Package spotgear details

Subset Profiling and Organizing Tools for Gel Electrophoresis Autoradiography in R

In many diseases, the targets of autoantibodies are incompletely defined. Although the technologies for autoantibody discovery have advanced dramatically over the past decade, each of these techniques generates hundreds of possibilities, which are onerous and expensive to validate. We set out to establish a method to greatly simplify autoantibody discovery, using a pre-filtering step to define subgroups with similar specificities based on migration of radiolabeled, immunoprecipitated proteins on sodium dodecyl sulfate (SDS) gels and autoradiography [Gel Electrophoresis and band detection on Autoradiograms (GEA)]. Human recognition of patterns is not optimal when the patterns are complex or scattered across many samples. Multiple sources of errors - including irrelevant intensity differences and warping of gels - have challenged automation of pattern discovery from autoradiograms. In this package, we address these limitations using a Bayesian hierarchical model with shrinkage priors for pattern alignment and spatial dewarping. The Bayesian model combines information from multiple gel sets and corrects spatial warping for coherent estimation of autoantibody signatures defined by presence or absence of a grid of landmark proteins. The pre-processing method creates more clearly separated clusters and improves the accuracy of autoantibody subset detection via hierarchical clustering.

Maintainer: Zhenke Wu < zhenkewu at umich.edu >

 
From within R, enter citation('spotgear')


To cite package 'spotgear' in publications use:

Zhenke Wu and Scott Zeger (2019). spotgear: Subset Profiling and
Organizing Tools for Gel Electrophoresis Autoradiography in R. R
package version 1.0.1.0002.

A BibTeX entry for LaTeX users is

@Manual{,
title = {spotgear: Subset Profiling and Organizing Tools for Gel Electrophoresis
Autoradiography in R},
author = {Zhenke Wu and Scott Zeger},
year = {2019},
note = {R package version 1.0.1.0002},
}

 

If you have any problems with this package you can open a new issue or check the already existing ones here.

 
 
To install this package, start R and enter:

source("https://oslerinhealth.org/oslerLite.R")

# Default Install
osler_install('spotgear')

# from GitHub
osler_install('spotgear', release = "stable", release_repo = "github")
osler_install('spotgear', release = "current", release_repo = "github")

More detailed installation instructions can be found here.

 

Initially submitted on October 7 2019 3:33PM
Last updated on October 7 2019 3:33PM
Package type standard
Source GitHub https://github.com/zhenkewu/spotgear GitHub
OslerinHealth GitHub https://github.com/oslerinhealth/spotgear GitHub
System requirementsJAGS (>= 4.1.0) (http://mcmc-jags.sourceforge.net)
DependsR (3.3)
Importsmsir (1.3), rjags(>=4-6), R2jags (0.5), coda (0.16), reshape2 (1.4)
Suggestsknitr, rmarkdown, MassSpecWavelet