ARscore

Phage ImmunoPrecipitation Sequencing (PhIP-Seq) is a massively multiplexed method for quantifying antibody reactivity to libraries of peptides. PhIP-seq analyses begin by identifying enriched antibody reactivity to individual peptides. However, studies frequently require understanding of aggregate reactivity to whole antigens or pathogens. ARscore provides a standardized approach for calculating aggregate reactivity to groups of peptides.

ARscore generates aggregate reactivity scores (ARscores) by comparing the average fold change of a group of peptides to distributions of average fold change from randomly selected peptides, using fitdistrplus¹ and limma². To expedite computation and evaluate extreme reactivity scores, random distributions are modeled as gamma distributions with paramaters that change regularly with the number of randomly selected peptides.

ARscore was initially implemented with VirScan (a PhIP-Seq library of viral peptides) to create a virus level reactivity metric (Viral ARscore, VARscore), using peptide enrichments determined with edgeR’s standard pipeline for identifying differential expression from read count data³⁴⁵.

Input PhIP-Seq data requires Larman Lab naming conventions for mock IP controls, samples, and peptide annotations. Peptide grouping is currently based on the taxon_species annotation column. Custom peptide groupings can be achieved by replacing this column.

For more information, see the package vignette using browseVignettes("ARscore").

Installation

ARscore

To install the package:

if(!requireNamespace("remotes")) install.packages("remotes")

remotes::install_github("wmorgen1/ARscore")

To load the package:

library(ARscore)

Footnotes

1. Delignette-Muller ML, Dutang C (2015). fitdistrplus: An R Package for Fitting Distributions. Journal of Statistical Software, 64(4), 1–34. ↩

2. 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. ↩

3. 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. ↩

4. 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. ↩

5. 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. ↩