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GRENITS (version 1.24.0)

Gene Regulatory Network Inference Using Time Series

Description

The package offers four network inference statistical models using Dynamic Bayesian Networks and Gibbs Variable Selection: a linear interaction model, two linear interaction models with added experimental noise (Gaussian and Student distributed) for the case where replicates are available and a non-linear interaction model.

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Version

Version

1.24.0

License

GPL (>= 2)

Maintainer

Edward Morrissey

Last Published

February 15th, 2017

Functions in GRENITS (1.24.0)

mcmc.defaultParams_nonLinear

Default Parameters for non-Linear Model
mcmc.defaultParams_Linear

Default Parameters for Linear Model
NonLinearNet

Dynamic Bayesian Network Inference Using Non-Linear Interactions
ReplicatesNet_gauss

Dynamic Bayesian Network Inference Using Linear Interactions and Gaussian Experimental Noise
mcmc.defaultParams_student

Default Parameters for Linear Model with Student distributed replicates
LinearNet

Dynamic Bayesian Network Inference Using Linear Interactions
analyse.output

Analysis Plots
mcmc.defaultParams_gauss

Default Parameters for Linear Model with Gaussian distributed replicates
read.chain

Read MCMC Chains
Athaliana_ODE_4NoiseReps

Gene expression time series generated with ODE model with added noise
plotPriors

Plot prior using parameter vector
ReplicatesNet_student

Dynamic Bayesian Network Inference Using Linear Interactions and Student Experimental Noise
Athaliana_ODE

Gene expression time series generated with ODE model