如何按组计算 FPKM 基因计数的平均值和 sd 并将平均值和 sd 组合为 dataframe？

Question

Luckly, the first step of calculating the mean and sd by group has been finished. Now I have the mean and sd result respectively. And what I wanna do is how to combind theme togather. No matter how easy or difficult the combination method but should the combination dataframe be simple or not complicated.

Here I will show you my calculate method and the only combination method I knew. I nead the other new combination method. Plz. My sample data and code below:

data<-data.frame(matrix(sample(1:1000,500),20,25))
names(data) <- c(paste0("Gene_", 1:25))
rownames(data)<-NULL
data$Name<-c(rep(paste0("Group_",1:10),each=2))
        
unique(data$Name)
## 1 group_by, only get one result each time
mm <- data %>% 
  group_by(data$Name) %>% 
  summarise(mean=mean(Gene_1))
mm

## 2 tapply, can get the mean of each column , but only one column each time.
mm <- data.frame(mean_Gene_1=tapply(data[,"Gene_1"],data$Name,mean))  
mm

## 3.aggregate, a powerful function , can get all the columns result by group.
mm <- aggregate(.~Name,data,mean) 
mm
        
## get the mean and sd dataframe.
mean <- aggregate(.~Name,data,mean) 
sd <- aggregate(.~Name,data,sd) 
        
## now combine the two dataframe usingt the same index "Name" and "gene"        
## I just learned one method from somebody in Stack overflow. 
## combine the two file 
data <- bind_rows(list(mean = mean, sd = sd), .id = "stat")
        
data_mean_sd <- data %>% 
  pivot_longer(-c(Name, stat), names_to = "Gene", values_to = "value") %>%
  pivot_wider(names_from = "stat", values_from = "value")

You know the result is right. But it's a large file though it's a example here. It includes many duplicated data. I hope somebody give me a better method to simplify my result.

I need your help.

Answer 1

I am not sure, would the approach below work for you? The last part is basically the same using pivot_longer and pivot_wider , but for the summarise part I used dplyr::across .

library(dplyr)
library(tidyr)

data<-data.frame(matrix(sample(1:1000,500),20,25))
names(data) <- c(paste0("Gene_", 1:25))
rownames(data)<-NULL
data$Name<-c(rep(paste0("Group_",1:10),each=2))


data %>% 
  group_by(Name) %>% 
  summarise(across(everything(),
                   list(mean = ~ mean(.x),
                        sd = ~ sd(.x)),
                   .names = "{col}__{fn}")) %>% 
  pivot_longer(-c(Name), names_to = "Gene", values_to = "value") %>% 
  separate(., Gene, into = c("Gene", "Stats"), sep = "__") %>% 
  pivot_wider(names_from = Stats, values_from = "value")

#> `summarise()` ungrouping output (override with `.groups` argument)
#> # A tibble: 250 x 4
#>    Name    Gene     mean     sd
#>    <chr>   <chr>   <dbl>  <dbl>
#>  1 Group_1 Gene_1   534. 556.  
#>  2 Group_1 Gene_2   294.  51.6 
#>  3 Group_1 Gene_3   262. 350.  
#>  4 Group_1 Gene_4   615  338.  
#>  5 Group_1 Gene_5    89   43.8 
#>  6 Group_1 Gene_6   322  263.  
#>  7 Group_1 Gene_7   696. 391.  
#>  8 Group_1 Gene_8   182. 101.  
#>  9 Group_1 Gene_9   582  139.  
#> 10 Group_1 Gene_10  184    2.83
#> # ... with 240 more rows

^{Created on 2021-01-27 by the reprex package (v0.3.0)}