bridge.3samples(bridge)
bridge.3samples()所属R语言包：bridge
Bayesian Robust Inference for Differential Gene Expression
鲁棒贝叶斯推理的差异表达基因
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Test for differentially expressed genes in a three sample set-up. This code can be used with both cDNA microarrays or Affymetrix chip.
在三个样本中差异表达基因的设置进行测试。此代码可以用cDNA微阵列或Affymetrix公司芯片。
用法----------Usage----------
bridge.3samples(sample1,sample2,sample3,B=1000,min.iter=0,batch=10,mcmc.obj=NULL,all.out=TRUE,verbose=FALSE,log=FALSE,robust=TRUE)
参数----------Arguments----------
参数：sample1
The matrix of intensity from the sample 1. Each row corresponds to a different gene.
从样品1矩阵的强度。每一行对应一个不同的基因。
参数：sample2
The matrix of intensity from the sample 2. Each row corresponds to a different gene.
从样品2矩阵的强度。每一行对应一个不同的基因。
参数：sample3
The matrix of intensity from the sample 3. Each row corresponds to a different gene.
3从样品基质的强度。每一行对应一个不同的基因。
参数：B
The number of iteration used in the MCMC algorithm.
MCMC算法的迭代次数。
参数：min.iter
The length of the burn-in period in the MCMC algorithm.min.iter should be less than B.
MCMC算法在烧伤期间的长度。min.iter应该小于B
参数：batch
The thinning value to be used in the MCMC. Only every batch-th iteration will be stored.
细化值将用于在MCMC方法。只有每batch次迭代将被保存。
参数：mcmc.obj
An object of type bridge2, as returned by bridge.2samples. mcmc.obj can be used to initialized the MCMC. If no mcmc.obj, the MCMC is initialized to the least squares estimates.
一个对象类型bridge2，返回bridge.2samples的。 mcmc.obj可以用来初始化的MCMC。如果没有mcmc.obj，初始化最小二乘估计的MCMC。
参数：all.out
A logical value indicating if all the parameters should be output. If all.out is FALSE, only the posterior mean is output. This could be used to save memory.
一个逻辑值，指出如果所有的参数应该是输出。 all.out如果是FALSE，只有后平均输出。这可以用来节省内存。
参数：verbose
A logical value indicating if the current MCMC iteration number should be printed out.
一个逻辑值，指出应打印出来，如果当前的MCMC迭代次数。
参数：log
A logical value indicating if the data are log transformed.
一个逻辑值，表明如果数据log转化。
参数：robust
A logical value indicating if a t model (robust==TRUE) or a Gaussian model (robust==TRUE) should be used. In the case of the t-model, the degrees of freedoms are estimated.
一个逻辑值，指出如果在模型（robust==TRUE）或高斯模型（robust==TRUE）的应用。在T-模型的情况下，自由的程度估计。
Details
详情----------Details----------
This code fits a robust Bayesian hierarchical model for testing for differential expression. Outliers are modeled explicitly using a $t$-distribution. The model includes an exchangeable prior for the variances which allow different variances for the genes but still shrink extreme empirical variances. This function DO NOT perform normalization. The data should be normalized before hands such as centering the mean expression of each experiment. More details can be found in the references below.
此代码适合稳健贝叶斯测试差异表达的层次模型。离群建模明确使用$ T $分布。该模型包括交换前的差异，允许不同的基因差异，但仍缩小极端的实证差异。此功能不执行规范化。应标准化之前，围绕平均每个实验的表达，如手的数据。更多细节，可以发现在下面的参考资料。
值----------Value----------
An object of type bridge3 containing the sampled values from the posterior distribution.
bridge3包含从后验分布的采样值类型的对象。
参数：gamma1
A matrix, each row contains the sampled values from the corresponding gene effect in sample 1.
A矩阵，每一行包含从相应的基因的作用在样品1的采样值。
参数：gamma2
A matrix, each row contains the sampled values from the corresponding gene effect in sample 2.
A矩阵，每一行包含从相应的基因的作用在样品2的采样值。
参数：gamma3
A matrix, each row contains the sampled values from the corresponding gene effect in sample 3.
A矩阵，每一行包含从相应的基因的作用在样品3的采样值。
参数：lambda.gamma1
A vector containing the sampled values for the precision of the gene effect prior for the component corresponding to sample 1.
一个向量之前包含的组件对应的样品1的基因效应的精度的采样值。
参数：lambda.gamma2
A vector containing the sampled values for the precision of the gene effect prior for the component corresponding to sample 2.
样品2对应的组件包含精密的基因效应的采样值之前的一个向量。
参数：lambda.gamma3
A vector containing the sampled values for the precision of the gene effect prior for the component corresponding to sample 3.
样品3对应的组件包含精密的基因效应的采样值前一个向量。
参数：lambda.gamma12
A vector containing the sampled values for the precision of the gene effect prior for the component where sample 1 and sample 2 are combined.
