lmFit(limma)
lmFit()所属R语言包：limma
Linear Model for Series of Arrays
阵列系列的线性模型
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Fit linear model for each gene given a series of arrays
适合一系列数组的每个基因的线性模型
用法----------Usage----------
lmFit(object,design=NULL,ndups=1,spacing=1,block=NULL,correlation,weights=NULL,method="ls",...)
参数----------Arguments----------
参数：object
object of class numeric, matrix, MAList, EList, marrayNorm, ExpressionSet or PLMset containing log-ratios or log-values of expression for a series of microarrays
对象类numeric，matrix，MAList，EList，marrayNorm，ExpressionSet或PLMset包含log比率或log一系列微阵列表达值
参数：design
the design matrix of the microarray experiment, with rows corresponding to arrays and columns to coefficients to be estimated. Defaults to the unit vector meaning that the arrays are treated as replicates.
芯片实验设计矩阵，与估计的系数阵列和列对应的行。默认的单位向量的含义，阵列被视为重复。
参数：ndups
positive integer giving the number of times each gene is printed on an array
正整数，给每一个基因的次数上印有一个数组
参数：spacing
positive integer giving the spacing between duplicate spots, spacing=1 for consecutive spots
正整数重复点之间的间距，spacing=1连续点
参数：block
vector or factor specifying a blocking variable on the arrays. Has length equal to the number of arrays. Must be NULL if ndups>2.
向量或指定数组变量阻塞的因素。有长度等于阵列数量。必须NULL如果ndups>2。
参数：correlation
the inter-duplicate or inter-technical replicate correlation
间重复或复制相关技术间
参数：weights
optional numeric matrix containing weights for each spot
可选的数字矩阵，包含每个点的权重
参数：method
character string, "ls" for least squares or "robust" for robust regression
字符串，"ls"最小二乘或"robust"稳健回归
参数：...
other optional arguments to be passed to lm.series, gls.series or mrlm
其他可选参数被传递到lm.series，gls.series或mrlm
Details
详情----------Details----------
This function fits multiple linear models. It accepts data from a experiment involving a series of microarrays with the same set of probes. A linear model is fitted to the expression data for each probe. The expression data should be log-ratios for two-color array platforms or log-expression values for one-channel platforms. (To fit linear models to the individual channels of two-color array data, see lmscFit.) The coefficients of the fitted models describe the differences between the RNA sources hybridized to the arrays. The probe-wise fitted model results are stored in a compact form suitable for further processing by other functions in the limma package.
此功能适合多个线性模型。它接受从实验的数据，涉及一系列的芯片组相同的探针。线性模型拟合每个探针的表达数据。表达式的数据应该是登录的比率为两色的阵列平台或通道平台的log表达式的值。 （为了适应两色阵列数据的个别通道的线性模型，看到lmscFit。）拟合模型系数描述阵列杂交的RNA来源之间的差异。探针明智的拟合模型结果存储在一个紧凑的形式，适合作进一步处理等功能在limma包。
The function allows for missing values and accepts quantitative weights through the weights argument. It also supports two different correlation structures. If block is not NULL then different arrays are assumed to be correlated. If block is NULL and ndups is greater than one then replicate spots on the same array are assumed to be correlated. It is not possible at this time to fit models with both a block structure and a duplicate-spot correlation structure simultaneously.
该功能允许缺失值和接受量化权重，通过weights参数。它也支持两种不同的相关结构。 block如果非NULL然后不同的阵列被假定为相关。如果block是NULL和ndups然后在同一阵列复制点大于一假设相关。在这个时候是不是块结构和重复的现场相关的结构，同时适合的车型。
If object is a matrix then it should contain log-ratios or log-expression data with rows corresponding to probes and columns to arrays. (A numeric vector is treated the same as a matrix with one column.) For objects of other classes, a matrix of expression values is taken from the appropriate component or slot of the object. If object is of class MAList or marrayNorm, then the matrix of log-ratios (M-values) is extracted. If object is of class ExpressionSet, then the expression matrix is extracted. (This may contain log-expression or log-ratio values, depending on the platform.) If object is of class PLMset then the matrix of chip coefficients chip.coefs is extracted.
如果object是一个矩阵，那么它应该包含相应的探针和列数组的行数比率或log表达数据。一个矩阵表达式的值（一个数值向量处理作为一列的矩阵相同。）对于其他类的对象，采取相应的组件或对象插槽。如果object类MAList或marrayNorm，然后log比矩阵（M值）中提取。如果object类ExpressionSet是，然后表达矩阵中提取。 （这可能包含log中表达或数比率值，取决于平台）。如果object类PLMset然后芯片系数矩阵chip.coefs提取。
The arguments design, ndups, spacing and weights will be extracted from the data object if available and do not normally need to set explicitly in the call. On the other hand, if any of these are set in the function call then they will over-ride the slots or components in the data object. If object is an PLMset, then weights are computed as 1/pmax(object@se.chip.coefs, 1e-05)^2. If object is an ExpressionSet object, then weights are not computed.
