Evaluation and Development of Statistical Methods for Data Harmonization in Molecular Prognostication

项目摘要 生存分析在生物医学转录组学研究中起着基础性作用， 患者预后和治疗反应的预测因子。虽然生存分析方法可用于 针对高维数和信号稀疏性的问题，数据问题的研究还很缺乏 与不同的实验处理相关的伪影，这是转录组学数据的关键特征。 已发表的研究通常通过借用为微分而开发的方法来处理伪影 表达分析，其中最流行的是微阵列数据的分位数归一化和缩放 用于测序数据的标准化。尽管对这种“标签外”用途持毫无根据的乐观态度，但我们发现， 标准化可能会扭曲标记在样本中的排序，并随后损害对 结果相关标志物和结果预测的准确性。因此，迫切需要重新-- 评估处理这些数据工件的现有方法，并专门为派生定制新方法 这样就可以准确地重复进行。在本提案中，我们将首先填写 microRNA（一类在基因调控中起重要作用的小RNA）的知识缺口 人类中的表达）使用真实分布和稳健基准的数据。然后我们将 开发新的方法来管理处理工件，利用生存回归框架。我们将 使用模拟工具评估新方法与现有方法相比的性能， 展示了他们的使用与应用程序的卵巢癌数据从癌症基因组图谱。我们的项目 预计将推进优化microRNA数据中的数据协调所需的知识， 加速将其可重复地转化为临床有用的预测因子，并为继续推进 RNA数据及其翻译中的这些问题。

PROJECT SUMMARY Survival analysis plays a foundational role in biomedical transcriptomics studies for developing reliable predictors of patient prognosis and treatment response. While survival analysis methods are available to address the issues of high dimensionality and signal sparsity, research is still lacking on the issue of data artifacts associated with disparate experimental handling, which is a pivotal feature of transcriptomics data. Published studies often deal with handling artifacts by borrowing methods that were developed for differential expression analysis, the most popular of which is quantile normalization for microarray data and scaling normalization for sequencing data. Despite the unfounded optimism for such ‘off-label’ uses, we found that normalization may distort a marker’s ordering across samples and subsequently compromise the detection of outcome-associated markers and the accuracy of outcome prediction. Thus, there is a pressing need to re- evaluate existing methods for dealing with these data artifacts and tailor new ones specifically for the derivation of molecular prognosticators so that it can be done accurately and reproducibly. In this proposal, we will first fill the knowledge gap for microRNAs (a class of small RNAs that play an important regulatory role of gene expression in humans) using data that are realistically distributed and robustly benchmarked. We will then develop new methods for managing handling artifacts, leveraging the survival regression framework. We will assess the performance of the new methods in comparison with existing methods using simulation tools and demonstrate their use with an application to ovarian cancer data from The Cancer Genome Atlas. Our project is expected to advance the knowledge needed for optimizing data harmonization in microRNA data and thus accelerating their reproducible translations to clinically useful predictors and for paving the way to press on these issues in RNA data and their translations.