analyse.output {GRENITS} | R Documentation |
Analyse output from network inference functions. Basic convergence and analysis plots.
analyse.output(output.folder, timeSeries = NULL)
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. |
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).
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. |
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
NonLinearNet
, LinearNet
,
ReplicatesNet_student
, ReplicatesNet_gauss
.
# 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)