Covariance matrix estimation for high-throughput biomedical data with interconnected communities
Estimating a covariance matrix is central to high-dimensional data analysis. Empirical
analyses of high-dimensional biomedical data, including genomics, proteomics …
analyses of high-dimensional biomedical data, including genomics, proteomics …
Multivariate Bayesian variable selection for multi-trait genetic fine mapping
T Canida, H Ke, S Chen, Z Ye… - Journal of the Royal …, 2024 - academic.oup.com
Genome-wide association studies (GWAS) have identified thousands of single-nucleotide
polymorphisms (SNPs) associated with complex traits, but determining the underlying …
polymorphisms (SNPs) associated with complex traits, but determining the underlying …
dCCA: detecting differential covariation patterns between two types of high-throughput omics data
Results We propose a novel approach called Differential Canonical Correlation Analysis
(dCCA) to capture differential covariation patterns between two multivariate vectors across …
(dCCA) to capture differential covariation patterns between two multivariate vectors across …
Bayesian indicator variable selection of multivariate response with heterogeneous sparsity for multi-trait fine mapping
T Canida, H Ke, S Chen, Z Ye, T Ma - arXiv preprint arXiv:2212.13294, 2022 - arxiv.org
As more data being collected nowadays, it is common to analyze multiple related responses
from the same study. Existing variable selection methods select variables for all responses …
from the same study. Existing variable selection methods select variables for all responses …
New Statistical Methods for High-Dimensional Interconnected Data With Uniform Blocks
Y Yang - 2023 - search.proquest.com
Empirical analyses of high-dimensional biomedical data, including genomics, proteomics,
microbiome, and neuroimaging data, consistently reveal the presence of strong modularity …
microbiome, and neuroimaging data, consistently reveal the presence of strong modularity …