频率表中行子集的频率

Question

我把计算给定子集中的突变数的方法复杂化了，我想知道如何利用一个频率表来计算每个包含突变的样本子类型的百分比。数据类似于以下数据：

Rownames gene1 gene2 ...gene40
Sample1  Mut   WT        WT
Sample2   WT   WT        WT
Sample3   Mut  WT        WT
Sample4   WT   MUT       MUT
...277

每个示例被归类在另一个框架中，样本按相同的$Subtype = "GS" "CIN" "MSI"顺序排列。

我想按表中每个基因的亚型计算突变geneX的百分比。

提前感谢！

Answer 1

ng <- 40
ns <- 277

set.seed(1)
m <- matrix(sample(c('WT','MUT'), ng * ns, TRUE), ns,
            dimnames = list(paste0('Sample', seq(ns)), paste0('Gene', seq(ng))))

data <- data.frame(m, stringsAsFactors = FALSE)
subtype <- sample(c("GS","CIN","MSI"), ns, TRUE)

如果你的数据看起来像

str(data)
'data.frame':   277 obs. of  40 variables:
  # $ Gene1 : chr  "WT" "WT" "MUT" "MUT" ...
  # $ Gene2 : chr  "WT" "WT" "MUT" "WT" ...
  # $ Gene3 : chr  "MUT" "WT" "WT" "WT" ...
  # $ Gene4 : chr  "MUT" "MUT" "MUT" "WT" ...

然后你就可以像

sp <- split(data, subtype)
(l <- lapply(sp, function(x) colMeans(x == 'MUT')))

## 
## 
## $CIN
##     Gene1     Gene2     Gene3     Gene4     Gene5     Gene6     Gene7     Gene8     Gene9 
## 0.5268817 0.4516129 0.4838710 0.4408602 0.4516129 0.4301075 0.4731183 0.5376344 0.4408602 
##    Gene10    Gene11    Gene12    Gene13    Gene14    Gene15    Gene16    Gene17    Gene18 
## 0.4193548 0.4946237 0.5698925 0.4301075 0.4838710 0.5053763 0.3978495 0.5161290 0.5483871 
##    Gene19    Gene20    Gene21    Gene22    Gene23    Gene24    Gene25    Gene26    Gene27 
## 0.3978495 0.5698925 0.5698925 0.4516129 0.4946237 0.5268817 0.5591398 0.4731183 0.4838710 
##    Gene28    Gene29    Gene30    Gene31    Gene32    Gene33    Gene34    Gene35    Gene36 
## 0.4946237 0.5161290 0.5161290 0.4301075 0.5698925 0.5376344 0.5161290 0.4516129 0.4301075 
##    Gene37    Gene38    Gene39    Gene40 
## 0.4731183 0.6021505 0.5483871 0.4731183 
## 
## $GS
##     Gene1     Gene2     Gene3     Gene4     Gene5     Gene6     Gene7     Gene8     Gene9 
## 0.4742268 0.4536082 0.5567010 0.4845361 0.4742268 0.5051546 0.5463918 0.4020619 0.4845361 
##    Gene10    Gene11    Gene12    Gene13    Gene14    Gene15    Gene16    Gene17    Gene18 
## 0.4329897 0.4536082 0.4948454 0.4948454 0.4639175 0.3711340 0.5051546 0.5154639 0.5876289 
##    Gene19    Gene20    Gene21    Gene22    Gene23    Gene24    Gene25    Gene26    Gene27 
## 0.5670103 0.5051546 0.5567010 0.5670103 0.5876289 0.5051546 0.4536082 0.5567010 0.5051546 
##    Gene28    Gene29    Gene30    Gene31    Gene32    Gene33    Gene34    Gene35    Gene36 
## 0.4639175 0.4329897 0.5154639 0.4639175 0.4639175 0.5773196 0.5257732 0.4948454 0.4329897 
##    Gene37    Gene38    Gene39    Gene40 
## 0.5360825 0.5257732 0.4742268 0.5051546 
## 
## $MSI
##     Gene1     Gene2     Gene3     Gene4     Gene5     Gene6     Gene7     Gene8     Gene9 
## 0.4367816 0.4827586 0.5172414 0.4597701 0.4252874 0.5402299 0.4827586 0.5057471 0.5172414 
##    Gene10    Gene11    Gene12    Gene13    Gene14    Gene15    Gene16    Gene17    Gene18 
## 0.5057471 0.5057471 0.5862069 0.5747126 0.5172414 0.4252874 0.5057471 0.5057471 0.5517241 
##    Gene19    Gene20    Gene21    Gene22    Gene23    Gene24    Gene25    Gene26    Gene27 
## 0.5057471 0.5057471 0.5747126 0.4597701 0.5517241 0.4597701 0.6321839 0.4252874 0.4712644 
##    Gene28    Gene29    Gene30    Gene31    Gene32    Gene33    Gene34    Gene35    Gene36 
## 0.4942529 0.4022989 0.5172414 0.5172414 0.4827586 0.4252874 0.5632184 0.4712644 0.5172414 
##    Gene37    Gene38    Gene39    Gene40 
## 0.5172414 0.4712644 0.5977011 0.4482759 

和

do.call('rbind', l)

#         Gene1     Gene2     Gene3     Gene4     Gene5     Gene6     Gene7     Gene8
# CIN 0.5268817 0.4516129 0.4838710 0.4408602 0.4516129 0.4301075 0.4731183 0.5376344
# GS  0.4742268 0.4536082 0.5567010 0.4845361 0.4742268 0.5051546 0.5463918 0.4020619
# MSI 0.4367816 0.4827586 0.5172414 0.4597701 0.4252874 0.5402299 0.4827586 0.5057471