analyse.output: Analysis Plots

In GRENITS: Gene Regulatory Network Inference Using Time Series

Description

Analyse output from network inference functions. Basic convergence and analysis plots.

Usage

analyse.output(output.folder, timeSeries = NULL)

Arguments

output.folder	Name of folder (including path) where chains are kept
timeSeries	Only used by NonLinearModel analysis. Data matrix containing gene expression time series. Where genes will be placed in rows and time points in columns.

Details

Read first two chains run and plot some basic convergence plots (ConvergencePlots.pdf), analysis plots (AnalysisPlots.pdf), as well as inferred network probabilities in two formats (NetworkProbability_List.txt and NetworkProbability_Matrix.txt).

Value

The output of the analysis will be four files (five if nonLinearNet). The contents of the two plot files change depending on the network inference function used.

ConvergencePlots.pdf	Basic convergence plots. The posterior means of each variable are compared.
AnalysisPlots.pdf	Heatmap plot of network link probabilities as well as marginal network uncertainty plot. A plot of the number of links predicted by the model for a given probability threshold. For ReplicatesNet_student, the posterior distribution of the degrees of freedom are also plotted. For NonLinearNet, the posterior of the smoothness parameter is plotted.
NetworkProbability_List.txt	Posterior probabilities for each network connection in list format, including posterior interaction strength for linear models. Can be imported with network analysis software such as cytoscape.
NetworkProbability_Matrix.txt	Posterior probabilities for each network connection in matrix format.
ProbNumParents.txt	Posterior probabilities for number of regulators for each gene.
InferredFunctionPlots.pdf	(Only for nonLinearNet) Posterior distribution of predicted functions. Data values are plotted as circles.

References

Morrissey, E.R., Juarez, M.A., Denby, K.J. and Burroughs, N.J. 2010. On reverse engineering of gene interaction networks using time course data with repeated measurements. Bioinformatics 2010; doi: 10.1093/bioinformatics/btq421

Morrissey, E.R., Juarez, M.A., Denby, K.J. and Burroughs, N.J. 2011 Inferring the time-invariant topology of a nonlinear sparse gene regulatory network using fully Bayesian spline autoregression Biostatistics 2011; doi: 10.1093/biostatistics/kxr009

See Also

NonLinearNet, LinearNet, ReplicatesNet_student , ReplicatesNet_gauss.

Examples

  # Load A. thaliana circadian clock ODE generated data
  data(Athaliana_ODE)
  # Folder where raw runs will be kept and analysed
  output.folder <- paste(tempdir(), "/Example_LinearNet",sep="")
  # Run network inference, place raw results in output.folder
  LinearNet(output.folder, Athaliana_ODE)
  # Analyse raw results, place analysis plots and files in output.folder
  analyse.output(output.folder)

Example output

Loading required package: Rcpp
Loading required package: RcppArmadillo
Loading required package: ggplot2
Created folder /work/tmp/tmp/RtmpwBF2II/Example_LinearNet
Started MCMC chain 1  ============= 
25% completed
Estimated runtime = 0.0334365 min
50% completed
75% completed
MCMC chain 1 finished!
Started MCMC chain 2  ============= 
25% completed
Estimated runtime = 0.0337859 min
50% completed
75% completed
MCMC chain 2 finished!
Analysis finished. Output plots can be found in folder: "/work/tmp/tmp/RtmpwBF2II/Example_LinearNet"

GRENITS documentation built on Nov. 8, 2020, 6:47 p.m.