New Statistical Methods for Medical Signals and Images

计算生物学和医学的大型数据集的分析是一个重要的挑战， 为Stacians。这些数据通常具有大量相关特征， 用于预测感兴趣的表型的信号。这种数据的实例包括DNA序列和GWAS， 质谱、MRI和EEG图像、RNAseq和蛋白质阵列，仅举几例。这个广泛的目标 正在进行的三名研究人员赠款是开发和研究统计技术，以加强分析 这些数据的解释。我们的团队结合了统计建模，算法开发， opment，并对这些技术进行了理论分析。在新的项目中，我们的重点是开发 利用已知或隐含的结构来提取有用的信息， 高维数据 更新将通过四个具体目标来实现这些目标。研究者将研究： 1.用于染色质结构建模的主曲线。我们提出了新的统计方法， 基于来自Hi-C测定的接触图对DNA的染色质结构进行建模。我们使用 技术的灵感来自主曲线，但应用于度量缩放的背景下，考虑到 考虑染色体沿着的局部结构。 2.将稀疏模型拟合到大数据和汇总数据。许多现代数据集（例如GWAS， 1 M SNP和500 K受试者）在计算上具有挑战性。我们提出计算的进步 使套索能够适应这种情况。通常，发表的GWAS研究的作者 出于隐私和其他原因，不共享原始数据。我们提出了近似拟合的技术， 这些模型的多变量版本仅给出通常报告的单变量汇总评分。 3.病毒学和遗传学中高维特征结构的估计。我们将利用低级结构- 序列数据的真实性，以比较不同的方法推断病毒蛋白质中的扇区。为 数量遗传学，我们将发展统计理论，方法和软件的特征分析， 多个变异水平，特别是遗传协方差矩阵。 4.带边信息的预测许多研究寻求生物标志物特征， 结果，如各种治疗下的疾病状态。我们提出了一种统计方法， 利用辅助信息，例如基因途径中的成员资格或每个基因途径的定量测量， 生物标志物，以增加在这些具有挑战性的领域中发现特征的能力。 通过共同努力，研究人员和他们的学生将把新的统计工具应用到巴西， callable可用的软件，遵循在此赠款的早期周期中建立的模式，其中我们的软件包 在斯坦福大学和世界各地的医学研究人员中得到了广泛的应用。

The analysis of large datasets from computational biology and medicine represents an important chal- lenge for Statisticians. These data typically have a large number of correlated features with relatively weak signals for predicting phenotypes of interest. Examples of such data includes DNA sequences and GWAS, mass-spectra, MRI and EEG images, RNAseq and protein arrays, to name a few. The broad goal of this ongoing three-investigator grant is to develop and study statistical techniques that enhance the analysis and interpretation of these data. Our team combines experience in statistical modeling, algorithmic devel- opment, and theoretical analysis of these techniques. In the new projects, our focus is the development of state-of-the art methods to exploit known or implied structure in order to extract useful information from high-dimensional data. The renewal will address these goals through four Specific Aims. The investigators will study: 1. Principal curves for modeling chromatin architecture. We propose new statistical methodology for modeling the chromatin structure of DNA based on contact maps derived from Hi-C assays. We use techniques inspired by principal curves, but applied in the context of metric scaling, that take into account local structure along the chromosome. 2. Fitting sparse models to large data and to summary data. Many modern datasets (e.g. GWAS with 1M SNPs and 500K subjects) are computationally challenging. We propose computational advances that enable the lasso to scale to such scenarios. Often the authors of published GWAS studies do not share the raw data for privacy and other reasons. We propose techniques for approximately fitting multivariate versions of these models given only the univariate summary scores typically reported. 3. Estimating high-dimensional eigenstructure in virology and genetics. We will exploit low rank struc- ture in sequence data to compare different methods for inference about sectors in viral proteins. For quantitative genetics, we will develop statistical theory, methods and software for eigenanalysis of multiple levels of variation, and specifically for genetic covariance matrices. 4. Prediction with side information. Many studies seek biomarker signatures that are predictive of outcomes such as disease status under various treatments. We propose a statistical approach for exploiting side information such as membership in gene pathways or quantitative measures for each biomarker in order to increase the power for discovering signatures in these challenging domains. Working together, the investigators and their students will implement the new statistical tools into publi- cally available software, following a pattern established in earlier cycles of this grant, in which our packages have found wide use among medical researchers both at Stanford and around the world.