  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.
