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评估基因芯片数据分析中基因选择的稳定性。

Assessing stability of gene selection in microarray data analysis.

作者信息

Qiu Xing, Xiao Yuanhui, Gordon Alexander, Yakovlev Andrei

机构信息

Department of Biostatistics and Computational Biology, University of Rochester, 601 Elmwood Avenue, Rochester, New York 14642, USA.

出版信息

BMC Bioinformatics. 2006 Feb 1;7:50. doi: 10.1186/1471-2105-7-50.

Abstract

BACKGROUND

The number of genes declared differentially expressed is a random variable and its variability can be assessed by resampling techniques. Another important stability indicator is the frequency with which a given gene is selected across subsamples. We have conducted studies to assess stability and some other properties of several gene selection procedures with biological and simulated data.

RESULTS

Using resampling techniques we have found that some genes are selected much less frequently (across sub-samples) than other genes with the same adjusted p-values. The extent to which this type of instability manifests itself can be assessed by a method introduced in this paper. The effect of correlation between gene expression levels on the performance of multiple testing procedures is studied by computer simulations.

CONCLUSION

Resampling represents a tool for reducing the set of initially selected genes to those with a sufficiently high selection frequency. Using resampling techniques it is also possible to assess variability of different performance indicators. Stability properties of several multiple testing procedures are described at length in the present paper.

摘要

背景

宣称差异表达的基因数量是一个随机变量,其变异性可以通过重采样技术进行评估。另一个重要的稳定性指标是给定基因在各个子样本中被选中的频率。我们已经开展了多项研究,以评估几种基因选择程序在生物学数据和模拟数据方面的稳定性及其他一些特性。

结果

通过重采样技术,我们发现一些基因(在各个子样本中)被选中的频率远低于其他具有相同校正p值的基因。本文介绍的一种方法可以评估这类不稳定性的表现程度。通过计算机模拟研究了基因表达水平之间的相关性对多重检验程序性能的影响。

结论

重采样是一种将最初选定的基因集缩减为选择频率足够高的基因的工具。使用重采样技术还可以评估不同性能指标的变异性。本文详细描述了几种多重检验程序的稳定性特性。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1f6e/1403808/bb48c085f091/1471-2105-7-50-1.jpg

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