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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., Comment: The code to reproduce the images presented in the manuscript is available in the Github repository: https://github.com/UCLouvain-CBIO/2023_scp_na

Sound data analysis is essential to retrieve meaningful biological information from single-cell proteomics experiments. This analysis is carried out by computational methods that are assembled into workflows, and their implementations influence the conclusions that can be drawn from the data. In this work, we explore and compare the computational workflows that have been used over the last four years and identify a profound lack of consensus on how to analyze single-cell proteomics data. We highlight the need for benchmarking of computational workflows, standardization of computational tools and data, as well as carefully designed experiments. Finally, we cover the current standardization efforts that aim to fill the gap and list the remaining missing pieces, and conclude with lessons learned from the replication of published single-cell proteomics analyses., Comment: All data used to create the figures in this article are available from the scpdata package scpdata. The R code to reproduce the figures is available at: https://github.com/UCLouvain-CBIO/2022-scp-data-analysis

Recent advances in sample preparation, processing and mass spectrometry (MS) have allowed the emergence of MS-based single-cell proteomics (SCP). We are currently developing a workflow for analyzing MS-SCP data. It relies on a standardized data structure combining Bioconductor classes used for singe-cell analyses, SingleCellExperiment, and for MS-based proteomics analyses, Features, facilitating re-use of current state-of-the-art software., Source code can be found on GitHub: https://github.com/cvanderaa/ABLS2020-Poster