  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.
