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R语言 WGCNA包 coClustering.permutationTest()函数中文帮助文档(中英文对照)

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发表于 2012-10-1 21:09:02 | 显示全部楼层 |阅读模式
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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