ATD Collaborative Research: Statistical Modeling of Short-Read Counts in RNA-Seq

快速、准确地检测生物硫酸盐不仅对遏制其潜在的危害很重要，而且对于确定潜在的医疗补救措施也很重要。广泛的研究表明，感染细胞中的某些基因对不同的病原体有不同的mRNA表达水平，因此准确识别对病原体有反应的基因并准确量化它们的表达差异是早期生物治疗检测的关键步骤。新兴的RNA-Seq技术提供了数千万个表达基因的短序列读取，这些短序列在映射到基因组后可以转换为准确地代表基因表达水平。然而，从序列读取到基因表达水平的转换仍然是有问题的。在这个项目中，研究人员和她的同事们将通过使用一大类灵活的非线性模型来建模RNA-Seq数据来解决这个问题，这些模型被称为充分降维(SDR)模型；提出了用于SDR模型的新的变量选择方法；并为所提出的方法的有效性建立了理论基础。因此，这项工作将产生一个强大的软件套件，用于从RNA-SEQ数据估计基因表达水平，并在统一的框架内识别对特定病原体做出反应的标记基因。该项目不仅解决了使用高通量测序技术进行生物处理检测中的一些新问题，而且产生了广泛适用于一般统计学习和预测问题的新的统计方法和理论。更具体地说，建议的方法(I)为分析超高维数据产生了创新的新方法，(Ii)激发了基因组学中新的定量研究路线，以及(Iii)为本科生和研究生提供了参与尖端统计和跨学科研究的独特教育体验。

Rapid and accurate detection of biothreat is important not only for containing its potential damages, but also for determining potential medical remedies. Extensive researches show that certain genes in infected cells have different mRNA expression levels for different pathogens.Thus, an accurate identification of the genes that react to pathogens and an accurate quantification of their expression variations are key steps in early biothreat detections. The emerging RNA-Seq technologies provide tens of millions of short sequence reads of the expressed genes, which, after mapping to the genome, can be converted to accurately represent gene expression levels. However, the conversion from sequence reads to gene expression levels is still problematic. In this project, The investigator and her colleagues will tackle this problem by modeling RNA-Seq data through a broad class of flexible nonlinear models, called sufficient dimension reduction (SDR) models; propose novel variable selection methods for SDR models; and develop theoretical underpinning of the effectiveness of the proposed methods. As a consequence, this effort will result in a powerful software suite for estimating gene expression levels from RNA-seq data and identifying marker genes reacting to specific pathogens in a unified framework. This project not only addresses some emerging issues in biothreat detections using high-throughput sequencing technologies, but also results in novel statistical methods andtheory broadly applicable to general statistical learning and prediction problems. More specifically, the proposed methods (i) produce innovative new methodologies for analyzing ultra-high dimensional data, (ii) inspire new lines of quantitative investigations in genomics, and (iii) offer a unique educational experience for both undergraduate and graduate students to participate in cutting-edge statistical and interdisciplinary research.