README.md

In lgatto/QFeaturesScpWorkshop2021: Reproducing a single-cell proteomics data analysis using QFeatures and scp

Single-cell proteomics data analysis using QFeatures and scp

Authors: Laurent Gatto and Christophe Vanderaa.

Overview

Description

Mass spectrometry (MS)-based single-cell proteomics (SCP) is an emerging field that requires a dedicated computational environment. QFeatures along with its extension scp allow for standardized analysis of SCP data. The workshop will start by introducing the QFeatures class and its functions to perform generic proteomics data analysis. We will then move to SCP and present how scp extends QFeatures to single-cell applications. The remainder of the workshop will be a hands-on session where attendees will be guided through the reproduction of a real-life analysis of published SCP data. Along the reproduction exercise, we will point out to current challenges that still need to be tackled computationally. This workshop is meant for inexperienced users that want to learn how to perform current state-of-the-art analysis of SCP data as well as experienced developers interested in contributing to an emerging and exciting single-cell technology.

This workshop is provided as two vignettes. The first vignette provides a general introduction to the QFeatures class in the general context of MS-based proteomics data manipulation. The second vignette focuses on single-cell application and introduces the scp package as an extension of QFeatures. This second vignette also provides exercises that give the attendee the opportunity to apply the learned concepts to reproduce a published analysis on a subset of a real data set.

Pre-requisites

• Basic knowledge of R syntax
• Familiarity with the SummarizedExperiment class
• Familiarity with the ggplot2 package

We recommend reading the paper that has published the SCP analysis that will be reproduced in this workshop:

Specht, Harrison, Edward Emmott, Aleksandra A. Petelski, R. Gray Huffman, David H. Perlman, Marco Serra, Peter Kharchenko, Antonius Koller, and Nikolai Slavov. 2021. "Single-Cell Proteomic and Transcriptomic Analysis of Macrophage Heterogeneity Using SCoPE2.” Genome Biology 22 (1): 50. link to article, link to preprint

R / Bioconductor packages used

QFeatures, scp, scpdata, MultiAssayExperiment, SingleCellExperiment

Time outline

This 90 min workshop will be split in three parts:

| Activity | Time | |-----------------------------------------------------|------| | Introduction to the QFeatures class and functions | 25m | | Introduction to SCP and the scp package | 20m | | Reproducing SCoPE2: a published SCP data analysis | 45m |

Learning goals

• Highlight the common steps to perform MS-based proteomics data analysis
• Understand the specificities when it comes to single-cell data
• Identify the current challenges in the SCP data analysis

Learning objectives

• Use QFeatures and scp to perform a real-life analysis of SCP data
• Integrate the workflow with other tools such as the tidyverse ecosystem for efficient data visualization or the SingleCellExperiment related tools for extending the current analysis workflow.

Running the workshop

There are 3 ways you can follow the workshop, listed from easiest to more advanced:

1. Read the static vignettes (available from the Articles tab at the top of this page). In this case, there are no software requirements, but you won't be able to run the code yourself as everything is already compiled.
2. Run the workshop on the cloud. You can make use of the orchestra server set up by the Bioconductor community. Follow the instruction about how to get access to the server.
3. Run the vignettes on your local machine. You can get access to the source files of the vignette here and run the code on your own computer. You can install all necessary packages to run the workshop locally by installing the following packages:

## Bioconductor packages
if (!requireNamespace("BiocManager", quietly = TRUE))
    install.packages("BiocManager")
if (BiocManager::version() < 3.14) stop("Your BiocManager version is too old.")
BiocManager::install("QFeatures")
BiocManager::install("scp")
BiocManager::install("scpdata")

If you want to avoid dependency and version issues when running the vignettes locally, the lgatto/qfeaturesscpworkshop2021 docker container has all packages necessary for running the workshop vignettes. The container can be downloaded and run with

docker run -e PASSWORD=bioc -p 8787:8787 lgatto/qfeaturesscpworkshop2021:latest

(you can choose any password, not only bioc, above)

Once running, navigate to https://localhost:8787/ and then login with user rstudio and password bioc.

License

The content of this workshop is provided under a CC-BY ShareAlike license.

To cite package 'QFeaturesScpWorkshop2021' in publications use:

  Laurent Gatto and Christophe Vanderaa (NA). QFeaturesScpWorkshop2021:
  Reproducing a single-cell proteomics data analysis using QFeatures
  and scp. R package version 0.1.0.
  https://github.com/lgatto/QFeaturesScpWorkshop2021

A BibTeX entry for LaTeX users is

  @Manual{,
    title = {QFeaturesScpWorkshop2021: Reproducing a single-cell proteomics data analysis using QFeatures and scp.},
    author = {Laurent Gatto and Christophe Vanderaa},
    note = {R package version 0.1.0},
    url = {https://github.com/lgatto/QFeaturesScpWorkshop2021},
  }

lgatto/QFeaturesScpWorkshop2021 documentation built on Dec. 21, 2021, 10:44 a.m.