| PCLassoReg {PCLassoReg} | R Documentation |
Group Regression Models for Risk Protein Complex Identification
Description
Two protein complex-based group regression models (PCLasso and PCLasso2) for risk protein complex identification. PCLasso is a prognostic model that identifies risk protein complexes associated with survival. PCLasso2 is a classification model that identifies risk protein complexes associated with classes. For more information, see Wang and Liu (2021) <doi:10.1093/bib/bbab212>.
Details
The PCLasso model accepts a protein expression matrix, survival data, and protein complexes for training the prognostic model, and makes predictions for new samples and identifies risk protein complexes associated with survival.
The PCLasso2 model accepts a protein expression matrix, a response vector, and protein complexes for training the classification model, and makes predictions for new samples and identifies risk protein complexes associated with classes.
Both PCLasso and PCLasso2 use grLasso as the penalty function. The
other two penalties grSCAD and grMCP can also be used for model
construction and risk protein complex identification. The package also
provides methods for plotting coefficient paths and cross-validation curves.
References
PCLasso2: a protein complex-based, group Lasso-logistic model for risk protein complex discovery. To be published.
PCLasso: a protein complex-based group lasso-Cox model for accurate prognosis and risk protein complex discovery. Brief Bioinform, 2021.
Park, H., Niida, A., Miyano, S. and Imoto, S. (2015) Sparse overlapping group lasso for integrative multi-omics analysis. Journal of computational biology: a journal of computational molecular cell biology, 22, 73-84.