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Mass spectrometry (MS) based single-cell proteomics (SCP) explores cellularheterogeneity by focusing on the functional effectors of the cells - proteins.However, extracting meaningful biological information from MS data is far fromtrivial, especially with single cells. Currently, data analysis workflows aresubstantially different from one research team to another. Moreover,it isdifficult to evaluate pipelines as ground truths are missing. Our team hasdeveloped the R/Bioconductor package called scp to provide a standardisedframework for SCP data analysis. It relies on the widely used QFeatures andSingleCellExperiment data structures. In addition, we used a design containingcell lines mixed in known proportions to generate controlled variability fordata analysis benchmarking. In this work, we provide a flexible data analysisprotocol for SCP data using the scp package together with comprehensiveexplanations at each step of the processing. Our main steps are quality controlon the feature and cell level, aggregation of the raw data into peptides andproteins, normalisation and batch correction. We validate our workflow usingour ground truth data set. We illustrate how to use this modular, standardisedframework and highlight some crucial steps.

Standardised workflow for mass spectrometry-based single-cell proteomics data processing and analysis using the scp package