Comparative assessment and novel strategy on methods for imputing proteomics data

Minjie Shen, Yi Tan Chang, Chiung Ting Wu, Sarah J. Parker, Georgia Saylor, Yizhi Wang, Guoqiang Yu, Jennifer E. Van Eyk, Robert Clarke, David M. Herrington, Yue Wang

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish (US)
Article number1067
JournalScientific reports
Volume12
Issue number1
DOIs
StatePublished - Dec 2022

Bibliographical note

Funding Information:
This work has been supported by the National Institutes of Health under Grants HL111362-05A1, HL133932, NS115658-01, and the Department of Defense under Grant W81XWH-18-1-0723 (BC171885P1).

Publisher Copyright:
© 2022, The Author(s).

PubMed: MeSH publication types

  • Journal Article
  • Research Support, N.I.H., Extramural
  • Research Support, U.S. Gov't, Non-P.H.S.

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