coClustering.permutationTest(WGCNA)
coClustering.permutationTest()所属R语言包：WGCNA
Permutation test for co-clustering
置换检验的合作聚类
译者：生物统计家园网 机器人LoveR
描述----------Description----------
This function calculates permutation Z statistics that measure how different the co-clustering of modules in a reference and test clusterings is from random.
此函数计算排列的Z统计量是随机测量不同模块的参考和测试的聚类的聚类。
用法----------Usage----------
coClustering.permutationTest(
clusters.ref, clusters.test,
tupletSize = 2,
nPermutations = 100,
unassignedLabel = 0,
randomSeed = 12345, verbose = 0, indent = 0)
参数----------Arguments----------
参数：clusters.ref
Reference input clustering. A vector in which each element gives the cluster label of an object.
参考输入聚类。一个向量中的每个元素给出了聚类标签的对象。
参数：clusters.test
Test input clustering. Must be a vector of the same size as cluster.ref.
测试输入聚类。必须是相同的大小的矢量作为cluster.ref。
参数：tupletSize
Co-clutering tuplet size.
的合作clutering tuplet的大小。
参数：nPermutations
Number of permutations to execute. Since the function calculates parametric p-values, a relatively small number of permutations (at least 50) should be sufficient.
的排列数来执行。由于函数计算参数p-值，一个相对小的排列数（至少50）应该是足够的。
参数：unassignedLabel
Optional specification of a clustering label that denotes unassigned objects. Objects with this label are excluded from the calculation.
可选规格的聚类标签，表示未分配的对象。这个标签的对象被排除在外的计算。
参数：randomSeed
Random seed for initializing the random number generator. If NULL, the generator is not initialized (useful for calling the function sequentially). The default assures reproducibility.
用于初始化的随机数发生器的随机种子。如果NULL，发电机未初始化（按顺序调用函数很有用）。默认情况下，保证可重复性。
参数：verbose
If non-zero, function will print out progress messages.
如果不为零，函数将打印出进度消息。
参数：indent
Indentation for progress messages. Each unit adds two spaces.
压痕取得进展的消息。每个单元增加两个空格。
Details
详细信息----------Details----------
This function performs a permutation test to determine whether observed co-clustering statistics are significantly different from those expected by chance. It returns the observed co-clustering as well as the permutation Z statistic, calculated as (observed - mean)/sd, where mean and sd are the mean and standard deviation of the co-clustering when the test clustering is repeatedly randomly permuted.
此功能进行置换的测试，以确定是否观察到的聚类统计显着不同于预期的机会。它返回所观察到的合作聚类，以及置换的Z统计，计算公式如下(observed - mean)/sd，其中mean和sd是合作聚类的均值和标准偏差时，测试聚类多次随机排列。
值----------Value----------
参数：observed
the observed co-clustering measures for clusters in clusters.ref
所观察到的聚类措施的聚类clusters.ref
参数：Z
permutation Z statics
排列Ž静
参数：permuted.mean
means of the co-clustering measures when the test clustering is permuted
合作聚类的装置测量时测试聚类置换
参数：permuted.sd
standard deviations of the co-clustering measures when the test clustering is permuted
合作聚类的标准偏差的测量当测试聚类置换
参数：permuted.cc
values of the co-clustering measure for each permutation of the test clustering. A matrix of dimensions (number of permutations)x(number of clusters in reference clustering).
值的每个排列的测试聚类合作聚类措施。维的矩阵（排列数）×（参照聚类簇数）。
（作者）----------Author(s)----------
Peter Langfelder
参考文献----------References----------
Reproducible? PLoS Comput Biol 7(1): e1001057. Co-clustering is discussed in the Methods Supplement (Supplementary text 1) of that article.
参见----------See Also----------
coClustering for calculation of the "observed" co-clustering measure modulePreservation for a large suite of module preservation statistics
coClustering“观察”的合作聚类措施modulePreservation的大套房模块保存的统计数据，计算
实例----------Examples----------
set.seed(1);
nModules = 5;
nGenes = 100;
cl1 = sample(c(1:nModules), nGenes, replace = TRUE);
cl2 = sample(c(1:nModules), nGenes, replace = TRUE);
cc = coClustering(cl1, cl2)
# Choose a low number of permutations to make the example fast[选择一个低的排列组合，快速的例子]
ccPerm = coClustering.permutationTest(cl1, cl2, nPermutations = 20, verbose = 1);
ccPerm$observed
ccPerm$Z
# Combine cl1 and cl2 to obtain clustering that is somewhat similar to cl1:[结合CL1和CL2，得到聚类是有点类似cl1的：]
cl3 = cl2;
from1 = sample(c(TRUE, FALSE), nGenes, replace = TRUE);
cl3[from1] = cl1[from1];
ccPerm = coClustering.permutationTest(cl1, cl3, nPermutations = 20, verbose = 1);
# observed co-clustering is higher than before:[观察聚类比以前高：]
ccPerm$observed
# Note the high preservation Z statistics:[注意保存Z统计量：]
ccPerm$Z
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 2012-10-1 21:09:02
