TANDEM: A Two-Stage Approach to Maximize Interpretability of Drug Response Models Based on Multiple Molecular Data Types
Version 1.0.1

A two-stage regression method that can be used when various input data types are correlated, for example gene expression and methylation in drug response prediction. In the first stage it uses the upstream features (such as methylation) to predict the response variable (such as drug response), and in the second stage it uses the downstream features (such as gene expression) to predict the residuals of the first stage. In our manuscript (Aben et al., 2016, ), we show that using TANDEM prevents the model from being dominated by gene expression and that the features selected by TANDEM are more interpretable.

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AuthorNanne Aben
Date of publication2017-04-07 12:33:36 UTC
MaintainerNanne Aben <nanne.aben@gmail.com>
LicenseGPL-2
Version1.0.1
Package repositoryView on CRAN
InstallationInstall the latest version of this package by entering the following in R:
install.packages("TANDEM")

Man pages

coef.tandem: Returns the regression coefficients from a TANDEM fit
example_data: A small artificial data set
nested.cv: Estimating predictive performance via nested cross-validation
predict.tandem: Creates a prediction using a tandem-object
relative.contributions: Determine the relative contribution per data type
tandem: Fits a TANDEM model by performing a two-stage regression

Functions

coef.tandem Man page Source code
example_data Man page
nested.cv Man page Source code
predict.tandem Man page Source code
relative.contributions Man page Source code
tandem Man page Source code

Files

inst
inst/doc
inst/doc/my-vignette.html
inst/doc/my-vignette.Rmd
inst/doc/my-vignette.R
NAMESPACE
NEWS.md
data
data/example_data.rda
R
R/data.R
R/functions.R
vignettes
vignettes/my-vignette.Rmd
README.md
MD5
build
build/vignette.rds
DESCRIPTION
man
man/tandem.Rd
man/relative.contributions.Rd
man/predict.tandem.Rd
man/nested.cv.Rd
man/coef.tandem.Rd
man/example_data.Rd
TANDEM documentation built on May 19, 2017, 8:42 p.m.