Cargando…

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...

Descripción completa

Detalles Bibliográficos
Autores principales: Chen, Kathleen M., Tan, Jie, Way, Gregory P., Doing, Georgia, Hogan, Deborah A., Greene, Casey S.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2018
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
_version_ 1783336900915888128
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
work_keys_str_mv AT chenkathleenm pathcoretidentifyingandvisualizinggloballycooccurringpathwaysinlargetranscriptomiccompendia
AT tanjie pathcoretidentifyingandvisualizinggloballycooccurringpathwaysinlargetranscriptomiccompendia
AT waygregoryp pathcoretidentifyingandvisualizinggloballycooccurringpathwaysinlargetranscriptomiccompendia
AT doinggeorgia pathcoretidentifyingandvisualizinggloballycooccurringpathwaysinlargetranscriptomiccompendia
AT hogandeboraha pathcoretidentifyingandvisualizinggloballycooccurringpathwaysinlargetranscriptomiccompendia
AT greenecaseys pathcoretidentifyingandvisualizinggloballycooccurringpathwaysinlargetranscriptomiccompendia