createSignatures4TB(RTools4TB)
createSignatures4TB()所属R语言包：RTools4TB
Creates a set of transcriptional signatures from a microarray dataset.
建立了一套从芯片集的转录签名。
译者：生物统计家园网 机器人LoveR
描述----------Description----------
This function is a wrapper to create sets of transcriptional signatures (as in the TranscriptomeBrowser Project, TBrowser,http://tagc.univ-mrs.fr/tbrowser). This function creates a "cdt" file containing a set of expression matrices (transcriptional signatures) separated by blank lines. Please note that it requires both MCL and Cluster 3.0 (see 'warnings section'). It accepts both a matrix or file name as input.
此功能是创建一个包装转录签名集（在TranscriptomeBrowser项目，TBrowser，http://tagc.univ-mrs.fr/tbrowser）。这个函数创建一个“CDT”文件，其中包含的表达矩阵集（转录签名）由空行隔开。请注意，它需要MCL和聚类3.0（见“警告一节”）。它接受一个矩阵作为输入或文件名。
用法----------Usage----------
createSignatures4TB(data = NULL, filename = NULL, path = ".", name = NULL, normalization = c("rank", "gaussian", "quantiles", "none"),
distance.method = c("spearman", "pearson", "euclidean", "spm", "spgm"), silent = FALSE, verbose = TRUE, k = 150, random = 3, memory.used = 1024, fdr = 10,
inflation = 2.0, median.center = FALSE, set.seed = 123, returnRank = FALSE)
参数----------Arguments----------
参数：data
a matrix, data.frame or ExpressionSet object.
matrix，data.frame或ExpressionSet对象。
参数：filename
a character string representing the file name to load.
代表的文件名来加载一个字符串。
参数：path
a character string representing the data directory.
一个字符串，代表数据目录。
参数：name
a prefix for the name of the created files.
创建的文件的名称的前缀。
参数：normalization
indicates whether data should be normalized prior to analysis (see details).
指示数据是否应标准化之前的分析（见详情）。
参数：distance.method
a method to compute the distance to the k-th nearest neighbor. One of "pearson" (Pearson's correlation coefficient-based distance), "spearman" (Spearman's rho-based distance), "euclidean", "spm" or "spgm". Note that the "spm" distance corresponds to the arithmetic mean of pearson- and spearman-based distance : ("pearson"+"spearman")/2 whereas "spgm" computes their geometric mean : sqrt("pearson"*"spearman").
一种方法来计算距离的k个近邻。 “皮尔逊（Pearson相关系数为基础的距离）”，“矛”（斯皮尔曼的RHO基于距离），“欧几里德”，“SPM”或“spgm”之一。请注意，“SPM”的距离对应的算术平均皮尔森，和Spearman距离：（“培+”矛“）/ 2的”spgm“，而其几何平均数计算：SQRT（”培“ *“矛”）
参数：silent
if set to TRUE, the progression of distance matrix calculation is not displayed.
如果设置为TRUE，距离矩阵计算的进展将不会显示。
参数：verbose
if set to TRUE the function runs verbosely.
如果设置为TRUE，函数运行冗长。
参数：k
the neighborhood size.
附近的大小。
参数：random
the number of simulated distributions S to compute. By default random = 3.
模拟分布的数S计算。默认情况下random = 3。
参数：memory.used
size of the memory used to store part of the distance matrix. The subsequent sub-matrix is used to computed simulated distances to the k-th nearest neighbor (see detail section of DBFMCL function).
大小的内存用于存储距离矩阵的一部分。随后子矩阵是用来模拟计算距离的k个近邻（见DBFMCL功能的细节部分）。
参数：fdr
an integer value corresponding to the false discovery rate (range: 0 to 100).
一个整数值，对应的错误发现率（范围：0到100）。
参数：inflation
the main control of MCL. Inflation affects cluster granularity. It is usually chosen somewhere in the range [1.2-5.0]. inflation = 5.0 will tend to result in fine-grained clusterings whereas inflation = 1.2 will tend to result in very coarse grained clusterings. By default, inflation = 2.0. Default setting gives very good results for microarray data.
韧带主要控制。通货膨胀影响聚类的粒度。它通常在某处选择范围[1.2-5.0]。 inflation = 5.0往往会导致细粒度的聚类，而inflation = 1.2往往会导致非常粗粒聚类。默认情况下，inflation = 2.0。默认设置芯片的数据提供了很好的结果。
参数：median.center
if set to TRUE, median-centering is applied to the rows of the matrix.
如果设置为TRUE，中位数定心适用于矩阵的行。
参数：set.seed
specify seeds for random number generator.
