plgem.fit(plgem)
plgem.fit()所属R语言包：plgem
PLGEM Fitting and Evaluation
PLGEM配件和评价
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Function for fitting and evaluating goodness of fit of a PLGEM on a set of replicated samples defined in an ExpressionSet.
装修和一组的复制样本在ExpressionSet定义一个PLGEM的拟合优度评估中的作用。
用法----------Usage----------
plgem.fit(data, covariate=1, fitCondition=1, p=10, q=0.5,
trimAllZeroRows=FALSE, zeroMeanOrSD=c("replace", "trim"), fittingEval=FALSE,
plot.file=FALSE, 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：covariate
integer, numeric or character; specifies the covariate to be used to fit the PLGEM. See Details for how to specify the covariate.
integer，numeric或character;指定被用来适应PLGEM协。详情请参阅如何指定covariate。
参数：fitCondition
integer, numeric or character; specifies the condition to be used to fit the PLGEM. See Details for how to specify the fitCondition.
integer，numeric或character;指定条件将要用于符合PLGEM的。详情请参阅如何指定fitCondition。
参数：p
integer (or coercible to integer); number of intervals used to partition the expression value range.
integer（或强制转换integer）;用于表达式的值范围分区的间隔数。
参数：q
numeric in [0,1]; the quantile of standard deviation used for PLGEM fitting.
numeric在[0,1];用于PLGEM拟合的标准偏差的分量。
参数：trimAllZeroRows
logical; if TRUE, rows in the data set containing only zero values are trimmed before fitting PLGEM.
logical如果TRUE，在数据行集只包含零值修剪配件PLGEM前。
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 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"（见详情）。
参数：fittingEval
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：plot.file
logical; if TRUE, a png file is written on the current working directory.
logical;如果TRUE，PNG文件写入当前工作目录。
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----------
plgem.fit fits a Power Law Global Error Model (PLGEM) to an ExpressionSet and optionally evaluates the quality of the fit. This PLGEM aims to find the mathematical relationship between standard deviation and mean gene expression values (or protein abundance levels) in a set of replicated microarray (or proteomics) samples, according to the following power law:
plgem.fit适合功法的全局误差模型（PLGEM）ExpressionSet并有选择地评估合适的质量。此PLGEM目的是找到一套复制芯片（蛋白质组学）样本的标准偏差和平均基因表达的价值观（或蛋白含量水平）之间的数学关系，根据以下的功法：
It has been demonstrated that this model fits to Affymetrix GeneChip datasets, as well as to datasets of normalized spectral counts obtained by mass spectrometry-based proteomics. Technically, two replicates are required and sufficient to fit a PLGEM. Having 3 or more replicates, of course, improves the fitting and is recommended (see References for details).
它已被证明，这种模式适合于Affymetrix基因数据集，以及基于质谱的蛋白质组学得到归谱计数的数据集。从技术上讲，两个复制是必需的，足以适应PLGEM。当然，有3个或更多的重复，提高了拟合和建议（详见参考文献）。
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 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.
有一对夫妇不同的方式来指定covariate如果integer或numeric是，它将被作为协的数量（以相同的顺序在该协变量出现在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 to specify on which experimental condition to fit the model. The available "condition names" are taken from unique(as.character(pData(data)[, covariate])). If fitCondition is given as a character, it will be taken as the condition name itself. If fitCondition 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来指定的实验条件，以适应模型。可用的条件名称“从unique(as.character(pData(data)[, covariate]))。如果fitCondition作为一个character，它将被作为条件的名称本身。 fitCondition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。
Setting trimAllZeroRows=TRUE is especially useful in proteomics data sets, where there is no guarantee of identifying a protein across all experimental conditions. Since PLGEM is fitted only to the data corresponding to a single experimental condition (as defined by fitCondition), it is possible to generate a non-negligible number of rows containing only zero values, even if there were no such rows in the original (complete) data set containing all experimental conditions.
