lmscFit(limma)
lmscFit()所属R语言包：limma
Fit Linear Model to Individual Channels of Two-Color Data
个别通道两色数据拟合线性模型
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Fit a linear model to the individual log-intensities for each gene given a series of two-color arrays
一系列两色阵列的每一个基因的线性模型适合个人log强度
用法----------Usage----------
lmscFit(object, design, correlation)
参数----------Arguments----------
参数：object
an MAList object or a list from which M and A values may be extracted
MAList对象或从M和A值可提取的列表
参数：design
a numeric matrix containing the design matrix for linear model in terms of the individual channels. The number of rows should be twice the number of arrays. The number of columns will determine the number of coefficients estimated for each gene.
包含在个人渠道方面的线性模型的设计矩阵数字矩阵。行数应该是阵列数量的两倍。列数将决定每一个基因的估计系数。
参数：correlation
numeric value giving the intra-spot correlation
给现场内的相关数值
Details
详情----------Details----------
For two color arrays, the channels measured on the same set of arrays are correlated. The M and A however are uncorrelated for each gene. This function fits a linear model to the set of M and A-values for each gene after re-scaling the M and A-values to have equal variances. The input correlation determines the scaling required. The input correlation is usually estimated using intraspotCorrelation before using lmscFit.
两种颜色的阵列，阵列同一组测量通道是相关的。 M和A但是每个基因无关。此功能适合线性模型的M集和每个基因后，重新调整M和A值有平等的差异A值。输入相关决定所需的缩放。相关的输入通常使用intraspotCorrelation使用lmscFit之前估计。
Missing values in M or A are not allowed.
失踪M或A不允许的值。
值----------Value----------
An object of class MArrayLM
一个对象的类MArrayLM
作者（S）----------Author(s)----------
Gordon Smyth
参考文献----------References----------
Proceedings of the 55th Session of the International Statistics Institute, 5-12 April 2005, Sydney, Australia, Paper 116. http://www.statsci.org/smyth/pubs/ISI2005-116.pdf
参见----------See Also----------
lm.fit.
lm.fit。
An overview of methods for single channel analysis in limma is given by 07.SingleChannel.
单通道分析方法在limma概述的07.SingleChannel。
举例----------Examples----------
if(require("sma")) {
# Subset of data from ApoAI case study in Limma User's Guide[在Limma用户指南apoAⅠ的案例研究的数据子集]
data(MouseArray)
# Avoid non-positive intensities[避免非积极的强度]
RG <- backgroundCorrect(mouse.data,method="normexp")
MA <- normalizeWithinArrays(RG,mouse.setup)
MA <- normalizeBetweenArrays(MA,method="Aq")
# Randomly choose 500 genes for this example[这个例子中随机选择的500个基因]
i <- sample(1:nrow(MA),500)
MA <- MA[i,]
targets <- data.frame(Cy3=I(rep("Pool",6)),Cy5=I(c("WT","WT","WT","KO","KO","KO")))
targets.sc <- targetsA2C(targets)
targets.sc$Target <- factor(targets.sc$Target,levels=c("Pool","WT","KO"))
design <- model.matrix(~Target,data=targets.sc)
corfit <- intraspotCorrelation(MA,design)
fit <- lmscFit(MA,design,correlation=corfit$consensus)
cont.matrix <- cbind(KOvsWT=c(0,-1,1))
fit2 <- contrasts.fit(fit,cont.matrix)
fit2 <- eBayes(fit2)
topTable(fit2,adjust="fdr")
}
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。
注2：由于是机器人自动翻译，难免有不准确之处，使用时仔细对照中、英文内容进行反复理解，可以帮助R语言的学习。
注3：如遇到不准确之处，请在本贴的后面进行回帖，我们会逐渐进行修订。
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发表于 2012-2-25 23:17:49
