Package: beer 1.17.0

Athena Chen

beer: Bayesian Enrichment Estimation in R

BEER implements a Bayesian model for analyzing phage-immunoprecipitation sequencing (PhIP-seq) data. Given a PhIPData object, BEER returns posterior probabilities of enriched antibody responses, point estimates for the relative fold-change in comparison to negative control samples, and more. Additionally, BEER provides a convenient implementation for using edgeR to identify enriched antibody responses.

Authors:Athena Chen [aut, cre], Rob Scharpf [aut], Ingo Ruczinski [aut]

beer_1.17.0.tar.gz
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manual.pdf |manual.html✨
card.svg |card.png
beer/json (API)
NEWS

# Install 'beer' in R:

install.packages('beer', repos = c('https://bioc.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/athchen/beer/issues

Uses libs:

jags– Just Another Gibbs Sampler for Bayesian MCMC - binary JAGS is Just Another Gibbs Sampler. It is a program for analysis of Bayesian hierarchical models using Markov Chain Monte Carlo (MCMC) simulation not wholly unlike BUGS. JAGS was written with three aims in mind: * To have an engine for the BUGS language that runs on Unix * To be extensible, allowing users to write their own functions, distributions and samplers. * To be a plaftorm for experimentation with ideas in Bayesian modelling This package contains the 'jags' binary as well as the associated shared library modules loaded by the binary.
c++– GNU Standard C++ Library v3

On BioConductor:beer-1.17.0(bioc 3.24)beer-1.15.1(bioc 3.23)

software statisticalmethod bayesian sequencing coverage jags cpp

5.66 score 11 stars 14 scripts 364 downloads 10 exports 70 dependencies

Last updated from:2ad6b135ea. Checks:1 NOTE, 7 ERROR, 2 OK. Indexed: yes.

Target	Result	Time
bioc-checks	NOTE	195
linux-devel-x86_64	ERROR	526
source / vignettes	OK	325
linux-release-x86_64	ERROR	454
macos-release-arm64	ERROR	345
macos-oldrel-arm64	ERROR	246
windows-devel	ERROR	794
windows-release	ERROR	904
windows-oldrel	ERROR	804
wasm-release	OK	146

Exports:beadsRR brew brewOne edgeROne edgeROneQLF getAB getBF getExpected guessEnriched runEdgeR

Dependencies:abind askpass BH Biobase BiocFileCache BiocGenerics BiocParallel bit bit64 blob cachem cli coda codetools cpp11 curl DBI dbplyr DelayedArray digest dplyr edgeR fastmap filelock formatR futile.logger futile.options generics GenomicRanges glue httr2 IRanges lambda.r lattice lifecycle limma locfit magrittr Matrix MatrixGenerics matrixStats memoise openssl PhIPData pillar pkgconfig progressr purrr R6 rappdirs rjags rlang RSQLite S4Arrays S4Vectors Seqinfo snow SparseArray statmod stringi stringr SummarizedExperiment sys tibble tidyr tidyselect utf8 vctrs withr XVector

Estimating Enrichment in PhIP-Seq Experiments with BEER

Athena Chen, Kai Kammers, Rob Scharpf, Ingo Ruczinski

Rendered frombeer.Rmdusingknitr::rmarkdownon May 30 2026.

Last update: 2022-06-30
Started: 2021-02-23

Citation

Development and contributors

Readme and manuals

Help Manual

Help page	Topics
Function to run the beads-only round robin using BEER	.beadsRRBeer
Function to run the beads-only round robin using edgeR	.beadsRREdgeR
Run BEER for all samples	.brewSamples
Function to check that the counts matrix only contains integers	.checkCounts
Function to check whether an assay will be overwritten	.checkOverwrite
Estimate edgeR dispersion parameters from the beads-only data using qCML	.edgeRBeads
Estimate edgeR dispersion parameters from the beads-only samples using Cox-Reid profile adjusted likelihood method for estimating dispersions.	.edgeRBeadsQLF
Derive beta shape parameters using edgeR dispersion estimates	.getABEdgeR
Wrapper function to derive MLE estimates of a, b from beads-only samples	.getABMLE
Helper function to derive MLE estimates of a, b from a vector of proportions	.getABMLEProp
Wrapper function to derive MOM estimates of a, b from beads-only samples	.getABMOM
Helper function to derive MOM estimates of a, b from a vector of proportions	.getABMOMProp
Guess super-enriched peptides based on edgeR fold-change estimates	.guessEnrichedEdgeR
Guess enriched peptides based on MLE estimates of the true fold-change	.guessEnrichedMLE
Clean-up specified assay names	.tidyAssayNames
Clean inputs for JAGS parameters	.tidyInputsJAGS
Clean up inputs for prior estimation	.tidyInputsPrior
Clean up inputs for identifying super-enriched peptides	.tidyInputsSE
Beads-only round robin	beadsRR
Bayesian Enrichment Estimation in R (BEER)	brew
Run BEER for one sample	brewOne
Run edgeR for one sample against all the beads-only samples.	edgeROne
Run edgeR for one sample against all the beads-only samples using edgeR's QLF Test for differential expression.	edgeROneQLF
Estimate beads-only shape parameters	getAB
Calculate Bayes Factors	getBF
Calculate expected read counts or proportion of reads	getExpected
Identifying clearly enriched peptides	guessEnriched
Derive initial estimates of unknown model parameters	guessInits
Run edgeR on PhIP-Seq data	runEdgeR
Summarize MCMC chain and return point estimates for BEER parameters	summarizeRun
Derive point estimates for c, pi, phi, and Z for a particular sample	summarizeRunOne