemmed hacksig: a unified and tidy R framework to easily compute gene expression signature scores
title_short hacksig: a unified and tidy R framework to easily compute gene expression signature scores
title_sort hacksig: a unified and tidy r framework to easily compute gene expression signature scores
topic Applications Notes
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9113261/
https://www.ncbi.nlm.nih.gov/pubmed/35561166
http://dx.doi.org/10.1093/bioinformatics/btac161
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