一个向量，包含精密的基因效应的样品1和样品2结合的组件前的采样值。
参数：lambda.gamma13
A vector containing the sampled values for the precision of the gene effect prior for the component where sample 1 and sample 3 are combined.
一个向量，包含精密的基因效应的样品1和样品3结合的组件前的采样值。
参数：lambda.gamma23
A vector containing the sampled values for the precision of the gene effect prior for the component where sample 2 and sample 3 are combined.
一个向量，结合样品2和样品3的组件包含精密的基因效应的采样值之前。
参数：lambda.gamma123
A vector containing the sampled values for the precision of the gene effect prior for the component where all the samples are combined.
一个向量，包含精密的基因效应的组成部分，所有的样品结合事先的采样值。
参数：lambda_eps1
A matrix, each row contains the sampled values from the corresponding gene precision in sample 1.
A矩阵，每一行包含在相应的基因样本1精密的采样值。
参数：lambda_eps2
A matrix, each row contains the sampled values from the corresponding gene precision in sample 2.
A矩阵，每一行包含在相应的基因样本2精密的采样值。
参数：lambda_eps3
A matrix, each row contains the sampled values from the corresponding gene precision in sample 3.
A矩阵，每一行包含从样品3中相应的基因精确的采样值。
参数：a.eps1
A vector containing the sampled values for the mean of the prior of the genes precision in sample 1.
一个向量，包含事先在样品1的基因精确的平均采样值。
参数：b.eps1
A vector containing the sampled values for the variance of the prior of the genes precision in sample 1.
样品1的基因精确的事先方差采样值的一个向量。
参数：a.eps2
A vector containing the sampled values for the mean of the prior of the genes precision in sample 2.
一个向量，包含平均样品2的基因精确的事先的采样值。
参数：b.eps2
A vector containing the sampled values for the variance of the prior of the genes precision in sample 2.
样品2的基因精确的事先方差采样值的一个向量。
参数：a.eps3
A vector containing the sampled values for the mean of the prior of the genes precision in sample 3.
一个向量，包含平均样品3的基因精确的事先的采样值。
参数：b.eps3
A vector containing the sampled values for the variance of the prior of the genes precision in sample 3.
一个向量，包含样品3的基因精确的事先方差的采样值。
参数：w1
A matrix, each element (i,j) correspond to the posterior mean of the sampled weights of replicate j in gene i and sample 1. To save memory, we only store the posterior means of the weigths.
一个矩阵，每个元素（I，J）对应复制j基因I和样品1的权重采样后的平均值。为了节省内存，我们只存储的weigths后的手段。
参数：w2
A matrix, each element (i,j) correspond to the posterior mean of the sampled weights of replicate j in gene i and sample 2. To save memory, we only store the posterior means of the weigths.
一个矩阵，每个元素（I，J）对应复制j样本在基因I和样品2的重量后的平均值。为了节省内存，我们只存储的weigths后的手段。
参数：w3
A matrix, each element (i,j) correspond to the posterior mean of the sampled weights of replicate j in gene i and sample 3. To save memory, we only store the posterior means of the weigths.
一个矩阵，每个元素（I，J）对应复制j样本在基因I和样品3的权重后的平均值。为了节省内存，我们只存储的weigths后的手段。
参数：w1
A matrix, each element (i,j) correspond to the posterior mean of the sampled weights of replicate j in gene i and sample 1. To save memory, we only store the posterior means of the weigths.
一个矩阵，每个元素（I，J）对应复制j基因I和样品1的权重采样后的平均值。为了节省内存，我们只存储的weigths后的手段。
参数：nu1
A matrix containing the sampled degrees of freedom in sample 1.
A矩阵中含有样品1自由的采样度。
参数：nu2
A matrix containing the sampled degrees of freedom in sample 2.
2样品中含有自由的采样度矩阵。
参数：nu3
A matrix containing the sampled degrees of freedom in sample 3.
样品3中含有自由的采样度矩阵。
参数：w.mix
The posterior mixing proportions in the mixture component.
混合物中的成分后的混合比例。
参数：prop.model
The posterior proportions of each component for each each gene.
每个组件的每个基因后的比例。
参数：move
The proportion of moves between components. This should be used as a diagnostic tool.
移动组件之间的比例。这应作为一个诊断工具。
作者（S）----------Author(s)----------
Raphael Gottardo
参考文献----------References----------
Raphael Gottardo, Adrian E. Raftery, Ka Yee Yeung, and Roger Bumgarner
参见----------See Also----------
bridge.2samples
bridge.2samples
举例----------Examples----------
sample1<-matrix(exp(rnorm(150)),50,3)
sample2<-matrix(exp(rnorm(200)),50,4)
sample3<-matrix(exp(rnorm(150)),50,3)
mcmc.bridge3<-bridge.3samples(sample1,sample2,sample3,B=10,min.iter=0,batch=10,mcmc.obj=NULL,all.out=TRUE,verbose=FALSE,robust=TRUE)
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。
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发表于 2012-2-25 13:55:27
