Question

我对计算给定子集中突变数量的方法过于复杂，我想知道如何利用频率表来计算包含突变的每个样本亚型的百分比。数据类似如下：

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

每个样本在另一个框架中分类，样本的顺序相同 $Subtype = "GS" "CIN" "MSI"

我想计算表中每个基因的亚型突变基因的百分比。

提前致谢！

Answer 1

JUnit

如果您的数据看起来像

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)

然后你可以像

那样

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" ...

和

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

频率表中行子集的频率

1 个答案: