Question

我有一个大数据框df1，看起来像这样：

          Gene  CB_1.1 CB_10.1 CB_10.2 CB_10.3
1         Gene1     10       0       0       0
2         Gene2    871       7       9       2
3         Gene3    490       2       5       8
4         Gene4     17       5       6       1
5         Gene5     75       1       1       1
6         Gene6    308       2       6       2

> dput(head(df1[,1:5]))
structure(list(X = c("Gene1", "Gene2", "Gene3", 
"Gene4", "Gene5", "Gene6"), CB_1.1 = c(10L, 
871L, 490L, 17L, 75L, 308L), CB_10.1 = c(0L, 7L, 2L, 5L, 1L, 
2L), CB_10.2 = c(0L, 9L, 5L, 6L, 1L, 6L), CB_10.3 = c(0L, 2L, 
8L, 1L, 1L, 2L)), row.names = c(NA, 6L), class = "data.frame")

和第二个数据帧df2，看起来像这样。

  tissue_subcluster    Class_2
1            CB_1.1     Neuron
2           CB_10.1     Neuron
3           CB_10.2 Non-Neuron
4           CB_10.3 Non-Neuron

> dput(head(df2[,c(7,9)]))
structure(list(tissue_subcluster = c("CB_1.1", "CB_10.1", "CB_10.2", 
"CB_10.3", "CB_11.1", "CB_11.2"), Class_2 = c("Neuron", "Non-Neuron", 
"Non-Neuron", "Non-Neuron", "Non-Neuron", "Non-Neuron")), row.names = c("1", 
"2", "3", "4", "5", "6"), class = "data.frame")

我想根据df1中Neuron或Non-neuron的因子df2中的值取平均值。这样看起来像这样：

          Gene Neuron_mean Non-Neuron_mean 
1         Gene1         5               0       
2         Gene2       439             5.5       
3         Gene3       246             6.2       
4         Gene4        11             3.5       
5         Gene5        38               1       
6         Gene6       155               4

我该怎么做？任何帮助表示赞赏！

Answer 1

这里是base R的一个选项。将列名称“ df1”与列“ tissue_subcluster”匹配，获得相应的“ Class_2”值，使用该值将“ df1”拆分为data.frame的list，在{{1}上循环}与list并获得sapply

rowMeans

数据

data.frame(Gene = df1$X, sapply(split.default(df1[-1], with(df2, 
   Class_2[match(names(df1)[-1], tissue_subcluster)])), rowMeans))
#   Gene Neuron Non.Neuron
#1 Gene1      5        0.0
#2 Gene2    439        5.5
#3 Gene3    246        6.5
#4 Gene4     11        3.5
#5 Gene5     38        1.0
#6 Gene6    155        4.0

Answer 2

对于大型数据集，这可能不是最佳方法，但是您可以使用tidyr和dplyr：

df1 %>%
  pivot_longer(cols=-Gene, names_to="tissue_subcluster") %>%
  left_join(df2, by="tissue_subcluster") %>%
  group_by(Gene, Class_2) %>%
  summarise(mean=mean(value)) %>%
  pivot_wider(names_from="Class_2", names_glue="{Class_2}_mean", values_from="mean")

返回

# A tibble: 6 x 3
  Gene          Neuron_mean `Non-Neuron_mean`
  <chr>               <dbl>             <dbl>
1 0610005C13Rik           5               0  
2 0610007P14Rik         439               5.5
3 0610009B22Rik         246               6.5
4 0610009E02Rik          11               3.5
5 0610009L18Rik          38               1  
6 0610009O20Rik         155               4

Answer 3

使用reshape库，

library(reshape)

out <- merge(melt(df1),df2, by.x = "variable", by.y = "tissue_subcluster")
cast(out, Gene~Class_2,mean)

给予

   Gene Neuron Non-Neuron
1 Gene1      5        0.0
2 Gene2    439        5.5
3 Gene3    246        6.5
4 Gene4     11        3.5
5 Gene5     38        1.0
6 Gene6    155        4.0

根据单独数据框中的因子查找行平均值

3 个答案:

数据