Peddada Shyamal D, Harris Shawn F, Davidov Ori
Biostatistics Branch, NIEHS, NIH, T. W. Alexander Dr. NC, 27709.
J Indian Soc Agric Stat. 2010;64(1):45-60.
A bootstrap based methodology is introduced for analyzing repeated measures/longitudinal microarray gene expression data over ordered categories. The proposed non-parametric procedure uses order-restricted inference to compare gene expressions among ordered experimental conditions. The null distribution for determining significance is derived by suitably bootstrapping the residuals. The procedure addresses two potential sources of correlation in the data, namely, (a) correlations among genes within a chip ("intra-chip" correlation), and (b) correlation within subject due to repeated/longitudinal measurements ("temporal" correlation). To make the procedure computationally efficient, the adaptive bootstrap methodology of Guo and Peddada (2008) is implemented such that the resulting procedure controls the false discovery rate (FDR) at the desired nominal level.
引入了一种基于自助法的方法，用于分析有序类别上的重复测量/纵向微阵列基因表达数据。所提出的非参数程序使用顺序受限推断来比较有序实验条件下的基因表达。通过对残差进行适当的自助抽样来推导用于确定显著性的零分布。该程序解决了数据中两个潜在的相关来源，即：(a)芯片内基因之间的相关性（“芯片内”相关性），以及(b)由于重复/纵向测量导致的受试者内部相关性（“时间”相关性）。为了使该程序计算高效，实施了Guo和Peddada（2008）的自适应自助法，以使所得程序在所需的名义水平上控制错误发现率（FDR）。
