https://github.com/lgatto/qfeaturesscpworkshop2021
QFeatures and scp Bioc2021 workshop
https://github.com/lgatto/qfeaturesscpworkshop2021
Last synced: 3 days ago
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QFeatures and scp Bioc2021 workshop
- Host: GitHub
- URL: https://github.com/lgatto/qfeaturesscpworkshop2021
- Owner: lgatto
- Created: 2021-03-09T15:16:28.000Z (about 4 years ago)
- Default Branch: main
- Last Pushed: 2021-08-12T16:09:55.000Z (over 3 years ago)
- Last Synced: 2025-03-30T19:22:59.810Z (about 1 month ago)
- Language: R
- Homepage: https://lgatto.github.io/QFeaturesScpWorkshop2021/
- Size: 175 MB
- Stars: 3
- Watchers: 3
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# 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](https://lgatto.github.io/QFeaturesScpWorkshop2021/articles/v01-QFeatures.html)
provides a general introduction to the `QFeatures` class in the
general context of MS-based proteomics data manipulation. The
[second vignette](https://lgatto.github.io/QFeaturesScpWorkshop2021/articles/v02-scp.html)
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](http://dx.doi.org/10.1186/s13059-021-02267-5),
[link to preprint](https://www.biorxiv.org/content/10.1101/665307v5)
### _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](http://app.orchestra.cancerdatasci.org/)
set up by the `Bioconductor` community. Follow the
[instruction](https://github.com/lgatto/QFeaturesScpWorkshop2021/tree/main/inst/Get_started_on_cloud.pdf)
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](https://github.com/lgatto/QFeaturesScpWorkshop2021/tree/main/vignettes)
and run the code on your own computer. You can install all
necessary packages to run the workshop locally by installing the
following packages:
```r
## 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](https://hub.docker.com/repository/docker/lgatto/qfeaturesscpworkshop2021)
docker container has all packages necessary for running the workshop
vignettes. The container can be downloaded and run with
```sh
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](https://creativecommons.org/licenses/by-sa/2.0/)
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},
}
```