MetaGSCA: A tool for meta-analysis of gene set differential coexpression.
Analyses of gene set differential coexpression may shed light on molecular mechanisms underlying phenotypes and diseases. However, differential coexpression analyses of conceptually similar individual studies are often inconsistent and underpowered to provide definitive results. Researchers can grea...
| Main Authors: | , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2021-05-01
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| Series: | PLoS Computational Biology |
| Online Access: | https://doi.org/10.1371/journal.pcbi.1008976 |
| _version_ | 1819008085040037888 |
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| author | Yan Guo Hui Yu Haocan Song Jiapeng He Olufunmilola Oyebamiji Huining Kang Jie Ping Scott Ness Yu Shyr Fei Ye |
| author_facet | Yan Guo Hui Yu Haocan Song Jiapeng He Olufunmilola Oyebamiji Huining Kang Jie Ping Scott Ness Yu Shyr Fei Ye |
| author_sort | Yan Guo |
| collection | DOAJ |
| description | Analyses of gene set differential coexpression may shed light on molecular mechanisms underlying phenotypes and diseases. However, differential coexpression analyses of conceptually similar individual studies are often inconsistent and underpowered to provide definitive results. Researchers can greatly benefit from an open-source application facilitating the aggregation of evidence of differential coexpression across studies and the estimation of more robust common effects. We developed Meta Gene Set Coexpression Analysis (MetaGSCA), an analytical tool to systematically assess differential coexpression of an a priori defined gene set by aggregating evidence across studies to provide a definitive result. In the kernel, a nonparametric approach that accounts for the gene-gene correlation structure is used to test whether the gene set is differentially coexpressed between two comparative conditions, from which a permutation test p-statistic is computed for each individual study. A meta-analysis is then performed to combine individual study results with one of two options: a random-intercept logistic regression model or the inverse variance method. We demonstrated MetaGSCA in case studies investigating two human diseases and identified pathways highly relevant to each disease across studies. We further applied MetaGSCA in a pan-cancer analysis with hundreds of major cellular pathways in 11 cancer types. The results indicated that a majority of the pathways identified were dysregulated in the pan-cancer scenario, many of which have been previously reported in the cancer literature. Our analysis with randomly generated gene sets showed excellent specificity, indicating that the significant pathways/gene sets identified by MetaGSCA are unlikely false positives. MetaGSCA is a user-friendly tool implemented in both forms of a Web-based application and an R package "MetaGSCA". It enables comprehensive meta-analyses of gene set differential coexpression data, with an optional module of post hoc pathway crosstalk network analysis to identify and visualize pathways having similar coexpression profiles. |
| first_indexed | 2024-12-21T00:34:52Z |
| format | Article |
| id | doaj.art-dfe3d22a633a472d9322b1fe6da9ec96 |
| institution | Directory Open Access Journal |
| issn | 1553-734X 1553-7358 |
| language | English |
| last_indexed | 2024-12-21T00:34:52Z |
| publishDate | 2021-05-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS Computational Biology |
| spelling | doaj.art-dfe3d22a633a472d9322b1fe6da9ec962022-12-21T19:21:49ZengPublic Library of Science (PLoS)PLoS Computational Biology1553-734X1553-73582021-05-01175e100897610.1371/journal.pcbi.1008976MetaGSCA: A tool for meta-analysis of gene set differential coexpression.Yan GuoHui YuHaocan SongJiapeng HeOlufunmilola OyebamijiHuining KangJie PingScott NessYu ShyrFei YeAnalyses of gene set differential coexpression may shed light on molecular mechanisms underlying phenotypes and diseases. However, differential coexpression analyses of conceptually similar individual studies are often inconsistent and underpowered to provide definitive results. Researchers can greatly benefit from an open-source application facilitating the aggregation of evidence of differential coexpression across studies and the estimation of more robust common effects. We developed Meta Gene Set Coexpression Analysis (MetaGSCA), an analytical tool to systematically assess differential coexpression of an a priori defined gene set by aggregating evidence across studies to provide a definitive result. In the kernel, a nonparametric approach that accounts for the gene-gene correlation structure is used to test whether the gene set is differentially coexpressed between two comparative conditions, from which a permutation test p-statistic is computed for each individual study. A meta-analysis is then performed to combine individual study results with one of two options: a random-intercept logistic regression model or the inverse variance method. We demonstrated MetaGSCA in case studies investigating two human diseases and identified pathways highly relevant to each disease across studies. We further applied MetaGSCA in a pan-cancer analysis with hundreds of major cellular pathways in 11 cancer types. The results indicated that a majority of the pathways identified were dysregulated in the pan-cancer scenario, many of which have been previously reported in the cancer literature. Our analysis with randomly generated gene sets showed excellent specificity, indicating that the significant pathways/gene sets identified by MetaGSCA are unlikely false positives. MetaGSCA is a user-friendly tool implemented in both forms of a Web-based application and an R package "MetaGSCA". It enables comprehensive meta-analyses of gene set differential coexpression data, with an optional module of post hoc pathway crosstalk network analysis to identify and visualize pathways having similar coexpression profiles.https://doi.org/10.1371/journal.pcbi.1008976 |
| spellingShingle | Yan Guo Hui Yu Haocan Song Jiapeng He Olufunmilola Oyebamiji Huining Kang Jie Ping Scott Ness Yu Shyr Fei Ye MetaGSCA: A tool for meta-analysis of gene set differential coexpression. PLoS Computational Biology |
| title | MetaGSCA: A tool for meta-analysis of gene set differential coexpression. |
| title_full | MetaGSCA: A tool for meta-analysis of gene set differential coexpression. |
| title_fullStr | MetaGSCA: A tool for meta-analysis of gene set differential coexpression. |
| title_full_unstemmed | MetaGSCA: A tool for meta-analysis of gene set differential coexpression. |
| title_short | MetaGSCA: A tool for meta-analysis of gene set differential coexpression. |
| title_sort | metagsca a tool for meta analysis of gene set differential coexpression |
| url | https://doi.org/10.1371/journal.pcbi.1008976 |
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