Description Usage Arguments Value References See Also Examples

Run Bayesian inference of non-linear interaction network. Non linear interactions are modelled using Penalised Splines. The function generates MCMC chains that can later be analysed.

1 2 3 | ```
NonLinearNet( resultsFolder, timeSeries, ParamVec = NULL,
chains = 2, user.seeds = NULL, Regulators = NULL,
fixMe = NULL)
``` |

`resultsFolder` |
Name of output folder. The folder will be created and the output of the run will be placed there. |

`timeSeries` |
Data matrix containing gene expression time series. Where genes will be placed in rows and time points in columns. Gene names may be included as row names. |

`ParamVec` |
A parameter vector created using "mcmc.defaultParams_nonLinear". If none is given, default parameters will be used. The vector contains parameters associated to the priors as well as MCMC run length. (See mcmc.defaultParams_nonLinear) |

`chains` |
Number of MCMC chains to run. |

`user.seeds` |
An optional vector with seeds to use for MCMC chains. |

`Regulators` |
An optional vector with the indices of which genes are regulators. If provided, all non-regulator genes will not be allowed to regulate. |

`fixMe` |
An optional matrix of size genes x genes, where columns represent regulators and rows regulated genes. The matrix informs the model of network connections known to be present/absent. For each position use either 0 (no regulation, fix off), 1 (known regulatory interaction, fix on) or NaN (no information, do not fix). |

For each chain run, a folder (chain1, chain2, ...) will be created and the output of the MCMC run will be placed there. The files will be Gamma_mcmc (the indicator variables of Gibbs variable selection), Lambda_mcmc (the precision of each regression), Mu_mcmc (the intercept of each regression), Rho_mcmc (the network connectivity parameter), Tau_mcmc (the "smoothness parameter"), all_f (posterior mean of all functions), all_f_sqr (posterior mean of the square of all functions) and Full_F_sqr (posterior mean of the square of the sum of all functions, for each regression). For the files all_f and all_f_sqr functions are placed in column-wise order. The file is filled by placing all interactions for each regression one after another.

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

`mcmc.defaultParams_nonLinear, analyse.output`

.

1 2 3 4 5 6 7 8 9 10 | ```
# Synthetic data
data(Athaliana_ODE)
# Reduced data set to 3 genes 20 TP for faster run
Athaliana_ODE.reduced <- Athaliana_ODE[c(1,3,5),1:20]
# Folder where raw runs will be kept and later analysed
output.folder <- paste(tempdir(), "/ExampleNonLinearNet", sep = "")
# Run network inference and place raw results in output.folder
NonLinearNet(output.folder , Athaliana_ODE.reduced)
# Analyse raw results, place analysis plots and files in output.folder
analyse.output(output.folder, Athaliana_ODE.reduced)
``` |

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