我有一个包含~30,000行和~17,000列的庞大数据集,以及character
元素的向量。
这是一个重新创建我的数据集的虚拟集
### Example
df <- data.frame(Gene=paste0("gene", 1:60), replicate(60, runif(60, min=0, max=100)))
colnames(df) <- c("GeneName", paste0("TisA.", 1:20), paste0("TisB.", 1:20), paste0("TisC.", 1:20))
genes <- sample(df$GeneName, 5)
head(df)
# GeneName TisA.1 TisA.2 TisA.3 TisA.4
#1 gene1 1.987621 17.936562 18.145417 59.43023
#2 gene2 60.031713 73.822846 93.946769 72.27633
#3 gene3 44.833748 47.890719 77.100497 39.45719
#4 gene4 44.662776 26.285659 30.087606 49.50682
#5 gene5 63.770411 6.469006 3.797708 68.17532
我需要匹配数据帧的向量中的元素,这可以通过
轻松完成 df.new <- df[df$GeneName %in% genes,]
然后,我想要的是,genes
中的每一个,为每个基因创建等级值,然后将等级加Tis
(A,B,C)
我可以使用例如一个gene
genes.ord <- sort(df.new[1,], decreasing = TRUE)
但是,我被困在这里,这将是为基因分配排名并按组排列这些排名的最快方法,即TisA
,TisB
和TisC
?
为澄清起见,每组有20个样本TisA.1, TisA.2, ..., TisA.20
所需的输出是:
GeneName TisA TisB TisC
gene4 24 32 10 ## these are random values to show sum of ranks for each of genes in the vector
gene1 14 12 20 ## these are random values to show sum of ranks for each of genes in the vector
gene40 4 92 12 ## these are random values to show sum of ranks for each of genes in the vector
gene2 64 2 40 ## these are random values to show sum of ranks for each of genes in the vector
gene15 84 32 9 ## these are random values to show sum of ranks for each of genes in the vector
P.S我的真实数据集中的一些值可以是0并在不同的列中重复
答案 0 :(得分:1)
直接使用tidyverse
df1 <- apply(df[-1], 1, rank, ties.method= "first")
df2 <- apply(df1, 2, function(x){
aggregate(x, list(sapply(strsplit(colnames(df), "[.]"), "[", 1)[-1]), sum)
})
df3 <- cbind.data.frame(df$GeneName, t(Reduce(cbind, lapply(df2, "[", 2))))
colnames(df3) <- c("GeneName", "TisA", "TisB", "TisC")
head(df3[order(df3$GeneName),])
GeneName TisA TisB TisC
gene1 698 620 512
gene10 525 653 652
gene11 631 598 601
gene12 487 679 664
gene13 688 579 563
gene14 674 581 575
或尝试使用baseR解决方案......有点复杂
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