Increasing the reliability of clinical microarray data analysis by systematic bia

描述(由申请人提供)： 微阵列分析被广泛认为将进一步加深我们对疾病表型的理解，并产生基于强健的基因表达特征的临床结果预测指标。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，如其噪声结构的了解不足，往往阻碍提取稳健的、生物学的和临床上有意义的结果。在这项资助中，我们提供了临床微阵列数据集包含显著水平的系统性偏差的初步证据。我们确定了观察到的技术偏差的来源，例如给定微阵列样本中的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中的虚假相关性，以及基因和临床变量之间的虚假关联。在这项提案中，我们正在开发一种方法，以纠正在各种常用的微阵列平台上产生的临床微阵列数据中的此类技术偏差。在具体目标2中，我们将评估临床微阵列数据集中系统偏差校正的总体影响。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出更好的相关性。我们的初步结果表明，建议的偏差校正显著提高了基因表达水平与已知生物学关系的一致性，因此它可能有助于从微阵列数据中提取临床相关结果。

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.