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

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.

Author
Nanne Aben
Date of publication
2016-09-20 00:58:59
Maintainer
Nanne Aben <nanne.aben@gmail.com>
License
GPL-2
Version
1.0.0

View on CRAN

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

Files in this package

TANDEM
TANDEM/inst
TANDEM/inst/doc
TANDEM/inst/doc/my-vignette.html
TANDEM/inst/doc/my-vignette.Rmd
TANDEM/inst/doc/my-vignette.R
TANDEM/NAMESPACE
TANDEM/data
TANDEM/data/example_data.rda
TANDEM/R
TANDEM/R/data.R
TANDEM/R/functions.R
TANDEM/vignettes
TANDEM/vignettes/my-vignette.Rmd
TANDEM/MD5
TANDEM/build
TANDEM/build/vignette.rds
TANDEM/DESCRIPTION
TANDEM/man
TANDEM/man/tandem.Rd
TANDEM/man/relative.contributions.Rd
TANDEM/man/predict.tandem.Rd
TANDEM/man/nested.cv.Rd
TANDEM/man/coef.tandem.Rd
TANDEM/man/example_data.Rd