Increasing the reliability of clinical microarray data analysis by systematic bia

描述（由申请人提供）： 微阵列分析被广泛期望进一步我们对疾病表型的理解，并产生基于基因表达特征的临床结果预测因子。然而，正如文献中经常证明的那样，对临床微阵列数据集的各种关键特征（例如其噪声结构）的理解不足，通常会阻碍提取稳健的、生物学上和临床上有意义的结果。在这项资助中，我们提供了初步证据，表明临床微阵列数据集包含显着水平的系统性偏差。我们确定了观察到的技术偏差的来源，例如给定微阵列样品中mRNA完整性的总体水平。我们发现，这会影响许多基因的表达水平，从而导致临床数据集的虚假相关性以及基因和临床变量之间的虚假关联。在这个建议中，我们正在开发一种方法，纠正临床微阵列数据中的技术偏差，这些数据是在各种常用的微阵列平台上产生的。在具体目标2中，我们将评估临床微阵列数据集系统偏倚校正的总体影响。我们将确定临床微阵列测量是否与独立验证或交叉验证显示更好的相关性。正如我们的初步结果表明，提出的偏差校正显着增加了一致性的基因表达水平与已知的生物学关系，因此，它可能会促进提取临床相关的结果，从微阵列数据。

DESCRIPTION (provided by applicant): Microarray analysis is widely expected to further our understanding of disease phenotypes and yield robust gene expression signature based predictors of clinical outcome. However, as it has been frequently demonstrated in the literature, insufficient understanding of the various key characteristics of clinical microarray data sets, such as their noise structure, often impedes extracting robust, biologically and clinically meaningful results. In this grant we provide preliminary evidence that clinical microarray data sets contain a significant level of systematic bias. We identified sources of the observed technical bias, such as the overall level of mRNA integrity in a given microarray sample. We showed that this affects the expression level of many genes in concert, thus causing spurious correlations in clinical data sets and false associations between genes and clinical variables. In this proposal we are developing a method that correct for such technical biases in clinical microarray data that are produced on the various generally used microarray platforms. In specific aim 2 we will evaluate the overall impact of systematic bias correction in clinical microarray data sets. We will determine whether clinical microarray measurements show better correlation with independent validation or during cross-validation. As our preliminary results indicate, the proposed bias correction significantly increased concordance of gene expression levels with known biological relationships therefore it will likely facilitate the extraction of clinically relevant results from microarray data.