Analysis of single-cell proteomics data
- Pipelines for data processing
- Increasing confident peptide identifications
- Visualizing LC-MS/MS data
- Community guidelines and recommendations
This section organizes computational pipelines for the analysis of single-cell proteomics data. These pipelines can be used with data generated from various methods for single-cell proteomics.
Pipelines for data processing
Pipelines from Slavov Laboratory
- The Scripts and Pipelines for Proteomics (SPP) provides computational utilities for single-cell proteomics data. The code originated from the SCoPE2 pipeline, which was abstracted and generalized so that it can be used for analyzing data from other projects, including data acquired by plexDIA, pSCoPE, and other mass-spec methods for single-cell proteomics.
- The SCoPE2 pipeline was developed to process the SCoPE2 data from Specht et al., 2019 and it has been generalized in the SPP collection of scripts and pipelines.
Pipelines by colleagues
- Chris Vanderaa and Laurent Gatto have developed a Bioconductor package scp for manipulating, processing, and analyzing mass spectrometry-based single-cell proteomics data. The package implemented workflows from the SCoPE2 pipeline using the ‘QFeatures’ package and added new functions.
- Erwin Schoof and colleagues developed SCeptre, a Python pipeline used for processing single-cell proteomics data generated using an isobaric carrier experimental design.
Increasing confident peptide identifications
DART-ID implements principled Bayesian frameworks for global retention time (RT) alignment and for incorporating RT estimates towards improved confidence estimates of peptide-spectrum-matches.
Visualizing LC-MS/MS data
Community guidelines and recommendations
Analyzing proteins from single cells by tandem mass spectrometry (MS) has become technically feasible. While such analysis has the potential to accurately quantify thousands of proteins across thousands of single cells, the accuracy and reproducibility of such results may be undermined by numerous factors affecting experimental design, sample preparation, data acquisition, and data analysis. Establishing community guidelines and standardized metrics will enhance rigor, data quality, and alignment between laboratories. The community has proposed best practices, quality controls, and data reporting guidelines to assist in the broad adoption of reliable quantitative workflows for single-cell proteomics.