PCLassoCox: A protein complex-based group lasso-Cox model for accurate...

In weiliu123/PCLassoCox: A protein complex-based group lasso-Cox model for accurate prognosis and risk protein complex discovery

Description

The PCLasso model is a prognostic model which selects important predictors at the protein complex level to achieve accurate prognosis and identify risk protein complexes.

Details

Index: This package was not yet installed at build time.
The PCLasso model accepts a gene expression matrix, survival data, and protein complexes for the PCLasso model, and makes predictions for new samples and identifies risk protein complexes.

PCLasso constructs a PCLasso model based on a gene expression matrix, survival data, and protein complexes.

predict.PCLasso makes predictions from a PCLasso model.

cv.PCLasso performs k-fold cross validations for the PCLasso model with grouped covariates over a grid of values for the regularization parameter lambda, and returns an optimal value for lambda.

predict.cv.PCLasso returns predictions from a fitted cv.PCLasso object, using the optimal value chosen for lambda.

plot.PCLasso produces a plot of the coefficient paths for a fitted PCLasso object.

plot.cv.PCLasso plots the cross-validation curve from a cv.PCLasso object, along with standard error bars.

References

PCLasso: a protein complex-based group lasso-Cox model for accurate prognosis and risk protein complex discovery. To be published.

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.

weiliu123/PCLassoCox documentation built on Dec. 23, 2021, 5:10 p.m.