NAguideR: performing and prioritizing missing value imputations for consistent bottom-up proteomic analyses

Abstract

Mass spectrometry (MS)-based quantitative proteomics experiments frequently generate data with missing values, which may profoundly affect downstream analyses. A wide variety of imputation methods have been established to deal with the missing-value issue. To date, however, there is a scarcity of efficient, systematic, and easy-to-handle tools that are tailored for proteomics community. Herein, we developed a user-friendly and powerful stand-alone software, NAguideR, to enable implementation and evaluation of different missing value methods offered by 23 widely used missing-value imputation algorithms. NAguideR further evaluates data imputation results through classic computational criteria and, unprecedentedly, proteomic empirical criteria, such as quantitative consistency between different charge-states of the same peptide, different peptides belonging to the same proteins, and individual proteins participating protein complexes and functional interactions. We applied NAguideR into three label-free proteomic datasets featuring peptide-level, protein-level, and phosphoproteomic variables respectively, all generated by data independent acquisition mass spectrometry (DIA-MS) with substantial biological replicates. The results indicate that NAguideR is able to discriminate the optimal imputation methods that are facilitating DIA-MS experiments over those sub-optimal and low-performance algorithms. NAguideR further provides downloadable tables and figures supporting flexible data analysis and interpretation. NAguideR is freely available at http://www.omicsolution.org/wukong/NAguideR/ and the source code: https://github.com/wangshisheng/NAguideR/.

DeepPhospho: Accelerate DIA phosphoproteome profiling by Deep Learning

“…2a). For the third dataset (U2OS DIA) which is another DIA library generated from phosphoproteome pro ling of U2OS cells 9 , DeepPhospho made equally accurate predictions of fragment ion intensity and iRT (Supplementary Figs. 2b, 2c).…”

“…2a upper). To determine which spectrum is more likely to be correct for a given phosphopeptide, we obtained the reference spectrum from the pro ling results of the same or very similar phosphoproteome samples analyzed by gold-standard DDA acquisition methods 1,9 (Supplementary Table 1). Notably, the majority of phosphopeptides (78.6% in RPE1 DIA data, 82.8% in U2OS DIA data) showed a stronger correlation between the reference and predicted spectra than between the library and predicted spectra (Fig.…”

“…However, the current DIA phosphoproteomic work ow faces a signi cant limitation which is the need of a high-quality spectral library to be constructed prior to data processing. In almost all reported DIA phophoproteomic analysis, a project-speci c DDA library was built through DDA analysis of extensively pre-fractionated or repeatedly injected samples 3,[6][7][8][9] . Although project-speci c DDA libraries afford a higher proteome coverage (i.e.…”

A Simple Optimization Workflow to Enable Precise and Accurate Imputation of Missing Values in Proteomic Datasets

“…However, the missing values are not eliminated with this state-of-the-art technology. DIA-MS data analysis, including scoring of peptide identification, retention time alignment, and calculating false discovery rates across a large number of samples, can be a significant contributor to introducing missing values at various steps of the analysis pipeline 15 .…”

“…Fragment level imputation subtly but consistently outperformed the peptide and protein level imputation. Fragment level imputation also resulted in slightly larger protein ID count, 2918 versus 3026 protein IDs quantified after fragment and peptide/protein level imputation respectively.In addition to the analysis of quantitative performance, we further evaluated the dilution series data set using the NAguideR tool15 , which provides a classic and proteomic criterion for evaluation of imputation methods for proteomic data sets. The classic criteria calculate normalized root mean square error (NRMSE), NRMSE based sum of ranks (SOR), the average correlation coefficient between the original and imputed values, and Procrustes statistical shape analysis (PSS).…”