Jie Peng

A supplementary text with additional details on FOD estimators, synthetic experiments results and the HCP D-MRI application.

We consider the problem of testing linear hypotheses under a multivariate regression model with a high-dimensional response and spiked noise covariance. The proposed family of tests consists of test statistics based on a weighted sum of projections of the data onto the estimated latent factor directions, with the weights acting as the regularization parameters. We establish asymptotic normality of the test statistics under the null hypothesis. We also establish the power characteristics of the tests and propose a data-driven choice of the regularization parameters under a family of local alternatives. The performance of the proposed tests is evaluated through a simulation study. Finally, the proposed tests are applied to the Human Connectome Project data to test for the presence of associations between volumetric measurements of human brain and behavioral variables. Supplementary materials for this article are …

BackgroundApplying directed acyclic graph (DAG) models to proteogenomic data has been shown effective for detecting causal biomarkers of complex diseases. However, there remain unsolved challenges in DAG learning to jointly model binary clinical outcome variables and continuous biomarker measurements.ResultsIn this paper, we propose a new tool, DAGBagM, to learn DAGs with both continuous and binary nodes. By using appropriate models, DAGBagM allows for either continuous or binary nodes to be parent or child nodes. It employs a bootstrap aggregating strategy to reduce false positives in edge inference. At the same time, the aggregation procedure provides a flexible framework to robustly incorporate prior information on edges.ConclusionsThrough extensive simulation experiments, we demonstrate that DAGBagM has superior performance compared to alternative strategies for modeling mixed …

A critical bottleneck step towards understanding the immune activation and suppression mechanisms in tumor samples is to identify transcriptional signals underlying the cell-cell communication between tumor cells and immune/stromal cells in tumor microenvironments (TME). Cell-to-cell communication extensively relies on interactions between secreted ligands and cell-surface receptors, which create a highly connected signaling network through many ligand-receptor paths. The latest advance in in situ omics analyses, such as spatial transcriptomic (ST) analysis, provide unique opportunities to directly characterize ligand-receptor signaling networks that powers cell-cell communication, which has not been feasible based on either bulk or single-cell omics data. In this paper, we focus on high grade serous ovarian cancer (HGSC), and propose a novel statistical method, DAGBagST, to characterize the ligand-receptor interaction networks between adjacent tumor and stroma cells in ovarian tumors based on spatial transcriptomic data. DAGBagST utilizes a directed acyclic graph (DAG) model with a novel approach to handle the zero-inflated distribution observed in the ST data. It also leverages existing ligand-receptor regulation databases as prior information, and employs a bootstrap aggregation strategy to achieve robust network estimation. We applied DAGBagST to ST datasets of tumor samples from four HGSC patients, and identified common and distinct ligand-receptor regulations between adjacent tumor and stromal grids across multiple tumors. These results cast light on biological processes relating to the communication between tumor …

In this article, we study time-varying graphical models based on data measured over a temporal grid. Such models are motivated by the needs to describe and understand evolving interacting relationships among a set of random variables in many real applications, for instance, the study of how stock prices interact with each other and how such interactions change over time. We propose a new model, LOcal Group Graphical Lasso Estimation (loggle), under the assumption that the graph topology changes gradually over time. Specifically, loggle uses a novel local group-lasso type penalty to efficiently incorporate information from neighboring time points and to impose structural smoothness of the graphs. We implement an ADMM-based algorithm to fit the loggle model. This algorithm utilizes blockwise fast computation and pseudo-likelihood approximation to improve computational efficiency. An R package loggle …

We propose a two-sample test for detecting the difference between mean vectors in a high-dimensional regime based on a ridge-regularized Hotelling’s T². To choose the regularization parameter, a method is derived that aims at maximizing power within a class of local alternatives. We also propose a composite test that combines the optimal tests corresponding to a specific collection of local alternatives. Weak convergence of the stochastic process corresponding to the ridge-regularized Hotelling’s T² is established and used to derive the cut-off values of the proposed test. Large sample properties are verified for a class of sub-Gaussian distributions. Through an extensive simulation study, the composite test is shown to compare favorably against a host of existing two-sample test procedures in a wide range of settings. The performance of the proposed test procedures is illustrated through an application to a breast …

Due to recent technological advances, large brain imaging data sets can now be collected. Such data are highly complex so extraction of meaningful information from them remains challenging. Thus, there is an urgent need for statistical procedures that are computationally scalable and can provide accurate estimates that capture the neuronal structures and their functionalities. We propose a fast method for estimating the fiber orientation distribution(FOD) based on diffusion MRI data. This method models the observed dMRI signal at any voxel as a convolved and noisy version of the underlying FOD, and utilizes the spherical harmonics basis for representing the FOD, where the spherical harmonic coefficients are adaptively and nonlinearly shrunk by using a James-Stein type estimator. To further improve the estimation accuracy by enhancing the localized peaks of the FOD, as a second step a super-resolution sharpening process is then applied. The resulting estimated FODs can be fed to a fiber tracking algorithm to reconstruct the white matter fiber tracts. We illustrate the overall methodology using both synthetic data and data from the Human Connectome Project.