mb.MANOVA(timecourse)
mb.MANOVA()所属R语言包：timecourse
Multivariate Empirical Bayes Analysis of Variance for Longitudinal Replicated
多元的经验Bayes方差分析复制纵向
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Computes the MB-statistics for longitudinal replicated developmental microarray time course
纵向复制的发展芯片日久计算MB统计
用法----------Usage----------
mb.MANOVA(object, times, D, size, nu = NULL, Lambda = NULL,
beta.d = NULL, beta = NULL, alpha.d = NULL, alpha = NULL,
condition.grp, time.grp = NULL, rep.grp = NULL, p = 0.02)
参数----------Arguments----------
参数：object
Required. An object of class matrix, MAList, marrayNorm, or ExpressionSet containing log-ratios or log-values of expression for a series of microarrays.
必需的。一个类的对象matrix，MAList，marrayNorm或ExpressionSet包含log比率或一系列微阵列表达log值。
参数：times
Required. A positive integer giving the number of time points.
必需的。一个正整数，给予的时间点。
参数：D
Required. A positive integer giving the number of biological conditions. D>1
必需的。生物条件的正整数。 D>1
参数：size
Required. A numeric matrix corresponding to the sample sizes for all genes across different biological conditions, when biological conditions are sorted in ascending order. Rows represent genes while columns represent biological conditions.
必需的。一个数字矩阵对应的所有基因在不同的生理条件，生物条件是按升序排序时的样本大小。行代表基因，列代表生物条件。
参数：nu
an optional positive value giving the degrees of moderation for the fully moderated Wilks' Lambda.
一个可选的积极价值给予充分主持威尔克斯的Lambda放缓的程度。
参数：Lambda
an optional numeric matrix giving the common covariance matrix for the fully moderated Wilks' Lambda.
一个可选的数字矩阵给予充分主持威尔克斯的Lambda共同协方差矩阵。
参数：beta.d
an optional numeric vector of length D giving the condition-specific scale parameters for the common covariance matrix of the expected time course vectors under the alternative.
一个可选的数字矢量长度D给予预期下的替代课程向量的共同协方差矩阵的特定条件的尺度参数。
参数：beta
an optional numeric value giving the scale parameter for the common covariance matrix of the common expected time course vector under the null.
可选的数值给人的共同预期的时间当然向量空下的共同协方差矩阵为尺度参数。
参数：alpha.d
an optional numeric matrix giving the condition-specific means of the expected time course vectors under the alternative.
一个可选的数字矩阵条件下的替代预期的时间当然向量具体手段。
参数：alpha
an optional numeric vector of length times giving the common mean of the expected time course vector under the null.
一个可选的数字矢量长度times共同平均预期的时间空下的当然向量。
参数：condition.grp
Required. A numeric or character vector with length equals to the number of arrays, assigning the biological condition group to each array.
必需的。一个数字或字符的矢量长度等于阵列的数量，分配给每个阵列组的生物条件。
参数：rep.grp
an optional numeric or character vector with length equals to the number of arrays, assigning the replicate group to each array.
一个可选的数字或字符向量长度等于阵列的数量，分配给每个阵列复制组。
参数：time.grp
an optional numeric vector with length equals to the number of arrays, assigning the time point group to each array.
可选数值向量长度等于阵列，每个阵列分配的时间点组。
参数：p
a numeric value between 0 and 1, assumed proportion of genes which are differentially expressed.
0和1之间的数值，假定这些差异表达基因的比例。
Details
详情----------Details----------
This function implements the multivariate empirical Bayes Analysis of Variance model for identifying genes with different temporal profiles across multiple biological conditions, as described in Tai (2005).
此功能实现多元的经验，确定基因与多个生物条件的不同时间剖面，大（2005）所述方差模型的Bayes分析。
值----------Value----------
Object of MArrayTC.
对象MArrayTC。
作者（S）----------Author(s)----------
Yu Chuan Tai <a href="mailto:yuchuan@stat.berkeley.edu">yuchuan@stat.berkeley.edu</a>
参考文献----------References----------
Ph.D. dissertation. Division of Biostatistics, University of California, Berkeley.
Microarrays, U. Nuber (ed.), BIOS Scientific Publishers Limited, Taylor & Francis, 4 Park Square, Milton Park, Abingdon OX14 4RN, Chapter 20.
参见----------See Also----------
timecourse Vignette.
timecourse小插曲。
举例----------Examples----------
SS <- matrix(c( 0.01, -0.0008, -0.003, 0.007, 0.002,
-0.0008, 0.02, 0.002, -0.0004, -0.001,
-0.003, 0.002, 0.03, -0.0054, -0.009,
0.007, -0.0004, -0.00538, 0.02, 0.0008,
0.002, -0.001, -0.009, 0.0008, 0.07), ncol=5)
sim.Sigma <- function()
{
S <- matrix(rep(0,25),ncol=5)
x <- mvrnorm(n=10, mu=rep(0,5), Sigma=10*SS)
for(i in 1:10)
S <- S+crossprod(t(x[i,]))
solve(S)
}
## Now let's simulate a dataset with three biological conditions[＃现在，让我们的模拟数据集有三个生物条件]
## 500 genes in total, 10 of them have different expected time course profiles[＃500的基因总数，其中10人有不同的预期时间当然概况]
## across biological conditions[＃横跨生物条件]
## the first condition has 3 replicates, while the second condition has 4 replicates, [＃第一个条件有3个重复，而第二个条件有4个重复，]
## and the third condition has 2 replicates. 5 time points for each condition.[＃和第三个条件有2个重复。每个条件的5个时间点。]
sim.data <- function(x, indx=1)
{
mu <- rep(runif(1,8,x[1]),5)
if(indx==1)
res <- c(as.numeric(t(mvrnorm(n=3, mu=mu+rnorm(5,sd=5), Sigma=sim.Sigma()))),
as.numeric(t(mvrnorm(n=4, mu=mu+rnorm(5,sd=3.2), Sigma=sim.Sigma()))),
as.numeric(t(mvrnorm(n=2, mu=mu+rnorm(5,sd=2), Sigma=sim.Sigma()))))
if(indx==0) res <- as.numeric(t(mvrnorm(n=9, mu=mu+rnorm(5,sd=3), Sigma=sim.Sigma())))
res
}
M <- matrix(rep(14,500*45), ncol=45)
M[1:10,] <- t(apply(M[1:10,],1,sim.data))
M[11:500,] <- t(apply(M[11:500,],1,sim.data, 0))
assay <- rep(c("1.2.04","2.4.04","3.5.04","5.21.04","7.17.04","9.10.04","12.1.04","1.2.05","4.1.05"),each=5)
trt <- c(rep(c("wildtype","mutant1"),each=15),rep("mutant1",5), rep("mutant2", 10))
# Caution: since "mutant1" < "mutant2" < "wildtype", the sample sizes should be in the order of 4,2,3, [注意：“mutant1”<“mutant2”<“野生”以来，样本大小应该在4,2,3顺序，]
# but NOT 3,4,2. [但不是3,4,2。]
size <- matrix(c(4,2,3), byrow=TRUE, nrow=500, ncol=3)
MB.multi <- mb.MANOVA(M, times=5, D=3, size=size, rep.grp=assay, condition.grp=trt)
plotProfile(MB.multi, stats="MB", type="b") # plots the no. 1 gene[图没有。 1基因]
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。
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发表于 2012-2-26 15:37:47
