switchBox-package: A package to train and apply K-Top-Scoring-Pair (KTSP) classifiers.

Description

The switchBox package allows to train and apply a K-Top-Scoring-Pair (KTSP) classifier with learning mechanism proposed in Afsari et al (AOAS, 2014) and as used by Marchionni et al (BMC Genomics, 2013). KTSP is an extension of the TSP classifier described by Geman and colleagues (Bioinformatics, 2005). The TSP algorithm is a simple binary classifier based on the reversal ordering across phenotypes of two measurements (e.g. gene expression reversals from normal to cancer.

Arguments

switchBox package features

The switchBox package contains several utilities enabling to:

A) Filter the features to be used to develop the classifier (i.e., differentially expressed genes); B) Compute the scores for all available feature pairs to identify the top performing TSP; C) Compute the scores for selected feature pairs to identify the top performing TSP; D) Identify the number of $K$ TSP to be used in the final classifier using the analysis of variance; E) Compute individual TSP votes for one class or the other and combine the votes based on user defined methods; F) Classify new samples based on the top KTSP based on various methods.

References

Afsari et al., "Rank Discriminants for Predicting Phenotypes from RNA Expression.", Annals of Applied Statistics, 2014, to appear.

Marchionni et al., "A simple and reproducible breast cancer prognostic test.", BMC Genomics, 2013, 14(1):336-342 http://www.ncbi.nlm.nih.gov/pubmed/23682826

Tan et al., "Simple decision rules for classifying human cancers from gene expression profiles.", Bioinformatics (2005) 21(20), 3896-3904. http://www.ncbi.nlm.nih.gov/pubmed/16105897

Xu et al., "Robust prostate cancer marker genes emerge from direct integration of inter-study microarray data" Bioinformatics (2005) 21(20), 3905-3911. http://www.ncbi.nlm.nih.gov/pubmed/16131522

Geman et al. "Classifying gene expression profiles from pairwise mRNA comparisons" Statistical applications in genetics and molecular biology (2004) 3.1 : 1071. http://www.ncbi.nlm.nih.gov/pubmed/16646797