In PhilipBerg/PaiR: Imputation and Significance Analysis of Proteomics Data

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.path = "man/figures/README-",
  out.width = "100%"
)

pair: Proteomics analysis in R

The goal of pair is to improve your proteomics data analysis with a specific focus on PTM data. PTM data is usually noisier than total proteomics and pair helps you normalize data, perform imputation, and make more robust statistical decisions. It includes plots to help you decide what normalization method to use and lets you visualize the statistical decision. In addition, it uses a recently developed gamma regression model to capture and normalize mean-variance trends in the data. Further, it uses a multiple imputation pipeline that helps correct for errors in the decision cause by imputation and generates a probability of error in the statistical decision for imputed data. For each individual imputation, it assumes that the mean can be correctly estimated from the data and then uses the gamma regression to estimate the variance given the mean.

Installation

pair is still under development but will be released to CRAN shortly.

#install.packages("pair")

In the meantime, you can download pair from github.

# install.packages("devtools")
devtools::install_github("PhilipBerg/pair", build_vignettes = TRUE)

Example

For examples see the vignette.

vignette('pair-tutorial')

PhilipBerg/PaiR documentation built on March 18, 2022, noon