Description Details Author(s) References

The recent advancement of high-throughput technologies has led to frequent utilization of gene expression and other "omics" data for toxicological, diagnostic or prognostic studies in and clinical applications. Unlike in classical predictions where the number of samples is greater than the number of variables (n>p), the challenge faced with prediction using "omics" data is that the number of parameters greatly exceeds the number of samples (p>>n). To solve this curse of dimensionality problem, several predictive functions have been proposed for direct and probabilistic classification and survival predictions. Nevertheless, these predictive functions have been shown to perform differently across datasets. Comparing predictive functions and choosing the best is computationally intensive and leads to selection bias. Thus, the question which function should one choose for a given dataset is to be ascertained. This package implements the approach proposed by Jong et al., (2016) to address this question.

The package allows one to determine an optimal predictive function among several functions for either binary direct classification, binary probabilistic classification or survival prediction for a given gene expression data. It also presents an interface to simulate gene expression data and to compare classification survival prediction functions on a given data. The most important workflow of this package is as follows:

Estimate the gene expression data characteristics using estimateDataCha

Fit a specific linear mixed effects model using fitLMEModel

Predict the performance of the functions on the given data using SPreFu

Plot the results of step 3 using plotSPreFu.

Other functions are(were) used for simulation studies presented in most of the references.

Victor L Jong, Kit CB Roes & Marinus JC Eijkemans

Maintainer: Victor L Jong <[email protected]>

Jong VL, Novianti PW, Roes KCB & Eijkemans MJC. Selecting a classification function for class prediction with gene expression data. Bioinformatics (2016) 32(12): 1814-1822;

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