权益分类	功能权益	普通用户	{{item.name}}会员
{{category.name}}	{{benefitItem.name}}

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

分子预测中数据协调统计方法的评估和开发

基本信息

批准号：
10303963
负责人：
Li-Xuan Qin
金额：
$ 49.63万
依托单位：
SLOAN-KETTERING INST CAN RESEARCH
依托单位国家：
美国
项目类别：
财政年份：
2021
资助国家：
美国
起止时间：
2021-09-03 至 2024-08-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10303963
关键词：
Address Adopted Algorithms Awareness Benchmarking Biography Biological Characteristics Classification Clinical Communities Data Data Analyses Data Set Derivation procedure Detection Development Disease Evaluation Foundations Funding Gene Expression Genes Goals Human Hybrids Knowledge Letters Malignant Female Reproductive System Neoplasm Malignant neoplasm of ovary Methods MicroRNAs Modeling Molecular Morphologic artifacts Outcome Ovarian Patients Performance Physicians Play Principal Investigator Prognosis Property Publishing RNA Reproducibility Research Research Design Role Sampling Scientist Secure Signal Transduction Small RNA Statistical Methods Stratification Survival Analysis The Cancer Genome Atlas Translations United States National Institutes of Health Untranslated RNA anticancer research base cBioPortal cancer genomics computerized tools data harmonization design differential expression experience genomic data high dimensionality innovation insight multidimensional data novel off-label use open source optimism outcome prediction prognostic simulation survival prediction tool transcriptomics treatment response virtual

项目摘要

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.

项目总结