Lin Simon M, Du Pan, Huber Wolfgang, Kibbe Warren A
Robert H. Lurie Comprehensive Cancer Center, Northwestern University, Chicago, IL, 60611, USA.
Nucleic Acids Res. 2008 Feb;36(2):e11. doi: 10.1093/nar/gkm1075. Epub 2008 Jan 4.
Variance stabilization is a step in the preprocessing of microarray data that can greatly benefit the performance of subsequent statistical modeling and inference. Due to the often limited number of technical replicates for Affymetrix and cDNA arrays, achieving variance stabilization can be difficult. Although the Illumina microarray platform provides a larger number of technical replicates on each array (usually over 30 randomly distributed beads per probe), these replicates have not been leveraged in the current log2 data transformation process. We devised a variance-stabilizing transformation (VST) method that takes advantage of the technical replicates available on an Illumina microarray. We have compared VST with log2 and Variance-stabilizing normalization (VSN) by using the Kruglyak bead-level data (2006) and Barnes titration data (2005). The results of the Kruglyak data suggest that VST stabilizes variances of bead-replicates within an array. The results of the Barnes data show that VST can improve the detection of differentially expressed genes and reduce false-positive identifications. We conclude that although both VST and VSN are built upon the same model of measurement noise, VST stabilizes the variance better and more efficiently for the Illumina platform by leveraging the availability of a larger number of within-array replicates. The algorithms and Supplementary Data are included in the lumi package of Bioconductor, available at: www.bioconductor.org.
方差稳定化是微阵列数据预处理中的一个步骤，它能极大地提升后续统计建模和推断的性能。由于Affymetrix和cDNA阵列的技术重复次数通常有限，实现方差稳定化可能具有挑战性。尽管Illumina微阵列平台在每个阵列上提供了更多的技术重复（每个探针通常有超过30个随机分布的珠子），但这些重复在当前的log2数据转换过程中并未得到利用。我们设计了一种方差稳定化转换（VST）方法，该方法利用了Illumina微阵列上可用的技术重复。我们通过使用Kruglyak珠子水平数据（2006年）和Barnes滴定数据（2005年），将VST与log2和方差稳定化归一化（VSN）进行了比较。Kruglyak数据的结果表明，VST可稳定阵列内珠子重复的方差。Barnes数据的结果显示，VST能够改善差异表达基因的检测，并减少假阳性识别。我们得出结论，尽管VST和VSN都基于相同的测量噪声模型，但通过利用更多的阵列内重复，VST在Illumina平台上能更好、更有效地稳定方差。算法和补充数据包含在Bioconductor的lumi包中，可从以下网址获取：www.bioconductor.org。
