Detecting biomarkers on label-free mass spectrometry data using biclustering
Sprache des Titels:
ISMB 2014 Proceedings
Mass spectrometry (MS) is a major tool in proteomics that is evolving at a rapid pace. Significant advances in instrumentation lead to a high-throughput resource field lacking of suitable data driven analysis tools. Major goals in this area involve the detection of reliable biomarkers and their quantitation. To tackle these challenges we propose a novel unsupervised approach utilizing the FABIA biclustering algorithm. The core application is to use the algorithm on MS level 1 data that is preclustered by retention time in order to find similar spectra over all samples. FABIA looks for samples as well as retention times that show similar patterns of m/z ratios. On the one hand the obtained biclusters facilitate the alignment of retention times and on the other hand they help to detect informative biomarkers. In a next step the results can further be utilized for protein quantitation.We show that our approach outperforms competing methods on benchmark data sets and therefore conclude that pivotal contributions to the detection of differentially expression proteins and their quantitation could be made.