  Building prediction models from mass-spectrometry data is challenging due to
the abundance of correlated features with varying degrees of zero-inflation,
leading to a common interest in reducing the features to a concise predictor
set with good predictive performance. In this study, we formally established
and examined regularized regression approaches, designed to address
zero-inflated and correlated predictors. In particular, we describe a novel
two-stage regularized regression approach (ridge-garrote) explicitly modelling
zero-inflated predictors using two component variables, comprising a ridge
estimator in the first stage and subsequently applying a nonnegative garrote
estimator in the second stage. We contrasted ridge-garrote with one-stage
methods (ridge, lasso) and other two-stage regularized regression approaches
(lasso-ridge, ridge-lasso) for zero-inflated predictors. We assessed the
predictive performance and predictor selection properties of these methods in a
comparative simulation study and a real-data case study to predict kidney
function using peptidomic features derived from mass-spectrometry. In the
simulation study, the predictive performance of all assessed approaches was
comparable, yet the ridge-garrote approach consistently selected more
parsimonious models compared to its competitors in most scenarios. While
lasso-ridge achieved higher predictive accuracy than its competitors, it
exhibited high variability in the number of selected predictors. Ridge-lasso
exhibited slightly superior predictive accuracy than ridge-garrote but at the
expense of selecting more noise predictors. Overall, ridge emerged as a
favourable option when variable selection is not a primary concern, while
ridge-garrote demonstrated notable practical utility in selecting a
parsimonious set of predictors, with only minimal compromise in predictive
accuracy.
