Investigating gene expression array with outliers and missing data in bladder cancer

International audienceIn this article, we present a methodology to perform selection among genes based on their expression in various groups of patients, in order to find new genetic markers for specific pathologies. Our approach is based on clustering the denoised data and computing a LASSO (Least Absolute Shrinkage and Selection Operator) estimator, in order to select the relevant genes. This latter belongs to the class of penalized regression estimators where the penalty is a multiple of the ℓ1-norm of the regression vector. Gene markers of the most severe tumor state are finally provided using the proposed approach

A Bregman-proximal point algorithm for robust non-negative matrix factorization with possible missing values and outliers - application to gene expression analysis

BackgroundNon-Negative Matrix factorization has become an essential tool for feature extraction in a wide spectrum of applications. In the present work, our objective is to extend the applicability of the method to the case of missing and/or corrupted data due to outliers.ResultsAn essential property for missing data imputation and detection of outliers is that the uncorrupted data matrix is low rank, i.e. has only a small number of degrees of freedom. We devise a new version of the Bregman proximal idea which preserves nonnegativity and mix it with the Augmented Lagrangian approach for simultaneous reconstruction of the features of interest and detection of the outliers using a sparsity promoting ℓ 1 penality.ConclusionsAn application to the analysis of gene expression data of patients with bladder cancer is finally proposed.KeywordsFeature extraction Non-negative matrix factorization Gene expression analysis Outliers and missing dat