Name Mode Size
..
workflows 040000
.gitignore 100644 0 kb
README.md
<!-- README.md is generated from README.Rmd. Please edit that file --> # PhIPData <!-- badges: start --> [![R-CMD-check](https://github.com/athchen/PhIPData/workflows/R-CMD-check/badge.svg)](https://github.com/athchen/PhIPData/actions) [![BioC status](http://www.bioconductor.org/shields/build/release/bioc/PhIPData.svg)](https://bioconductor.org/checkResults/release/bioc-LATEST/PhIPData) [![Codecov test coverage](https://codecov.io/gh/athchen/PhIPData/branch/main/graph/badge.svg)](https://codecov.io/gh/athchen/PhIPData?branch=main) <!-- badges: end --> The `PhIPData` class is used to store experimental results from phage-immunoprecipitation sequencing (PhIP-set) experiments in a matrix-like container. Building on the [`RangedSummarizedExperiment`](https://bioconductor.org/packages/release/bioc/html/SummarizedExperiment.html) class, `PhIPData` contains all of functionality of `SummarizedExperiments` and includes additional operations to facilitate analysis with PhIP-seq data. Like `SummarizedExperiments`, a key feature of `PhIPData` is the coordination of metadata when subsetting `PhIPData` objects. For example, if you wanted to examine experimental data for peptides from one particular virus, you can subset the experimental data and the associated peptide annotation with one command. This ensures all metadata (for samples, peptides, etc.) remain synced with the experimental data throughout analysis. ## Installation We recommend installing the stable release version of `PhIPData` in Bioconductor. This can be done using `BiocManager`: ``` r if (!require("BiocManager")) install.packages("BiocManager") BiocManager::install("PhIPData") ``` To load the package: ``` r library(PhIPData) ``` ## Components of a PhIPData Object As reflected in the figure below, the structure of a `PhIPData` object is nearly identical to the structure of a `SummarizedExperiment`/`RangedSummarizedExperiment` object. Each object contains at least three assays of data. These assays are: - `counts`: matrix of raw read counts, - `logfc`: matrix of log10 estimated fold-changes (in comparison to negative control samples), - `prob`: matrix of probabilities (p-values or posterior probabilities) associated with whether a sample shows an enriched antibody response to the particular peptide. The rows of a `PhIPData` object represent peptides of interest and the columns represent samples. Sample and peptide metadata are stored in `DataFrame`s. Each row of the metadata `DataFrame` specifies the peptide/sample, and the columns represent different features associated with the peptides/samples. In addition to sample- and peptide-specific metadata, experimental metadata such as associated papers, experimental parameters, sequencing dates, etc. are stored in a list-like component named `metadata`. <div class="figure"> <img src="vignettes/extras/PhIPData.png" alt="Schematic of a PhIPData object. Commands used to access each component of the object are listed underneath its visual representation. Code in black indicates functions specific to `PhIPData` objects while functions in red extend `SummarizedExperiment` functions. Here, `pd` is a generic `PhIPData` object." width="\maxwidth" /> <p class="caption"> Schematic of a PhIPData object. Commands used to access each component of the object are listed underneath its visual representation. Code in black indicates functions specific to `PhIPData` objects while functions in red extend `SummarizedExperiment` functions. Here, `pd` is a generic `PhIPData` object. </p> </div> ## Example Use For an example of how to use `PhIPData` objects, please see the package vignette.