example_data

datasets

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.

Nanne Aben

TANDEM

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

example_data function

A small artificial data set, containing 200 samples, 20 upstream features and 20 downstream features<dl class="dl-horizontal">
 <dt>x</dt><dd>A 200 x 40 feature matrix</dd>
 <dt>y</dt><dd>A 200 x 1 response vector</dd>
 <dt>upstream</dt><dd>A 40 x 1 boolean vector indicating which features are upstream features</dd>
 <dt>data_types</dt><dd>A 40 x 1 vector indicating for each feature to which data type they belong</dd>
</dl>

Format

A small artificial data set — example_data

A small artificial data set, containing 200 samples, 20 upstream features and 20 downstream features<dl class='dl-horizontal'>
 <dt>x</dt><dd>A 200 x 40 feature matrix</dd>
 <dt>y</dt><dd>A 200 x 1 response vector</dd>
 <dt>upstream</dt><dd>A 40 x 1 boolean vector indicating which features are upstream features</dd>
 <dt>data_types</dt><dd>A 40 x 1 vector indicating for each feature to which data type they belong</dd>
</dl>

example_data: A small artificial data set

Description

Usage

Arguments

Format