Network analysis of TCGA and GTEx gene expression datasets for identification of trait-associated biomarkers in human cancer

Advances in high-throughput sequencing technologies now yield unprecedented volumes of OMICs data with opportunities to conduct systematic data analyses and derive novel biological insights. Here, we provide protocols to perform differential-expressed gene analysis of TCGA and GTEx RNA-Seq data from...

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Autores principales: Chen, Huey-Miin, MacDonald, Justin A.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2022
Materias:
Protocol
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8841814/
https://www.ncbi.nlm.nih.gov/pubmed/35199033
http://dx.doi.org/10.1016/j.xpro.2022.101168
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author Chen, Huey-Miin
MacDonald, Justin A.
author_facet Chen, Huey-Miin
MacDonald, Justin A.
author_sort Chen, Huey-Miin
collection PubMed
description Advances in high-throughput sequencing technologies now yield unprecedented volumes of OMICs data with opportunities to conduct systematic data analyses and derive novel biological insights. Here, we provide protocols to perform differential-expressed gene analysis of TCGA and GTEx RNA-Seq data from human cancers, complete integrative GO and network analyses with focus on clinical and survival data, and identify differential correlation of trait-associated biomarkers. For complete details on the use and execution of this protocol, please refer to Chen and MacDonald (2021).
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spelling pubmed-88418142022-02-22 Network analysis of TCGA and GTEx gene expression datasets for identification of trait-associated biomarkers in human cancer Chen, Huey-Miin MacDonald, Justin A. STAR Protoc Protocol Advances in high-throughput sequencing technologies now yield unprecedented volumes of OMICs data with opportunities to conduct systematic data analyses and derive novel biological insights. Here, we provide protocols to perform differential-expressed gene analysis of TCGA and GTEx RNA-Seq data from human cancers, complete integrative GO and network analyses with focus on clinical and survival data, and identify differential correlation of trait-associated biomarkers. For complete details on the use and execution of this protocol, please refer to Chen and MacDonald (2021). Elsevier 2022-02-07 /pmc/articles/PMC8841814/ /pubmed/35199033 http://dx.doi.org/10.1016/j.xpro.2022.101168 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
Chen, Huey-Miin
MacDonald, Justin A.
Network analysis of TCGA and GTEx gene expression datasets for identification of trait-associated biomarkers in human cancer
title Network analysis of TCGA and GTEx gene expression datasets for identification of trait-associated biomarkers in human cancer
title_full Network analysis of TCGA and GTEx gene expression datasets for identification of trait-associated biomarkers in human cancer
title_fullStr Network analysis of TCGA and GTEx gene expression datasets for identification of trait-associated biomarkers in human cancer
title_full_unstemmed Network analysis of TCGA and GTEx gene expression datasets for identification of trait-associated biomarkers in human cancer
title_short Network analysis of TCGA and GTEx gene expression datasets for identification of trait-associated biomarkers in human cancer
title_sort network analysis of tcga and gtex gene expression datasets for identification of trait-associated biomarkers in human cancer
topic Protocol
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8841814/
https://www.ncbi.nlm.nih.gov/pubmed/35199033
http://dx.doi.org/10.1016/j.xpro.2022.101168
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