run.plgem(plgem)
run.plgem()所属R语言包：plgem
Wrapper for Power Law Global Error Model (PLGEM) analysis method
包装为电力法的全局误差模型（PLGEM）分析方法
译者：生物统计家园网 机器人LoveR
描述----------Description----------
This function automatically performs PLGEM fitting and evaluation, determination of observed and resampled PLGEM-STN values, and selection of differentially expressed genes/proteins (DEG) using the PLGEM method.
此功能自动进行拟合和评价PLGEM，观察和重新取样PLGEM-STN值的测定，并选择差异表达的基因/蛋白质（二甘醇）使用PLGEM方法。
用法----------Usage----------
run.plgem(esdata, signLev=0.001, rank=100, covariate=1,
baselineCondition=1, Iterations="automatic", trimAllZeroRows=FALSE,
zeroMeanOrSD=c("replace", "trim"), fitting.eval=TRUE,
plotFile=FALSE, writeFiles=FALSE, Verbose=FALSE)
参数----------Arguments----------
参数：esdata
an object of class ExpressionSet; see Details for important information on how the phenoData slot of this object will be interpreted by the function.
类对象ExpressionSet;看到phenoData这个对象的槽将如何解释函数的重要信息的详细信息。
参数：signLev
numeric vector; significance level(s) for the DEG selection. Value(s) must be in (0,1).
数字向量;显着性水平为二甘醇的选择（S）。值（S），必须在（0,1）。
参数：rank
integer (or coercible to integer); the number of genes or proteins to be selected according to their PLGEM-STN rank. Only used if number of available replicates is too small to perform resampling (see Details).
integer（或强制转换integer）;基因或蛋白质的数量，根据他们的PLGEM-STN级被选中。如果只用可用的号码复制太小进行重采样（见详情）。
参数：covariate
integer, numeric or character; specifies the covariate to be used to distinguish the various experimental conditions from one another. See Details for how to specify the covariate.
integer，numeric或character;指定被用来区分各种实验条件下，从一个协。详情请参阅如何指定covariate。
参数：baselineCondition
integer, numeric or character; specifies the condition to be treated as the baseline. See Details for how to specify the baselineCondition.
integer，numeric或character;指定的条件，要作为基准处理。详情请参阅如何指定baselineCondition。
参数：Iterations
number of iterations for the resampling step; if "automatic" it is automatically determined.
重采样步迭代的数量;如果"automatic"它会自动确定。
参数：trimAllZeroRows
logical; if TRUE, rows in the data set containing only zero values are trimmed before fitting PLGEM. See help page of function plgem.fit for details.
logical如果TRUE，在数据行集只包含零值修剪配件PLGEM前。见有助于详情页的功能plgem.fit。
参数：zeroMeanOrSD
either NULL or character; what should be done if a row with non-positive mean or zero standard deviation is encountered before fitting PLGEM? Current options are one of "replace" or "trim". Partial matching is used to switch between the options and setting the value to NULL will cause the default behaviour to be enforced, i.e. to "replace". See help page of function plgem.fit for details.
无论是NULL或character;与非阳性均值或标准差为零的行遇到装修PLGEM之前是否应该做些什么呢？当前选项"replace"或"trim"。部分匹配使用之间切换的选项和设定值NULL会导致默认行为予以强制执行，即"replace"。见有助于详情页的功能plgem.fit。
参数：fitting.eval
logical; if TRUE, the fitting is evaluated generating a diagnostic plot.
logical如果TRUE，拟合的评估产生了诊断的图。
参数：plotFile
logical; if TRUE, the generated plot is written on a file.
logical;如果TRUE，生成的图被写上文件。
参数：writeFiles
logical; if TRUE, the generated list of DEG is written on disk file(s).
logical;如果TRUE，二甘醇生成的名单写在磁盘上的文件（S）。
参数：Verbose
logical; if TRUE, comments are printed out while running.
logical;如果TRUE评论打印出来，而运行。
Details
详情----------Details----------
The phenoData slot of the ExpressionSet given as input is expected to contain the necessary information to distinguish the various experimental conditions from one another. The columns of the pData are referred to as "covariates". There has to be at least one covariate defined in the input ExpressionSet. The sample attributes according to this covariate must be distinct for samples that are to be treated as distinct experimental conditions and identical for samples that are to be treated as replicates.
phenoData输入插槽ExpressionSet预计包含必要的信息来区分彼此在各种实验条件。 pData列被称为“协变量”。必须有至少一个协变量定义输入ExpressionSet。样品的属性，根据该协变量必须是不同的样品，被视为不同的实验条件和相同的样品，重复治疗治疗。
There is a couple different ways how to specify the covariate: If an integer or a numeric is given, it will be taken as the covariate number (in the same order in which the covariates appear in the colnames of the pData). If a character is given, it will be taken as the covariate name itself (in the same way the covariates are specified in the colnames of the pData). By default, the first covariate appearing in the colnames of the pData is used.
