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PathAct: a novel method for pathway analysis using gene expression profiles
We developed PathAct, a novel method for pathway analysis to investigate the biological and clinical implications of the gene expression profiles. The advantage of PathAct in comparison with the conventional pathway analysis methods is that it can estimate pathway activity levels for individual pati...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Biomedical Informatics
2013
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3670121/ https://www.ncbi.nlm.nih.gov/pubmed/23750088 http://dx.doi.org/10.6026/97320630009394 |
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author | Mogushi, Kaoru Tanaka, Hiroshi |
author_facet | Mogushi, Kaoru Tanaka, Hiroshi |
author_sort | Mogushi, Kaoru |
collection | PubMed |
description | We developed PathAct, a novel method for pathway analysis to investigate the biological and clinical implications of the gene expression profiles. The advantage of PathAct in comparison with the conventional pathway analysis methods is that it can estimate pathway activity levels for individual patient quantitatively in the form of a pathway-by-sample matrix. This matrix can be used for further analysis such as hierarchical clustering and other analysis methods. To evaluate the feasibility of PathAct, comparison with frequently used gene-enrichment analysis methods was conducted using two public microarray datasets. The dataset #1 was that of breast cancer patients, and we investigated pathways associated with triple-negative breast cancer by PathAct, compared with those obtained by gene set enrichment analysis (GSEA). The dataset #2 was another breast cancer dataset with disease-free survival (DFS) of each patient. Contribution by each pathway to prognosis was investigated by our method as well as the Database for Annotation, Visualization and Integrated Discovery (DAVID) analysis. In the dataset #1, four out of the six pathways that satisfied p < 0.05 and FDR < 0.30 by GSEA were also included in those obtained by the PathAct method. For the dataset #2, two pathways (“Cell Cycle” and “DNA replication”) out of four pathways by PathAct were commonly identified by DAVID analysis. Thus, we confirmed a good degree of agreement among PathAct and conventional methods. Moreover, several applications of further statistical analyses such as hierarchical cluster analysis by pathway activity, correlation analysis and survival analysis between pathways were conducted. |
format | Online Article Text |
id | pubmed-3670121 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2013 |
publisher | Biomedical Informatics |
record_format | MEDLINE/PubMed |
spelling | pubmed-36701212013-06-07 PathAct: a novel method for pathway analysis using gene expression profiles Mogushi, Kaoru Tanaka, Hiroshi Bioinformation Hypothesis We developed PathAct, a novel method for pathway analysis to investigate the biological and clinical implications of the gene expression profiles. The advantage of PathAct in comparison with the conventional pathway analysis methods is that it can estimate pathway activity levels for individual patient quantitatively in the form of a pathway-by-sample matrix. This matrix can be used for further analysis such as hierarchical clustering and other analysis methods. To evaluate the feasibility of PathAct, comparison with frequently used gene-enrichment analysis methods was conducted using two public microarray datasets. The dataset #1 was that of breast cancer patients, and we investigated pathways associated with triple-negative breast cancer by PathAct, compared with those obtained by gene set enrichment analysis (GSEA). The dataset #2 was another breast cancer dataset with disease-free survival (DFS) of each patient. Contribution by each pathway to prognosis was investigated by our method as well as the Database for Annotation, Visualization and Integrated Discovery (DAVID) analysis. In the dataset #1, four out of the six pathways that satisfied p < 0.05 and FDR < 0.30 by GSEA were also included in those obtained by the PathAct method. For the dataset #2, two pathways (“Cell Cycle” and “DNA replication”) out of four pathways by PathAct were commonly identified by DAVID analysis. Thus, we confirmed a good degree of agreement among PathAct and conventional methods. Moreover, several applications of further statistical analyses such as hierarchical cluster analysis by pathway activity, correlation analysis and survival analysis between pathways were conducted. Biomedical Informatics 2013-04-30 /pmc/articles/PMC3670121/ /pubmed/23750088 http://dx.doi.org/10.6026/97320630009394 Text en © 2013 Biomedical Informatics This is an open-access article, which permits unrestricted use, distribution, and reproduction in any medium, for non-commercial purposes, provided the original author and source are credited. |
spellingShingle | Hypothesis Mogushi, Kaoru Tanaka, Hiroshi PathAct: a novel method for pathway analysis using gene expression profiles |
title | PathAct: a novel method for pathway analysis using gene expression profiles |
title_full | PathAct: a novel method for pathway analysis using gene expression profiles |
title_fullStr | PathAct: a novel method for pathway analysis using gene expression profiles |
title_full_unstemmed | PathAct: a novel method for pathway analysis using gene expression profiles |
title_short | PathAct: a novel method for pathway analysis using gene expression profiles |
title_sort | pathact: a novel method for pathway analysis using gene expression profiles |
topic | Hypothesis |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3670121/ https://www.ncbi.nlm.nih.gov/pubmed/23750088 http://dx.doi.org/10.6026/97320630009394 |
work_keys_str_mv | AT mogushikaoru pathactanovelmethodforpathwayanalysisusinggeneexpressionprofiles AT tanakahiroshi pathactanovelmethodforpathwayanalysisusinggeneexpressionprofiles |