rTranscriptData(simone)
rTranscriptData()所属R语言包：simone
Simulation of artificial transcriptomic data
模拟人工转录组数据
译者：生物统计家园网 机器人LoveR
描述----------Description----------
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
模拟高斯样本，可以模仿转录组数据，根据一个给定的网络，无论是稳态或时间过程的数据。当几个网络，多个样本
用法----------Usage----------
graph,
...,
mu = rep(0, p),
sigma = 0.1)
参数----------Arguments----------
参数：n
integer or vector of integer indicating the sample sizes of each task
整数或向量的整数，表示每个任务的样本规模，
参数：graph
a simone.network object typically generated either by rNetwork or coNetwork
simone.network对象通常产生rNetwork或coNetwork
参数：...
additional simone.network objects in case of multiple sample generation
额外的simone.network在多个样品一代的情况下对象
参数：mu
if the network(s) is(are) directed, mu is the offset of the VAR(1) model that is used to generate the time-course data; if undirected, mu is the offset of the Gaussian vector.
如果网络（s）是（）执导，mu是VAR（1）模型，该模型被用来生成的时间过程数据的偏移量，如果无向，mu的偏移量高斯矢量。
参数：sigma
standard deviation of the noise term used in the simulation process
在模拟过程中使用的噪声项的标准偏差
Details
详细信息----------Details----------
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.
如果网络是导演，时间当然是根据一个VAR（1）模型模拟数据。如果网络是无向的，稳态模拟一个独立，同分布的样本的高斯矢量数据所产生的。
In both cases, samples are generated on the basis of Θ, as provided by graph$Theta.
在这两种情况下，对样品进行Θ的的基础上产生的，如所提供graph$Theta。
If the network is directed, samples are generated according to the following VAR(1) process:
如果网络的指示，产生样本，根据以下的VAR（1）过程：
<center> <table> <tr> <td> X<sub>0</sub> follows N(0,σ) </td> </tr> <tr> <td> X<sub>t</sub> = μ + Θ X<sub>t-1</sub> + ε<sub>t</sub>, for all t= 1,..., n</td> </tr> <tr> <td> ε<sub>t</sub> follows N(0,σ).</td> </tr> </table> </center>
<CENTER> <TABLE>文章快照所述<SUB> 0 </ sub>的如下N（0，σ）</ TD> </ TR>文章快照所述<SUB> T </子> =μ+Θ所述<SUB> T-1 </ SUB> +ε<SUB> T </ sub>的，对所有的t = 1，...，N </ TD> </ TR>文章快照ε<SUB> T </ sub>的如下N（0，σ）。</ TD> </ TR> </ TABLE> </ CENTER>
If the network is undirected, samples are generated according to the following Gaussian vector: <center> <table> <tr> <td> X<sub>i</sub> = μ + t(Θ<sup>-1/2</sup>) U<sub>i</sub> + ε<sub>i</sub>, for all i in 1, ..., n,</td> </tr> <tr> <td> U<sub>i</sub> follows N(0,1) </td> </tr> <tr> <td> ε<sub>i</sub> follows N(0,σ).</td> </tr> </table> </center> Numerically, Θ<sup>-1/2</sup> is computed with the Cholesky decomposition of the pseudo-inverse of Θ.
如果网络是无向的，样本是根据下面的高斯矢量<CENTER> <TABLE>文章快照：X <SUB>我</ SUB> =μ+ T（Θ<SUP> -1 / 2 </ SUP>）U <SUB>我</ SUB> +ε<SUB> </ sub>的，所有我在1，...，N，</ TD> </ TR> <TR > <TD>ü<SUB>我</ sub>的如下N（0,1）</ TD> </ TR>文章快照ε<SUB> </ sub>的如下N（0，与SIGMA ;) </ TD> </ TR> </ TABLE> </中心>数值，Θ<SUP> -1 / 2 </ sup>的计算Cholesky分解的伪逆Θ。
值----------Value----------
Returns a list comprising : <table summary="R valueblock"> <tr valign="top"><td>X</td> <td> matrix of simulated gene expression data, n observations in rows, genes in columns</td></tr> <tr valign="top"><td>tasks</td> <td> factor indicating the tasks corresponding to the simulated gene expression data in case of multiple networks.</td></tr> </table>
返回一个列表，包括：<table summary="R valueblock"> <tr valign="top"> <TD> X</ TD> <TD>矩阵的模拟基因表达数据，n观察行，列中的基因</ TD> </ TR> <tr valign="top"> <TD> tasks </ TD> <TD>因素对应于模拟基因表达数据的情况下，指示的任务多个网络。</ TD> </ TR> </ TABLE>
（作者）----------Author(s)----------
J. Chiquet, C. Charbonnier
参见----------See Also----------
rNetwork, coNetwork.
rNetwork，coNetwork。
实例----------Examples----------
## time-Course data generation[＃课程时间数据生成]
##-----------------------------[＃-----------------------------]
# generate a directed network[生成有向网络]
n <- 20
p <- 5
g <- rNetwork(p, pi=5, directed=TRUE)
# Generate the data, data2 noisier than data1[生成的数据，噪音比DATA1 DATA2]
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[＃稳态数据生成]
##-----------------------------[＃-----------------------------]
# generate an undirected network[生成一个无向网络]
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)[推理的Theta（这里没有尺寸问题，因为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[理论西塔的比较和推断的Theta]
par(mfrow=c(1,2))
image(g$Theta, main = "Theoretical Theta")
image(b, main = "Inferred Theta")
## time-course multitask data generation[时间当然多任务数据生成]
##--------------------------------------[＃--------------------------------------]
# start by generating the networks[从产生的网络]
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[生成的数据]
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(1n[2]),X[tasks == 2,], type= "l", main="Dataset from child 2",
xlab = "time points", ylab = "level of expression")
detach(data)
转载请注明:出自 生物统计家园网(http://www.biostatistic.net)。
注：
注1：为了方便大家学习，本文档为生物统计家园网机器人LoveR翻译而成，仅供个人R语言学习参考使用，生物统计家园保留版权。
注2：由于是机器人自动翻译，难免有不准确之处，使用时仔细对照中、英文内容进行反复理解，可以帮助R语言的学习。
注3：如遇到不准确之处，请在本贴的后面进行回帖，我们会逐渐进行修订。
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发表于 2012-9-30 02:51:55
