A combined test for feature selection on sparse metaproteomics data - an alternative to missing value imputation

Abstract

One of the difficulties encountered in the statistical analysis of metaproteomics data is the high proportion of missing values, which are usually treated by imputation. Nevertheless, imputation methods are based on restrictive assumptions regarding missingness mechanisms, namely "at random" or "not at random". To circumvent these limitations in the context of feature selection in a multi-class comparison, we propose a univariate selection method that combines a test of association between missingness and classes, and a test for difference of observed intensities between classes. This approach implicitly handles both missingness mechanisms. We performed a quantitative and qualitative comparison of our procedure with imputation-based feature selection methods on two experimental data sets. Whereas we observed similar performances in terms of prediction, the feature ranking from various imputation-based methods was strongly divergent. We showed that the combined test reaches a compromise by correlating reasonably with other methods.