New Statistical Methods for Medical Signals and Images

对来自计算生物学和医学的大数据集的分析代表了一个重要的挑战- 统计学家的挑战。这些数据通常具有大量具有相对弱相关性的要素 用于预测感兴趣表型的信号。这种数据的例子包括DNA序列和GWA， 质谱学，核磁共振和脑电图像，RNAseq和蛋白质阵列，仅举几例。这样做的总体目标是 正在进行的三名研究人员的资助是开发和研究统计技术，以增强分析 以及对这些数据的解释。我们的团队结合了统计建模、算法开发方面的经验- 并对这些技术进行了理论分析。在新的项目中，我们的重点是发展 利用已知或隐含的结构来提取有用信息 高维数据。 此次更新将通过四个具体的fic目标来解决这些目标。调查人员将研究： 1.染色质结构建模的主曲线。我们提出了新的统计方法 基于Hi-C分析得出的接触图对DNA染色质结构进行建模。我们用 技术灵感来自主曲线，但应用于公制缩放的环境中，这些技术考虑到 说明染色体上的局部结构。 2.将稀疏模型与大数据和汇总数据进行拟合。许多现代数据集(例如，具有 100万个SNP和50万个受试者)在计算上具有挑战性。我们提出了计算方面的改进 这使得套索能够根据这种情况进行调整。通常，已发表的GWAS研究的作者会这样做 出于隐私和其他原因，不分享原始数据。我们提出了近似fi集的一些技巧 这些模型的多变量版本只给出了通常报告的单变量汇总分数。 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- 真的在序列数据中比较不同的方法来推断病毒蛋白质的片段。为 数量遗传学，我们将开发用于特征分析的统计理论、方法和软件 多水平的变异，特别是遗传协方差矩阵的fi。 4.带边信息的预测。许多研究都在寻找能够预测 在不同治疗下的结果，如疾病状况。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或每个基因途径的定量测量 生物标记物，以增加在这些具有挑战性的领域中发现签名的能力。 调查人员和他们的学生将共同努力，将新的统计工具应用于公众- Cally Available软件，遵循在此赠与的早期周期中建立的模式，在该模式中，我们的包 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.