Robust Summarization and Inference in Proteome-wide Label-free Quantification.

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 dis

SUBMITTER: Sticker A 

PROVIDER: S-EPMC7338080 | biostudies-literature | 2020 Jul

REPOSITORIES: biostudies-literature