The current state of single-cell proteomics data analysis

TL;DR

This paper reviews the current state of computational workflows in single-cell proteomics data analysis, emphasizing the lack of consensus, the need for benchmarking, and standardization efforts to improve reproducibility and reliability.

Contribution

It systematically compares recent workflows, highlights gaps in standardization, and discusses ongoing efforts to establish benchmarks and best practices in the field.

Findings

Significant variability exists in analysis workflows.

Standardization and benchmarking are urgently needed.

Current efforts aim to improve reproducibility.

Abstract

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.