ARscape is a tool for the statistical analysis of Phage ImmunoPrecipitation Sequencing (PhIP-Seq) data. It provides a standardized approach for calculating aggregate reactivity - quantifying antibody responses not just to individual peptides, but to whole species, such as pathogens or allergens.
This package is a statistical evolution of the original ARscore algorithm. It aims to improve the sensitivity and specificity of aggregate reactivity profiles by introducing calibrated null distribution modeling and refined p-value estimation.
PhIP-seq analyses typically begin by identifying enriched antibody reactivity to individual peptides. However, biological insights frequently require aggregating these signals to the protein or organism level.
ARscape generates reactivity scores by comparing the average fold change of a biologically related group of peptides against a background distribution of randomly selected peptides.
While retaining the core logic of the original algorithm, ARscape refactors the code, calibrates the statistical engine, and implements diagnostics:
- Calibrated Gamma Fitting: The fit of gamma functions to sampled null distributions is now explicitly calibrated to handle edge cases in null distribution tails more robustly.
- P-value Calibration: The computation of p-values has been calibrated, reducing false discovery rates by permitting p-value based aggregate reactivity cutoffs.
- Modern Infrastructure: The package has been refactored for current R versions (4.4+), utilizing testthat for unit testing and modularized functions.
ARscape is a distinct fork and significant refactoring of the ARscore package.
- Original Concept & Algorithm: Developed by William R. Morgenlander, PhD.
- Current Maintainer & Refactoring: August F. Jernbom, PhD
I gratefully acknowledge the foundational work provided by the original ARscore implementation. If you use ARscape in your research, please cite both this repository and the original methodology.
You can install the development version of ARscape from GitHub with:
install.packages("remotes")
remotes::install_github("jernbom/ARscape")To load the package:
library(ARscape)
Input PhIP-Seq data generally follows Larman Lab naming conventions for mock IP controls, samples, and peptide annotations.
Peptide grouping is currently based on the taxon_species annotation
column, though this can be customized by users.
(Note: Documentation for new calibrated functions is currently in progress.)
The underlying statistical approach relies on:
- fitdistrplus: Delignette-Muller ML, Dutang C (2015). fitdistrplus: An R Package for Fitting Distributions. Journal of Statistical Software, 64(4), 1–34.
- limma: Ritchie ME, Phipson B, Wu D, Hu Y, Law CW, Shi W, Smyth GK (2015). limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Research, 43(7), e47.
- edgeR:
- Robinson MD, McCarthy DJ and Smyth GK (2010). edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26, 139-140.
- McCarthy DJ, Chen Y and Smyth GK (2012). Differential expression analysis of multifactor RNA-Seq experiments with respect to biological variation. Nucleic Acids Research 40, 4288-4297.
- Chen Y, Lun ATL, Smyth GK (2016). From reads to genes to pathways: differential expression analysis of RNA-Seq experiments using Rsubread and the edgeR quasi-likelihood pipeline. F1000Research 5, 1438.