Collaborative Research: Spatial Model-based Methods for RNA-seq Analysis

RNA测序（RNA-seq）是利用下一代超高通量测序技术绘制和定量转录组的一项强大的新技术。尽管非常有希望，但RNA-seq产生的大量数据，大量的偏差和短读比对的不确定性给研究人员在分析RNA-seq数据时带来了艰巨的挑战。目前的大多数分析程序枚举每个外显子内的标签总数，并使用规范化计数作为表达式度量。这些方法忽略了外显子内测序深度的变化和相关性，这可能导致不太准确的表达测量。由于相邻碱基的读取计数之间的相关性取决于它们之间的距离，因此称为空间相关性。大的碱基特异性变异和碱基间的空间相关性使得朴素的方法，如对RNA-seq数据进行平均正常化和量化基因/异构体表达，无效。在地理统计学、空间流行病学和图像处理等领域中，空间数据的位置特异性变异和空间相关性是许多空间数据的突出特征，并在空间统计学的文献中得到了研究。在这个项目中，研究者建议应用和扩展植根于空间统计学的思想、模型和方法来建模和分析RNA-seq数据。特别是，研究人员开发了空间泊松混合效应模型，包括层次模型和混合模型，以适应RNA-seq数据中存在的偏差、变异和相关性，从而准确估计基因/异构体表达水平，促进基因/异构体表达比较和新的转录结构或活性发现。此外，研究人员将应用所提出的方法来分析来自前列腺癌和牛皮癣转录组研究的真实RNA-seq数据。在全基因组范围内监测基因表达水平对于理解许多生物过程的机制非常重要。在过去的十年中，微阵列已经成为实验室测量基因表达水平的主要工具。最近，RNA-seq，一种新兴的新技术，已经被证明在测量基因表达谱方面比微阵列提供了关键的优势。然而，现有的从RNA-seq数据中定量表达水平的方法是粗糙和不令人满意的。这极大地削弱了RNA-seq在基因组和转录组学研究中的作用。在本项目中，研究者仔细研究了RNA-seq数据的独特性，提出了一系列先进的统计模型，旨在开发有效和高效的RNA-seq数据分析方法。从这个项目中产生的方法将极大地有利于快速增长的研究人员社区，他们正在计划进行RNA-seq实验与数据分析。此外，这个项目也对统计方法发展的进步作出了重大贡献。研究人员还将开发和支持基于该项目产生的方法的RNA-seq数据分析的开源计算机软件，并使其在网上免费向公众提供。

RNA sequencing (RNA-seq) is a powerful new technology for mapping and quantifying transcriptomes using next generation ultra-high-throughput sequencing technologies. Although extremely promising, massive data produced by RNA-seq, substantial biases, and uncertainty in short read alignment pose daunting challenges for researchers when analyzing RNA-seq data. Most of the current analytic programs enumerate total number of tags landed within each exon and use normalized counts as the expression measure. Such methods ignore variation and correlation in sequencing depth within an exon, which may result in less accurate expression measures. Because the correlation between the read counts of adjacent bases depends on the distance between them, it is referred to as spatial correlation. Large base-specific variations and between-bases spatial correlations make naive approaches, such as averaging to normalizing RNA-seq data and quantifying gene/isoform expressions, ineffective. The presence of location-specific variation as well as spatial correlation is an outstanding characteristic of many spatial data in Geostatistics, Spatial Epidemiology, and image processing, and it has been studied in the literature of Spatial Statistics. In this project, the investigators propose to apply and extend the ideas, models and methodologies rooted in Spatial Statistics to model and analyze RNA-seq data. In particular, the investigators develop spatial Poisson mixed effects models including a hierarchical model and a mixture model to accommodate biases, variations, and correlations present in RNA-seq data so as to accurately estimate gene/isoform expression levels and to facilitate gene/isoform expression comparison and novel transcript structure or activities discovery. Furthermore, the investigators will apply the proposed methods to analyze real RNA-seq data generated from prostate cancer and psoriasis transcriptomic studies. Monitoring gene expression levels genome-wide is important for understanding the mechanisms of many biological processes. In the past decade, microarray has been the main tool in laboratories for measuring gene expression levels. Recently, RNA-seq, an emerging new technology, has been shown to offer key advantages over microarray in measuring gene expression profiles. However, existing methods for quantifying expression levels from RNA-seq data are crude and unsatisfactory. This greatly compromises the power of RNA-seq for genomic and transcriptomic studies. In this project, having carefully investigated the unique characteristics of RNA-seq data, the investigators propose a series of advanced statistical models, and aim to develop effective and efficient methods for RNA-seq data analysis. The methods generated from this project will greatly benefit a fast growing community of researchers who are planning to conduct RNA-seq experiments with data analysis. Furthermore, this project also constitutes a significant contribution to the advance of statistical methodology development. The investigators will also develop and support open-source computer software for RNA-seq data analysis based on the methods resulting from this project and make it freely available to the public online.