Package: nempi 1.15.0

Martin Pirkl

nempi: Inferring unobserved perturbations from gene expression data

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.

Authors:Martin Pirkl [aut, cre]

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NEWS

# Install 'nempi' in R:
install.packages('nempi', repos = c('https://bioc.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Bug tracker:https://github.com/cbg-ethz/nempi/issues

On BioConductor:nempi-1.13.0(bioc 3.20)nempi-1.12.0(bioc 3.19)

softwaregeneexpressiondifferentialexpressiondifferentialmethylationgenesignalingpathwaysnetworkclassificationneuralnetworknetworkinferenceatacseqdnaseqrnaseqpooledscreenscrisprsinglecellsystemsbiology

4.90 score 2 stars 2 scripts 148 downloads 4 exports 104 dependencies

Last updated 20 days agofrom:f9f289f6c4. Checks:OK: 7. Indexed: yes.

TargetResultDate
Doc / VignettesOKOct 30 2024
R-4.5-winOKOct 30 2024
R-4.5-linuxOKOct 30 2024
R-4.4-winOKOct 30 2024
R-4.4-macOKOct 30 2024
R-4.3-winOKOct 30 2024
R-4.3-macOKOct 30 2024

Exports:classpinempinempibspifit

Dependencies:abindamapapclusterbdsmatrixBHBiocGenericsBiocManagerBoolNetBoutrosLab.plotting.generalclasscliclueclustercolorspacecorpcorcpp11data.tabledeldirDEoptimRdipteste1071ellipseepiNEMfansifarverfastclusterfastICAflexclustflexmixfpcgdataggdendroggmggplot2gluegmodelsgraphgridExtragtablegtoolshexbinigraphinfotheointerpisobandjpegkernlablabelinglatex2explatticelatticeExtralifecyclelimmaLinnormlmtestmagrittrMASSMatrixmatrixStatsmclustmgcvminetmnemmodeltoolsmunsellnaturalsortnlmennetpcalgpermutepillarpkgconfigpngprabclusproxyR6randomForestRBGLRColorBrewerRcppRcppArmadilloRcppEigenRgraphvizrlangrobustbaseRtsnescalessfsmiscsnowsnowfallstatmodstringistringrtibbletsneutf8vcdvctrsveganviridisLitewesandersonwithrXMLzoo

| Nested Effects Models-based perturbation inference: | Inference of unobserved perturbations from gene expression profiles.

Martin Pirkl, Niko Beerenwinkel

Rendered fromnempi.Rmdusingknitr::rmarkdownon Oct 30 2024.

Last update: 2022-09-23
Started: 2019-09-02