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

快速准确地检测生物威胁不仅对于遏制其潜在的损害，而且对于确定潜在的医疗补救措施都很重要。大量研究表明，不同病原体感染细胞后，细胞内某些基因的mRNA表达水平不同，因此，准确识别与病原体反应的基因，并对其表达变化进行准确定量，是早期生物威胁检测的关键步骤。新兴的RNA-Seq技术提供了数千万个表达基因的短序列读段，在映射到基因组后，可以将其转换为准确表示基因表达水平。然而，从序列读数到基因表达水平的转换仍然存在问题。 在这个项目中，研究人员和她的同事将通过广泛的灵活的非线性模型（称为充分降维（SDR）模型）对RNA-Seq数据进行建模来解决这个问题;为SDR模型提出新的变量选择方法;并为所提出的方法的有效性发展理论基础。因此，这项工作将产生一个强大的软件套件，用于从RNA-seq数据中估计基因表达水平，并在统一的框架中识别对特定病原体反应的标记基因。 该项目不仅解决了使用高通量测序技术进行生物威胁检测的一些新问题，而且还产生了广泛适用于一般统计学习和预测问题的新统计方法和理论。更具体地说，所提出的方法（i）产生用于分析超高维数据的创新新方法，（ii）激发基因组学定量研究的新路线，以及（iii）为本科生和研究生提供独特的教育体验，以参与尖端的统计和跨学科研究。

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.