R语言 plgem包 plgem.resampledStn()函数中文帮助文档(中英文对照)

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plgem.resampledStn(plgem)
plgem.resampledStn()所属R语言包：plgem

                                        Computation of Resampled PLGEM-STN Statistics
                                         对重采样PLGEM-STN统计计算

                                         译者：生物统计家园网 机器人LoveR

描述----------Description----------

This function computes resampled signal-to-noise ratio (STN) values using PLGEM fitting parameters (obtained via a call to function plgem.fit) to detect differential expression in an ExpressionSet, containing either microarray or proteomics data.
此函数计算重采样信号信噪比（STN）的值，使用PLGEM拟合参数检测plgem.fit差异表达，包含任何芯片或蛋白质组学数据（获得通过调用函数ExpressionSet）。


用法----------Usage----------


  plgem.resampledStn(data, plgemFit, covariate=1, baselineCondition=1,
    iterations="automatic", verbose=FALSE)



参数----------Arguments----------

参数：data
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：plgemFit
list; the output of function plgem.fit.
list;输出功能plgem.fit。


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：verbose
logical; if TRUE, comments are printed out while running.
logical;如果TRUE评论打印出来，而运行。


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。


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。

PLGEM-STN values are a measure of the degree of differential expression
PLGEM-STN值是衡量差异表达的程度

where:
其中：

plgem.resampledStn determines the resampled PLGEM-STN values for each gene or protein in data using a resampling approach; see References for details. The number of iterations should be chosen depending on the number of available replicates of the condition used for fitting the model.
plgem.resampledStn决定了每个基因或蛋白质中data使用重采样的方法重新取样PLGEM-STN值，详见参考文献。应选择取决于可用的数拟合模型所使用的条件复制的迭代次数。


值----------Value----------

A list of two elements:
一个list两部分组成：


参数：RESAMPLED.STN
matrix of resampled PLGEM-STN values, with rownames identical to those in data, and colnames representing the different number of replicates found in the different comparisons; see References for details.
matrix重采样PLGEM-STN值，rownamesdata，colnames代表在不同的比较发现不同数量的复制;见参考相同。


参数：REPL.NUMBER
the number of replicates found for each experimental condition; see References for details.
复制的数量，每个实验条件;详见参考。


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.pValue, plgem.deg, run.plgem
plgem.fit，plgem.obsStn，plgem.pValue，plgem.deg，run.plgem


举例----------Examples----------


  data(LPSeset)
  LPSfit <- plgem.fit(data=LPSeset)
  LPSobsStn <- plgem.obsStn(data=LPSeset, plgemFit=LPSfit)
  set.seed(123)
  LPSresampledStn <- plgem.resampledStn(data=LPSeset, plgemFit=LPSfit)
  plot(density(LPSresampledStn[["RESAMPLED.STN"]], bw=0.01), col="black", lwd=2,
    xlab="PLGEM STN values",
    main="Distribution of observed\nand resampled PLGEM-STN values")
  lines(density(LPSobsStn[["PLGEM.STN"]], bw=0.01), col="red")
  legend("topright", legend=c("resampled", "observed"), col=c("black", "red"),
    lwd=2:1)

转载请注明:出自 生物统计家园网(http://www.biostatistic.net)。


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