Expected Signal Given Observed Foreground Under Normal+Exp Model
预期信号在正常+ EXP模型由于观测到的前景
译者:生物统计家园网 机器人LoveR
描述----------Description----------
Adjust foreground intensities for observed background using Normal+Exp Model. This function is called by backgroundCorrect and is not normally called directly by the user.
调整使用正常+ EXP模型为观察背景的前景强度。 backgroundCorrect通常不会由用户直接调用这个函数被调用。
用法----------Usage----------
normexp.signal(par, x)
参数----------Arguments----------
参数:par
numeric vector containing the parameters of the Normal+Exp distribution, see normexp.fit for details.
数字向量正常+ EXP分布参数,看到normexp.fit详情。
参数:x
numeric vector of (background corrected) intensities
(背景校正)强度的数值向量
Details
详情----------Details----------
In general the vector normmean is computed conditional on background at each spot.
一般的矢量normmean计算背景有条件的每个点。
A comparison of background correction methods for two-colour microarrays. Bioinformatics http://bioinformatics.oxfordjournals.org/cgi/content/abstract/btm412
Microarray background correction: maximum likelihood estimation for the normal-exponential convolution. Biostatistics 10, 352-363. http://biostatistics.oxfordjournals.org/cgi/content/abstract/kxn042
参见----------See Also----------
normexp.fit
normexp.fit
An overview of background correction functions is given in 04.Background.
背景校正功能概述04.Background的。