Search

Your search keyword '"Park, Jun Young"' showing total 2,147 results
Start Over Author "Park, Jun Young"
2,147 results on '"Park, Jun Young"'

5. Rank-adaptive covariance testing with applications to genomics and neuroimaging

Author

Veitch, David, He, Yinqiu, and Park, Jun Young

Subjects
Statistics - Methodology, Mathematics - Statistics Theory, Statistics - Applications
Abstract

In biomedical studies, testing for differences in covariance offers scientific insights beyond mean differences, especially when differences are driven by complex joint behavior between features. However, when differences in joint behavior are weakly dispersed across many dimensions and arise from differences in low-rank structures within the data, as is often the case in genomics and neuroimaging, existing two-sample covariance testing methods may suffer from power loss. The Ky-Fan(k) norm, defined by the sum of the top Ky-Fan(k) singular values, is a simple and intuitive matrix norm able to capture signals caused by differences in low-rank structures between matrices, but its statistical properties in hypothesis testing have not been studied well. In this paper, we investigate the behavior of the Ky-Fan(k) norm in two-sample covariance testing. Ultimately, we propose a novel methodology, Rank-Adaptive Covariance Testing (RACT), which is able to leverage differences in low-rank structures found in the covariance matrices of two groups in order to maximize power. RACT uses permutation for statistical inference, ensuring an exact Type I error control. We validate RACT in simulation studies and evaluate its performance when testing for differences in gene expression networks between two types of lung cancer, as well as testing for covariance heterogeneity in diffusion tensor imaging (DTI) data taken on two different scanner types.

Published
2023

35. Bidimensional linked matrix factorization for pan-omics pan-cancer analysis

Author

Lock, Eric F., Park, Jun Young, and Hoadley, Katherine A.

Subjects
Statistics - Machine Learning, Computer Science - Machine Learning, Quantitative Biology - Quantitative Methods, Statistics - Applications, Statistics - Methodology
Abstract

Several modern applications require the integration of multiple large data matrices that have shared rows and/or columns. For example, cancer studies that integrate multiple omics platforms across multiple types of cancer, pan-omics pan-cancer analysis, have extended our knowledge of molecular heterogenity beyond what was observed in single tumor and single platform studies. However, these studies have been limited by available statistical methodology. We propose a flexible approach to the simultaneous factorization and decomposition of variation across such bidimensionally linked matrices, BIDIFAC+. This decomposes variation into a series of low-rank components that may be shared across any number of row sets (e.g., omics platforms) or column sets (e.g., cancer types). This builds on a growing literature for the factorization and decomposition of linked matrices, which has primarily focused on multiple matrices that are linked in one dimension (rows or columns) only. Our objective function extends nuclear norm penalization, is motivated by random matrix theory, gives an identifiable decomposition under relatively mild conditions, and can be shown to give the mode of a Bayesian posterior distribution. We apply BIDIFAC+ to pan-omics pan-cancer data from TCGA, identifying shared and specific modes of variability across 4 different omics platforms and 29 different cancer types., Comment: 26 pages, 5 figures

Published
2020

41. Integrative Factorization of Bidimensionally Linked Matrices

Author

Park, Jun Young and Lock, Eric F.

Subjects
Statistics - Machine Learning, Computer Science - Machine Learning, Quantitative Biology - Quantitative Methods, Statistics - Methodology
Abstract

Advances in molecular "omics'" technologies have motivated new methodology for the integration of multiple sources of high-content biomedical data. However, most statistical methods for integrating multiple data matrices only consider data shared vertically (one cohort on multiple platforms) or horizontally (different cohorts on a single platform). This is limiting for data that take the form of bidimensionally linked matrices (e.g., multiple cohorts measured on multiple platforms), which are increasingly common in large-scale biomedical studies. In this paper, we propose BIDIFAC (Bidimensional Integrative Factorization) for integrative dimension reduction and signal approximation of bidimensionally linked data matrices. Our method factorizes the data into (i) globally shared, (ii) row-shared, (iii) column-shared, and (iv) single-matrix structural components, facilitating the investigation of shared and unique patterns of variability. For estimation we use a penalized objective function that extends the nuclear norm penalization for a single matrix. As an alternative to the complicated rank selection problem, we use results from random matrix theory to choose tuning parameters. We apply our method to integrate two genomics platforms (mRNA and miRNA expression) across two sample cohorts (tumor samples and normal tissue samples) using the breast cancer data from TCGA. We provide R code for fitting BIDIFAC, imputing missing values, and generating simulated data., Comment: 27 pages, 4 figures

Published
2019
Full Text
View/download PDF

Catalog

Books, media, physical & digital resources
See catalog results