[](https://codecov.io/github/lgatto/pRoloc?branch=master)
# A unifying bioinformatics framework for spatial proteomics
<img src="https://raw.githubusercontent.com/Bioconductor/BiocStickers/master/pRoloc/pRoloc.png" height="200"><img src="https://raw.githubusercontent.com/Bioconductor/BiocStickers/master/pRoloc/pRolocdata.png" height="200"><img src="https://raw.githubusercontent.com/Bioconductor/BiocStickers/master/pRoloc/pRolocGUI.png" height="200">
The `pRoloc` suite set of software offers a complete software pipeline
to analyse, visualise and interpret mass spectrometry-based spatial
proteomics data such, for example, as LOPIT (Localization of Organelle
Proteins by Isotope Tagging), PCP (Protein Correlation Profiling) or
hyperLOPIT (hyperplexed LOPIT). The suite includes
[`pRoloc`](http://www.bioconductor.org/packages/release/bioc/html/pRoloc.html),
the mail software that focuses on data analysis using state-of-the-art
machine learning,
[`pRolocdata`](http://bioconductor.org/packages/release/data/experiment/html/pRolocdata.html),
that distributes numerous datasets, and [`pRolocGUI`](https://lgatto.github.io/pRolocGUI/), that offers
interactive visualisations dedicated to spatial proteomics. The
software are distributed as part of the
R/[Bioconductor](http://bioconductor.org/) project.
## Getting started
The `pRoloc` software comes with ample
documentation. The
[main tutorial](https://lgatto.github.io/pRoloc/articles/v01-pRoloc-tutorial.html) provides
a broad overview of the package and its functionality. See the
*Articles* tab for additional manuals.
[`pRolocGUI`](http://www.bioconductor.org/packages/release/bioc/html/pRolocGUI.html)
also offer several documentation files.
Here are a set of
[video tutorial](https://www.youtube.com/playlist?list=PLvIXxpatSLA2loV5Srs2VBpJIYUlVJ4ow)
that illustrate the `pRoloc` framework.
## Help
Post your questions on the
[Bioconductor support site](https://support.bioconductor.org/),
tagging it with the package name `pRoloc` (the maintainer will
automatically be notified by email). If you identify a bug or have a
feature request, please open an
[issue](https://github.com/lgatto/pRoloc/issues) on the github
development page.
## Installation
The preferred installation procedure uses the Bioconductor
infrastructure:
```c
## unless BiocManager is already installed
install.packages("BiocManager")
## then
BiocManager::install("pRoloc")
BiocManager::install("pRolocdata")
BiocManager::install("pRolocGUI")
```
### Pre-release/development version
The pre-release/development code on github can also be installed using
`BiocManager::install`, as shown below. Note that this requires a
working R build environment (i.e `Rtools` on Windows - see
[here](https://github.com/lgatto/teachingmaterial/wiki/R-package)). New
pre-release features might not be documented not thoroughly tested and
could substantially change prior to release. Use at your own risks.
```c
## unless BiocManager is already installed
install.packages("BiocManager")
## then, install from github
BiocManager::install("lgatto/pRoloc")
BiocManager::install("lgatto/pRolocdata")
BiocManager::install("lgatto/pRolocGUI")
```
## References:
For refences about the software, how to use it and spatial proteomics
data analysis:
* Crook OM, Breckels LM, Lilley KS, Kirk PWD, Gatto L. A Bioconductor
workflow for the Bayesian analysis of spatial proteomics [version 1;
peer review: awaiting peer review]. F1000Research 2019, 8:446
(https://doi.org/10.12688/f1000research.18636.1)
* Breckels LM, Mulvey CM, Lilley KS and Gatto L. A Bioconductor
workflow for processing and analysing spatial proteomics data
[version 2; peer review: 2 approved]. F1000Research 2018, 5:2926
(https://doi.org/10.12688/f1000research.10411.2)
* Gatto L, Breckels LM, Burger T, Nightingale DJ, Groen AJ, Campbell
C, Nikolovski N, Mulvey CM, Christoforou A, Ferro M, Lilley KS. *A
foundation for reliable spatial proteomics data analysis* Mol Cell
Proteomics. 2014 Aug;13(8):1937-52. doi:
10.1074/mcp.M113.036350. Epub 2014 May 20. [PubMed PMID:
24846987](http://www.ncbi.nlm.nih.gov/pubmed/24846987)
* Gatto L, Breckels LM, Wieczorek S, Burger T, Lilley
KS. *Mass-spectrometry-based spatial proteomics data analysis using
pRoloc and pRolocdata* Bioinformatics. 2014 May 1;30(9):1322-4. doi:
10.1093/bioinformatics/btu013. Epub 2014 Jan 11. [PubMed PMID:
24413670](http://www.ncbi.nlm.nih.gov/pubmed/24413670).
Specific algorithms available in the software:
* Breckels LM, Gatto L, Christoforou A, Groen AJ, Lilley KS, Trotter
MW. *The effect of organelle discovery upon sub-cellular protein
localisation* J Proteomics. 2013 Aug 2;88:129-40. doi:
10.1016/j.jprot.2013.02.019. Epub 2013 Mar 21. [PubMed PMID:
23523639](http://www.ncbi.nlm.nih.gov/pubmed/23523639).
* Breckels LM, Holden S, Wojnar D, Mulvey CMM, Christoforou A, Groen
AJ, Kohlbacher O, Lilley KS and Gatto L. *Learning from
heterogeneous data sources: an application in spatial proteomics*
2015 biorXiv, doi: http://dx.doi.org/10.1101/022152
* Oliver M Crook, Claire M Mulvey, Paul D. W. Kirk, Kathryn S Lilley,
Laurent Gatto *A Bayesian Mixture Modelling Approach For Spatial
Proteomics* PLOS Computational Biology
doi:[10.1371/journal.pcbi.1006516](https://doi.org/10.1371/journal.pcbi.1006516)
#### More resource
* R and Bioconductor for proteomics
[web page](http://lgatto.github.io/RforProteomics/) and
[package](http://www.bioconductor.org/packages/release/data/experiment/html/RforProteomics.html)
* Bioconductor proteomics [workflow](http://bioconductor.org/help/workflows/proteomics/)
## Contributing
Contributions to the package are more than welcome. If you want to
contribute to this package, you should follow the same conventions as
the rest of the functions whenever it makes sense to do so. Feel free
to get in touch (preferable opening a
[github issue](https://github.com/lgatto/pRoloc/issues/)) to discuss
any suggestions.
Please note that this project is released with a
[Contributor Code of Conduct](https://github.com/lgatto/pRoloc/blob/master/CONDUCT.md).
By participating in this project you agree to abide by its terms.
