Garrett-Mayer Elizabeth, Parmigiani Giovanni, Zhong Xiaogang, Cope Leslie, Gabrielson Edward
Division of Biostatistics, The Hollings Cancer Center, Medical University of South Carolina, Charleston, SC 29425, USA.
Biostatistics. 2008 Apr;9(2):333-54. doi: 10.1093/biostatistics/kxm033. Epub 2007 Sep 14.
Investigations of transcript levels on a genomic scale using hybridization-based arrays have led to formidable advances in our understanding of the biology of many human illnesses. At the same time, these investigations have generated controversy because of the probabilistic nature of the conclusions and the surfacing of noticeable discrepancies between the results of studies addressing the same biological question. In this article, we present simple and effective data analysis and visualization tools for gauging the degree to which the findings of one study are reproduced by others and for integrating multiple studies in a single analysis. We describe these approaches in the context of studies of breast cancer and illustrate that it is possible to identify a substantial biologically relevant subset of the human genome within which hybridization results are reliable. The subset generally varies with the platforms used, the tissues studied, and the populations being sampled. Despite important differences, it is also possible to develop simple expression measures that allow comparison across platforms, studies, laboratories and populations. Important biological signals are often preserved or enhanced. Cross-study validation and combination of microarray results requires careful, but not overly complex, statistical thinking and can become a routine component of genomic analysis.
使用基于杂交的阵列在基因组规模上对转录本水平进行研究，极大地推动了我们对许多人类疾病生物学的理解。与此同时，这些研究引发了争议，原因在于结论的概率性质以及针对同一生物学问题的研究结果之间出现了明显差异。在本文中，我们展示了简单有效的数据分析和可视化工具，用于衡量一项研究的结果被其他研究重现的程度，以及在单一分析中整合多项研究。我们在乳腺癌研究的背景下描述这些方法，并表明有可能在人类基因组中识别出一个与生物学高度相关的重要子集，在该子集中杂交结果是可靠的。该子集通常会因所使用的平台、所研究的组织以及所采样的人群而有所不同。尽管存在重要差异，但也有可能开发出简单的表达量度，以便在不同平台、研究、实验室和人群之间进行比较。重要的生物学信号通常得以保留或增强。跨研究验证和微阵列结果的合并需要谨慎但不过于复杂的统计思维，并且可以成为基因组分析的常规组成部分。
