plgem.obsStn(plgem)
plgem.obsStn()所属R语言包：plgem
Computation of Observed PLGEM-STN Statistics
观察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 observed 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.
此函数计算观察到的信号信噪比（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.obsStn(data, plgemFit, covariate=1, baselineCondition=1,
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。
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.obsStn determines the observed PLGEM-STN values for each gene or protein in data; see References for details.
plgem.obsStn决定观察每个基因或蛋白质在dataPLGEM-STN值;有关详细信息，请参阅参考文献。
值----------Value----------
A list of two elements:
一个list两部分组成：
参数：fit
the input plgemFit.
输入plgemFit。
参数：PLGEM.STN
a matrix of observed PLGEM-STN values. The rownames of this matrix are identical to the rownames of data. The colnames represent the different experimental conditions that were compared to the baseline.
一个matrix的观察PLGEM-STN值。这个矩阵的rownames相同rownamesdata。 colnames代表不同的实验条件进行了比较，基线。
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.resampledStn, plgem.pValue, plgem.deg, run.plgem
plgem.fit，plgem.resampledStn，plgem.pValue，plgem.deg，run.plgem
举例----------Examples----------
data(LPSeset)
LPSfit <- plgem.fit(data=LPSeset)
LPSobsStn <- plgem.obsStn(data=LPSeset, plgemFit=LPSfit)
head(LPSobsStn[["PLGEM.STN"]])
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。
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发表于 2012-2-26 11:13:52
