Package: ternarynet 1.56.0

McCall N. Matthew

ternarynet: Ternary Network Estimation

Gene-regulatory network (GRN) modeling seeks to infer dependencies between genes and thereby provide insight into the regulatory relationships that exist within a cell. This package provides a computational Bayesian approach to GRN estimation from perturbation experiments using a ternary network model, in which gene expression is discretized into one of 3 states: up, unchanged, or down). The ternarynet package includes a parallel implementation of the replica exchange Monte Carlo algorithm for fitting network models, using MPI.

Authors:Matthew N. McCall <[email protected]>, Anthony Almudevar <[email protected]>, David Burton <[email protected]>, Harry Stern <[email protected]>

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manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
ternarynet/json (API)

# Install 'ternarynet' in R:

install.packages('ternarynet', repos = c('https://bioc-release.r-universe.dev', 'https://cloud.r-project.org'))

Uses libs:

c++– GNU Standard C++ Library v3

On BioConductor:ternarynet-1.57.0(bioc 3.24)ternarynet-1.56.0(bioc 3.23)

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

software cellbiology graphandnetwork network bayesian cpp

3.30 score 3 scripts 85 exports 19 dependencies

Last updated from:8a6bb8df77 (on RELEASE_3_23). Checks:1 WARNING, 13 OK. Indexed: no.

Target	Result	Time
bioc-checks	WARNING	152
linux-devel-arm64	OK	154
linux-devel-x86_64	OK	163
source / vignettes	OK	193
linux-release-arm64	OK	216
linux-release-x86_64	OK	176
macos-release-arm64	OK	108
macos-release-x86_64	OK	251
macos-oldrel-arm64	OK	149
macos-oldrel-x86_64	OK	180
windows-devel	OK	122
windows-release	OK	121
windows-oldrel	OK	154
wasm-release	OK	133

Exports:attractorSummary backupStage backupStage<-beta0 beta0<-chi0 chi0<-degreeObjMin degreeObjMin<-degreeObjs degreeObjs<-delta delta<-edgePenalty edgePenalty<-epsilon epsilon<-experimentNames experimentNames<-finalTemperature finalTemperature<-geneNames geneNames<-graphObjMin graphObjMin<-graphObjs graphObjs<-graphPosterior inputParams inputParams<-m0 m0<-maxDegree maxDegree<-maxStage maxStage<-maxTransition maxTransition<-minScore minScore<-ne ne<-neighborDegree neighborDegree<-newScore newScore<-pAddParent pAddParent<-parallelFit perturbationObj perturbationObj<-perturbationType perturbationType<-pExchangeParent pExchangeParent<-plotFit plotPost plotTraces pNeighborhood pNeighborhood<-predictAttractor rho rho<-scores scores<-scoreType scoreType<-simulateSteadyState stageCount stageCount<-steadyStateObj steadyStateObj<-tableObjMin tableObjMin<-tableObjs tableObjs<-ternaryFit ternaryFitParameters ternaryPost tnetfit tnetpost traces traces<-xSeed xSeed<-

Dependencies:BH BiocParallel cli codetools cpp11 formatR futile.logger futile.options glue igraph lambda.r lattice lifecycle magrittr Matrix pkgconfig rlang snow vctrs