设置trimAllZeroRows=TRUE是特别有用的蛋白质组学数据集，那里是没有确定一种蛋白质，在所有实验条件的保证。由于PLGEM只装到一个单一的实验条件（定义相应的数据fitCondition），它可以生成一个只包含零值的行不可忽略的，即使有没有这样的行原（完整）的数据集包含了所有的实验条件。
Setting zeroMeanOrSD="replace" (the current default, for backward compatibility) will cause the function to replace zero or negative means with the smallest positive mean found in the data set and to replace zero standard deviations with the smallest non-zero standard deviation found in the data set. Setting zeroMeanOrSD="trim" is the current recommended option, especially for spectral counting proteomics data sets that are typically characterized by a high data granularity or for microarray data sets with a small number of replicates. In both cases, there are chances for data values for a same gene or protein to be identical across replicates (and therefore with zero standard deviation) by chance alone. Note that setting trimAllZeroRows=TRUE does not guarantee that there will be no rows with zero mean or zero standard deviation.
设置zeroMeanOrSD="replace"（当前默认为向后兼容）会导致功能来取代零或负的手段与最小正意味着在数据集，以取代非零最小的标准偏差的标准偏差为零在数据集。设置zeroMeanOrSD="trim"是目前推荐的选项，尤其是对光谱计数，通常是由一个高数据粒度或微阵列数据特征的蛋白质组学数据集，设置少量的复制。在这两种情况下，有机会跨重复相同的机会单独（并因此与标准差为零）为同一基因或蛋白质的数据值。注意，设置trimAllZeroRows=TRUE不保证会有零均值或标准差为零的无行。
If argument fittingEval is set to TRUE, a graphical control of the goodness of the PLGEM fitting is produced and a plot containing four panels is generated. The top-left panel shows the power law, characterized by a "SLOPE" and an "INTERCEPT". The top-right panel represents the distribution of model residuals. The bottom-left reports the contour plot of ranked residuals. The bottom-right panel finally shows the relationship between the distribution of observed residuals and the normal distribution. A good fit normally gives a horizontal symmetric rank-plot and a near normal distribution of residuals.
如果参数fittingEval设置为TRUE，一个善良的PLGEM拟合图形控制生产，并产生包含四个小组的图。左上角的面板显示的功法，由一个“斜坡”和“拦截”的特点。右上角的面板模型残差分布。左下角的报告排名残差的等高线图。最后右下角的面板显示观测残差分布和正态分布之间的关系。通常一个不错的选择给出了水平对称秩图和附近的一个正常的残差分布。
Warnings are issued if the fitted PLGEM slope is above 1 or under 0.5, if the adjusted r^2 is below 0.95 or if the Pearson correlation coefficient is below 0.85. These are the ranges of values inside which most GeneChip MAS5 dataset and NSAF proteomics dataset have been empirically observed to lie (see References).
警告发出，如果拟合PLGEM坡是高于或低于0.5，如果调整r^2是Pearson相关系数低于0.95或如果低于0.85。这些值的范围内最MAS5基因芯片数据集和NSAF蛋白质组学的集已被经验观察撒谎（见参考资料）。
值----------Value----------
A list of six elements (see Details):
一个list六大要素（见详情）：
参数：SLOPE
the slope of the fitted PLGEM.
斜坡装PLGEM。
参数：INTERCEPT
the intercept of the fitted PLGEM.
拦截装PLGEM。
参数：DATA.PEARSON
the Pearson correlation coefficient between the log(sd) and the log(mean) in the original data.
之间log(sd)和log(mean)在原始数据的Pearson相关系数。
参数：ADJ.R2.MP
the adjusted r^2 of PLGEM fitted on the modelling points.
调整后的r^2PLGEM上装有建模点。
参数：COVARIATE
a character indicating the covariate used for fitting.
character显示为装修中使用的协。
参数：FIT.CONDITION
a character indicating the condition used for fitting.
character显示为装修中使用的条件。
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.obsStn, plgem.resampledStn, plgem.pValue, plgem.deg, run.plgem
plgem.obsStn，plgem.resampledStn，plgem.pValue，plgem.deg，run.plgem
举例----------Examples----------
data(LPSeset)
LPSfit <- plgem.fit(data=LPSeset, fittingEval=TRUE)
as.data.frame(LPSfit)
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 11:13:45
