Durbin Blythe P, Rocke David M
Center for Image Processing and Integrated Computing, University of California, Davis 95616, USA.
Bioinformatics. 2004 Mar 22;20(5):660-7. doi: 10.1093/bioinformatics/btg464. Epub 2004 Jan 22.
Authors of several recent papers have independently introduced a family of transformations (the generalized-log family), which stabilizes the variance of microarray data up to the first order. However, for data from two-color arrays, tests for differential expression may require that the variance of the difference of transformed observations be constant, rather than that of the transformed observations themselves.
We introduce a transformation within the generalized-log family which stabilizes, to the first order, the variance of the difference of transformed observations. We also introduce transformations from the 'started-log' and log-linear-hybrid families which provide good approximate variance stabilization of differences. Examples using control-control data show that any of these transformations may provide sufficient variance stabilization for practical applications, and all perform well compared to log ratios.
最近几篇论文的作者独立引入了一族变换（广义对数族），该变换能将微阵列数据的方差稳定到一阶。然而，对于双色阵列的数据，差异表达检验可能要求变换后观测值之差的方差恒定，而非变换后观测值本身的方差恒定。
我们在广义对数族中引入一种变换，它能将变换后观测值之差的方差稳定到一阶。我们还引入了来自“起始对数”族和对数线性混合族的变换，它们能为差异提供良好的近似方差稳定。使用对照-对照数据的示例表明，这些变换中的任何一种都可为实际应用提供足够的方差稳定，并且与对数比率相比，所有变换的表现都很好。
