scplainer: using linear models to understand mass spectrometry-based single-cell proteomics data (source code)

The repository contains the scripts to reproduce the results and figures of the scplainer manuscript.

scplainer: using linear models to understand mass spectrometry-based single-cell proteomics data Christophe Vanderaa, Laurent Gatto bioRxiv 2023.12.14.571792; doi: https://doi.org/10.1101/2023.12.14.571792.

Abstract

Analysing mass spectrometry (MS)-based single-cell proteomics (SCP) data is challenging. The data analysis must address numerous problems that are inherent to both MS-based proteomics technologies and single-cell experiments. This has led to the development of complex and divergent data processing workflows within the field. In this work, we present scplainer, a principled and standardised approach for extracting meaningful insights from SCP data. The approach relies on minimal data processing combined with linear modelling. The approach is a simple yet powerful approach for exploring and interpreting various types of SCP data. scplainer performs variance analysis, differential abundance analysis and component analysis while streamlining the visualization of the results. This thorough exploration enhances our capacity to gain a deeper understanding of the biological processes hidden in the data. Finally, we demonstrate that scplainer corrects for technical variability, and even enables the integration of data sets from different SCP experiments. The approach effectively generates high-quality data that is amenable to perform downstream analyses. In conclusion, this work reshapes the analysis of SCP data by moving efforts from dealing with the technical aspects of data analysis to focusing on answering biologically relevant questions.

Installation instructions

Participants should install a recent version of R (version >= 4.3.1) and run the following code chunk to install the necessary dependencies:

install.packages(c("BiocManager", "remotes"))
BiocManager::install(version = "3.18", ask = FALSE)
pkgs <- c("QFeatures", "SingleCellExperiment", "scp", "scpdata",
          "ggplot2", "dplyr", "patchwork", "scater")
BiocManager::install(pkgs)

For the second part, also run the following command to install the scplainer modelling code:

BiocManager::install("UCLouvain-CBIO/scp")

Useful links

The scplainer approach is implemented in scp

Vanderaa, Christophe, and Laurent Gatto. 2021. “Replication of Single-Cell Proteomics Data Reveals Important Computational Challenges.” Expert Review of Proteomics 18 (10): 835–43.

Data were retrieved from scpdata

Vanderaa, Christophe, and Laurent Gatto. 2023. “The Current State of Single-Cell Proteomics Data Analysis.” Current Protocols 3 (1): e658.

Repository organisation

• 1_processing contains R scripts to perform scplainer's minimal data processing approach, one script per SCP data set.
• 2_modelling contains R scripts to perform splainer's modelling approach, one script per SCP data set.
• 3_make_figures contains R scripts to generate the figures from the article.

Reproducing the analysis

You can reproduce the analysis presented in the scplainer paper on your local machine using Docker. You must first install Docker. Then, pull the image from the DockerHub repository.

docker pull cvanderaa/scplainer_paper_docker:latest

Then, clone the scplainer GitHub repo

Reproducing analysis through a Rstudio interface

Cd in github repo

You can start a Rstudio session within a Docker container using the following command through your computer terminal:

  docker run -e PASSWORD=bioc -p 8787:8787 -v `pwd`:/home/rstudio/2024-scplainer/ cvanderaa/scplainer_paper_docker:latest

Note you should use %CD% instead of pwd when using Windows.

Open your browser and go to http://localhost:8787. The USER is rstudio and the password is bioc. See the DockerHub repository for more detailed information on getting started with Docker.

Once the Rstudio/R session opened, you have access to all the scripts and any saved file within the session will also be saved on your local computer.

Reproducing analysis through the command line

If you don't want to run the analysis in Rstudio through the browser, you can access the environment from the command line using:

docker run  -v `pwd`:/home/rstudio/2024-scplainer/ -it cvanderaa/scplainer_paper_docker:latest bash

Note to future self

Build the docker image

If new dependencies are required, update the Dockerfile accordingly. Build the image (make sure to cd in the 2024-scplainer/Docker folder):

docker build -t cvanderaa/scplainer_paper_docker:latest .

Publish the docker image

When complete, push the new image to DockerHub:

docker push cvanderaa/scplainer_paper_docker:latest

Run complete analysis

Run the following command to fully reproduce the analysis:

docker run  -v `pwd`:/home/rstudio/2024-scplainer/ -it cvanderaa/scplainer_paper_docker:latest bash

cd 2024-scplainer/
make all
make clean

This will automatically update all figures in figs/ and intermediate data is stored in data/. make clean will remove any unnecessary intermediated files.