Revisiting the thorny issue of missing values in single-cell proteomics

TL;DR

This paper discusses the challenges of missing data in single-cell proteomics, evaluates imputation methods, and provides recommendations for better handling and reporting of missing values in the field.

Contribution

It offers a comprehensive review of missing value issues, highlights key challenges, and proposes guidelines for improved data management in single-cell proteomics.

Findings

Identifies five main challenges in missing value management.

Analyzes advantages and drawbacks of imputation methods.

Provides recommendations for reporting and handling missing data.

Abstract

Missing values are a notable challenge when analysing mass spectrometry-based proteomics data. While the field is still actively debating on the best practices, the challenge increased with the emergence of mass spectrometry-based single-cell proteomics and the dramatic increase in missing values. A popular approach to deal with missing values is to perform imputation. Imputation has several drawbacks for which alternatives exist, but currently imputation is still a practical solution widely adopted in single-cell proteomics data analysis. This perspective discusses the advantages and drawbacks of imputation. We also highlight 5 main challenges linked to missing value management in single-cell proteomics. Future developments should aim to solve these challenges, whether it is through imputation or data modelling. The perspective concludes with recommendations for reporting missing values, for reporting methods that deal with missing values and for proper encoding of missing values.