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

项目总结 生存分析在生物医学转录学研究中发挥着基础性作用，以开发可靠的 患者预后和治疗反应的预测因子。虽然生存分析方法可以用来 针对高维和信号稀疏的问题，目前对数据问题的研究还比较缺乏 与不同的实验处理相关的人工制品，这是转录组数据的关键特征。 已发表的研究通常通过借用为区分而开发的方法来处理人工制品 表达式分析，其中最流行的是微阵列数据的分位数归一化和缩放 对测序数据进行标准化。尽管人们对这种“标签外”的使用持毫无根据的乐观态度，但我们发现 归一化可能会扭曲标记在样本中的排序，并随后危及对 结果相关标记物和结果预测的准确性。因此，迫切需要重新-- 评估处理这些数据构件的现有方法，并专门为派生定制新的方法 分子预报器，以便它可以准确和重复性地进行。在这份提案中，我们将首先填充 MicroRNAs(一类对基因起重要调节作用的小RNA)的知识缺口 人类的表达)使用真实分布和稳健基准的数据。到时候我们会的 利用生存回归框架，开发管理处理构件的新方法。我们会 使用模拟工具与现有方法进行比较，评估新方法的性能 通过对癌症基因组图谱中卵巢癌数据的应用，展示它们的用途。我们的项目 预计将促进优化microRNA数据中的数据协调所需的知识，从而 加速它们的可重复性转化为临床有用的预测指标，并为进一步研究铺平道路 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.