lpe(LPE)
lpe()所属R语言包：LPE
Evaluates local pooled error significance test
评估当地汇集错误显着性检验
译者：生物统计家园网 机器人LoveR
描述----------Description----------
The local pooled error test attempts to reduce dependence on the within-gene estimates in tests for differential expression, by pooling error estimates within regions of similar intensity. Note that with the large number of genes there will be genes with low within-gene error estimates by chance, so that some signal-to-noise ratios will be large regardless of mean expression intensities and fold-change. The local pooled error attempts to avert this by combining within-gene error estimates with those of genes with similar expression intensity.
汇集的地方错误测试，试图减少依赖类似强度的区域内汇集的错误估计，在测试的差异表达的基因内估计。请注意，大量的基因将基因与低基因内偶然的错误估计，因此，将一些信号信噪比大，无论平均表达强度和倍数变化。汇集的地方错误试图避免这种类似的表达强度的基因相结合的基因内的误差估计。
用法----------Usage----------
lpe(x, y, basevar.x,basevar.y, df=10, array.type="olig",
probe.set.name=NULL, trim.percent=5)
参数----------Arguments----------
参数：x
Replicated data from first experimental condition (as matrix or data-frame) </table>
从第一次的实验条件下（如矩阵或数据框）</ TABLE>复制数据
参数：y
Replicated data from second experimental condition (as matrix or data-frame) </table>
从第二次实验条件下（如矩阵或数据框）</ TABLE>复制数据
参数：basevar.x
Baseline distribution of first condition obtained from function baseOlig.error
从功能baseOlig.error基线分配的首要条件
参数：basevar.y
Baseline distribution of second condition obtained from function baseOlig.error
从功能baseOlig.error基线分布的第二个条件
参数：df
Degrees of freedom used in fitting smooth.spline to estimates of var.M for bins in A
在A箱的var.M估计在装修smooth.spline为使用的自由度
参数：array.type
Currently supports oligo arrays
目前支持寡核苷酸阵列
参数：probe.set.name
Gene IDs. By default if they are not provided then 1,2,3,... is assigned as GeneID
基因标识。默认情况下，如果他们不提供的，然后1,2,3，...分配作为GeneID的
参数：trim.percent
Percent of (A, var.M) estimates to trim from low end of A
（一，var.M）的百分之估计从一个低端的修剪
Details
详情----------Details----------
The LPE test statistic numerator is the difference in medians between the two experimental conditions. The test statistic denominator is the combined pooled standard error for the two experimental conditions obtained by looking up the var.M from each baseOlig.error variance function. The conversion to p-values is based on the Gaussian distribution for difference if order statistics (medians). The user may select both the smoother degrees of freedom (smaller is smoother) and the trim percent to obtain a variance function to suit particular issues i.e. variability of genes with low expression intensity.
液相外延的检验统计量的分子是在两者之间的实验条件下的中位数的差异。检验统计量的分母是合并汇集寻找var.M从每个baseOlig.error方差函数得到的两个实验条件的标准错误。 p值的转换是基于高斯分布的差异，如果次序统计量（中位数）。用户可以选择自由的平滑度（较小的平滑）和修剪％，获得了变异的功能，以满足特定的变异，即基因表达强度低的问题。
值----------Value----------
Data frame including x, median of x, y, median of y, median difference of (x,y), pooled standard deviation of difference, LPE p-value, outlier flag, probability of an outlier within x or y.
数据框包括x的中位数，X，Y，y的中位数，中位数差异（X，Y），汇集差异的标准偏差，液相p值，离群标志，离群内X或Y的概率。
作者（S）----------Author(s)----------
Nitin Jain<a href="mailto:nitin.jain@pfizer.com">nitin.jain@pfizer.com</a>
参考文献----------References----------
differentially expressed genes with a small number of replicated microarrays, Bioinformatics, 1945-1951.
举例----------Examples----------
# Loading the library and the data[载入库和数据]
library(LPE)
data(Ley)
dim(Ley)
# Gives 12488*7 [给12488 * 7]
# First column is ID.[第一列是ID。]
# Subsetting the data[子集的数据]
subset.Ley <- Ley[1:1000,]
subset.Ley[,2:7] <- preprocess(subset.Ley[,2:7],data.type="MAS5")
# Finding the baseline distribution of condition 1 and 2.[寻找条件1和2的基准分配。]
var.11 <- baseOlig.error.step1(subset.Ley[,2:4])
var.1 <- baseOlig.error(subset.Ley[,2:4], q=0.01)
var.2 <- baseOlig.error(subset.Ley[,5:7], q=0.01)
# Applying LPE[应用液相外延]
lpe.result <- lpe(subset.Ley[,2:4],subset.Ley[,5:7], var.1, var.2,
probe.set.name=subset.Ley[,1])
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 23:40:30
