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Abstract:
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Applications of high-throughput proteomics are gaining popularity in recent decade, specifically in the analysis of human cancer. For example recent assays such as multiplexed kinase inhibitor beads (MIBs) coupled with quantitative mass spectrometry (MS) have the ability to quickly measures changes in the landscape of activated human kinases in response to treatment, allowing the rational design of kinase inhibitor combination therapies. However, several issues complicate the analysis of data from MS experiments, including batch effects, run-to-run global shifts in peptide intensities, and large amounts of intensity-dependent missing data. We detail a rank-based approach to the analysis of high-throughput proteomic data arising from labeled and label-free proteomic experiments, adjusting for technical factors and missingness across MS experiments. We demonstrate our method's applicability in determining peptides and proteins significantly differentially expressed in MIB-MS kinome experiments in cancer data and compare its performance to existing methods.
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