backgroundCorrect(limma)
backgroundCorrect()所属R语言包：limma
Correct Intensities for Background
正确的背景强度
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Background correct microarray expression intensities.
背景正确的微阵列表达强度。
用法----------Usage----------
backgroundCorrect(RG, method="auto", offset=0, printer=RG$printer, normexp.method="saddle", verbose=TRUE)
backgroundCorrect.matrix(E, Eb=NULL, method="auto", offset=0, printer=NULL, normexp.method="saddle", verbose=TRUE)
参数----------Arguments----------
参数：RG
a numeric matrix, EListRaw or RGList object.
数字矩阵，EListRaw或RGList对象。
参数：E
numeric matrix containing foreground intensities.
数值矩阵包含前台强度。
参数：Eb
numeric matrix containing background intensities.
数字矩阵包含背景强度。
参数：method
character string specifying correction method. Possible values are "auto", "none", "subtract", "half", "minimum", "movingmin", "edwards" or "normexp". If RG is a matrix, possible values are restricted to "none" or "normexp". The default "auto" is interpreted as "subtract" if background intensities are available or "normexp" if they are not.
字符串指定的校正方法。可能值"auto"，"none"，"subtract"，"half"，"minimum"，"movingmin"，"edwards"或"normexp"的 。如果RG是一个矩阵，可能值限制"none"或"normexp"。默认的"auto"被解释为"subtract"如果背景强度，如果他们不提供或"normexp"。
参数：offset
numeric value to add to intensities
添加到强度的数值
参数：printer
a list containing printer layout information, see PrintLayout-class. Ignored if RG is a matrix.
一个列表，其中包含打印机的布局信息，请参阅PrintLayout-class。如果RG是一个矩阵忽略。
参数：normexp.method
character string specifying parameter estimation strategy used by normexp, ignored for other methods. Possible values are "saddle", "mle", "rma" or "rma75".
字符串指定参数估计战略由normexp使用，忽略其他方法。可能的值是"saddle"，"mle"，"rma"或"rma75"。
参数：verbose
logical. If TRUE, progress messages are sent to standard output
逻辑。如果TRUE，进展的消息被发送到标准输出
Details
详情----------Details----------
This function implements the background correction methods reviewed or developed in Ritchie et al (2007) and Silver at al (2009). Ritchie et al (2007) recommend method="normexp" whenever RG contains local background estimates. Silver et al (2009) shows that either normexp.method="mle" or normexp.method="saddle" are excellent options for normexp. If RG contains morphological background estimates instead (available from SPOT or GenePix image analysis software), then method="subtract" performs well.
此功能实现里奇等人（2007年）和银人（2009）在审查或开发的背景校正方法。里奇等人（2007）建议method="normexp"时RG包含了当地的背景估计。银等（2009）显示，要么normexp.method="mle"或normexp.method="saddle"是normexp的优秀选择。如果RG包含形态学的背景估计，而不是从SPOT或的GenePix图像分析软件，然后method="subtract"做得好。
If method="none" then no correction is done, i.e., the background intensities are treated as zero. If method="subtract" then the background intensities are subtracted from the foreground intensities. This is the traditional background correction method, but is not necessarily recommended. If method="movingmin" then the background estimates are replaced with the minimums of the backgrounds of the spot and its eight neighbors, i.e., the background is replaced by a moving minimum of 3x3 grids of spots.
如果method="none"然后不改正的完成，即背景强度视为零。如果method="subtract"然后在背景强度从前台强度减去。这是传统的背景校正方法，但不一定建议。如果method="movingmin"然后背景估计与现货和八个邻国，即背景最低取代，由一个3x3的网格点移动最低的背景被替换。
The remaining methods are all designed to produce positive corrected intensities. If method="half" then any intensity which is less than 0.5 after background subtraction is reset to be equal to 0.5. If method="minimum" then any intensity which is zero or negative after background subtraction is set equal to half the minimum of the positive corrected intensities for that array. If method="edwards" a log-linear interpolation method is used to adjust lower intensities as in Edwards (2003). If method="normexp" a convolution of normal and exponential distributions is fitted to the foreground intensities using the background intensities as a covariate, and the expected signal given the observed foreground becomes the corrected intensity. This results in a smooth monotonic transformation of the background subtracted intensities such that all the corrected intensities are positive.
