Abstract
Background Many proteomic investigations summarize the quantitative information across multiple spectral features into protein-level conclusions. Data-independent spectral acquisition (DIA) now generates a lot of interest, as it allows us to quantify many spectral features in a single run. However, the disadvantage of DIA experiments as compared, e.g., to Selected Reaction Monitoring (SRM) is that the features are subject to interferences and noise. We argue that between-run variation provides an additional insight for distinguishing good-quality and noisy DIA features. To appropriately use the quantitative between-run variation, it is important to account for the properties experimental design, and distinguish random artifacts from the biological changes. We have previously proposed a method (Chang et al., ASMS 2013) that accounts for the experimental design to eliminate features with low information content.
Highlights
Many proteomic investigations summarize the quantitative information across multiple spectral features into protein-level conclusions
In this project we emphasized that conducting regularization helps us avoid exploring every subset of features exhaustively, and allows us to conduct hypothesis tests later on so that we would be able to control the false discovery rate of the feature selection process
We evaluated our proposed approach by using three datasets that have some notion of ground truth: an extensive simulation study, a controlled mixture where proteins were spiked into a complex background in known concentrations, and a study of 232 plasma samples, where 18 proteins were quantified in both SWAH and Selected Reaction Monitoring (SRM) mode in presence of heavy labeled reference peptides
Summary
Many proteomic investigations summarize the quantitative information across multiple spectral features into protein-level conclusions. Results In this project we emphasized that conducting regularization helps us avoid exploring every subset of features exhaustively, and allows us to conduct hypothesis tests later on so that we would be able to control the false discovery rate of the feature selection process.
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