nempi
Inferring unobserved perturbations from gene expression data
Bioconductor version: 3.23 · Package version: 1.20.0
Other Bioconductor versions
devel is the development version; release is the current stable one.
3.24 (devel), 3.23 (release)
Takes as input an incomplete perturbation profile and differential gene expression in log odds and infers unobserved perturbations and augments observed ones. The inference is done by iteratively inferring a network from the perturbations and inferring perturbations from the network. The network inference is done by Nested Effects Models.
Installation
if (!require("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install("nempi")Details
| Maintainer | Martin Pirkl <martinpirkl@yahoo.de> |
| Author | Martin Pirkl [aut, cre] |
| License | GPL-3 |
| URL | https://github.com/cbg-ethz/nempi/ |
| Bug Reports | https://github.com/cbg-ethz/nempi/issues |
| Source branch | RELEASE_3_23 |
| Build report | Bioconductor build system, r-universe |
| biocViews | ATACSeq, CRISPR, Classification, DNASeq, DifferentialExpression, DifferentialMethylation, GeneExpression, GeneSignaling, Network, NetworkInference, NeuralNetwork, Pathways, PooledScreens, RNASeq, SingleCell, Software, SystemsBiology |
| Package Short Url | https://bioconductor.org/packages/nempi/ |
Citation
From within R, enter citation("nempi"):
Martin Pirkl. nempi: Inferring unobserved perturbations from gene expression data. doi:10.18129/B9.bioc.nempi, R package version 1.20.0, https://bioconductor.org/packages/nempi.
Generated from the package metadata; it may differ from the package's own citation.
Documentation
Download
Follow the installation instructions to use this package in your R session.
| Source package | nempi_1.20.0.tar.gz |
| Windows binary (x86_64) | nempi_1.20.0.zip |
| macOS binary (arm64) | nempi_1.20.0.tgz |
| macOS binary (x86_64) | nempi_1.20.0.tgz |
Dependencies
Depends: R (>= 4.1), mnem
Imports: e1071, nnet, randomForest, naturalsort, graphics, stats, utils, matrixStats, epiNEM
Suggests: knitr, BiocGenerics, rmarkdown, RUnit, BiocStyle