1
我的同事萨曼莎问了一个不清楚的问题,所以在这里我问这里的问题。 她有一个变量goterms
,包含所有要分析的数据帧。R:通过检查参考集从列表生成数据帧
goterms <- c('df1','df2','df3')
的interestedGO
变量包含每个goterm
与ILMN号的列表。所以第一个列表包含了df1
等的ILMN代码。
df1 <- c("ILMN_1665132", "ILMN_1691487", "ILMN_1716446", "ILMN_1769383",
"ILMN_1772387", "ILMN_1783910", "ILMN_1784863")
df2 <- c("ILMN_1651599", "ILMN_1652693", "ILMN_1652825", "ILMN_1653324",
"ILMN_1655595", "ILMN_1656057", "ILMN_1659077", "ILMN_1659923",
"ILMN_1659947", "ILMN_1662322", "ILMN_1662619", "ILMN_1664565",
"ILMN_1665132", "ILMN_1665738", "ILMN_1665859")
df3 <- c("ILMN_1661695", "ILMN_1665132", "ILMN_1716446", "ILMN_1737314",
"ILMN_1772387", "ILMN_1784863", "ILMN_1796094", "ILMN_1800317",
"ILMN_1800512", "ILMN_1807074")
interestedGO <- list(df1,df2,df3)
该xx2
是一个比较集。变量xx2
包含所有可能的ILMN号码的子集。
xx2 <- c("ILMN_1691487", "ILMN_1716446", "ILMN_1769383","ILMN_1832921")
x
是一种参考集。变量x
包含所有可能的ILMN号码。
x <- c("ILMN_1665132", "ILMN_1691487", "ILMN_1716446", "ILMN_1769383", "ILMN_1772387",
"ILMN_1783910", "ILMN_1784863","ILMN_1651599", "ILMN_1652693", "ILMN_1652825",
"ILMN_1653324", "ILMN_1655595","ILMN_1656057", "ILMN_1659077", "ILMN_1659923",
"ILMN_1659947", "ILMN_1662322","ILMN_1662619", "ILMN_1664565", "ILMN_1665132",
"ILMN_1665738", "ILMN_1665859","ILMN_1661695", "ILMN_1665132", "ILMN_1716446",
"ILMN_1737314", "ILMN_1772387","ILMN_1784863", "ILMN_1796094", "ILMN_1800317",
"ILMN_1800512", "ILMN_1807074")
所有这些变量的目标是与相应ILMN代码检查每个goterm
如果他们是在referenceset xx2
。为了检查这一点,使用了匹配函数,并且所有没有匹配项都给出了0,并且匹配值被替换为1.为了便于对所有goterms
实验进行概述,我想创建一个类似于下面的循环,检查它的每个基因都在参考集x
中。最终结果必须是data.frame
,比较data.frame
中每个goterm
的结果。
test <- list()
for (i in 1:length(goterms)) {
goilmn <- as.data.frame(interestedGO[i])
resultILMN <- match(goilmn[,1], xx2, nomatch=0)
resultILMN[resultILMN!=0] <- 1
result <- cbind(goilmn, resultILMN)
colnames(result) <- c('x', 'result')
zz <- merge(result, x, all=TRUE)
zz[is.na(zz)] <- 0
test[[i]] <- matrix(resultloop)
}
最终输出将是就像这样:
1 ILMN_1651599 0 0 0
2 ILMN_1652693 0 0 0
3 ILMN_1652825 0 0 0
4 ILMN_1653324 0 0 0
5 ILMN_1655595 0 0 0
6 ILMN_1656057 0 0 0
7 ILMN_1659077 0 0 0
8 ILMN_1659923 0 0 0
9 ILMN_1659947 0 0 0
10 ILMN_1661695 0 0 0
11 ILMN_1662322 0 0 0
12 ILMN_1662619 0 0 0
13 ILMN_1664565 0 0 0
14 ILMN_1665132 0 0 0
15 ILMN_1665132 0 0 0
16 ILMN_1665132 0 0 0
17 ILMN_1665738 0 0 0
18 ILMN_1665859 0 0 0
19 ILMN_1691487 0 0 1
20 ILMN_1716446 1 0 1
21 ILMN_1716446 1 0 1
22 ILMN_1737314 0 0 0
23 ILMN_1769383 0 0 1
24 ILMN_1772387 0 0 0
25 ILMN_1772387 0 0 0
26 ILMN_1783910 0 0 0
27 ILMN_1784863 0 0 0
28 ILMN_1784863 0 0 0
29 ILMN_1796094 0 0 0
30 ILMN_1800317 0 0 0
31 ILMN_1800512 0 0 0
32 ILMN_1807074 0 0 0
谁能帮助我? 谢谢!
+1不错。我正在研究类似的方法,但您的解决方案非常紧凑。 – Andrie 2011-05-12 10:02:11
这是Briljant!非常感谢! – Lisann 2011-05-12 10:06:20