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Article: Pathway-based meta-analysis for partially paired transcriptomics analysis

TitlePathway-based meta-analysis for partially paired transcriptomics analysis
Authors
KeywordsMeta‐analysis
Partially overlapping samples
Pathway analysis
Psoriasis
Issue Date2020
PublisherWiley-Blackwell Publishing Ltd. The Journal's web site is located at http://onlinelibrary.wiley.com/journal/10.1002/(ISSN)1759-2887
Citation
Research Synthesis Methods, 2020, v. 11 n. 1, p. 123-133 How to Cite?
AbstractPathway-based differential expression analysis allows the incorporation of biological domain knowledge into transcriptomics analysis to enhance our understanding of disease mechanisms. To integrate information among multiple studies at the pathway level, pathway-based meta-analysis can be performed. Paired or partially paired samples are common in biomedical research. However, there are currently no existing pathway-based meta-analysis methods appropriate for paired or partially paired study designs. In this study, we developed a pathway-based meta-analysis approach for paired or partially paired samples. Meta-analysis on the transcriptomics profiles were conducted using p-value-based, rank-based, and effect size-based algorithms. The application of our approach was demonstrated using partially paired data from psoriasis transcriptomics studies. Upon combining six transcriptomics studies, genes related to the cell cycle and DNA replication pathways are found to be highly perturbed in psoriatic lesional skin samples. Results were validated externally with independent RNA-Seq data. Comparison with existing pathway meta-analysis methods revealed consistent results, with our method showing higher detection power. This study demonstrated the utility of our newly developed pathway-based meta-analysis that allows the incorporation of partially paired or paired samples. The proposed framework can be applied to omics data including but not limited to transcriptomics data.
Persistent Identifierhttp://hdl.handle.net/10722/281766
ISSN
2019 Impact Factor: 5.299
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorFung, WT-
dc.contributor.authorWu, JT-
dc.contributor.authorChan, WMM-
dc.contributor.authorChan, HH-
dc.contributor.authorPang, H-
dc.date.accessioned2020-03-27T04:22:18Z-
dc.date.available2020-03-27T04:22:18Z-
dc.date.issued2020-
dc.identifier.citationResearch Synthesis Methods, 2020, v. 11 n. 1, p. 123-133-
dc.identifier.issn1759-2879-
dc.identifier.urihttp://hdl.handle.net/10722/281766-
dc.description.abstractPathway-based differential expression analysis allows the incorporation of biological domain knowledge into transcriptomics analysis to enhance our understanding of disease mechanisms. To integrate information among multiple studies at the pathway level, pathway-based meta-analysis can be performed. Paired or partially paired samples are common in biomedical research. However, there are currently no existing pathway-based meta-analysis methods appropriate for paired or partially paired study designs. In this study, we developed a pathway-based meta-analysis approach for paired or partially paired samples. Meta-analysis on the transcriptomics profiles were conducted using p-value-based, rank-based, and effect size-based algorithms. The application of our approach was demonstrated using partially paired data from psoriasis transcriptomics studies. Upon combining six transcriptomics studies, genes related to the cell cycle and DNA replication pathways are found to be highly perturbed in psoriatic lesional skin samples. Results were validated externally with independent RNA-Seq data. Comparison with existing pathway meta-analysis methods revealed consistent results, with our method showing higher detection power. This study demonstrated the utility of our newly developed pathway-based meta-analysis that allows the incorporation of partially paired or paired samples. The proposed framework can be applied to omics data including but not limited to transcriptomics data.-
dc.languageeng-
dc.publisherWiley-Blackwell Publishing Ltd. The Journal's web site is located at http://onlinelibrary.wiley.com/journal/10.1002/(ISSN)1759-2887-
dc.relation.ispartofResearch Synthesis Methods-
dc.subjectMeta‐analysis-
dc.subjectPartially overlapping samples-
dc.subjectPathway analysis-
dc.subjectPsoriasis-
dc.titlePathway-based meta-analysis for partially paired transcriptomics analysis-
dc.typeArticle-
dc.identifier.emailWu, JT: joewu@hku.hk-
dc.identifier.emailChan, WMM: chanwmm@hku.hk-
dc.identifier.emailChan, HH: hhlchan@hkucc.hku.hk-
dc.identifier.emailPang, H: herbpang@hku.hk-
dc.identifier.authorityWu, JT=rp00517-
dc.identifier.authorityChan, WMM=rp02394-
dc.identifier.authorityPang, H=rp01857-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1002/jrsm.1381-
dc.identifier.pmid31682084-
dc.identifier.scopuseid_2-s2.0-85074858677-
dc.identifier.hkuros309576-
dc.identifier.volume11-
dc.identifier.issue1-
dc.identifier.spage123-
dc.identifier.epage133-
dc.identifier.isiWOS:000495485700001-
dc.publisher.placeUnited Kingdom-
dc.identifier.issnl1759-2879-

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