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README.Rmd
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README.Rmd
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---
output: github_document
always_allow_html: true
---
<!-- README.md is generated from README.Rmd. Please edit that file -->
```{r, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.path = "man/figures/README-",
out.width = "100%"
)
```
# RexMS
<!-- badges: start -->
[](https://lifecycle.r-lib.org/articles/stages.html#experimental)
[](https://bioconductor.org/checkResults/release/bioc-LATEST/RexMS)
[](https://bioconductor.org/checkResults/devel/bioc-LATEST/RexMS)
[](http://bioconductor.org/packages/stats/bioc/RexMS/)
[](https://support.bioconductor.org/tag/RexMS)
[](https://bioconductor.org/packages/release/bioc/html/RexMS.html#since)
[](http://bioconductor.org/checkResults/devel/bioc-LATEST/RexMS/)
[](https://bioconductor.org/packages/release/bioc/html/RexMS.html#since)
[](https://github.com/ococrook/RexMS/actions/workflows/R-CMD-check-bioc.yaml)
[](https://app.codecov.io/gh/ococrook/RexMS?branch=devel)
<!-- badges: end -->
The goal of `RexMS` is to analyse hydrogen deuterium exchange mass spectrometry
(HDX-MS) data at the residue level. The package is designed to be used with
data from various platforms that have already been process at Spectrum-level.
`RexMS` takes the processed peptide-level data and infers the residue level
deuterium uptake. The underlying model is a Bayesian non-parametric model that
recasts HDX-MS data analysis as a (latent) change-point detection problem.
The unique benefits of this model are the following:
(1) It can provide statistical confidence at the level of residues (e.g.
a probability of change)
(2) Borrow statistical power from overlapping peptides
(3) Infer uptake patterns that are hidden at the peptide-level because of
averaging
(4) Provide global and per-residue resolution metrics
(5) It can perform single protein analysis, differential analysis and
conformational signature analysis (many states/compounds), linking results
to downstream functional outcomes
(6) You can build predictive models with the inferred uptakes using partial
least squares discriminant analysis (PLS-DA).
(7) You can costumize the model to your specific needs.
## Documentation
The official documentation is available at
[ococrook.github.io/RexMS](https://ococrook.github.io/RexMS/). We encourage
use to look there and in particular the vignettes. We suggest reading through
all of the vignettes first so you understand which part of `RexMS` you need to use.
## Installation instructions
Get the latest stable `R` release from [CRAN](http://cran.r-project.org/). Then install `RexMS` from [Bioconductor](http://bioconductor.org/) using the following code:
Note that you cannot currently install from Bioconductor
```{r 'install', eval = FALSE}
if (!requireNamespace("BiocManager", quietly = TRUE)) {
install.packages("BiocManager")
}
BiocManager::install("RexMS")
```
To install the development version of `RexMS`, it's easiest to use the
`remotes` package.
```{r 'install_dev', eval = FALSE}
if (!requireNamespace("remotes", quietly = TRUE)) {
install.packages("remotes")
}
remotes::install_github("ococrook/RexMS")
```
## Example
RexMS has structure visualisation at part of its core functionality.
Below is an example of how to use the `hdx_to_pdb_colours` function to map HDX-MS data onto a PDB file.
```{r example, eval = requireNamespace('RexMS'), eval = FALSE}
library("RexMS")
library(NGLVieweR)
# generate random HDX data
v <- matrix(rnorm(n = 477), nrow = 1)
colnames(v) <- seq.int(ncol(v)) # residue numbering
v2 <- v[, seq.int(344, 477), drop = FALSE]
colnames(v2) <- seq.int(ncol(v2))
pdb_filepath <- system.file("extdata", "test_BRD4.pdb", mustWork = TRUE, package = "RexMS")
# generate a protection-deprotection colour mapping
mycolor_parameters <- hdx_to_pdb_colours(v2, pdb = pdb_filepath, cmap_name = "ProtDeprot")
# Note this will open in a view
view_structure(pdb_filepath = pdb_filepath, color_parameters = mycolor_parameters)
```
The expected output is a 3D structure of the protein with the HDX-MS data mapped
onto it. The colouring is based on the protection-deprotection scale.The
following image is indicative and may not be the same as the output you will see.
## Shiny Application
You may be interested in interesting ways to display your data or interact
with our results. Here are links to some shiny applications that use `RexMS`:
https://olivercrook.shinyapps.io/BRD4-ReX/
and
https://olivercrook.shinyapps.io/ConformationalSignatureAnalysis/
## Citation
Below is the citation output from using `citation('RexMS')` in R. Please
run this yourself to check for any updates on how to cite __RexMS__.
```{r 'citation', eval = requireNamespace('RexMS')}
print(citation("RexMS"), bibtex = TRUE)
```
Please note that the `RexMS` was only made possible thanks to many other
R and bioinformatics software authors, which are cited either
in the vignettes and/or the paper(s) describing this package.
## Code of Conduct
Please note that the `RexMS` project is released with a
[Contributor Code of Conduct](http://bioconductor.org/about/code-of-conduct/).
By contributing to this project, you agree to abide by its terms.
## Development tools
* Continuous code testing is possible thanks to
[GitHub actions](https://www.tidyverse.org/blog/2020/04/usethis-1-6-0/)
through `r BiocStyle::CRANpkg('usethis')`, `r BiocStyle::CRANpkg('remotes')`,
and `r BiocStyle::CRANpkg('rcmdcheck')` customized to
use [Bioconductor's docker containers](https://www.bioconductor.org/help/docker/)
and `r BiocStyle::Biocpkg('BiocCheck')`.
* Code coverage assessment is possible thanks
to [codecov](https://codecov.io/gh) and `r BiocStyle::CRANpkg('covr')`.
* The code is styled automatically thanks to `r BiocStyle::CRANpkg('styler')`.
* The documentation is formatted thanks to `r BiocStyle::CRANpkg('devtools')`
and `r BiocStyle::CRANpkg('roxygen2')`.
For more details, check the `dev` directory.
This package was developed using `r BiocStyle::Biocpkg('biocthis')`.