lpNet: Linear Programming Model for Network Inference

lpNet aims at infering biological networks, in particular signaling and gene networks. For that it takes perturbation data, either steady-state or time-series, as input and generates an LP model which allows the inference of signaling networks. For parameter identification either leave-one-out cross-validation or stratified n-fold cross-validation can be used.

Author
Bettina Knapp, Marta R. A. Matos, Johanna Mazur, Lars Kaderali
Date of publication
None
Maintainer
Lars Kaderali <lars.kaderali@tu-dresden.de>
License
Artistic License 2.0
Version
2.6.0

View on Bioconductor

Man pages

calcActivation
Calculate Activation Matrix
calcPrediction
Calculate Predicted Observation.
calcRangeLambda
Compute Range Of Penalty Parameter Lambda.
CV
Cross-validation
doILP
Do The Network Inference With The Linear Programming...
generateTimeSeriesNetStates
Generate Time Series Network States
getAdja
Get Adjacency Matrix.
getBaseline
Get Baseline Vector.
getEdgeAnnot
Get the annotation of the edges.
getObsMat
Get Observation Matrix.
getSampleAdja
Get The Sample Adjacency.
lpNet-package
Network Inference Of Perturbation Data Using a Linear...
summarizeRepl
Summarize Replicate Measurements

Files in this package

lpNet/DESCRIPTION
lpNet/NAMESPACE
lpNet/R
lpNet/R/calcActivation.R
lpNet/R/calcPredictionKfoldCV.R
lpNet/R/calcPredictionLOOCV.R
lpNet/R/calcRangeLambda.R
lpNet/R/doILP.R
lpNet/R/doILP_steadyState.R
lpNet/R/doILP_timeSeries.R
lpNet/R/generateTimeSeriesNetStates.r
lpNet/R/getAdja.R
lpNet/R/getBaseline.R
lpNet/R/getEdgeAnnot.R
lpNet/R/getObsMat.R
lpNet/R/getSampleAdja.R
lpNet/R/getSampleAdjaMAD.R
lpNet/R/kfoldCV.R
lpNet/R/loocv.R
lpNet/R/summarizeRepl.R
lpNet/build
lpNet/build/vignette.rds
lpNet/inst
lpNet/inst/NEWS
lpNet/inst/doc
lpNet/inst/doc/vignette_lpNet.R
lpNet/inst/doc/vignette_lpNet.Rnw
lpNet/inst/doc/vignette_lpNet.pdf
lpNet/man
lpNet/man/CV.Rd
lpNet/man/calcActivation.Rd
lpNet/man/calcPrediction.Rd
lpNet/man/calcRangeLambda.Rd
lpNet/man/doILP.Rd
lpNet/man/generateTimeSeriesNetStates.Rd
lpNet/man/getAdja.Rd
lpNet/man/getBaseline.Rd
lpNet/man/getEdgeAnnot.Rd
lpNet/man/getObsMat.Rd
lpNet/man/getSampleAdja.Rd
lpNet/man/lpNet-package.Rd
lpNet/man/summarizeRepl.Rd
lpNet/tests
lpNet/tests/runitCalcActivation.R
lpNet/tests/runitCalcPredictionKfoldCV.R
lpNet/tests/runitCalcPredictionKfoldCV_timeSeries.R
lpNet/tests/runitCalcPredictionLOOCV.R
lpNet/tests/runitCalcPredictionLOOCV_timeSeries.R
lpNet/tests/runitCalcRangeLambda.R
lpNet/tests/runitDoILP.R
lpNet/tests/runitDoILP_timeSeries.R
lpNet/tests/runitGenerateTimeSeriesNetStates.R
lpNet/tests/runitGetAdja.R
lpNet/tests/runitGetBaseline.R
lpNet/tests/runitGetEdgeAnnot.R
lpNet/tests/runitGetObsMat.R
lpNet/tests/runitGetSampleAdja.R
lpNet/tests/runitGetSampleAdjaMAD.R
lpNet/tests/runitKfoldCV.R
lpNet/tests/runitKfoldCV_timeSeries.R
lpNet/tests/runitLOOCV.R
lpNet/tests/runitLOOCV_timeSeries.R
lpNet/vignettes
lpNet/vignettes/references.bib
lpNet/vignettes/vignette_lpNet.Rnw