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Xiangqin Cui
Researcher at University of Alabama at Birmingham
Publications - 90
Citations - 7785
Xiangqin Cui is an academic researcher from University of Alabama at Birmingham. The author has contributed to research in topics: Gene & Polycystic kidney disease. The author has an hindex of 35, co-authored 89 publications receiving 7289 citations. Previous affiliations of Xiangqin Cui include United States Department of Agriculture & University of North Texas Health Science Center.
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Journal ArticleDOI
Microarray data analysis: from disarray to consolidation and consensus.
TL;DR: In just a few years, microarrays have gone from obscurity to being almost ubiquitous in biological research, and points of consensus are emerging about the general approaches that warrant use and elaboration.
Journal ArticleDOI
Statistical tests for differential expression in cDNA microarray experiments.
Xiangqin Cui,Gary A. Churchill +1 more
TL;DR: Analysis of variance (ANOVA) can be used, and the mixed ANOVA model is a general and powerful approach for microarray experiments with multiple factors and/or several sources of variation.
Journal ArticleDOI
Repeatability of published microarray gene expression analyses.
John P. A. Ioannidis,David B. Allison,Catherine A. Ball,Issa Coulibaly,Xiangqin Cui,Aedín C. Culhane,Mario Falchi,Mario Falchi,Cesare Furlanello,Laurence Game,Giuseppe Jurman,Jon Mangion,Tapan Mehta,Michael Nitzberg,Grier P. Page,Grier P. Page,Enrico Petretto,Enrico Petretto,Vera van Noort +18 more
TL;DR: Evaluated the replication of data analyses in 18 articles on microarray-based gene expression profiling published in Nature Genetics in 2005–2006, finding that Repeatability of published microarray studies is apparently limited.
Journal ArticleDOI
Improved statistical tests for differential gene expression by shrinking variance components estimates.
TL;DR: An estimator of the error variance that can borrow information across genes using the James-Stein shrinkage concept is developed and a new test statistic (FS) is constructed that shows best or nearly best power for detecting differentially expressed genes over a wide range of simulated data.
Journal ArticleDOI
Erratum: Microarray data analysis: from disarray to consolidation and consensus
TL;DR: The y- axis label for Figure 1d was incorrect and the correct y-axis label should be .