analyseMA(daMA)
analyseMA()所属R语言包：daMA
ANALYSIS OF FACTORIAL MICROARRAY EXPERIMENTS
因子微阵列实验分析
译者：生物统计家园网 机器人LoveR
描述----------Description----------
analyseMA is used for the ananlysis of factorial two-colour microarray experiments based on the experimental design, a user-defined matrix containing the experimental question in contrast form and a vector to discern vectorial contrasts from contrasts given in matrix form.
analyseMA用于三维CFD的阶乘两色芯片实验，根据实验设计，用户定义的矩阵包含在实验对比的形式和向量，以辨别向量在矩阵形式给出的对比反差的问题的。
用法----------Usage----------
analyseMA( data, design, id, cmat, cinfo, padj=c("none","bonferroni","fdr"), tol=1e-06 )
参数----------Arguments----------
参数：data
a matrix of size G \times N containing the normalized and/or standardized data to be analyzed, where G is the number of spots under investigation and N is the number of arrays used in the experiment. The matrix should contain one row for each spot. The matrix should contain as many columns as arrays involved in the experiment, such that each column contains the data for one single array. The matrix should not contain any ID variables, which are entered separately. Missing values should be entered as NA.
大小的矩阵G \times N含有规范化和/或标准化的数据进行分析，其中G是根据调查和N点的数量是实验中所用的阵列。每个点矩阵应该包含一个行。应包含许多实验中所涉及的阵列，这样每一列包含一个单一阵列中的数据列的矩阵。矩阵不应该包含任何标识变量，这是单独订立的。遗漏值应输入为NA。
参数：design
the design matrix of size N \times (K+2), where K is the number of experimental conditions. This is the design matrix X known from linear model theory and its elements are typically 0, 1, or -1. A 0 means that the associated parameter does not apply for the corresponding observation (i.e., row). The first two columns are reserved for the two dyes and are usually filled up with 1 and -1, respectively.
N \times (K+2)设计矩阵的大小，其中K是实验条件。这是通常称为线性模型的理论和它的元素的X是设计矩阵0，1或-1。 0值表示相关参数并不适用于相应的观察（即行）。保留两种染料的前两列，分别为1和-1，通常是充满了。
参数：id
an ID vector of length G for the identification of the spots.
鉴定点的长度G ID向量。
参数：cmat
a matrix describing the p experimental questions (contrasts) to be analysed in the experiment. The matrix can be composed of vectorial contrasts (a single row of the matrix) and of contrasts in matrix form (several rows of the matrix), e.g. an A \times B interaction effect in a 3 \times 2 design. All contrasts have to be combined into one matrix (using rbind for instance).
在实验中进行分析的矩阵描述的P实验问题（对比）。矩阵可组成矢量对比（单列的矩阵）和对比矩阵形式（矩阵的几行），例如A \times B3 \times 2设计中的互动效应。所有对比都必须结合成一个矩阵（例如使用rbind）。
参数：cinfo
a vector of length p describing the grouping of the contrast matrix rows in vector or matrix form. E.g. if the design matrix contains three contrasts in vector form, cinfo = rep(1,3), if it contains two vectorial contratst and one as matrix with three rows, cinfo=c(1,1,3).
对比矩阵的行向量或矩阵形式描述了分组的长度P级的向量。例如如果设计矩阵包含三个对比向量形式，CINFO =（1,3）代表，如果它包含两个的矢量contratst和三行为基质，CINFO = C（1,1,3）。
参数：padj
a quoted string indicating the multiplicity adjustment that should be used. "none" - no multiplicity adjustment, "bonferroni" - Bonferroni single step adjustment, "fdr" - linear step-up procedure of Benjamini and Hochberg.
一个带引号的字符串，表示应该使用多重调整。 “无” - 无多重性的调整，“邦弗朗尼” - 邦弗朗尼单步调整，“FDR” - 线性升压过程Benjamini和Hochberg。
参数：tol
A value indicating the tolerance for contrast estimability check
一个值，该值指示的对比估计性检查的耐受性
Details
详情----------Details----------
The analysis is perfomed separately for each spot. For each spot, arrays with NA values are dropped. Then, for each experimental question (either contrast vector or contrast matrix) a check on the estimabilty of the resulting linear function is done. If the linear function of interest is estimable, t- or F-tests (whichever is appropriate) are computed and the associated unadjusted $P-$values are computed. Multiplicity adjustment is done over the number of spots only.
分别为每个点，俟分析。对于每一个点，无值的数组被丢弃。然后，对每个实验的问题（或者相反向量或对比矩阵）上产生的线性函数estimabilty的检查已经完成。如果感兴趣的线性函数是难能可贵的，T或F检验（以适用者为准）计算和相关未经调整的美元价值的P-$计算。只有斑点的数量做了多重调整。
值----------Value----------
a G \times (4p+3) matrix with the following row-wise components.
G \times (4p+3)矩阵具有以下行明智的组件。
参数：(i)
the first column contains the ID
第一列包含的ID
参数：(ii)
columns 2 though p+1 contain the estimates of the linear function (in case of vectorial contrasts) or the dregrees of freedom for the quadratic form in the numerator (in case of contrasts given in matrix form and that F-tests are used), depending on cinfo.
列2 P +1虽然包含线性函数的（矢量反差的情况下）或自由分子的二次形式（矩阵形式给出的对比和F检验的情况下使用）dregrees估计，取决于对CINFO。
参数：(iii)
columns p+2 through 2p+1 contain the test statistics (either t- or F-tests, depending on cinfo)
通过2P +1 P +2列包含的测试统计数据（无论是T-或F-测试，CINFO）
参数：(iv)
columns 2p+2 through 3p+1 contain the raw P-values, associated to the t- and F-tests
2P +2通过3P +1列包含原始的P-值，相关的T-和F-测试
参数：(v)
column 3p+2 contains the mean square error
3P +2包含列的均方误差
参数：(vi)
column 3p+3 contains the residual degrees of freedom
列3P +3包含自由的残留度
参数：(vii)
columns 3p+4 through 4p+3 contain the multiplicity adjusted P-values, associated to the raw P-values, as long as a multiplicty adjustment method has been selected
3P +4通过4P +3列包含多重调整P值，相关原材料的P值，已被选定作为multiplicty调整方法
作者（S）----------Author(s)----------
Jobst Landgrebe (jlandgr1@gwdg.de) and Frank Bretz (bretz@bioinf.uni-hannover.de)
参考文献----------References----------
two colour factorial microarray experiments", submitted. http://www.microarrays.med.uni-goettingen.de/
举例----------Examples----------
## Not run: result <- analyseMA( data=data.3x2, design=designs.composite$BSBSBS, id=id.3x2,[＃无法运行结果< - analyseMA（数据= data.3x2的，设计designs.composite美元BSBSBS，ID = id.3x2的，]
cmat=cmatB.AB, cinfo=c(1,3), padj=c("fdr"), tol=1e-06 ) # analyse a dataset with[分析一个数据集]
# 30012 spots and 18 arrays. The design[30012点和18个阵列。设计]
# is 3x2 with 3 replicates, the[与3 3x2的复制，]
# contrasts of interest are the main effect[利益的反差是主要的影响]
# B and the interaction effect AxB.[B和互动效应AXB。]
## End(Not run)[＃结束（不运行）]
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 2012-2-25 16:13:12
