baldur

Bayesian proteomics data analysis

https://github.com/philipberg/baldur

Repository

Bayesian proteomics data analysis

README.Rmd

---
output: github_document
---



```{r, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.path = "man/figures/README-",
  out.width = "100%"
)
```

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[![CRAN status](https://www.r-pkg.org/badges/version/baldur)](https://CRAN.R-project.org/package=baldur)


# Baldur

The goal of Baldur is to shine light on your proteomics data.
Baldur is a hierarchical Bayesian model that uses an empirical Bayes method to estimate hyperparamters for the variance and measurement specific uncertainty.
It then estimates the posterior of the difference in means between different conditions for each peptide/protein/PTM.
Finally, it integrates the posterior to estimate the probability of error.

## Installation
You can install the development version of `baldur` from this github or the stable version from CRAN.
Importantly, you first need to follow the instructions for installing `rstan` [https://github.com/stan-dev/rstan/wiki/RStan-Getting-Started](https://github.com/stan-dev/rstan/wiki/RStan-Getting-Started) and make sure that is working.
Then you can install `baldur` accordingly:

For the stable release please install from CRAN:
```{r, eval = FALSE}
install.packages('baldur')
```

Or you can install the developmental version of `baldur` from this github:
```{r, eval = FALSE}
devtools::install_github('PhilipBerg/baldur', build_vignettes = T)
```

Note that Ubuntu operating systems can require `pandoc` [https://pandoc.org/](https://pandoc.org/) to compile the vignettes.

For Windows, the developmental version of `rstan` is sometimes needed to install `baldur`.

## Example

Please see the vignettes for examples `vignette('baldur_yeast_tutorial')` and `vignette('baldur_ups_tutorial')`.

## Reference
Berg, Philip, and George Popescu. "Baldur: Bayesian Hierarchical Modeling for Label-Free Proteomics with Gamma Regressing Mean-Variance Trends" Molecular & Cellular Proteomics (2023): 2023-12.
https://doi.org/10.1016/j.mcpro.2023.100658

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