Clinical biomarker discovery by SWATH-MS based label-free quantitative proteomics: impact of criteria for identification of differentiators and data normalization method.
BACKGROUND
SWATH-MS has emerged as the strategy of choice for biomarker discovery due to the proteome coverage achieved in acquisition and provision to re-interrogate the data. However, in quantitative analysis using SWATH, each sample from the comparison group is run individually in mass spectrometer and the resulting inter-run variation may influence relative quantification and identification of biomarkers. Normalization of data to diminish this variation thereby becomes an essential step in SWATH data processing. In most reported studies, data normalization methods used are those provided in instrument-based data analysis software or those used for microarray data. This study, for the first time provides an experimental evidence for selection of normalization method optimal for biomarker identification.


METHODS
The efficiency of 12 normalization methods to normalize SWATH-MS data was evaluated based on statistical criteria in 'Normalyzer'-a tool which provides comparative evaluation of normalization by different methods. Further, the suitability of normalized data for biomarker discovery was assessed by evaluating the clustering efficiency of differentiators, identified from the normalized data based on p-value, fold change and both, by hierarchical clustering in Genesis software v.1.8.1.


RESULTS
Conventional statistical criteria identified VSN-G as the optimal method for normalization of SWATH data. However, differentiators identified from VSN-G normalized data failed to segregate test and control groups. We thus assessed data normalized by eleven other methods for their ability to yield differentiators which segregate the study groups. Datasets in our study demonstrated that differentiators identified based on p-value from data normalized with Loess-R stratified the study groups optimally.


CONCLUSION
This is the first report of experimentally tested strategy for SWATH-MS data processing with an emphasis on identification of clinically relevant biomarkers. Normalization of SWATH-MS data by Loess-R method and identification of differentiators based on p-value were found to be optimal for biomarker discovery in this study. The study also demonstrates the need to base the choice of normalization method on the application of the data.
['Narasimhan|Mythreyi|M|', 'Kannan|Sadhana|S|', 'Chawade|Aakash|A|', 'Bhattacharjee|Atanu|A|', 'Govekar|Rukmini|R|0000-0003-0515-2276']
D015415:Biomarkers D016022:Case-Control Studies D066264:Datasets as Topic D003937:Diagnosis, Differential D004926:Escherichia coli D005069:Evaluation Studies as Topic D006367:HeLa Cells D006801:Humans D020014:K562 Cells D013058:Mass Spectrometry D010446:Peptide Fragments D020543:Proteome D040901:Proteomics D012015:Reference Standards D012016:Reference Values D012984:Software D013194:Staining and Labeling D015003:Yeasts
Proteomic Normalization SWATH p-value Fold change LC–MS