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bioconductor-beer

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.

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beer

Workflows

Standard Workflow

Identify enriched antibody responses to peptides in PhIP-seq data using edgeR and Bayesian (BEER) modeling.

library(beer)

# Load simulated PhIPData
data_path <- system.file("extdata/sim_data.rds", package = "beer")
sim_data <- readRDS(data_path)

# 1. Run edgeR to estimate peptide-specific dispersion
edgeR_out <- runEdgeR(sim_data, assay.names = c(logfc = "edgeR_logfc", prob = "edgeR_logpval"))

# 2. Run BEER (brew) to identify enriched peptides (hits)
assay_locations <- c(phi = "beer_fc_marg", phi_Z = "beer_fc_cond", Z = "beer_prob", c = "sampleInfo", pi = "sampleInfo")
beer_out <- brew(edgeR_out, assay.names = assay_locations)

# Identify hits by combining posterior probabilities and super-enriched peptides
was_run <- matrix(rep(beer_out$group != "beads", each = nrow(beer_out)), nrow = nrow(beer_out))
are_se <- was_run & is.na(assay(beer_out, "beer_prob"))
assay(beer_out, "beer_hits") <- assay(beer_out, "beer_prob") > 0.5 | are_se

# 3. Run beads-only round robin to estimate false positive rates
beer_beadsRR <- beadsRR(beer_out, method = "beer", assay.names = assay_locations)

# 4. Calculate expected reads/proportions and Bayes factors
expected_data <- getExpected(sim_data)
bf_data <- getBF(beer_out)

Input: A PhIPData object containing PhIP-seq read counts; Output: A PhIPData object updated with enrichment hits, posterior probabilities, and Bayes factors.

When to Use

Analyzing PhIP-seq (phage immunoprecipitation sequencing) data to identify enriched antibody responses to peptides.
When you want to run standard differential expression analysis using runEdgeR on PhIP-seq data.
When you want to apply a Bayesian hierarchical model via brew to estimate posterior probabilities of enrichment and relative fold-changes.
When you want to estimate false positive rates using a beads-only round robin via beadsRR.

When NOT to Use

For general RNA-seq differential expression without a PhIP-seq design or negative control (beads-only) samples. Use standard edgeR or DESeq2 directly.

Data Requirements

A PhIPData object (e.g., loaded from sim_data.rds in the vignette) containing read counts for samples and peptides, with designated beads-only control samples.

Key Parameters

assay.names (NULL): Named vector specifying where to store summarized MCMC output (e.g., phi, phi_Z, Z, c, pi).
beadsRR (FALSE): Logical indicating whether to run beads-only round robin within brew or runEdgeR.
de.method ("exactTest"): Method for edgeR differential expression, either "exactTest" or "glmQLFTest".
a_pi (2): Prior shape parameter for the beta distribution describing the proportion of enriched peptides.
b_pi (300): Prior shape parameter for the beta distribution describing the proportion of enriched peptides.
a_phi (1.25): Prior shape parameter for the gamma distribution describing fold-change for enriched peptides.
b_phi (0.1): Prior shape parameter for the gamma distribution describing fold-change for enriched peptides.
fc (1): Minimum fold-change for an enriched peptide.

Best Practices

Run runEdgeR first to quickly identify enriched peptides and provide a baseline comparison.
Identify and exclude "super-enriched" peptides (e.g., fold-change > 15) before running the MCMC to speed up BEER execution.
Use parallelization via BiocParallel::SerialParam() or BiocParallel::SnowParam() passed to BPPARAM to speed up the Bayesian model.
Verify MCMC convergence by saving samples to a directory and plotting trace/density plots using coda or plot.

Common Pitfalls

MCMC sampler getting stuck during execution. Fix: Ensure prior beta shape parameters (e.g., estimated via getAB) are greater than one.
Extremely slow execution times for large datasets. Fix: Set a conservative threshold for removing super-enriched peptides to reduce the number of peptides modeled by MCMC.

Alternatives

edgeR directly for standard negative binomial differential expression.
DESeq2 for alternative parametric differential expression analysis.

Citations

Chen A, Kammers K, Larman HB, Scharpf R, Ruczinski I (2022). Detecting antibody reactivities in phage immunoprecipitation sequencing data. bioRxiv.
Robinson MD, McCarthy DJ and Smyth GK (2010). edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics.

References

Homepage: bioconductor.org/packages/beer
Vignette: https://bioconductor.org/packages/release/bioc/vignettes/beer/inst/doc/beer.html

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