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.
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(outdated version: htmlpdf)
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:
Alternatively, it can be installed from GitHub. This requires the package devtools:
Please restart R before loading the package and its documentation:
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 (outdated version: html pdf)