pumaComb(puma)
pumaComb()所属R语言包：puma
Combining replicates for each condition
结合复制的每个条件
译者：生物统计家园网 机器人LoveR
描述----------Description----------
This function calculates the combined (from replicates) signal for each condition using Bayesian models. The inputs are gene expression levels and the probe-level standard deviations associated with expression measurements for each gene on each chip. The outputs include gene expression levels and standard deviation for each condition.
此功能为每个使用贝叶斯模型的条件计算合并（复制）信号。输入基因表达水平和每个芯片上的每个基因表达测量探针级标准偏差。输出包括基因表达水平，并为每个条件的标准偏差。
用法----------Usage----------
pumaComb(
eset
, design.matrix=NULL
, method="em"
, numOfChunks=1000
, save_r=FALSE
, cl=NULL
, parallelCompute=FALSE
)
参数----------Arguments----------
参数：eset
An object of class ExpressionSet.
对象类ExpressionSet。
参数：design.matrix
A design matrix.
一个设计矩阵。
参数：method
Method "map" uses MAP of a hierarchical Bayesion model with Gamma prior on the between-replicate variance (Gelman et.al. p.285) and shares the same variance across conditions. This method is fast and suitable for the case where there are many conditions. Method "em" uses variational inference of the same hierarchical Bayesian model as in method "map" but with conjugate prior on between-replicate variance and shares the variance across conditions. This is generaly much slower than "map", but is recommended where there are few conditions (as is usually the case).
“图”使用方法的与伽玛层次Bayesion，模型图的复制之间的差异（吉尔曼等人p.285）之前和共享跨相同的条件方差。这种方法是快速的和适宜的情况下，有许多条件。法“EM”使用“图”的方法，但相同的分层贝叶斯模型的共轭变推理之前复制之间的差异和股份跨条件方差。这是generaly要比慢的“图”，但建议，其中有几个条件（通常情况下）。
参数：numOfChunks
An integer defining how many chunks the data is divided into before processing. There is generally no need to change the default value.
一个整数，定义多少块数据被分为前处理。一般不存在需要更改默认值。
参数：save_r
Will save an internal variable r to a file. Used for debugging purposes.
将内部变量r文件保存。用于调试目的。
参数：cl
A "cluster" object. See makeCluster function from snow package for more details (if available).
一个“聚类”的对象。看到makeCluster从snow了解更多的细节（如果可用）的功能。
参数：parallelCompute
Boolean identifying whether processing in parallel should occur.
布尔确定是否应该发生在并行处理。
Details
详情----------Details----------
It is generally recommended that data is normalised prior to using this function. Note that the default behaviour of mmgmos is to normalise data so this shouldn't generally be an issue. See the function pumaNormalize for more details on normalisation.
一般建议，数据标准化之前，使用此功能。请注意，mmgmos的默认行为是规范的数据，所以这个一般不应是一个问题。见功能pumaNormalize标准化更多细节。
值----------Value----------
The result is an ExpressionSet object.
结果是ExpressionSet对象。
作者（S）----------Author(s)----------
Xuejun Liu, Marta Milo, Neil D. Lawrence, Magnus Rattray
参考文献----------References----------
gene expression, Bioinformatics, 22:2107-2113.
参见----------See Also----------
Related methods pumaNormalize, bcomb, mmgmos and pumaDE
相关的方法pumaNormalize，bcomb，mmgmos和pumaDE
举例----------Examples----------
# Next 4 lines commented out to save time in package checks, and saved version used[接下来的4行注释掉包检查，以节省时间，并保存版本使用]
# if (require(affydata)) {[（要求（affydata））{]
# data(Dilution)[数据（稀释）]
# eset_mmgmos <- mmgmos(Dilution)[< - mmgmos eset_mmgmos（稀释）]
# }[}]
data(eset_mmgmos)
# Next line shows that eset_mmgmos has 4 arrays, each of which is a different[下一行显示，eset_mmgmos有4阵列，其中每一个不同]
# condition (the experimental design is a 2x2 factorial, with both liver and[条件（实验设计是一个2x2的因子，肝脏和]
# scanner factors)[扫描器的因素）]
pData(eset_mmgmos)
# Next line shows expression levels of first 3 probe sets[下一行显示，前3个探针组的表达水平]
exprs(eset_mmgmos)[1:3,]
# Next line used so eset_mmgmos only has information about the liver factor[下一行使用，所以eset_mmgmos只有肝因子的信息]
# The scanner factor will thus be ignored, and the two arrays of each level[扫描器的因素将被忽略，每个级别的两个数组]
# of the liver factor will be treated as replicates[肝因子，将被视为重复]
pData(eset_mmgmos) <- pData(eset_mmgmos)[,1,drop=FALSE]
# To save time we'll just use 100 probe sets for the example[为了节省时间，我们只使用100个探针组的例子]
eset_mmgmos_100 <- eset_mmgmos[1:100,]
eset_comb <- pumaComb(eset_mmgmos_100)
# We can see that the resulting ExpressionSet object has just two conditions[我们可以看到，产生ExpressionSet对象只有两个条件]
# and 1 expression level for each condition[1每种条件下的表达水平]
pData(eset_comb)
exprs(eset_comb)[1:3,]
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 2012-2-26 11:36:17