其余的方法都产生积极的校正强度。如果method="half"然后任何强度小于0.5背景减法后复位等于0.5。如果method="minimum"然后任何强度是零或负的背景减法后设置等于积极纠正该阵列的强度最低的一半。如果method="edwards"的log线性插值法是用来调整在爱德华兹（2003年）的低强度。如果method="normexp"正常和指数分布的卷积装有前景强度作为协的背景强度，并观测到的前景预期的信号成为纠正的力度。在顺利转型的背景下单调这结果减去强度等，所有的校正强度是积极的。
The normexp method is available in a number of variants depending on how the model parameters are estimated, and these are selected by normexp.method. Here "saddle" gives the saddle-point approximation to maximum likelihood from Ritchie et al (2007) and improved by Silver et al (2009), "mle" gives exact maximum likelihood from Silver at al (2009), "rma" gives the background correction algorithm from the RMA-algorithm for Affymetrix microarray data as implemented in the affy package, and "rma75" gives the RMA-75 method from McGee and Chen (2006). In practice "mle" performs well and is nearly as fast as "saddle", but "saddle" is the default for backward compatibility. See normexp.fit for more details.
normexp方法是如何估计模型参数，根据变种的数量，这些选择normexp.method的。这儿"saddle"给出了鞍点逼近里奇等人（2007）的可能性最大，银等（2009），提高了"mle"人（2009）给出了确切的最大似然从银， "rma"实施的RMA在affy包Affymetrix的基因芯片数据的算法给出的背景校正算法，和"rma75"提供的RMA-75麦吉和陈（2006）的方法。在实践中："mle"执行良好，几乎快"saddle"，但"saddle"是向后兼容的默认。看到normexp.fit更多细节。
The offset can be used to add a constant to the intensities before log-transforming, so that the log-ratios are shrunk towards zero at the lower intensities. This may eliminate or reverse the usual 'fanning' of log-ratios at low intensities associated with local background subtraction.
offset可以用来添加一个常数改造前log，使log比缩水接近零的低强度的强度。这可能消除或扭转在与当地背景减法的低强度的log比通常的“煽动”。
Background correction (background subtraction) is also performed by the normalizeWithinArrays method for RGList objects, so it is not necessary to call backgroundCorrect directly unless one wants to use a method other than simple subtraction. Calling backgroundCorrect before normalizeWithinArrays will over-ride the default background correction.
还进行背景校正（背景减法）normalizeWithinArraysRGList对象的方法，所以它是没有必要调用backgroundCorrect直接，除非你想使用其他方法比简单的减法。调用backgroundCorrect前normalizeWithinArrays将超过骑的默认背景校正。
值----------Value----------
A matrix, EListRaw or RGList object in which foreground intensities have been background corrected and any components containing background intensities have been removed.
A矩阵，EListRaw或RGList前景强度已经背景校正和背景强度包含任何组件已被删除的对象。
作者（S）----------Author(s)----------
Gordon Smyth
参考文献----------References----------
Bioinformatics 19, 825-833.
Parameter estimation for the exponential-normal convolution model for background correction of Affymetrix GeneChip data. Stat Appl Genet Mol Biol, Volume 5, Article 24.
A comparison of background correction methods for two-colour microarrays. Bioinformatics 23, 2700-2707. http://bioinformatics.oxfordjournals.org/cgi/content/abstract/btm412
Microarray background correction: maximum likelihood estimation for the normal-exponential convolution model. Biostatistics 10, 352-363. http://biostatistics.oxfordjournals.org/cgi/content/abstract/kxn042
参见----------See Also----------
kooperberg, neqc.
kooperberg，neqc。
An overview of background correction functions is given in 04.Background.
背景校正功能概述04.Background的。
举例----------Examples----------
RG <- new("RGList", list(R=c(1,2,3,4),G=c(1,2,3,4),Rb=c(2,2,2,2),Gb=c(2,2,2,2)))
backgroundCorrect(RG)
backgroundCorrect(RG, method="half")
backgroundCorrect(RG, method="minimum")
backgroundCorrect(RG, offset=5)
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 2012-2-25 23:12:04
