Description Usage Arguments Details Value References See Also Examples

View source: R/AnalyseOutput_MCMC.R

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

1 | ```
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`

.

1 2 3 4 5 6 7 8 | ```
# 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)
``` |

```
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"
```

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