解释R码函数

我想进行路径富集分析。 我有21个重要基因列表，我想检查多种类型的途径（即检查KEGG途径、GOterms、复合物等的富集情况）

我在一篇旧的生物化学文章中找到了这个代码示例。然而，我很难适应它为自己

首先,， 1-这意味着什么？我不知道这个多冒号语法

hyperg <- Category:::.doHyperGInternal

---

hyperg出现错误的原因是，您似乎没有从bioconductor安装
类别
软件包。我怀疑这是因为三重冒号操作符
：
。此运算符非常类似于双冒号运算符
：
。使用
：：
可以从包中访问导出的对象而无需加载，而
：：
允许访问非导出对象（在本例中，使用
类别中的
hyperg
函数）。如果安装了
类别
软件包，则代码运行时不会出错

关于
sapply
声明：

pVals.by.pathway<-t(sapply(genes.by.pathway, hyperg.test, significant.genes, all.geneIDs))

此循环完成后，
sapply
函数将输出简化为矩阵。然而，通过采用转置
t
，输出更加用户友好

一般来说，当试图理解复杂的
apply
语句时，我发现最好将它们分成更小的部分，看看对象本身是什么样子。
代码的行为是否与您预期的不一样，或者您只是想更好地理解它？不清楚您是否有问题或只是需要信息。@cdeterman-两者，我都不太理解，尤其是最后一行，这使得很难修改它，我得到了这个错误>pVals.by.pathway谢谢，这很有帮助，我不知道您可以通过这种方式将参数传递给函数
library(KEGGREST)
library(org.Hs.eg.db)

     # created named list, length 449, eg:
     # path:hsa00010: "Glycolysis / Gluconeogenesis"

pathways <- keggList("pathway", "hsa")

     # make them into KEGG-style human pathway identifiers
human.pathways <- sub("path:", "", names(pathways))

   # for demonstration, just use the first ten pathways

demo.pathway.ids <- head(human.pathways, 10)
demo.pathways <- setNames(keggGet(demo.pathway.ids), demo.pathway.ids)

genes.by.pathway <- lapply(demo.pathways, function(demo.pathway) {
     demo.pathway$GENE[c(TRUE, FALSE)]
      })

all.geneIDs <- keys(org.Hs.eg.db)

   # chose one of these for demonstration.  the first (a whole genome random
   # set of 100 genes)  has very little enrichment, the second, a random set
   # from the pathways themselves,  has very good enrichment in some pathways

set.seed(123)
significant.genes <- sample(all.geneIDs, size=100)
#significant.genes <- sample(unique(unlist(genes.by.pathway)), size=10)

   # the hypergeometric distribution is traditionally explained in terms of
   # drawing a sample of balls from an urn containing black and white balls.
   # to keep the arguments straight (in my mind at least), I use these terms
   # here also

hyperg <- Category:::.doHyperGInternal
hyperg.test <-
    function(pathway.genes, significant.genes, all.genes, over=TRUE)
{
    white.balls.drawn <- length(intersect(significant.genes, pathway.genes))
    white.balls.in.urn <- length(pathway.genes)
    total.balls.in.urn <- length(all.genes)
    black.balls.in.urn <- total.balls.in.urn - white.balls.in.urn
    balls.pulled.from.urn <- length(significant.genes)
    hyperg(white.balls.in.urn, black.balls.in.urn,
           balls.pulled.from.urn, white.balls.drawn, over)
}

pVals.by.pathway <-
    t(sapply(genes.by.pathway, hyperg.test, significant.genes, all.geneIDs))

print(pVals.by.pathway)

pVals.by.pathway<-t(sapply(genes.by.pathway, hyperg.test, significant.genes, all.geneIDs))

pVals.by.pathway <-
  t(sapply(genes.by.pathway, hyperg.test, significant.genes=significant.genes, all.genes=all.geneIDs))