ternarynet: A Computational Bayesian Approach to Ternary Network Estimation

Introduction | Getting Started | Session Info

Rendered fromternarynet.Rnwusingutils::Sweave

Last update: 2021-04-15
Started: 2013-11-01

Help page	Topics
Summarize Attractors	attractorSummary
Network Topology	graphPosterior
Fit ternary network models using parallel tempering	parallelFit
Network Fit Plot	plotFit
Network Posterior Plot	plotPost
Network Fit Traces	plotTraces
Predict the attractor(s) resulting from a given perturbation	predictAttractor
Simulate Steady State Data	simulateSteadyState
Ternary Network Fit	class:ternaryFit degreeObjMin degreeObjMin,ternaryFit-method degreeObjMin<- degreeObjMin<-,ternaryFit-method dim,ternaryFit-method experimentNames experimentNames,ternaryFit-method experimentNames<- experimentNames<-,ternaryFit-method finalTemperature finalTemperature,ternaryFit-method finalTemperature<- finalTemperature<-,ternaryFit-method geneNames geneNames,ternaryFit-method geneNames<- geneNames<-,ternaryFit-method graphObjMin graphObjMin,ternaryFit-method graphObjMin<- graphObjMin<-,ternaryFit-method initialize,ternaryFit-method inputParams inputParams,ternaryFit-method inputParams<- inputParams<-,ternaryFit-method minScore minScore,ternaryFit-method minScore<- minScore<-,ternaryFit-method newScore newScore,ternaryFit-method newScore<- newScore<-,ternaryFit-method perturbationObj perturbationObj,ternaryFit-method perturbationObj<- perturbationObj<-,ternaryFit-method show,ternaryFit-method stageCount stageCount,ternaryFit-method stageCount<- stageCount<-,ternaryFit-method steadyStateObj steadyStateObj,ternaryFit-method steadyStateObj<- steadyStateObj<-,ternaryFit-method tableObjMin tableObjMin,ternaryFit-method tableObjMin<- tableObjMin<-,ternaryFit-method ternaryFit ternaryFit-class ternaryFit-methods traces traces,ternaryFit-method traces<- traces<-,ternaryFit-method xSeed xSeed,ternaryFit-method xSeed<- xSeed<-,ternaryFit-method
Ternary Network Fitting Parameters	backupStage backupStage,ternaryFitParameters-method backupStage<- backupStage<-,ternaryFitParameters-method beta0 beta0,ternaryFitParameters-method beta0<- beta0<-,ternaryFitParameters-method chi0 chi0,ternaryFitParameters-method chi0<- chi0<-,ternaryFitParameters-method class:ternaryFitParameters delta delta,ternaryFitParameters-method delta<- delta<-,ternaryFitParameters-method edgePenalty edgePenalty,ternaryFitParameters-method edgePenalty<- edgePenalty<-,ternaryFitParameters-method epsilon epsilon,ternaryFitParameters-method epsilon<- epsilon<-,ternaryFitParameters-method initialize,ternaryFitParameters-method m0 m0,ternaryFitParameters-method m0<- m0<-,ternaryFitParameters-method maxDegree maxDegree,ternaryFitParameters-method maxDegree<- maxDegree<-,ternaryFitParameters-method maxStage maxStage,ternaryFitParameters-method maxStage<- maxStage<-,ternaryFitParameters-method maxTransition maxTransition,ternaryFitParameters-method maxTransition<- maxTransition<-,ternaryFitParameters-method ne ne,ternaryFitParameters-method ne<- ne<-,ternaryFitParameters-method neighborDegree neighborDegree,ternaryFitParameters-method neighborDegree<- neighborDegree<-,ternaryFitParameters-method pAddParent pAddParent,ternaryFitParameters-method pAddParent<- pAddParent<-,ternaryFitParameters-method perturbationType perturbationType,ternaryFitParameters-method perturbationType<- perturbationType<-,ternaryFitParameters-method pExchangeParent pExchangeParent,ternaryFitParameters-method pExchangeParent<- pExchangeParent<-,ternaryFitParameters-method pNeighborhood pNeighborhood,ternaryFitParameters-method pNeighborhood<- pNeighborhood<-,ternaryFitParameters-method rho rho,ternaryFitParameters-method rho<- rho<-,ternaryFitParameters-method scoreType scoreType,ternaryFitParameters-method scoreType<- scoreType<-,ternaryFitParameters-method show,ternaryFitParameters-method ternaryFitParameters ternaryFitParameters-class ternaryFitParameters-methods
Ternary Network Posterior	class:ternaryPost degreeObjs degreeObjs,ternaryPost-method degreeObjs<- degreeObjs<-,ternaryPost-method dim,ternaryPost-method experimentNames,ternaryPost-method experimentNames<-,ternaryPost-method geneNames,ternaryPost-method geneNames<-,ternaryPost-method graphObjs graphObjs,ternaryPost-method graphObjs<- graphObjs<-,ternaryPost-method initialize,ternaryPost-method inputParams,ternaryPost-method inputParams<-,ternaryPost-method perturbationObj,ternaryPost-method perturbationObj<-,ternaryPost-method scores scores,ternaryPost-method scores<- scores<-,ternaryPost-method show,ternaryPost-method steadyStateObj,ternaryPost-method steadyStateObj<-,ternaryPost-method tableObjs tableObjs,ternaryPost-method tableObjs<- tableObjs<-,ternaryPost-method ternaryPost ternaryPost-class ternaryPost-methods
Ternary Network Fitting	tnetfit
Ternary Network Posterior Sampling	tnetpost

Package: ternarynet 1.56.0

ternarynet: Ternary Network Estimation

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