Abstract
Sequential windowed acquisition of all theoretical fragment ion mass spectra (SWATH-MS) has emerged as one of the most popular techniques for label-free proteome quantification in current pharmacoproteomic research. It provides more comprehensive detection and more accurate quantitation of proteins comparing with the traditional techniques. The performance of SWATH-MS is highly susceptible to the selection of processing method. Till now, ≥27 methods (transformation, normalization, and missing-value imputation) are sequentially applied to construct numerous analysis chains for SWATH-MS, but it is still not clear which analysis chain gives the optimal quantification performance. Herein, the performances of 560 analysis chains for quantifying pharmacoproteomic data were comprehensively assessed. Firstly, the most complete set of the publicly available SWATH-MS based pharmacoproteomic data were collected by comprehensive literature review. Secondly, substantial variations among the performances of various analysis chains were observed, and the consistently well-performed analysis chains (CWPACs) across various datasets were for the first time generalized. Finally, the log and power transformations sequentially followed by the total ion current normalization were discovered as one of the best performed analysis chains for the quantification of SWATH-MS based pharmacoproteomic data. In sum, the CWPACs identified here provided important guidance to the quantification of proteomic data and could therefore facilitate the cutting-edge research in any pharmacoproteomic studies requiring SWATH-MS technique.
Highlights
The pharmacoproteomics has been widely applied to various aspects of current pharmaceutical researches by discovering disease-related genes (Mrozek et al, 2013; Quiros et al, 2017; Zeng et al, 2017) or new drug targets (Li et al, 2018; Saei et al, 2018), constructing pharmacology screening model (Hauser et al, 2005), and revealing the drug mechanism of action (Yue et al, 2016; Zhu et al, 2018), resistance (Paul et al, 2016), and toxicity (Tan et al, 2017; Wang et al, 2017b)
Considering the huge amount of possible analysis chains [560 in total, taking nontransformation, non-normalization, and non-imputation into account adopted by previous studies (Guo et al, 2015; Liu et al, 2015; Wu et al, 2016)] by randomly integrating those processing methods, it is essential to comprehensively evaluate the performance of all analysis chains to identify the optimal one for specific pharmacoproteomic dataset
Because some analysis chains may not be able to result in a pooled intragroup median absolute deviation (PMAD) value, there were slight variations among the number of analysis chains for different benchmark datasets
Summary
The pharmacoproteomics has been widely applied to various aspects of current pharmaceutical researches by discovering disease-related genes (Mrozek et al, 2013; Quiros et al, 2017; Zeng et al, 2017) or new drug targets (Li et al, 2018; Saei et al, 2018), constructing pharmacology screening model (Hauser et al, 2005), and revealing the drug mechanism of action (Yue et al, 2016; Zhu et al, 2018), resistance (Paul et al, 2016), and toxicity (Tan et al, 2017; Wang et al, 2017b). Considering the huge amount of possible analysis chains [560 in total, taking nontransformation, non-normalization, and non-imputation into account adopted by previous studies (Guo et al, 2015; Liu et al, 2015; Wu et al, 2016)] by randomly integrating those processing methods, it is essential to comprehensively evaluate the performance of all analysis chains to identify the optimal one for specific pharmacoproteomic dataset.
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