PhIPData defines an S4 class for phage-immunoprecipitation sequencing (PhIP-seq) experiments. Buliding upon the RangedSummarizedExperiment class, PhIPData enables users to coordinate metadata with experimental data in analyses. Additionally, PhIPData provides specialized methods to subset and identify beads-only samples, subset objects using virus aliases, and use existing peptide libraries to populate object parameters.
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PhIPData defines an S4 class for phage-immunoprecipitation sequencing (PhIP-seq) experiments. Buliding upon the RangedSummarizedExperiment class, PhIPData enables users to coordinate metadata with experimental data in analyses. Additionally, PhIPData provides specialized methods to subset and identify beads-only samples, subset objects using virus aliases, and use existing peptide libraries to populate object parameters.
library(PhIPData)
# 1. Define dimensions and simulate data matrices
n_samples <- 5L
n_peps <- 10L
counts_dat <- matrix(sample(1:1e6, n_samples*n_peps, replace = TRUE), nrow = n_peps)
logfc_dat <- matrix(rnorm(n_samples*n_peps, mean = 0, sd = 10), nrow = n_peps)
prob_dat <- matrix(rbeta(n_samples*n_peps, shape1 = 1, shape2 = 1), nrow = n_peps)
# 2. Define metadata
peptide_meta <- data.frame(pep_id = paste0("pep_", 1:n_peps))
sample_meta <- data.frame(
sample_name = paste0("sample", 1:n_samples),
group = c("ctrl", "beads", "trt", "trt", "trt")
)
# 3. Construct PhIPData object and subset beads-only samples
phip_obj <- PhIPData(counts_dat, logfc_dat, prob_dat, peptide_meta, sample_meta)
beads_only <- subsetBeads(phip_obj)
Note: Inputs are parallel matrices of counts, log fold-changes, probabilities, and metadata data frames; output is a coordinated PhIPData object.
subsetBeads() wrapper.setAlias(), getAlias(), and standard subset() operations.DESeq2 or edgeR because PhIPData is specifically tailored for PhIP-seq antibody enrichment assays.SingleCellExperiment because PhIPData is designed for bulk phage-display sequencing experiments.QFeatures or MSnbase because PhIPData is designed for high-throughput sequencing of phage libraries.counts (non-negative numeric values), logfc (log2 estimated fold-changes), and prob (enrichment probabilities/p-values).DataFrame or data.frame containing peptide identifiers, and optionally start/end positions (pos_start, pos_end).DataFrame or data.frame containing sample names and group designations (e.g., "beads", "ctrl", "trt").librarySize() and propReads(), controls whether output vectors/matrices retain row and column names.group with a "beads" level to enable automatic identification of beads-only control samples via subsetBeads().makeLibrary() to save peptide annotations as reusable templates, avoiding redundant re-importing of large peptide metadata files.PhIPData objects to DGEList using as(phip_obj, "DGEList") when transitioning to edgeR for differential enrichment analysis.pos_start and pos_end are missing from the peptide metadata, the constructor replaces them with 0 and prints a warning.PhIPData object without counts, logfc, or prob will result in empty matrices being initialized for the missing assays."hiv" vs "HIV") are case-sensitive; ensure consistent casing when calling getAlias().No reviews yet. Tell others what you thought.
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