weightedComLik(edgeR)
weightedComLik()所属R语言包：edgeR
Weighted Common Log-Likelihood
加权常见的对数似然
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Allow a flexible approach to accounting for a potential dependence of the dispersion on the abundance (expression level) of tags/genes by calculating a weighted 'common' log-likelihood for each gene.
让分散的潜力依赖于标记/基因的丰度（表达水平）计算每个基因加权的“共同”log的可能性，以灵活的会计方法。
用法----------Usage----------
weightedComLik(object,l0,prop.used=0.25)
weightedComLikMA(object,l0,prop.used=0.05)
参数----------Arguments----------
参数：object
DGEList object with (at least) elements counts (table of unadjusted counts) and samples (data frame containing information about experimental group, library size and normalization factor for the library size)
DGEList对象（至少）元素counts（未经调整数表）和samples（数据框包含有关实验组，库的大小和归一化因子的资料库的大小）
参数：l0
matrix of the conditional log-likelihood evaluated at a variety of values for the dispersion (on the delta scale, phi/(1 + phi)) for each tag/gene. The matrix has number of rows equal to the number of tags/genes and number of columns equal to the number of grid values (between 0 and 1) for the dispersion at which the conditional log-likelihood is evaluated.
矩阵的每个标签/基因在多种色散值（增量规模，phi/(1 + phi)）评估条件的记录的可能性。矩阵有标签/列等于有条件的对数似然被评为分散的网格值（0和1之间）的基因和数量相等的行数。
参数：prop.used
scalar giving the proportion of tags/genes in the whole dataset to use in computing the weighted common log-likelihood for each tag/gene. Default value is 0.25, i.e. a quarter of the tags/genes in the dataset, for weightedComLik and 0.05 for weightedComLikMA.
标/整个数据集使用在常见的对数似然加权计算每个标记/基因标记基因的比例。默认值是0.25，即一季度的标签/ DataSet中的基因，weightedComLik和0.05weightedComLikMA。
Details
详情----------Details----------
Genes are ordered based on abundance (expression level) and for a given gene, a proportion of the genes close to it are used to compute the common log-likelihood with decreasing weight given to the genes further from the given gene. Weighting is done using the tricube weighting function for weightedComLik. Computation can be slow relative to other functions in edgeR, especially if the number of genes or the number of grid values (i.e. the dimensions of l0) are large. weightedComLikMA uses a moving average to do the weighting (using movingAverageByCol) and so is much faster than weightedComLik.
基因排序的基础上丰富表达水平，对于一个给定的基因，该基因的比例接近它被用来计算减少重量进一步从特定基因的基因共同对数似然。使用weightedComLik的tricube加权函数加权完成。计算可以在其他功能相对缓慢edgeR的基因数目或网格值的数量（即L0尺寸），尤其是如果是大的。 weightedComLikMA使用移动平均加权（使用movingAverageByCol）等是远远比weightedComLik快。
值----------Value----------
matrix of weighted common log-likelihood values computed for each gene at each grid value for the dispersion. The matrix returned has the same dimensions as l0.
矩阵加权常见的对数似然值计算每个基因在每个格值的分散。返回的矩阵具有相同的尺寸为L0。
作者（S）----------Author(s)----------
Davis McCarthy
举例----------Examples----------
counts<-matrix(rnbinom(20,size=1,mu=10),nrow=5)
d<-DGEList(counts=counts,group=rep(1:2,each=2),lib.size=rep(c(1000:1001),2))
d<-estimateCommonDisp(d)
ntags<-nrow(d$counts)
y<-splitIntoGroups(new("DGEList",list(counts=d$pseudo.alt,samples=d$samples)))
grid.vals<-seq(0.001,0.999,length.out=10)
l0<-0
for(i in 1:length(y)) {
l0<-condLogLikDerDelta(y[[i]],grid.vals,der=0,doSum=FALSE)+l0
}
m0 <- ntags*weightedComLik(d,l0,prop.used=0.25) # Weights sum to 1, so need to multiply by number of tags to give this the same weight overall as the regular common likelihood[权重的总和为1，所以需要乘以标签数给这个经常共同可能性相同重量的整体]
# Or use the moving-average method[或使用移动平均法]
m1 <- ntags*weightedComLikMA(d,l0,prop.used=0.05)
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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 17:11:46
