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NBIA: a network-based integrative analysis framework – applied to pathway analysis
With the explosion of high-throughput data, effective integrative analyses are needed to decipher the knowledge accumulated in biological databases. Existing meta-analysis approaches in systems biology often focus on hypothesis testing and neglect real expression changes, i.e. effect sizes, across i...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7060280/ https://www.ncbi.nlm.nih.gov/pubmed/32144346 http://dx.doi.org/10.1038/s41598-020-60981-9 |
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author | Nguyen, Tin Shafi, Adib Nguyen, Tuan-Minh Schissler, A. Grant Draghici, Sorin |
author_facet | Nguyen, Tin Shafi, Adib Nguyen, Tuan-Minh Schissler, A. Grant Draghici, Sorin |
author_sort | Nguyen, Tin |
collection | PubMed |
description | With the explosion of high-throughput data, effective integrative analyses are needed to decipher the knowledge accumulated in biological databases. Existing meta-analysis approaches in systems biology often focus on hypothesis testing and neglect real expression changes, i.e. effect sizes, across independent studies. In addition, most integrative tools completely ignore the topological order of gene regulatory networks that hold key characteristics in understanding biological processes. Here we introduce a novel meta-analysis framework, Network-Based Integrative Analysis (NBIA), that transforms the challenging meta-analysis problem into a set of standard pathway analysis problems that have been solved efficiently. NBIA utilizes techniques from classical and modern meta-analysis, as well as a network-based analysis, in order to identify patterns of genes and networks that are consistently impacted across multiple studies. We assess the performance of NBIA by comparing it with nine meta-analysis approaches: Impact Analysis, GSA, and GSEA combined with classical meta-analysis methods (Fisher’s and the additive method), plus the three MetaPath approaches that employ multiple datasets. The 10 approaches have been tested on 1,737 samples from 27 expression datasets related to Alzheimer’s disease, acute myeloid leukemia (AML), and influenza. For all of the three diseases, NBIA consistently identifies biological pathways relevant to the underlying diseases while the other 9 methods fail to capture the key phenomena. The identified AML signature is also validated on a completely independent cohort of 167 AML patients. In this independent cohort, the proposed signature identifies two groups of patients that have significantly different survival profiles (Cox p-value 2 × 10(−6)). The NBIA framework will be included in the next release of BLMA Bioconductor package (http://bioconductor.org/packages/release/bioc/html/BLMA.html). |
format | Online Article Text |
id | pubmed-7060280 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-70602802020-03-18 NBIA: a network-based integrative analysis framework – applied to pathway analysis Nguyen, Tin Shafi, Adib Nguyen, Tuan-Minh Schissler, A. Grant Draghici, Sorin Sci Rep Article With the explosion of high-throughput data, effective integrative analyses are needed to decipher the knowledge accumulated in biological databases. Existing meta-analysis approaches in systems biology often focus on hypothesis testing and neglect real expression changes, i.e. effect sizes, across independent studies. In addition, most integrative tools completely ignore the topological order of gene regulatory networks that hold key characteristics in understanding biological processes. Here we introduce a novel meta-analysis framework, Network-Based Integrative Analysis (NBIA), that transforms the challenging meta-analysis problem into a set of standard pathway analysis problems that have been solved efficiently. NBIA utilizes techniques from classical and modern meta-analysis, as well as a network-based analysis, in order to identify patterns of genes and networks that are consistently impacted across multiple studies. We assess the performance of NBIA by comparing it with nine meta-analysis approaches: Impact Analysis, GSA, and GSEA combined with classical meta-analysis methods (Fisher’s and the additive method), plus the three MetaPath approaches that employ multiple datasets. The 10 approaches have been tested on 1,737 samples from 27 expression datasets related to Alzheimer’s disease, acute myeloid leukemia (AML), and influenza. For all of the three diseases, NBIA consistently identifies biological pathways relevant to the underlying diseases while the other 9 methods fail to capture the key phenomena. The identified AML signature is also validated on a completely independent cohort of 167 AML patients. In this independent cohort, the proposed signature identifies two groups of patients that have significantly different survival profiles (Cox p-value 2 × 10(−6)). The NBIA framework will be included in the next release of BLMA Bioconductor package (http://bioconductor.org/packages/release/bioc/html/BLMA.html). Nature Publishing Group UK 2020-03-06 /pmc/articles/PMC7060280/ /pubmed/32144346 http://dx.doi.org/10.1038/s41598-020-60981-9 Text en © The Author(s) 2020 Open Access This 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 license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license 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 license, visit http://creativecommons.org/licenses/by/4.0/. |
spellingShingle | Article Nguyen, Tin Shafi, Adib Nguyen, Tuan-Minh Schissler, A. Grant Draghici, Sorin NBIA: a network-based integrative analysis framework – applied to pathway analysis |
title | NBIA: a network-based integrative analysis framework – applied to pathway analysis |
title_full | NBIA: a network-based integrative analysis framework – applied to pathway analysis |
title_fullStr | NBIA: a network-based integrative analysis framework – applied to pathway analysis |
title_full_unstemmed | NBIA: a network-based integrative analysis framework – applied to pathway analysis |
title_short | NBIA: a network-based integrative analysis framework – applied to pathway analysis |
title_sort | nbia: a network-based integrative analysis framework – applied to pathway analysis |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7060280/ https://www.ncbi.nlm.nih.gov/pubmed/32144346 http://dx.doi.org/10.1038/s41598-020-60981-9 |
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