R语言 miRNApath包 miRNApath-package()函数中文帮助文档(中英文对照)

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miRNApath-package(miRNApath)
miRNApath-package()所属R语言包：miRNApath

                                         miRNApath: Pathway Enrichment for miRNA Expression Data
                                         miRNApath：miRNA表达数据的途径富集

                                         译者：生物统计家园网 机器人LoveR

描述----------Description----------

This package provides methods for assessing the statistical  over-representation of miRNA effects on gene sets, using supplied miRNA-to-gene associations. Because these associations are notably many-to-many (one miRNA to many genes; one gene affected by many miRNAs) the assessment is complex and warrants perhaps different approaches than are classically performed on differential gene expression datasets.
这个包提供了用于评估基因组统计miRNA的作用表示过，使用提供的miRNA基因协会的方法。由于这些协会，特别是许多一对多（一个miRNA的许多基因，一个基因，许多miRNA的影响）比经典差的基因表达数据集上进行评估是复杂的，认股权证也许不同的方法。


Details

详情----------Details----------


作者（S）----------Author(s)----------



James M. Ward

Maintainer: James M. Ward <jmw86069@gmail.com>




参考文献----------References----------

in Alzheimer's disease brain and CSF yields putative  biomarkers and insights into disease pathways, Journal of Alzheimer's Disease 14, 27-41.

参见----------See Also----------

loadmirnapath, filtermirnapath, loadmirnatogene, loadmirnapathways, runEnrichment
loadmirnapath，filtermirnapath，loadmirnatogene，loadmirnapathways，runEnrichment


举例----------Examples----------


## Not run: [＃无法运行：]
## Start with miRNA data from this package[从这个包＃开始的miRNA数据]
data(mirnaobj);

## Write a file as example of required input[＃写的一个文件所需的输入为例]
write.table(mirnaobj@mirnaTable, file = "mirnaTable.txt",
    quote = FALSE, row.names = FALSE, col.names = TRUE, na = "",
    sep = "\t");

## Now essentially load it back, but assign column headers[＃现在基本上加载它，但指定的列标题]
mirnaobj <- loadmirnapath( mirnafile = "mirnaTable.txt",
    pvaluecol = "P-value", groupcol = "GROUP",
    mirnacol = "miRNA Name", assayidcol = "ASSAYID" );

## Start with miRNA data from this package[从这个包＃开始的miRNA数据]
data(mirnaobj);

## Write a file as example of required input[＃写的一个文件所需的输入为例]
write.table(mirnaobj@mirnaGene, file = "mirnaGene.txt",
    quote = FALSE, row.names = FALSE, col.names = TRUE, na = "",
    sep = "\t");

## Load the miRNA to gene associations[＃加载的miRNA基因协会]
mirnaobj <- loadmirnatogene( mirnafile = "mirnaGene.txt",
    mirnaobj = mirnaobj, mirnacol = "miRNA Name",
    genecol = "Entrez Gene ID",
    columns = c(assayidcol = "ASSAYID") );

## Write a file as example of required input[＃写的一个文件所需的输入为例]
write.table(mirnaobj@mirnaPathways, file = "mirnaPathways.txt",
    quote = FALSE, row.names = FALSE, col.names = TRUE, na = "",
    sep = "\t");

## Load the gene to pathway associations[＃加载基因通路协会]
mirnaobj <- loadmirnapathways( mirnaobj = mirnaobj,
    pathwayfile = "mirnaPathways.txt",
    pathwaycol = "Pathway Name", genecol = "Entrez Gene ID");

## Annotate hits by filtering by P-value 0.05[＃注解命中过滤的P-值0.05]
mirnaobj <- filtermirnapath( mirnaobj, pvalue = 0.05,
    expression = NA, foldchange = NA );

## Now run enrichment test[＃现在运行铀浓缩试验]
mirnaobj <- runEnrichment( mirnaobj=mirnaobj, Composite=TRUE,
   groups=NULL, permutations=0 );

## Print out a summary table of significant results[＃打印出重大成果汇总表]
finaltable <- mirnaTable( mirnaobj, groups=NULL, format="Tall",
    Significance=0.1, pvalueTypes=c("pvalues") );
finaltable[1:4,];

## Example which calls heatmap function on the resulting data[＃调用所产生的数据热图功能的范例]
widetable <- mirnaTable( mirnaobj, groups=NULL, format="Wide",
    Significance=0.1, na.char=NA, pvalueTypes=c("pvalues") );
## Assign 1 to NA values, assuming they're all equally[＃指定1到NA值，假设他们同样]
## non-significant[＃非显着]
widetable[is.na(widetable)] <- 1;

## Display a heatmap of the result across sample groups[＃显示的结果，整个样本组的热图]
pathwaycol <- mirnaobj@columns["pathwaycol"];
pathwayidcol <- mirnaobj@columns["pathwayidcol"];
rownames(widetable) <- apply(widetable[,c(pathwaycol,
   pathwayidcol)], 1, function(i)paste(i, collapse="-"));
wt <- as.matrix(widetable[3:dim(widetable)[2]], mode="numeric")
heatmap(wt, scale="col");

## Show results where pathways are shared in four or more[＃显示结果在四个或更多的共享途径]
## sample groups[＃样品组]
pathwaySubset <- apply(wt, 1, function(i)
{
   length(i[i < 1]) >= 4;
} )
heatmap(wt[pathwaySubset,], scale="row");

## End(Not run)[＃结束（不运行）]

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