Increasing the reliability of clinical microarray data analysis by systematic bia

通过系统偏差提高临床微阵列数据分析的可靠性

• 批准号: 7569798
• 负责人: Zoltan Szallasi
• 金额: $ 8.54万
• 依托单位: BOSTON CHILDREN'S HOSPITAL
• 依托单位国家: 美国
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-09-01 至 2011-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7569798
• 关键词: Affect; Algorithms; Benchmarking; Biological; Biological Markers; Bion; Characteristics; Chemotherapy-Oncologic Procedure; Classification; Clinical; Clinical Data; Colon Carcinoma; Data; Data Analyses; Data Set; Exons; Gene Chips; Gene Expression; Gene Expression Profile; Gene Expression Profiling; Generations; Genes; Grant; Literature; Measurement; Measures; Messenger RNA; Methods; Metric; Microarray Analysis; Noise; Outcome; Price; Publishing; RNA; RNA Degradation; Research Personnel; Reverse Transcriptase Polymerase Chain Reaction; Sampling; Source; Structure; Systematic Bias; Text; Validation; base; clinically relevant; disease phenotype; evaluation/testing; improved; response; therapeutic target

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.

描述（由申请人提供）： 广泛期望微阵列分析进一步了解我们对疾病表型的理解，并产生基于临床结果的基于稳健基因表达签名的预测指标。但是，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的理解不足，例如它们的噪声结构，通常会阻碍提取可靠，生物学和临床意义上有意义的结果。在这笔赠款中，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偏见。我们确定了观察到的技术偏见的来源，例如给定的微阵列样本中mRNA完整性的总体水平。我们表明，这会影响许多基因的表达水平，从而导致临床数据集和基因与临床变量之间的错误关联的虚假相关性。在此提案中，我们正在开发一种方法，该方法可以纠正在各种普通使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见。在特定目标2中，我们将评估临床微阵列数据集中系统偏置校正的总体影响。我们将确定临床微阵列测量是否显示出与独立验证或交叉验证期间更好的相关性。正如我们的初步结果所示，拟议的偏差校正显着增加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的结果。