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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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.
runEdgeR on PhIP-seq data.brew to estimate posterior probabilities of enrichment and relative fold-changes.beadsRR.edgeR or DESeq2 directly.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.phi, phi_Z, Z, c, pi).brew or runEdgeR.runEdgeR first to quickly identify enriched peptides and provide a baseline comparison.BiocParallel::SerialParam() or BiocParallel::SnowParam() passed to BPPARAM to speed up the Bayesian model.coda or plot.getAB) are greater than one.edgeR directly for standard negative binomial differential expression.DESeq2 for alternative parametric differential expression analysis.まだレビューはありません。使ってみた感想をお寄せください。
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日本語の概要は準備中です。原文の説明を表示しています。