Abstract
Label-Free Quantitative mass spectrometry based workflows for differential expression (DE) analysis of proteins impose important challenges on the data analysis because of peptide-specific effects and context dependent missingness of peptide intensities. Peptide-based workflows, like MSqRob, test for DE directly from peptide intensities and outperform summarization methods which first aggregate MS1 peptide intensities to protein intensities before DE analysis. However, these methods are computationally expensive, often hard to understand for the non-specialized end-user, and do not provide protein summaries, which are important for visualization or downstream processing. In this work, we therefore evaluate state-of-the-art summarization strategies using a benchmark spike-in dataset and discuss why and when these fail compared with the state-of-the-art peptide based model, MSqRob. Based on this evaluation, we propose a novel summarization strategy, MSqRobSum, which estimates MSqRob's model parameters in a two-stage procedure circumventing the drawbacks of peptide-based workflows. MSqRobSum maintains MSqRob's superior performance, while providing useful protein expression summaries for plotting and downstream analysis. Summarizing peptide to protein intensities considerably reduces the computational complexity, the memory footprint and the model complexity, and makes it easier to disseminate DE inferred on protein summaries. Moreover, MSqRobSum provides a highly modular analysis framework, which provides researchers with full flexibility to develop data analysis workflows tailored toward their specific applications.
Highlights
Label-Free Quantitative mass spectrometry based workflows for differential expression (DE) analysis of proteins impose important challenges on the data analysis because of peptide-specific effects and context dependent missingness of peptide intensities
We introduced MSqRobSum, a novel summarization-based method for Label-free quantitation (LFQ) which offers stable protein intensity estimation and high-performance protein DE analysis
MSqRob uses the information on all peptides during statistical inference and outperforms all summarization-based methods, which can only carry out inference on the protein summaries
Summary
Label-Free Quantitative mass spectrometry based workflows for differential expression (DE) analysis of proteins impose important challenges on the data analysis because of peptide-specific effects and context dependent missingness of peptide intensities. In LFQ, each sample is separately analyzed on the mass spectrometer, and differential expression is obtained by comparing relative intensities between runs for the same identified peptide (1). This workflow induces challenging data analysis problems. The identification issue and the peptide specific effects on quantification have a severe impact on the downstream summarization of peptide intensities toward protein abundances (5) Because of these issues, simple summarization methods such as the mean or median peptide intensity are known to give unreliable protein abundance estimates (5) and more advanced summarization strategies have been proposed for LFQ data in the literature (6, 7, 8, 9). None of the summarization strategies outperforms the others across all conditions
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