compare.proto.cor(genefu)
compare.proto.cor()所属R语言包：genefu
Function to statistically compare correlation to prototypes
功能统计学比较相关的原型
译者：生物统计家园网 机器人LoveR
描述----------Description----------
This function performs a statistical comparison of the correlation coefficients as computed between each probe and prototype.
执行此功能的相关系数计算每个探针和原型之间的统计比较。
用法----------Usage----------
compare.proto.cor(gene.cor, proto.cor, nn,
p.adjust.m = c("none", "holm", "hochberg", "hommel", "bonferroni", "BH", "BY", "fdr"))
参数----------Arguments----------
参数：gene.cor
Correlation coefficients between the probes and each of the prototypes.
探针和原型之间的相关系数。
参数：proto.cor
Pairwise correlation coefficients of the prototypes.
成对相关系数的原型。
参数：nn
Number of samples used to compute the correlation coefficients between the probes and each of the prototypes.
探针和原型之间的相关系数来计算的样本数。
参数：p.adjust.m
Correction method as defined in p.adjust.
校正方法定义在p.adjust。
值----------Value----------
Data frame with probes in rows and with three columns: "proto" is the prototype to which the probe is the most correlated, "cor" is the actual correlation, and "signif" is the (corrected) p-value for the superiority of the correlation to this prototype compared to the second highest correlation.
数据框与行的探针和三列：“原”的原型，该探针是最相关的，“心病”是实际的相关，“signif”（修正）的P-值相关的优势，这个原型相比，第二次的相关性最高。
作者（S）----------Author(s)----------
Benjamin Haibe-Kains
参见----------See Also----------
compute.proto.cor.meta, compute.pairw.cor.meta
compute.proto.cor.meta，compute.pairw.cor.meta
举例----------Examples----------
## load VDX dataset[＃加载VDX的数据集]
data(vdxs)
## load NKI dataset[＃负载NKI日经指数集]
data(nkis)
## reduce datasets[＃减少数据集]
ginter <- intersect(annot.vdxs[ ,"EntrezGene.ID"], annot.nkis[ ,"EntrezGene.ID"])
ginter <- ginter[!is.na(ginter)][1:30]
myx <- unique(c(match(ginter, annot.vdxs[ ,"EntrezGene.ID"]),
sample(x=1:nrow(annot.vdxs), size=20)))
data2.vdxs <- data.vdxs[ ,myx]
annot2.vdxs <- annot.vdxs[myx, ]
myx <- unique(c(match(ginter, annot.nkis[ ,"EntrezGene.ID"]),
sample(x=1:nrow(annot.nkis), size=20)))
data2.nkis <- data.nkis[ ,myx]
annot2.nkis <- annot.nkis[myx, ]
## mapping of datasets[＃数据集的映射]
datas <- list("VDX"=data2.vdxs,"NKI"=data2.nkis)
annots <- list("VDX"=annot2.vdxs, "NKI"=annot2.nkis)
datas.mapped <- map.datasets(datas=datas, annots=annots, do.mapping=TRUE)
## define some prototypes[＃定义一些原型]
protos <- paste("geneid", ginter[1:3], sep=".")
## compute meta-estimate of correlation coefficients to the three prototype genes[＃元计算相关系数估计的三个原型基因]
probecor <- compute.proto.cor.meta(datas=datas.mapped$datas, proto=protos,
method="pearson")
## compute meta-estimate of pairwise correlation coefficients between prototypes[＃计算原型之间的成对相关系数估计元]
datas.proto <- lapply(X=datas.mapped$datas, FUN=function(x, p) {
return(x[ ,p,drop=FALSE]) }, p=protos)
protocor <- compute.pairw.cor.meta(datas=datas.proto, method="pearson")
## compare correlation coefficients to each prototype[＃每个原型比较相关系数]
res <- compare.proto.cor(gene.cor=probecor$cor, proto.cor=protocor$cor,
nn=probecor$cor.n, p.adjust.m="fdr")
head(res)
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 18:48:04
