Package: TANDEM 1.0.3
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, <doi:10.1093/bioinformatics/btw449>), we show that using TANDEM prevents the model from being dominated by gene expression and that the features selected by TANDEM are more interpretable.
Authors:
TANDEM_1.0.3.tar.gz
TANDEM_1.0.3.zip(r-4.5)TANDEM_1.0.3.zip(r-4.4)TANDEM_1.0.3.zip(r-4.3)
TANDEM_1.0.3.tgz(r-4.4-any)TANDEM_1.0.3.tgz(r-4.3-any)
TANDEM_1.0.3.tar.gz(r-4.5-noble)TANDEM_1.0.3.tar.gz(r-4.4-noble)
TANDEM_1.0.3.tgz(r-4.4-emscripten)TANDEM_1.0.3.tgz(r-4.3-emscripten)
TANDEM.pdf |TANDEM.html✨
TANDEM/json (API)
NEWS
# Install 'TANDEM' in R: |
install.packages('TANDEM', repos = c('https://nanne-aben.r-universe.dev', 'https://cloud.r-project.org')) |
- example_data - A small artificial data set
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated 5 years agofrom:6b32438a5e. Checks:OK: 7. Indexed: yes.
Target | Result | Date |
---|---|---|
Doc / Vignettes | OK | Nov 15 2024 |
R-4.5-win | OK | Nov 15 2024 |
R-4.5-linux | OK | Nov 15 2024 |
R-4.4-win | OK | Nov 15 2024 |
R-4.4-mac | OK | Nov 15 2024 |
R-4.3-win | OK | Nov 15 2024 |
R-4.3-mac | OK | Nov 15 2024 |
Exports:nested.cvrelative.contributionstandem
Dependencies:codetoolsforeachglmnetiteratorslatticeMatrixRcppRcppEigenshapesurvival
Readme and manuals
Help Manual
Help page | Topics |
---|---|
Returns the regression coefficients from a TANDEM fit | coef.tandem |
A small artificial data set | example_data |
Estimating predictive performance via nested cross-validation | nested.cv |
Creates a prediction using a tandem-object | predict.tandem |
Determine the relative contribution per data type | relative.contributions |
Fits a TANDEM model by performing a two-stage regression | tandem |