的论点design，ndups，spacing和weights将提取的数据object如果可以，一般不需要设置在通话中明确。另一方面，如果其中任何一个在功能设置调用，那么他们将骑槽或数据object组件。如果object是PLMset，然后重量计算为1/pmax(object@se.chip.coefs, 1e-05)^2。如果object一个ExpressionSet对象，然后重量不计算。
If the argument block is used, then it is assumed that ndups=1.
如果参数block使用，那么它被认为ndups=1。
The correlation argument has a default value of 0.75, but in normal use this default value should not be relied on and the correlation value should be estimated using the function duplicateCorrelation. The default value is likely to be too high in particular if used with the block argument.
correlation参数有一个0.75的默认值，但在正常使用此默认值不应依赖和相关值应使用的功能duplicateCorrelation估计。默认值是可能的，特别是过高，如果block参数。
The actual linear model computations are done by passing the data to one the lower-level functions lm.series, gls.series or mrlm. The function mrlm is used if method="robust". If method="ls", then gls.series is used if a correlation structure has been specified, i.e., if ndups>1 or block is non-null and correlation is different from zero. If method="ls" and there is no correlation structure, lm.series is used.
实际的线性模型计算，将数据传递到一个低级别的功能做了lm.series，gls.series或mrlm。功能mrlm如果method="robust"。如果method="ls"，gls.series如果已指定相关结构，即，如果ndups>1或block非空correlation不同于为零。如果method="ls"“有没有相关的结构，lm.series使用。
值----------Value----------
Object of class MArrayLM
对象类MArrayLM
作者（S）----------Author(s)----------
Gordon Smyth
参见----------See Also----------
An overview of linear model functions in limma is given by 06.LinearModels.
线性模型功能概述limma由06.LinearModels给出。
举例----------Examples----------
# Simulate gene expression data for 100 probes and 6 microarrays[模拟100个探针和6微阵列基因表达数据]
# Microarray are in two groups[芯片是在两组]
# First two probes are differentially expressed in second group[前两个探针在第二组的差异表达]
# Std deviations vary between genes with prior df=4[标准偏差不同基因之间事先DF = 4]
sd <- 0.3*sqrt(4/rchisq(100,df=4))
y <- matrix(rnorm(100*6,sd=sd),100,6)
rownames(y) <- paste("Gene",1:100)
y[1:2,4:6] <- y[1:2,4:6] + 2
design <- cbind(Grp1=1,Grp2vs1=c(0,0,0,1,1,1))
options(digit=3)
# Ordinary fit[普通合适]
fit <- lmFit(y,design)
fit <- eBayes(fit)
fit
as.data.frame(fit[1:10,2])
# Various ways of summarising or plotting the results[总结或策划的结果的各种方式]
topTable(fit,coef=2)
qqt(fit$t[,2],df=fit$df.residual+fit$df.prior)
abline(0,1)
volcanoplot(fit,coef=2,highlight=2)
# Various ways of writing results to file[结果写入文件的各种方式]
## Not run: write.fit(fit,file="exampleresults.txt")[＃无法运行：write.fit（合适的，文件=“exampleresults.txt”）]
## Not run: write.table(fit,file="exampleresults2.txt")[＃无法运行：write.table（合适的，文件=“exampleresults2.txt”）]
# Fit with correlated arrays[适合与关联数组]
# Suppose each pair of arrays is a block[假设对每个阵列是一个块]
block <- c(1,1,2,2,3,3)
dupcor <- duplicateCorrelation(y,design,block=block)
dupcor$consensus.correlation
fit3 <- lmFit(y,design,block=block,correlation=dupcor$consensus)
# Fit with duplicate probes[适合重复的探针]
# Suppose two side-by-side duplicates of each gene[假设每一个基因的两个侧端重复]
rownames(y) <- paste("Gene",rep(1:50,each=2))
dupcor <- duplicateCorrelation(y,design,ndups=2)
dupcor$consensus.correlation
fit4 <- lmFit(y,design,ndups=2,correlation=dupcor$consensus)
fit4 <- eBayes(fit3)
dim(fit4)
topTable(fit4,coef=2)
# Fold-change thresholding[倍数式的变化阈值]
fit <- lmFit(y,design)
fit <- treat(fit,lfc=0.1)
topTreat(fit,coef=2)
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 23:17:39
