Feature Selection Methods for Protein Biomarker Discovery from Proteomics or Multi-omics Data.

Abstract Untargeted mass spectrometry (MS)-based proteomics provides a powerful platform for protein biomarker discovery, but clinical translation depends on the selection of a small number of proteins for downstream verification and validation. Due to the small sample size of typical discovery studies, protein markers identified from discovery data may not be generalizable to independent datasets. In addition, a good protein marker identified using a discovery platform may be difficult to implement in verification and validation platforms. Moreover, although multi-omics characterization is being increasingly used in discovery cohort studies, there is no existing method for multi-omics-facilitated protein biomarker selection. Here, we present ProMS, a computational algorithm for protein marker selection. The algorithm is based on the hypothesis that a phenotype is characterized by a few underlying biological functions, each manifested by a group of co-expressed proteins. A weighted k- medoids clustering algorithm is applied to all univariately informative proteins to identify both co-expressed protein clusters and a representative protein for each cluster as markers. In two clinically-important classification problems, ProMS shows superior performance compared with existing feature selection methods. ProMS can be extended to the multi-omics setting (ProMS_mo) through a constrained weighted k-medoids clustering algorithm, and the protein panels selected by ProMS_mo show improved performance on independent test data compared with ProMS. In addition to superior performance, ProMS and ProMS_mo also have two unique strengths. First, the feature clusters enable functional interpretation of the selected protein markers. Second, the feature clusters provide an opportunity to select replacement protein markers, facilitating a robust transition to the verification and validation platforms. In summary, this study provides a unified and effective computational framework for selecting protein biomarkers using proteomics or multi-omics data. The software implementation is publicly available at https://github.com/bzhanglab/proms .