Hsiao A, Worrall D S, Olefsky J M, Subramaniam S
Department of Bioengineering, UC San Diego, La Jolla, CA 92093, USA.
Bioinformatics. 2004 Nov 22;20(17):3108-27. doi: 10.1093/bioinformatics/bth371. Epub 2004 Jun 24.
Microarrays are becoming an increasingly common tool for observing changes in gene expression over a large cross section of the genome. This experimental tool is particularly valuable for understanding the genome-wide changes in gene transcription in response to thiazolidinedione (TZD) treatment. The TZD class of drugs is known to improve insulin-sensitivity in diabetic patients, and is clinically used in treatment regimens. In cells, TZDs bind to and activate the transcriptional activity of peroxisome proliferator-activated receptor gamma (PPAR-gamma). Large-scale array analyses will provide some insight into the mechanisms of TZD-mediated insulin sensitization. Unfortunately, a theoretical basis for analyzing array data has not kept pace with the rapid adoption of this tool. The methods that are commonly used, particularly the fold-change approach and the standard t-test, either lack statistical rigor or resort to generalized statistical models that do not accurately estimate variability at low replicate numbers.
We introduce a statistical framework that models the dependence of measurement variance on the level of gene expression in the context of a Bayesian hierarchical model. We compare several methods of parameter estimation and subsequently apply these to determine a set of genes in 3T3-L1 adipocytes that are differentially regulated in response to TZD treatment. When the number of experimental replicates is low (n = 2-3), this approach appears to qualitatively preserve an equivalent degree of specificity, while vastly improving sensitivity over other comparable methods. In addition, the statistical framework developed here can be readily applied to understand the implicit assumptions made in traditional fold-change approaches to array analysis.
微阵列正日益成为一种常见工具，用于观察基因组大片段上基因表达的变化。这种实验工具对于理解噻唑烷二酮（TZD）治疗后全基因组范围内基因转录的变化尤为有价值。已知TZD类药物可改善糖尿病患者的胰岛素敏感性，并在临床治疗方案中使用。在细胞中，TZD与过氧化物酶体增殖物激活受体γ（PPAR-γ）结合并激活其转录活性。大规模阵列分析将为TZD介导的胰岛素增敏机制提供一些见解。不幸的是，分析阵列数据的理论基础未能跟上该工具的迅速采用。常用的方法，特别是倍数变化法和标准t检验，要么缺乏统计严谨性，要么诉诸于不能准确估计低重复次数下变异性的广义统计模型。
我们引入了一个统计框架，该框架在贝叶斯层次模型的背景下对测量方差与基因表达水平的依赖性进行建模。我们比较了几种参数估计方法，随后将这些方法应用于确定3T3-L1脂肪细胞中一组因TZD治疗而受到差异调节的基因。当实验重复次数较低（n = 2 - 3）时，这种方法似乎在定性上保持了同等程度的特异性，同时与其他可比方法相比大大提高了灵敏度。此外，这里开发的统计框架可以很容易地应用于理解传统倍数变化法在阵列分析中所做的隐含假设。