指定随机数发生器的种子。
参数：returnRank
This argument modifies the output. Given a set of elements conserved after the filtering step of the DBFMCL algorithm, if returnRank = TRUE their expression values are replaced by their corresponding ranks in the input matrix.
这种说法修改输出。鉴于一套后的DBFMCL算法过滤步骤保守的元素，如果returnRank = TRUE的表达值及其相应职级所取代，在输入矩阵。
Details
详情----------Details----------
The Markov Cluster Algorithm was written by S. Van Dongen (see reference section). Cluster was originally written by Michael Eisen (http://rana.lbl.gov/EisenSoftware.htm). The command line version of Cluster version 3.0 was created by Michiel de Hoon, together with Seiya Imoto and Satoru Miyano.
马尔可夫聚类算法的书面由S.范栋勤（见参考文献部分）。最初是由迈克尔艾森（http://rana.lbl.gov/EisenSoftware.htm）的聚类。 Cluster版本3.0的命令行版本创建由米歇尔·胡恩，连同斗士井本和宫野悟。
警告----------Warnings----------
With the current implementation, this function works only on UNIX-like plateforms.
这个功能与目前的执引号况，工作类UNIX plateforms。
Cluster 3.0 should be installed in its command-line only version:
聚类应安装在它的命令行版本3.0：
Please see http://bonsai.ims.u-tokyo.ac.jp/~mdehoon/software/cluster/software.htm for further informations.
请~mdehoon http://bonsai.ims.u-tokyo.ac.jp/ /软件/为进一步信息聚类/ software.htm的。
wget http://bonsai.ims.u-tokyo.ac.jp/~mdehoon/software/cluster/cluster-1.36.tar.gz
wget http://bonsai.ims.u-tokyo.ac.jp/~mdehoon/software/cluster/cluster-1.36.tar.gz
tar xvfz cluster-1.36.tar.gz
tar xvfz cluster-1.36.tar.gz
cd cluster-1.36/
cd cluster-1.36/
./configure --without-x
./configure --without-x
make
make
sudo make install
sudo make install
# You should get cluster in your path
# You should get cluster in your path
cluster -v
cluster -v
MCL should be installed:
MCL应安装：
# Download the latest version of mcl (the script has been tested successfully with the 06-058 version).
# Download the latest version of mcl (the script has been tested successfully with the 06-058 version).
wget http://micans.org/mcl/src/mcl-latest.tar.gz
wget http://micans.org/mcl/src/mcl-latest.tar.gz
# Uncompress and install mcl
# Uncompress and install mcl
tar xvfz mcl-latest.tar.gz
tar xvfz mcl-latest.tar.gz
cd mcl-xx-xxx
cd mcl-xx-xxx
./configure
./configure
make
make
sudo make install
sudo make install
# You should get mcl in your path
# You should get mcl in your path
mcl -h
mcl -h
作者（S）----------Author(s)----------
Bergon A., Lopez F., Textoris J., Granjeaud S. and Puthier D.
参考文献----------References----------
flexible toolbox to explore productively the transcriptional landscape of the Gene Expression Omnibus database. PLoSONE, 2008;3(12):e4001.
参见----------See Also----------
DBFMCL,heatmapFromCDT,plotGeneExpProfiles,getSignatures,getExpressionMatrix
DBFMCL，heatmapFromCDT，plotGeneExpProfiles，getSignatures，getExpressionMatrix
举例----------Examples----------
## Not run: [＃无法运行：]
## with an artificial dataset[＃与人工数据集]
m <- matrix(rnorm(80000), nc=20)
m[1:100,1:10] <- m[1:100,1:10] + 4
m[101:200,11:20] <- m[101:200,11:20] + 3
m[201:300,5:15] <- m[201:300,5:15] + -2
res <- createSignatures4TB(data = m, name="artificial", distance.method = "pearson", median.center=TRUE, k = 25)
plotGeneExpProfiles(res)
allsign <- heatmapFromCDT("artificial.dataMods.cdt")
plotGeneExpProfiles(res, signature=1)
heatmapFromCDT("artificial.dataMods.cdt", signature=1)
## with a real dataset[＃一个真实的数据集]
library(ALL)
data(ALL)
exp <- createSignatures4TB(data = ALL , name="ALLdataset", distance.method = "pearson", median.center=TRUE, k = 100)
plotGeneExpProfiles(exp, signatures=1)
plotGeneExpProfiles(res)
allsign <- heatmapFromCDT("ALLdataset.dataMods.cdt")
si1 <- heatmapFromCDT("ALLdataset.dataMods.cdt", signature=1)
## 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。
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 13:33:07
