nem - estimating Nested Effects Models from data
The R/Bioconductor package 'nem' allows to reconstruct features of pathways from the nested structure of perturbation effects. It takes as input
- a set of perturbed pathway components
- high-dimensional phenotypic readout of these perturbations (e.g. gene expression or morphological profiles).
The output is a directed graph representing the phenotypic hierarchy. The package contains an example dataset on Drosophila immune response and a vignette explaining its application.
Download and Installation
You can download the newest release from the package's Bioconductor page.
To install this package directly in R type:
source("http://bioconductor.org/biocLite.R")
biocLite("nem")
References:
-
Analyzing Gene Perturbation Screens With Nested Effects Models in R and Bioconductor
H. Fröhlich, T. Beißbarth, A. Tresch, D. Kostka, J. Jacob, R. Spang, F. Markowetz. Bioinformatics, 2008 Nov 1;24(21):2549-50
[ PMID: 18718939 | doi:10.1093/bioinformatics/btn446 ]
The software package combines the methods introduced in several papers:
-
Structure Learning in Nested Effects Models
A. Tresch, F. Markowetz. Statistical Applications in Genetics and Molecular Biology: Vol.7: Iss.1, Article 9, 2008.
[ PMID: 18312214 | bepress | arxiv:0710.448lv2 ] -
Non-transcriptional pathway features reconstructed from secondary effects of RNA interference
F. Markowetz, J. Bloch, R. Spang
Bioinformatics 2005 21: 4026-4032.
[ PMID: 16159925 | doi:10.1093/bioinformatics/bti662 | pdf ] -
Nested Effects Models for High-Dimensional Phenotyping Screens
F. Markowetz, D. Kostka, O.G. Troyanskaya, R. Spang.
Bioinformatics 2007 23(13):i305-i312 (ISMB/ECCB 2007 proceedings)
[ PMID 17646311 | doi:10.1093/bioinformatics/btm178 ] -
Large scale statistical inference of signaling pathways from RNAi and microarray data
H. Froehlich, M. Fellmann, H. Sueltmann, A. Poustka, T. Beissbarth.
BMC Bioinformatics 2007, 8:386
[ PMID 17937790 | doi:10.1186/1471-2105-8-386 ] -
Estimating large scale signaling networks through nested effects models from
intervention effects in microarray data
H. Froehlich , M. Fellmann, H. Sueltmann, A. Poustka, T. Beissbarth
Bioinformatics, 2007 (GCB 2007 proceedings)
[ PMID: 18227117 | doi:10.1093/bioinformatics/btm634]
