Peptide intensities from mass spectra are increasingly used for relative quantitation of proteins in complex samples. However, numerous issues inherent to the mass spectrometry workflow turn quantitative proteomic data analysis into a crucial challenge. We and others have shown that modeling at the peptide level outperforms classical summarization-based approaches, which typically also discard a lot of proteins at the data preprocessing step. Peptide-based linear regression models, however, still suffer from unbalanced datasets due to missing peptide intensities, outlying peptide intensities and overfitting. Here, we further improve upon peptide-based models by three modular extensions: ridge regression, improved variance estimation by borrowing information across proteins with empirical Bayes and M-estimation with Huber weights. We illustrate our method on the CPTAC spike-in study and on a study comparing wild-type and ArgP knock-out Francisella tularensis proteomes. We show that the fold change estimates of our robust approach are more precise and more accurate than those from stateof-the-art summarization-based methods and peptidebased regression models, which leads to an improved sensitivity and specificity. We also demonstrate that ionization competition effects come already into play at very low spike-in concentrations and confirm that analyses with peptide-based regression methods on peptide intensity values aggregated by charge state and modification status (e.g. High-throughput LC-MS-based proteomic workflows are widely used to quantify differential protein abundance between samples. Relative protein quantification can be achieved by stable isotope labeling workflows such as metabolic (1, 2) and postmetabolic labeling (3-6). These types of experiments generally avoid run-to-run differences in the measured peptide (and thus protein) content by pooling and analyzing differentially labeled samples in a single run. Labelfree quantitative (LFQ) 1 workflows become increasingly popular as the often expensive and time-consuming labeling protocols are omitted. Moreover, LFQ proteomics allows for more flexibility in comparing samples and tends to cover a larger area of the proteome at a higher dynamic range (7,8). Nevertheless, the nature of the LFQ protocol makes shotgun proteomic data analysis a challenging task. Missing values are omnipresent in proteomic data generated by data-dependent acquisition workflows, for instance because of low-abundant peptides that are not always fragmented in complex peptide mixtures and a limited number of modifications and mutations that can be accounted for in the feature search. Moreover, the overall abundance of a peptide is determined by the surroundings of its corresponding cleavage sites as these influence protease cleavage efficiency (9). Similarly, some peptides are more easily ionized than others (10). These issues not only lead to missing peptides, but also increase variability in individual peptide intensities. The discrete nature of MS1 sampling following continuous ...

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Quantitative label-free mass spectrometry is increasingly used to analyze the proteomes of complex biological samples. However, the choice of appropriate data analysis methods remains a major challenge. We therefore provide a rigorous comparison between peptide-based models and peptide-summarization-based pipelines. We show that peptide-based models outperform summarization-based pipelines in terms of sensitivity, specificity, accuracy, and precision. We also demonstrate that the predefined FDR cutoffs for the detection of differentially regulated proteins can become problematic when differentially expressed (DE) proteins are highly abundant in one or more samples. Care should therefore be taken when data are interpreted from samples with spiked-in internal controls and from samples that contain a few very highly abundant proteins. We do, however, show that specific diagnostic plots can be used for assessing differentially expressed proteins and the overall quality of the obtained fold change estimates. Finally, our study also illustrates that imputation under the "missing by low abundance" assumption is beneficial for the detection of differential expression in proteins with low abundance, but it negatively affects moderately to highly abundant proteins. Hence, imputation strategies that are commonly implemented in standard proteomics software should be used with care.