A Hierarchical Integrative Group LASSO (HiGLASSO) Framework for Analyzing Environmental Mixtures

面向多组学数据的 LASSO 与带惩罚因子的整合 LASSO（IPF-LASSO）方法比较：具有 I 类错误控制的变量选择

Comparison of the LASSO and Integrative LASSO with Penalty Factors (IPF-LASSO) methods for multi-omics data: Variable selection with Type I error control

本文比较 LASSO 与 IPF-LASSO，并结合稳定性选择控制假阳性，评估模态特异惩罚参数在独立、相关高维组学数据及乳腺癌数据中的变量选择效果。

PIntMF: Penalized Integrative Matrix Factorization Method for Multi-Omics Data

It is more and more common to explore the genome at diverse levels and not only at a single omic level. Through integrative statistical methods, omics data have the power to reveal new biological processes, potential biomarkers, and subgroups of a cohort. The matrix factorization (MF) is a unsupervised statistical method that allows giving a clustering of individuals, but also revealing relevant omic variables from the various blocks. Here, we present PIntMF (Penalized Integrative Matrix Factorization), a model of MF with sparsity, positivity and equality constraints.To induce sparsity in the …

Integrative Multi-View Reduced-Rank Regression: Bridging Group-Sparse and Low-Rank Models

Multi-view data have been routinely collected in various fields of science and engineering. A general problem is to study the predictive association between multivariate responses and multi-view predictor sets, all of which can be of high dimensionality. It is likely that only a few views are relevant to prediction, and the predictors within each relevant view contribute to the prediction collectively rather than sparsely. We cast this new problem under the familiar multivariate regression framework and propose an integrative reduced-rank regression (iRRR), where each view has its own low-rank…

Individual Data Protected Integrative Regression Analysis of High-dimensional Heterogeneous Data

Evidence-based decision making often relies on meta-analyzing multiple studies, which enables more precise estimation and investigation of generalizability. Integrative analysis of multiple heterogeneous studies is, however, highly challenging in the ultra high dimensional setting. The challenge is even more pronounced when the individual level data cannot be shared across studies, known as DataSHIELD constraint (Wolfson et al., 2010). Under sparse regression models that are assumed to be similar yet not identical across studies, we propose in this paper a novel integrative estimation procedur…

Integrative Generalized Convex Clustering Optimization and Feature Selection for Mixed Multi-View Data

In mixed multi-view data, multiple sets of diverse features are measured on the same set of samples. By integrating all available data sources, we seek to discover common group structure among the samples that may be hidden in individualistic cluster analyses of a single data-view. While several techniques for such integrative clustering have been explored, we propose and develop a convex formalization that will inherit the strong statistical, mathematical and empirical properties of increasingly popular convex clustering methods. Specifically, our Integrative Generalized Convex Clustering Opt…

Structured penalized regression for drug sensitivity prediction

Large-scale {\it in vitro} drug sensitivity screens are an important tool in personalized oncology to predict the effectiveness of potential cancer drugs. The prediction of the sensitivity of cancer cell lines to a panel of drugs is a multivariate regression problem with high-dimensional heterogeneous multi-omics data as input data and with potentially strong correlations between the outcome variables which represent the sensitivity to the different drugs. We propose a joint penalized regression approach with structured penalty terms which allow us to utilize the correlation structure between …