Revisiting the Thorny Issue of Missing Values in Single-Cell Proteomics

Missing values are a notable challenge when analyzing
mass spectrometry-based
proteomics data. While the field is still actively debating 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 modeling. The perspective concludes
with recommendations for reporting missing values, for reporting methods
that deal with missing values, and for proper encoding of missing
values.