Abstract

Missing values are a major issue in quantitative proteomics analysis. While many methods have been developed for imputing missing values in high-throughput proteomics data, a comparative assessment of imputation accuracy remains inconclusive, mainly because mechanisms contributing to true missing values are complex and existing evaluation methodologies are imperfect. Moreover, few studies have provided an outlook of future methodological development. We first re-evaluate the performance of eight representative methods targeting three typical missing mechanisms. These methods are compared on both simulated and masked missing values embedded within real proteomics datasets, and performance is evaluated using three quantitative measures. We then introduce fused regularization matrix factorization, a low-rank global matrix factorization framework, capable of integrating local similarity derived from additional data types. We also explore a biologically-inspired latent variable modeling strategy—convex analysis of mixtures—for missing value imputation and present preliminary experimental results. While some winners emerged from our comparative assessment, the evaluation is intrinsically imperfect because performance is evaluated indirectly on artificial missing or masked values not authentic missing values. Nevertheless, we show that our fused regularization matrix factorization provides a novel incorporation of external and local information, and the exploratory implementation of convex analysis of mixtures presents a biologically plausible new approach.

Details

Title
Comparative assessment and novel strategy on methods for imputing proteomics data
Author
Shen Minjie 1 ; Yi-Tan, Chang 1 ; Wu Chiung-Ting 1 ; Parker, Sarah J 2 ; Saylor, Georgia 3 ; Wang, Yizhi 1 ; Yu, Guoqiang 1 ; Van Eyk Jennifer E 2 ; Clarke, Robert 4 ; Herrington, David M 3 ; Wang, Yue 1 

 Virginia Polytechnic Institute and State University, Department of Electrical and Computer Engineering, Arlington, USA (GRID:grid.438526.e) (ISNI:0000 0001 0694 4940) 
 Advanced Clinical Biosystems Research Institute, Cedars Sinai Medical Center, Los Angeles, USA (GRID:grid.50956.3f) (ISNI:0000 0001 2152 9905) 
 Wake Forest University, Department of Internal Medicine, Winston-Salem, USA (GRID:grid.241167.7) (ISNI:0000 0001 2185 3318) 
 The Hormel Institute, University of Minnesota, Austin, USA (GRID:grid.17635.36) (ISNI:0000000419368657) 
Publication year
2022
Publication date
2022
Publisher
Nature Publishing Group
e-ISSN
20452322
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2621430629
Copyright
© The Author(s) 2022. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.