runSPIA(graphite)
runSPIA()所属R语言包：graphite
Run SPIA analysis
运行SPIA分析
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Run topological analysis on expression dataset.
表达数据集上运行的拓扑分析。
用法----------Usage----------
runSPIA(de, all, pathwaySetName, ...)
参数----------Arguments----------
参数：de
A named vector containing log2 fold-changes of the differentially expressed genes. The names of this numeric vector are Entrez gene IDs.
命名向量的log2倍的差异表达基因的变化。这个数字向量的名称是Entrez基因标识。
参数：all
A vector with the Entrez IDs in the reference set. If the data was obtained from a microarray experiment, this set will contain all genes present on the specific array used for the experiment. This vector should contain all names of the 'de' argument.
参考集的一个向量与Entrez的标识。如果从芯片实验获得的数据，本集将包含所有基因的实验中使用的特定阵列上。这个向量应该包含所有名称的德的说法。
参数：pathwaySetName
A list of pathways like kegg, nci or reactome.
KEGG，NCI或reactome的的途径。
参数：...
Additional options to pass to spia.
额外的选项，通过spia。
Details
详情----------Details----------
The spia option "organism" is internally used. It is an error use it in the additional options.
内部使用SPIA选项“有机体”。使用它，这是一个在其他选项的错误。
值----------Value----------
The same of spia, without KEGG links. A data frame containing the ranked pathways and various statistics: pSize is the number of genes on the pathway; NDE is the number of DE genes per pathway; tA is the observed total preturbation accumulation in the pathway; pNDE is the probability to observe at least NDE genes on the pathway using a hypergeometric model; pPERT is the probability to observe a total accumulation more extreme than tA only by chance; pG is the p-value obtained by combining pNDE and pPERT; pGFdr and pGFWER are the False Discovery Rate and respectively Bonferroni adjusted global p-values; and the Status gives the direction in which the pathway is perturbed (activated or inhibited).
SPIA相同，没有KEGG链接。一个数据框包含的排名途径和各种统计：本命令是通路上的基因数目;濒死体验是每个通路的基因数目; TA是观测到的总preturbation积累的途径; pNDE的概率是观察至少无损检测基因上使用超几何模型的途径; pPERT的概率是观察总积累超过TA只是偶然的极端; PG是p值通过结合pNDE和pPERT获得; pGFdr和pGFWER是假发现率分别邦弗朗尼调整全球p值和状态提供了途径扰动（激活或抑制）的方向。
参考文献----------References----------
Kusanovic JP, Romero R. A novel signaling pathway impact analysis. Bioinformatics. 2009 Jan 1;25(1):75-82.
Microarray Experiments, 2008, Bioinformatics, 2009, 25(1):75-82.
A systems biology approach for pathway level analysis. Genome Research, 17, 2007.
参见----------See Also----------
For other details please referer to spia
其他细节请的refererspia
举例----------Examples----------
if (require(SPIA) && require(hgu133plus2.db)) {
data(colorectalcancer)
x <- hgu133plus2ENTREZID
top$ENTREZ <- unlist(as.list(x[top$ID]))
top <- top[!is.na(top$ENTREZ), ]
top <- top[!duplicated(top$ENTREZ), ]
tg1 <- top[top$adj.P.Val < 0.05, ]
DE_Colorectal = tg1$logFC
names(DE_Colorectal) <- as.vector(tg1$ENTREZ)
ALL_Colorectal <- top$ENTREZ
prepareSPIA(biocarta[1:20], "biocartaEx")
runSPIA(de=DE_Colorectal, all=ALL_Colorectal, "biocartaEx")
}
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。
|
发表于 2012-2-25 21:04:07
