| name | bioconductor-phipdata |
| description | 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. |
PhIPData
Workflows
Standard Workflow
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)
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)
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")
)
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.
When to Use
- Storing PhIP-seq experimental results: Manage raw read counts, log2 estimated fold-changes, and enrichment probabilities in a single coordinated container.
- Identifying and subsetting control samples: Quickly isolate beads-only control samples using the specialized
subsetBeads() wrapper.
- Managing peptide libraries and virus aliases: Query and subset specific viral species using
setAlias(), getAlias(), and standard subset() operations.
When NOT to Use
- For general RNA-seq differential expression: Use
DESeq2 or edgeR because PhIPData is specifically tailored for PhIP-seq antibody enrichment assays.
- For single-cell transcriptomics: Use
SingleCellExperiment because PhIPData is designed for bulk phage-display sequencing experiments.
- For mass spectrometry-based proteomics: Use
QFeatures or MSnbase because PhIPData is designed for high-throughput sequencing of phage libraries.
Data Requirements
- Assay Matrices: Three parallel matrices of identical dimensions:
counts (non-negative numeric values), logfc (log2 estimated fold-changes), and prob (enrichment probabilities/p-values).
- Peptide Metadata: A
DataFrame or data.frame containing peptide identifiers, and optionally start/end positions (pos_start, pos_end).
- Sample Metadata: A
DataFrame or data.frame containing sample names and group designations (e.g., "beads", "ctrl", "trt").
Key Parameters
- counts (matrix): Raw read counts or pseudocounts for each peptide and sample.
- logfc (matrix): Log2 estimated fold-changes compared to negative controls.
- prob (matrix): Probabilities or p-values associated with enriched antibody response.
- withDimnames (TRUE): In
librarySize() and propReads(), controls whether output vectors/matrices retain row and column names.
Best Practices
- Ensure that the sample metadata contains a column named
group with a "beads" level to enable automatic identification of beads-only control samples via subsetBeads().
- Use
makeLibrary() to save peptide annotations as reusable templates, avoiding redundant re-importing of large peptide metadata files.
- Coerce
PhIPData objects to DGEList using as(phip_obj, "DGEList") when transitioning to edgeR for differential enrichment analysis.
Common Pitfalls
- Missing peptide start/end positions: If
pos_start and pos_end are missing from the peptide metadata, the constructor replaces them with 0 and prints a warning.
- Missing required assays: Attempting to construct a
PhIPData object without counts, logfc, or prob will result in empty matrices being initialized for the missing assays.
- Case-sensitivity in aliases: Keys in the alias database (e.g.,
"hiv" vs "HIV") are case-sensitive; ensure consistent casing when calling getAlias().
Alternatives
- SummarizedExperiment: For general-purpose coordinated representation of rectangular biological assay data.
- RangedSummarizedExperiment: For genomic-coordinate-linked assay data.
- edgeR (DGEList): For standard differential count analysis.
Citations
- Chen A, Scharpf R, Ruczinski I (2026). PhIPData: A Container for PhIP-Seq Experiments. R package version 1.12.0.
References
