R 频率表中行子集的频率
我把计算给定子集中突变数量的方法复杂化了，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。数据如下所示：R 频率表中行子集的频率,r,subset,frequency,R,Subset,Frequency,我把计算给定子集中突变数量的方法复杂化了，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。数据如下所示： 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进行分类，样本的顺序相同 $Subtype=“GS”“C
Rownames gene1 gene2 ...gene40
Sample1 Mut WT WT
Sample2 WT WT WT
Sample3 Mut WT WT
Sample4 WT MUT MUT
...277
$Subtype=“GS”“CIN”“MSI”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)
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
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
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