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。这些生物医学数据通常具有大量相关特征， 用于预测感兴趣的表型的弱信号。实例包括DNA序列和GWAS， 质谱、RNAseq和蛋白质阵列。这项正在进行的三项研究补助金的广泛目标是： velop和研究统计技术，加强这些数据的分析和解释。球队 结合了统计建模、算法开发和理论分析方面的经验。通过 四个具体目标，新项目的重点是发展和验证最先进的统计 使用结构的方法从高维数据中学习，以促进人类健康。 1.智能监督学习在“组学”设置中，有大量的功能， 往往表现出相当大的相关性。这一目标提出了一种统计方法，即自动识别套索， 该模型拟合套索回归模型，该模型使用分层模型自适应地选择特征聚类， 基于聚类的方法，强制执行树尊重解决方案的概念。将在以下日期进行验证： 基因表达和质谱数据，并扩展到其他监督学习设置研究。 2.从具有FDR对照的GWAS概括统计中选择SNP。全基因组关联研究 通常报告单个SNP的汇总统计表型的发现。这一目标 开发了一种统计方法来识别因果SNP，同时控制错误发现率。它 使用基于连锁不平衡数据的SNP相关矩阵的估计， 多变量套索拟合和模型X敲除技术，并将在英国生物库数据上进行验证。 3.高维遗传协方差矩阵的推断。大遗传量的统计估计 需要协方差矩阵来了解全表型范围内的遗传变异是否是集中的。 在相对较少的性状组合中进行了试验，并对进化和多效性产生了影响。这一目标将 探索限制性最大似然法中的偏倚，并研究替代参数和非参数 通过渐近近似和模拟的估计方法。 4.多实例学习的混合套索。人们通常知道一个人是否生病，但不知道是哪一种 他们的免疫细胞对特定疾病有反应，也不是活检组织的哪些部分对特定疾病有反应。 有病每个患者都有一个标签，但数据实例更细粒度。目的 是预测每个数据实例的标签。本项目提出了一种监督学习方法 基于混合物和套索，验证病毒序列和质谱数据。 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， 按照本赠款早期周期建立的模式，

The analysis of large datasets from computational biology and medicine represents an important chal- lenge for Statisticians. These biomedical data typically have a large number of correlated features with rel- atively weak signals for predicting phenotypes of interest. Examples include DNA sequences and GWAS, mass-spectra, RNAseq and protein arrays. The broad goal of this ongoing three-investigator grant is to de- velop and study statistical techniques that enhance the analysis and interpretation of these data. The team combines experience in statistical modeling, algorithmic development, and theoretical analysis. Through four Specific Aims, the new projects focus on development and validation of state-of-the art statistical methods to use structure to learn from high-dimensional data to advance human population health. 1. Cluster-aware supervised learning. In “omics” settings, there are a large number of features that often exhibit sizable correlations. This aim proposes the Cluster-Aware Lasso, a statistical method which fits a lasso regression model that adaptively selects clusters of features using a hierarchical clustering-based approach, enforcing a notion of a tree-respecting solution. It will be validated on gene expression and mass spec data, and extensions to other supervised learning settings studied. 2. SNP Selection from GWAS summary statistics with FDR control. Genome-wide association studies often report findings for phenotypes in terms of summary statistics for individual SNPs. This aim develops a statistical method to identify causal SNPs while controlling the False Discovery Rate. It uses an estimate of the SNP correlation matrix based on linkage-disequilibrium data, an approximate multivariate lasso fit and model-X knockoff techniques, and will be validated on UK Biobank data. 3. Inference for high-dimensional genetic covariance matrices. Statistical estimation of large genetic covariance matrices is needed to learn whether genetic variation at phenome-wide scale is concen- trated in relatively few trait combinations, with implications for evolution and pleiotropy. This aim will explore biases in Restricted Maximum Likelihood and study alternative parametric and nonparametric methods of estimation both by asymptotic approximation and simulation. 4. Mixture lasso for multiple instance learning. It is often known whether a person is sick, but not which of their immune cells are responding to a particular illness, nor which parts of biopsied tissue are diseased. There is a label only for each patient, but data instances on a more granular level. The aim is to predict the labels of each data instance. This project proposes a supervised learning method based on mixtures and the lasso, with validation on viral sequence and mass spectrometry data. 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.