In rauschenberger/semisup: Semi-Supervised Mixture Model

output: github_document

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.path = "README-"
)

Scope

Implements a parametric semi-supervised mixture model. The permutation test detects markers with main or interactive effects, without distinguishing them. Possible applications include genome-wide association studies and differential expression analyses.

Installation

The package semisup depends on R >= 3.0.0, and is available from Bioconductor:

if (!requireNamespace("BiocManager", quietly=TRUE))
    install.packages("BiocManager")
BiocManager::install("semisup")

Alternatively, it can be installed from GitHub. This requires the package devtools:

devtools::install_github("rauschenberger/semisup",build_vignettes=TRUE)

Please restart R before loading the package and its documentation:

library(semisup)
utils::help(semisup)
utils::vignette("semisup")

Reference

A Rauschenberger, RX Menezes, MA van de Wiel, NM van Schoor, and MA Jonker (2020). Semi-supervised mixture test for detecting markers associated with a quantitative trait. Manuscript in preparation. (outdated version: html pdf)