Seagull: lasso, group lasso and sparse-group lasso regularization for linear regression models via proximal gradient descent

Background Statistical analyses of biological problems in life sciences often lead to high-dimensional linear models. To solve the corresponding system of equations, penalization approaches are often the methods of choice. They are especially useful in case of multicollinearity, which appears if the number of explanatory variables exceeds the number of observations or for some biological reason. Then, the model goodness of fit is penalized by some suitable function of interest. Prominent examples are the lasso, group lasso and sparse-group lasso. Here, we offer a fast and numerically cheap implementation of these operators via proximal gradient descent. The grid search for the penalty parameter is realized by warm starts. The step size between consecutive iterations is determined with backtracking line search. Finally,seagull-the R package presented here- produces complete regularization paths. Results Publicly available high-dimensional methylation data are used to compareseagullto the established R packageSGL. The results of both packages enabled a precise prediction of biological age from DNA methylation status. But even though the results ofseagullandSGLwere very similar (R-2 > 0.99),seagullcomputed the solution in a fraction of the time needed bySGL. Additionally,seagullenables the incorporation of weights for each penalized feature. Conclusions The following operators for linear regression models are available inseagull: lasso, group lasso, sparse-group lasso and Integrative LASSO with Penalty Factors (IPF-lasso). Thus,seagullis a convenient envelope of lasso variants.