baldur: Bayesian Hierarchical Modeling for Label-Free Proteomics (original) (raw)

Statistical decision in proteomics data using a hierarchical Bayesian model. There are two regression models for describing the mean-variance trend, a gamma regression or a latent gamma mixture regression. The regression model is then used as an Empirical Bayes estimator for the prior on the variance in a peptide. Further, it assumes that each measurement has an uncertainty (increased variance) associated with it that is also inferred. Finally, it tries to estimate the posterior distribution (by Hamiltonian Monte Carlo) for the differences in means for each peptide in the data. Once the posterior is inferred, it integrates the tails to estimate the probability of error from which a statistical decision can be made. See Berg and Popescu for details (<doi:10.1101/2023.05.11.540411>).

Version:	0.0.3
Depends:	R (≥ 4.2.0)
Imports:	dplyr (≥ 1.0.9), magrittr (≥ 2.0.3), methods, purrr (≥ 0.3.4), Rcpp (≥ 0.12.0), RcppParallel (≥ 5.0.1), rstan (≥ 2.26.0), rstantools (≥ 2.2.0), stats, stringr (≥ 1.0.4), tidyr (≥ 1.2.0), rlang (≥ 1.0.2), Rdpack (≥ 2.4), multidplyr (≥ 0.1.1), ggplot2 (≥ 3.3.6), tibble (≥ 3.1.7), viridisLite (≥ 0.4.1), lifecycle
LinkingTo:	BH (≥ 1.66.0), Rcpp (≥ 0.12.0), RcppEigen (≥ 0.3.3.3.0), RcppParallel (≥ 5.0.1), rstan (≥ 2.26.0), StanHeaders (≥ 2.26.0)
Suggests:	knitr, rmarkdown
Published:	2023-09-18
DOI:	10.32614/CRAN.package.baldur
Author:	Philip Berg [aut, cre]
Maintainer:	Philip Berg
BugReports:	https://github.com/PhilipBerg/baldur/issues
License:	MIT + file
URL:	https://github.com/PhilipBerg/baldur
NeedsCompilation:	yes
SystemRequirements:	GNU make
Language:	en-US
Materials:	README NEWS
CRAN checks:	baldur results

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