Nonlinear ridge regression improves cell-type-specific differential expression analysis.

ABSTRACT:

Background

Epigenome-wide association studies (EWAS) and differential gene expression analyses are generally performed on tissue samples, which consist of multiple cell types. Cell-type-specific effects of a trait, such as disease, on the omics expression are of interest but difficult or costly to measure experimentally. By measuring omics data for the bulk tissue, cell type composition of a sample can be inferred statistically. Subsequently, cell-type-specific effects are estimated by linear regression that includes terms representing the interaction between the cell type proportions and the trait. This approach involves two issues, scaling and multicollinearity.

Results

First, although cell composition is analyzed in linear scale, differential methylation/expression is ana

SUBMITTER: Takeuchi F 

PROVIDER: S-EPMC7986289 | biostudies-literature | 2021 Mar

REPOSITORIES: biostudies-literature