Nonlinear Ridge Regression Improves Robustness of Cell-Type-Specific Differential Expression Studies

Abstract

<jats:title>Abstract</jats:title> <jats:p><jats:bold>Background:</jats:bold> 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.<jats:bold>Results:</jats:bold> First, although cell composition is analyzed in linear scale, differential methylation/expression is analyzed suitably in the logit/log scale. To simultaneously analyze two scales, we developed nonlinear regression. Second, we show that the interaction terms are highly collinear, which is obstructive to ordinary regression. To cope with the multicollinearity, we applied ridge regularization. In simulated and real data, the improvement was modest by nonlinear regression and substantial by ridge regularization. <jats:bold>Conclusion:</jats:bold> Nonlinear ridge regression performed cell-type-specific association test on bulk omics data more robustly than previous methods. The omicwas package for R implements nonlinear ridge regression for cell-type-specific EWAS, differential gene expression and QTL analyses. The software is freely available from https://github.com/fumi-github/omicwas</jats:p>