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A Nonlinear Mixture Model based Unsupervised Variable Selection in Genomics and Proteomics

Typical scenarios occurring in genomics and proteomics involve small number of samples and large number 
 
of variables. Thus, variable selection is necessary for creating disease prediction models robust to 
 
overfitting. We propose an unsupervised variable selection method based on sparseness constrained 
 
decomposition of a sample. Decomposition is based on nonlinear mixture model comprised of test sample 
 
and a reference sample representing negative (healthy) class. Geometry of the model enables automatic 
 
selection of component comprised of disease related variables. Proposed unsupervised variable selection 
 
method is compared with 3 supervised and 1 unsupervised variable selection methods on two-class 
 
problems using 3 genomic and 2 proteomic data sets. Obtained results suggest that proposed method could 
 
perform better than supervised methods on unseen data of the same cancer type.