有一对夫妇如何指定covariate不同的方式：如果一个integer或numeric是，它将被作为协号（其中在同一顺序中的协变量出现colnames）pData。如果character，它将被作为协变量的名称本身（协变量，以同样的方式指定colnamespData）。默认情况下，协出现在colnamespData使用。
Similarly, there is a couple different ways how to specify which experimental condition to treat as the baseline. The available "condition names" are taken from unique(as.character(pData(data)[, covariate])). If baselineCondition is given as a character, it will be taken as the condition name itself. If baselineCondition is given as an integer or a numeric value, it will be taken as the condition number (in the same order of appearance as in the "condition names"). By default, the first condition name is used.
同样，有一对夫妇不同的方式如何指定治疗的实验条件作为基准。可用的条件名称“从unique(as.character(pData(data)[, covariate]))。如果baselineCondition作为一个character，它将被作为条件的名称本身。 baselineCondition如果给出integer或一个numeric值，它将被作为条件数（外观相同的顺序在“条件名称”）。默认情况下，第一个条件的名称使用。
The model is fitted on the most replicated condition. When more conditions exist with the maximum number of replicates, the condition providing the best fit is chosen (based on the adjusted r^2). If there is again a tie, the first one is arbitrarily taken.
该模型的拟合上最复制的条件。当更多的条件，存在的最大数量的复制，条件，提供最合适的选择（根据调整后的r^2）。如果有又是一条领带，第一个被任意剥夺。
If less than 3 replicates are provided for the condition used for fitting, then the selection is based on ranking according to the observed PLGEM-STN values. In this case the first rank genes or proteins are selected for each comparison.
如果少于3个重复为装修中使用的条件，然后选择基础上的排名根据观测到的PLGEM-STN值。在这种情况下，第一rank基因或蛋白质被选中为每个比较。
Otherwise DEG are selected comparing the observed and resampled PLGEM-STN values at the signLev significance level(s), based on p-values obtained via a call to function plgem.pValue. See References for details.
否则，二甘醇是选择比较观察和重新取样PLGEM-STN值在signLev显着水平（S），基于p-值，通过调用函数plgem.pValue获得。有关详细信息，请参阅参考。
值----------Value----------
A list of four elements:
一个list四个要素：
参数：fit
the input plgemFit.
输入plgemFit。
参数：PLGEM.STN
a matrix of observed PLGEM-STN values (see plgem.obsStn for details).
观察PLGEM-STN值matrix（见plgem.obsStn细节）。
参数：p-value
a matrix of p-values (see plgem.pValue for details).
matrix的p值（见plgem.pValue细节）。
参数：significant
a list with a number of elements equal to the number of different significance levels (delta) used as input. If ranking method is used due to insufficient number of replicates (see Details), this list will be of length 1 and named firstXXX, where XXX is the number provided by argument rank. Each element of this list is again a list, whose number of elements correspond to the number of performed comparisons (i.e. the number of conditions in the starting ExpressionSet minus the baseline). Each of these second level elements is a character vector of significant gene/protein names that passed the statistical test at the corresponding significance level.
list不同的显着水平（delta）作为输入的数量相等的元素的数量。如果排名的方法是使用复制数量不足（见详情），这个名单将是长度为1名为firstXXX，XXX是参数rank提供的检测号码。这个名单中的每个元素又是一个列表的元素对应的数量进行比较（即条件出发ExpressionSet减去基准）。这些第二级元素，每一个character重要的基因/蛋白名字，通过相应的显着性水平的统计检验的矢量。
作者（S）----------Author(s)----------
Mattia Pelizzola <a href="mailto:mattia.pelizzola@gmail.com">mattia.pelizzola@gmail.com</a>
Norman Pavelka <a href="mailto:normanpavelka@gmail.com">normanpavelka@gmail.com</a>
参考文献----------References----------
Ricciardi-Castagnoli P. A power law global error model for the identification of differentially expressed genes in microarray data. BMC Bioinformatics. 2004 Dec 17; 5:203; http://www.biomedcentral.com/1471-2105/5/203.
Florens L, Washburn MP. Statistical similarities between transcriptomics and quantitative shotgun proteomics data. Mol Cell Proteomics. 2008 Apr; 7(4):631-44; http://www.mcponline.org/cgi/content/abstract/7/4/631.
参见----------See Also----------
plgem.fit, plgem.obsStn, plgem.resampledStn, plgem.pValue, plgem.deg, plgem.write.summary
plgem.fit，plgem.obsStn，plgem.resampledStn，plgem.pValue，plgem.deg，plgem.write.summary
举例----------Examples----------
data(LPSeset)
set.seed(123)
LPSdegList <- run.plgem(esdata=LPSeset, fitting.eval=FALSE)
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 11:14:17
