analyse.output {GRENITS} | R Documentation

## Analysis Plots

### 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)

[Package GRENITS version 1.39.0 Index]