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Integrating neuroimaging and gene expression data using the imaging transcriptomics toolbox

The integration of neuroimaging and transcriptomics data, Imaging Transcriptomics, is becoming increasingly popular but standardized workflows for its implementation are still lacking. We describe the Imaging Transcriptomics toolbox, a new package that implements a full imaging transcriptomics pipel...

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Detalles Bibliográficos
Autores principales: Giacomel, Alessio, Martins, Daniel, Frigo, Matteo, Turkheimer, Federico, Williams, Steven C.R., Dipasquale, Ottavia, Veronese, Mattia
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9036395/
https://www.ncbi.nlm.nih.gov/pubmed/35479111
http://dx.doi.org/10.1016/j.xpro.2022.101315
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author Giacomel, Alessio
Martins, Daniel
Frigo, Matteo
Turkheimer, Federico
Williams, Steven C.R.
Dipasquale, Ottavia
Veronese, Mattia
author_facet Giacomel, Alessio
Martins, Daniel
Frigo, Matteo
Turkheimer, Federico
Williams, Steven C.R.
Dipasquale, Ottavia
Veronese, Mattia
author_sort Giacomel, Alessio
collection PubMed
description The integration of neuroimaging and transcriptomics data, Imaging Transcriptomics, is becoming increasingly popular but standardized workflows for its implementation are still lacking. We describe the Imaging Transcriptomics toolbox, a new package that implements a full imaging transcriptomics pipeline using a user-friendly, command line interface. This toolbox allows the user to identify patterns of gene expression which correlates with a specific neuroimaging phenotype and perform gene set enrichment analyses to inform the biological interpretation of the findings using up-to-date methods. For complete details on the use and execution of this protocol, please refer to Martins et al. (2021).
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spelling pubmed-90363952022-04-26 Integrating neuroimaging and gene expression data using the imaging transcriptomics toolbox Giacomel, Alessio Martins, Daniel Frigo, Matteo Turkheimer, Federico Williams, Steven C.R. Dipasquale, Ottavia Veronese, Mattia STAR Protoc Protocol The integration of neuroimaging and transcriptomics data, Imaging Transcriptomics, is becoming increasingly popular but standardized workflows for its implementation are still lacking. We describe the Imaging Transcriptomics toolbox, a new package that implements a full imaging transcriptomics pipeline using a user-friendly, command line interface. This toolbox allows the user to identify patterns of gene expression which correlates with a specific neuroimaging phenotype and perform gene set enrichment analyses to inform the biological interpretation of the findings using up-to-date methods. For complete details on the use and execution of this protocol, please refer to Martins et al. (2021). Elsevier 2022-04-15 /pmc/articles/PMC9036395/ /pubmed/35479111 http://dx.doi.org/10.1016/j.xpro.2022.101315 Text en © 2022 The Author(s) https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
spellingShingle Protocol
Giacomel, Alessio
Martins, Daniel
Frigo, Matteo
Turkheimer, Federico
Williams, Steven C.R.
Dipasquale, Ottavia
Veronese, Mattia
Integrating neuroimaging and gene expression data using the imaging transcriptomics toolbox
title Integrating neuroimaging and gene expression data using the imaging transcriptomics toolbox
title_full Integrating neuroimaging and gene expression data using the imaging transcriptomics toolbox
title_fullStr Integrating neuroimaging and gene expression data using the imaging transcriptomics toolbox
title_full_unstemmed Integrating neuroimaging and gene expression data using the imaging transcriptomics toolbox
title_short Integrating neuroimaging and gene expression data using the imaging transcriptomics toolbox
title_sort integrating neuroimaging and gene expression data using the imaging transcriptomics toolbox
topic Protocol
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9036395/
https://www.ncbi.nlm.nih.gov/pubmed/35479111
http://dx.doi.org/10.1016/j.xpro.2022.101315
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