Multigroup Equivalence Analysis for High-Dimensional Expression Data. Yang, C., Bartolucci, A. A., & Cui, X. Cancer Informatics, 14s2:CIN.S17304, January, 2015. ![Multigroup Equivalence Analysis for High-Dimensional Expression Data [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex Hypothesis tests of equivalence are typically known for their application in bioequivalence studies and acceptance sampling. Their application to gene expression data, in particular high-dimensional gene expression data, has only recently been studied. In this paper, we examine how two multigroup equivalence tests, the F-test and the range test, perform when applied to microarray expression data. We adapted these tests to a well-known equivalence criterion, the difference ratio. Our simulation results showed that both tests can achieve moderate power while controlling the type I error at nominal level for typical expression microarray studies with the benefit of easy-to-interpret equivalence limits. For the range of parameters simulated in this paper, the F-test is more powerful than the range test. However, for comparing three groups, their powers are similar. Finally, the two multigroup tests were applied to a prostate cancer microarray dataset to identify genes whose expression follows a prespecified trajectory across five prostate cancer stages.
@article{yang_multigroup_2015,
title = {Multigroup {Equivalence} {Analysis} for {High}-{Dimensional} {Expression} {Data}},
volume = {14s2},
issn = {1176-9351, 1176-9351},
url = {http://journals.sagepub.com/doi/10.4137/CIN.S17304},
doi = {10.4137/CIN.S17304},
abstract = {Hypothesis tests of equivalence are typically known for their application in bioequivalence studies and acceptance sampling. Their application to gene expression data, in particular high-dimensional gene expression data, has only recently been studied. In this paper, we examine how two multigroup equivalence tests, the F-test and the range test, perform when applied to microarray expression data. We adapted these tests to a well-known equivalence criterion, the difference ratio. Our simulation results showed that both tests can achieve moderate power while controlling the type I error at nominal level for typical expression microarray studies with the benefit of easy-to-interpret equivalence limits. For the range of parameters simulated in this paper, the F-test is more powerful than the range test. However, for comparing three groups, their powers are similar. Finally, the two multigroup tests were applied to a prostate cancer microarray dataset to identify genes whose expression follows a prespecified trajectory across five prostate cancer stages.},
language = {en},
urldate = {2019-08-13},
journal = {Cancer Informatics},
author = {Yang, Celeste and Bartolucci, Alfred A. and Cui, Xiangqin},
month = jan,
year = {2015},
pages = {CIN.S17304}
}
Downloads: 0
{"_id":"EuFsnwi5sHn8HTShe","bibbaseid":"yang-bartolucci-cui-multigroupequivalenceanalysisforhighdimensionalexpressiondata-2015","authorIDs":[],"author_short":["Yang, C.","Bartolucci, A. A.","Cui, X."],"bibdata":{"bibtype":"article","type":"article","title":"Multigroup Equivalence Analysis for High-Dimensional Expression Data","volume":"14s2","issn":"1176-9351, 1176-9351","url":"http://journals.sagepub.com/doi/10.4137/CIN.S17304","doi":"10.4137/CIN.S17304","abstract":"Hypothesis tests of equivalence are typically known for their application in bioequivalence studies and acceptance sampling. Their application to gene expression data, in particular high-dimensional gene expression data, has only recently been studied. In this paper, we examine how two multigroup equivalence tests, the F-test and the range test, perform when applied to microarray expression data. We adapted these tests to a well-known equivalence criterion, the difference ratio. Our simulation results showed that both tests can achieve moderate power while controlling the type I error at nominal level for typical expression microarray studies with the benefit of easy-to-interpret equivalence limits. For the range of parameters simulated in this paper, the F-test is more powerful than the range test. However, for comparing three groups, their powers are similar. Finally, the two multigroup tests were applied to a prostate cancer microarray dataset to identify genes whose expression follows a prespecified trajectory across five prostate cancer stages.","language":"en","urldate":"2019-08-13","journal":"Cancer Informatics","author":[{"propositions":[],"lastnames":["Yang"],"firstnames":["Celeste"],"suffixes":[]},{"propositions":[],"lastnames":["Bartolucci"],"firstnames":["Alfred","A."],"suffixes":[]},{"propositions":[],"lastnames":["Cui"],"firstnames":["Xiangqin"],"suffixes":[]}],"month":"January","year":"2015","pages":"CIN.S17304","bibtex":"@article{yang_multigroup_2015,\n\ttitle = {Multigroup {Equivalence} {Analysis} for {High}-{Dimensional} {Expression} {Data}},\n\tvolume = {14s2},\n\tissn = {1176-9351, 1176-9351},\n\turl = {http://journals.sagepub.com/doi/10.4137/CIN.S17304},\n\tdoi = {10.4137/CIN.S17304},\n\tabstract = {Hypothesis tests of equivalence are typically known for their application in bioequivalence studies and acceptance sampling. Their application to gene expression data, in particular high-dimensional gene expression data, has only recently been studied. In this paper, we examine how two multigroup equivalence tests, the F-test and the range test, perform when applied to microarray expression data. We adapted these tests to a well-known equivalence criterion, the difference ratio. Our simulation results showed that both tests can achieve moderate power while controlling the type I error at nominal level for typical expression microarray studies with the benefit of easy-to-interpret equivalence limits. For the range of parameters simulated in this paper, the F-test is more powerful than the range test. However, for comparing three groups, their powers are similar. Finally, the two multigroup tests were applied to a prostate cancer microarray dataset to identify genes whose expression follows a prespecified trajectory across five prostate cancer stages.},\n\tlanguage = {en},\n\turldate = {2019-08-13},\n\tjournal = {Cancer Informatics},\n\tauthor = {Yang, Celeste and Bartolucci, Alfred A. and Cui, Xiangqin},\n\tmonth = jan,\n\tyear = {2015},\n\tpages = {CIN.S17304}\n}\n\n","author_short":["Yang, C.","Bartolucci, A. A.","Cui, X."],"key":"yang_multigroup_2015","id":"yang_multigroup_2015","bibbaseid":"yang-bartolucci-cui-multigroupequivalenceanalysisforhighdimensionalexpressiondata-2015","role":"author","urls":{"Paper":"http://journals.sagepub.com/doi/10.4137/CIN.S17304"},"downloads":0},"bibtype":"article","biburl":"https://bibbase.org/zotero/romain_jacob","creationDate":"2020-03-11T17:05:50.236Z","downloads":0,"keywords":[],"search_terms":["multigroup","equivalence","analysis","high","dimensional","expression","data","yang","bartolucci","cui"],"title":"Multigroup Equivalence Analysis for High-Dimensional Expression Data","year":2015,"dataSources":["GSdyza34Yogv8tQFT"]}