Description Usage Arguments Value Author(s) References See Also Examples

Function `simuDataREM`

simulates data under the Ornstein-Uhlenbeck (OU) (or Brownian Motion; BM) process-based random-effects mixture (REM) model.

1 2 | ```
simuDataREM(pars.mtx, dt, T, ntime, nrep, nsize, times,
method = c("eigen", "svd", "chol"), model = c("OU", "BM"))
``` |

`pars.mtx` |
A |

`dt` |
Increment in times. |

`T` |
Maximum time. |

`ntime` |
Number of time points to simulate data for. Needs to be same as the length of vector |

`nrep` |
Number of replicates. |

`nsize` |
An integer vector containing sizes of simulated clusters. |

`times` |
Vector of length |

`method` |
Method to compute the determinant of the covariance matrix in the calculation of the multivariate normal density. Required. Method choices are: "chol" for Choleski decomposition, "eigen" for eigenvalue decomposition, and "svd" for singular value decomposition. |

`model` |
Model to generate realizations of the mean vector of a mixture component. Required. Choices are: "OU" for an Ornstein-Uhlenbeck process (a.k.a. the mean-reverting process) and "BM" for a Brown motion (without drift). |

`means` |
A matrix of |

`data` |
A matrix of |

Audrey Q. Fu

Fu, A. Q., Russell, S., Bray, S. and Tavare, S. (2013) Bayesian clustering of replicated time-course gene expression data with weak signals. The Annals of Applied Statistics. 7(3) 1334-1361.

`plotSimulation`

for plotting simulated data.

`outputData`

for writing simulated data and parameter values used in simulation into external files.

`DIRECT`

for clustering the data.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 | ```
## Not run:
# Simulate replicated time-course gene expression profiles
# from OU processes
# Simulation parameters
times = c(0,5,10,15,20,25,30,35,40,50,60,70,80,90,100,110,120,150)
ntime=length (times)
nrep=4
nclust = 6
npars = 8
pars.mtx = matrix (0, nrow=nclust, ncol=npars)
# late weak upregulation or downregulation
pars.mtx[1,] = c(0.05, 0.1, 0.5, 0, 0.16, 0.1, 0.4, 0.05)
# repression
pars.mtx[2,] = c(0.05, 0.1, 0.5, 1, 0.16, -1.0, 0.1, 0.05)
# early strong upregulation
pars.mtx[3,] = c(0.05, 0.5, 0.2, 0, 0.16, 2.5, 0.4, 0.15)
# strong repression
pars.mtx[4,] = c(0.05, 0.5, 0.2, 1, 0.16, -1.5, 0.4, 0.1)
# low upregulation
pars.mtx[5,] = c(0.05, 0.3, 0.3, -0.5, 0.16, 0.5, 0.2, 0.08)
# late strong upregulation
pars.mtx[6,] = c(0.05, 0.3, 0.3, -0.5, 0.16, 0.1, 1, 0.1)
nsize = rep(40, nclust)
# Generate data
simudata = simuDataREM (pars=pars.mtx, dt=1, T=150,
ntime=ntime, nrep=nrep, nsize=nsize, times=times, method="svd", model="OU")
# Display simulated data
plotSimulation (simudata, times=times,
nsize=nsize, nrep=nrep, lty=1, ylim=c(-4,4), type="l", col="black")
# Write simulation parameters and simulated data
# to external files
outputData (datafilename= "simu_test.dat", parfilename= "simu_test.par",
meanfilename= "simu_test_mean.dat", simudata=simudata, pars=pars.mtx,
nitem=sum(nsize), ntime=ntime, nrep=nrep)
## End(Not run)
``` |

DIRECT documentation built on May 1, 2019, 8:08 p.m.

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