SAFEGUI：使基因表达数据中基于重采样的分类显著性检验变得轻松。
SAFEGUI: resampling-based tests of categorical significance in gene expression data made easy.
作者信息
Gatti Daniel M, Sypa Myroslav, Rusyn Ivan, Wright Fred A, Barry William T
机构信息
Department of Environmental Sciences & Engineering, Department of Biostatistics, University of North Carolina, Chapel Hill, NC 27599, USA.
出版信息
Bioinformatics. 2009 Feb 15;25(4):541-2. doi: 10.1093/bioinformatics/btn655. Epub 2008 Dec 19.
SUMMARY
A large number of websites and applications perform significance testing for gene categories/pathways in microarray data. Many of these packages fail to account for expression correlation between transcripts, with a resultant inflation in Type I error. Array permutation and other resampling-based approaches have been proposed as solutions to this problem. SAFEGUI provides a user-friendly graphical interface for the assessment of categorical significance in microarray studies, while properly accounting for the effects of correlations among genes. SAFEGUI incorporates both permutation and more recently proposed bootstrap algorithms that are demonstrated to be more powerful in detecting differential expression across categories of genes.
AVAILABILITY
摘要
大量网站和应用程序对微阵列数据中的基因类别/通路进行显著性检验。这些软件包中的许多都没有考虑转录本之间的表达相关性，从而导致I型错误率升高。阵列置换和其他基于重采样的方法已被提出作为解决这一问题的方案。SAFEGUI为微阵列研究中的分类显著性评估提供了一个用户友好的图形界面，同时适当地考虑了基因间相关性的影响。SAFEGUI结合了置换算法和最近提出的自举算法，这些算法在检测跨基因类别的差异表达方面表现得更加强大。
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