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

Simulates a Gaussian sample that mimics transcriptomic data, according to a given network, either steady-state or time-course data. When several networks are given, multiple samples are generated.

1 2 3 4 5 | ```
rTranscriptData(n,
graph,
...,
mu = rep(0, p),
sigma = 0.1)
``` |

`n` |
integer or vector of integer indicating the sample sizes of each task |

`graph` |
a |

`...` |
additional |

`mu` |
if the network(s) is(are) directed, |

`sigma` |
standard deviation of the noise term used in the simulation process |

If the network is directed, time-course data are simulated according to a VAR(1) model. If the network is undirected, steady-state data are generated by simulating an independent, identically distributed sample of a Gaussian vector.

In both cases, samples are generated on the basis of Θ, as provided by `graph$Theta`

.

If the network is directed, samples are generated according to the following VAR(1) process:

X_{0} follows N(0,σ) |

X_{t} = μ + Θ X_{t-1} + ε_{t}, for all t= 1,..., n |

ε_{t} follows N(0,σ). |

If the network is undirected, samples are generated according to the following Gaussian vector:

X_{i} = μ + t(Θ^{-1/2}) U_{i} + ε_{i}, for all i in 1, ..., n, |

U_{i} follows N(0,1) |

ε_{i} follows N(0,σ). |

Returns a list comprising :

`X` |
matrix of simulated gene expression data, |

`tasks` |
factor indicating the tasks corresponding to the simulated gene expression data in case of multiple networks. |

J. Chiquet, C. Charbonnier

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 43 44 45 46 47 48 49 50 51 52 53 | ```
## time-Course data generation
##-----------------------------
# generate a directed network
n <- 20
p <- 5
g <- rNetwork(p, pi=5, directed=TRUE)
# Generate the data, data2 noisier than data1
data1 <- rTranscriptData(n,g)
data2 <- rTranscriptData(n,g,sigma=1)
matplot(1:n, data1$X,type= "l", xlab = "time points",
ylab = "level of expression", col=rainbow(n,start=2/6,end = 3/6),
ylim = range(c(data1$X,data2$X)),
main="data2 (blue) generated with more noise than data1 (green)")
matlines(1:n,data2$X,type= "l",col = rainbow(n,start=4/6,end=5/6))
## steady-state data generation
##-----------------------------
# generate an undirected network
p <- 10
g <- rNetwork(p, pi=10)
data <- rTranscriptData(n=1000,g, sigma=0)
attach(data)
# Inference of Theta (here without dimension problems since p << n)
b <- sapply(1:p,function(x){
tmp <- -solve(t(X[,-x]) %*% X[,-x]) %*% t(X[,-x]) %*% X[,x]
res <- rep(NA,10)
res[-x] <- tmp
res[x] <- 1
return(res)
}
)
detach(data)
# comparison of theoretical Theta and inferred Theta
par(mfrow=c(1,2))
image(g$Theta, main = "Theoretical Theta")
image(b, main = "Inferred Theta")
## time-course multitask data generation
##--------------------------------------
# start by generating the networks
ancestor <- rNetwork(p=5, pi=5, name="ancestor", directed=TRUE)
child1 <- coNetwork(ancestor, 1, name = "child 1")
child2 <- coNetwork(ancestor, 1, name = "child 2")
# generate the data
n <- c(20,20)
data <- rTranscriptData(n,child1,child2)
attach(data)
par(mfrow=c(2,1))
matplot(1:(n[1]),X[tasks ==1,],type= "l", main="Dataset from child 1",
xlab = "time points", ylab = "level of expression")
matplot(1:(n[2]),X[tasks == 2,], type= "l", main="Dataset from child 2",
xlab = "time points", ylab = "level of expression")
detach(data)
``` |

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