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PathCORE-T: identifying and visualizing globally co-occurring pathways in large transcriptomic compendia
BACKGROUND: Investigators often interpret genome-wide data by analyzing the expression levels of genes within pathways. While this within-pathway analysis is routine, the products of any one pathway can affect the activity of other pathways. Past efforts to identify relationships between biological...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6029133/ https://www.ncbi.nlm.nih.gov/pubmed/29988723 http://dx.doi.org/10.1186/s13040-018-0175-7 |
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author | Chen, Kathleen M. Tan, Jie Way, Gregory P. Doing, Georgia Hogan, Deborah A. Greene, Casey S. |
author_facet | Chen, Kathleen M. Tan, Jie Way, Gregory P. Doing, Georgia Hogan, Deborah A. Greene, Casey S. |
author_sort | Chen, Kathleen M. |
collection | PubMed |
description | BACKGROUND: Investigators often interpret genome-wide data by analyzing the expression levels of genes within pathways. While this within-pathway analysis is routine, the products of any one pathway can affect the activity of other pathways. Past efforts to identify relationships between biological processes have evaluated overlap in knowledge bases or evaluated changes that occur after specific treatments. Individual experiments can highlight condition-specific pathway-pathway relationships; however, constructing a complete network of such relationships across many conditions requires analyzing results from many studies. RESULTS: We developed PathCORE-T framework by implementing existing methods to identify pathway-pathway transcriptional relationships evident across a broad data compendium. PathCORE-T is applied to the output of feature construction algorithms; it identifies pairs of pathways observed in features more than expected by chance as functionally co-occurring. We demonstrate PathCORE-T by analyzing an existing eADAGE model of a microbial compendium and building and analyzing NMF features from the TCGA dataset of 33 cancer types. The PathCORE-T framework includes a demonstration web interface, with source code, that users can launch to (1) visualize the network and (2) review the expression levels of associated genes in the original data. PathCORE-T creates and displays the network of globally co-occurring pathways based on features observed in a machine learning analysis of gene expression data. CONCLUSIONS: The PathCORE-T framework identifies transcriptionally co-occurring pathways from the results of unsupervised analysis of gene expression data and visualizes the relationships between pathways as a network. PathCORE-T recapitulated previously described pathway-pathway relationships and suggested experimentally testable additional hypotheses that remain to be explored. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s13040-018-0175-7) contains supplementary material, which is available to authorized users. |
format | Online Article Text |
id | pubmed-6029133 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-60291332018-07-09 PathCORE-T: identifying and visualizing globally co-occurring pathways in large transcriptomic compendia Chen, Kathleen M. Tan, Jie Way, Gregory P. Doing, Georgia Hogan, Deborah A. Greene, Casey S. BioData Min Software Article BACKGROUND: Investigators often interpret genome-wide data by analyzing the expression levels of genes within pathways. While this within-pathway analysis is routine, the products of any one pathway can affect the activity of other pathways. Past efforts to identify relationships between biological processes have evaluated overlap in knowledge bases or evaluated changes that occur after specific treatments. Individual experiments can highlight condition-specific pathway-pathway relationships; however, constructing a complete network of such relationships across many conditions requires analyzing results from many studies. RESULTS: We developed PathCORE-T framework by implementing existing methods to identify pathway-pathway transcriptional relationships evident across a broad data compendium. PathCORE-T is applied to the output of feature construction algorithms; it identifies pairs of pathways observed in features more than expected by chance as functionally co-occurring. We demonstrate PathCORE-T by analyzing an existing eADAGE model of a microbial compendium and building and analyzing NMF features from the TCGA dataset of 33 cancer types. The PathCORE-T framework includes a demonstration web interface, with source code, that users can launch to (1) visualize the network and (2) review the expression levels of associated genes in the original data. PathCORE-T creates and displays the network of globally co-occurring pathways based on features observed in a machine learning analysis of gene expression data. CONCLUSIONS: The PathCORE-T framework identifies transcriptionally co-occurring pathways from the results of unsupervised analysis of gene expression data and visualizes the relationships between pathways as a network. PathCORE-T recapitulated previously described pathway-pathway relationships and suggested experimentally testable additional hypotheses that remain to be explored. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s13040-018-0175-7) contains supplementary material, which is available to authorized users. BioMed Central 2018-07-03 /pmc/articles/PMC6029133/ /pubmed/29988723 http://dx.doi.org/10.1186/s13040-018-0175-7 Text en © The Author(s). 2018 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided 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 Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Software Article Chen, Kathleen M. Tan, Jie Way, Gregory P. Doing, Georgia Hogan, Deborah A. Greene, Casey S. PathCORE-T: identifying and visualizing globally co-occurring pathways in large transcriptomic compendia |
title | PathCORE-T: identifying and visualizing globally co-occurring pathways in large transcriptomic compendia |
title_full | PathCORE-T: identifying and visualizing globally co-occurring pathways in large transcriptomic compendia |
title_fullStr | PathCORE-T: identifying and visualizing globally co-occurring pathways in large transcriptomic compendia |
title_full_unstemmed | PathCORE-T: identifying and visualizing globally co-occurring pathways in large transcriptomic compendia |
title_short | PathCORE-T: identifying and visualizing globally co-occurring pathways in large transcriptomic compendia |
title_sort | pathcore-t: identifying and visualizing globally co-occurring pathways in large transcriptomic compendia |
topic | Software Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6029133/ https://www.ncbi.nlm.nih.gov/pubmed/29988723 http://dx.doi.org/10.1186/s13040-018-0175-7 |
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