Super outline exercises

Our introductory course of student letter and online live data mining course have a history of more than three years , We have cultivated wave after wave of excellent students and students . The content shared in this issue is not what we talked about in class , Instead, I gave a super outline exercise that I could do on tiptoe , Inspire students to take the initiative to learn , Instead of just waiting to be fed .

Let's take a look at the excellent student Xuqian's sharing ：

R Language super outline exercises

( Xuqian, an excellent student of the student trust skill tree )

This is one of the online live courses of Marathon teaching at present R Expansion questions of language part , The latest issue is next Monday ：

• data mining （GEO,TCGA, unicellular ）2022 year 6 Lunar field , Get a quick look at some bioinformatics Application Charts
• Introduction to student letters -2022 year 6 Lunar field , Your first bioinformatics lesson

I see that some students in the class have written a super outline problem solving method , Great idea , But now it is no longer beyond the outline , Next GEO Teacher Xiaojie will certainly emphasize this routine operation , Here are two new ideas for reference ：

One 、merge

No matter soft and exp Medium ID Have you been , Direct cross merge , No more %in% And row operation , The code is as follows ：

exp <- read.csv("exp.csv")
anno <- read.table("soft.txt",header = T,sep = "\t")### The annotation file is consistent with the column name corresponding to the expression matrix probe name 
colnames(exp)[1] <- "ID" 
exp_new <- merge(exp,anno,by="ID")
## According to the same idea , Keep only the first gene name 
exp_new <- exp_new[!duplicated(exp_new$GeneName),]
rownames(exp_new) <- exp_new$GeneName ### The gene name changes to the line name 
exp_new <- as.matrix(exp_new[,2:7])### To a matrix 

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Two 、 Sacrifice our artifact tidyverse

Treatment of duplicate genes , In practice, I prefer to take the line with the largest average value . So you can use artifact dplyr And other packages to do , so to speak Hadley God changed R The ecology of . The specific code is as follows , You will know how to get there by running step by step , You can select the code before the pipe symbol , Press Ctrl+Enter, In this way, the unchecked code will not run , Add them one by one to see what the pipe symbol operates ：

library(dplyr)
library(tibble)
colnames(exp)[1] <- "ID"
exp_new <- exp %>% 
  # Merge probe information 
  inner_join(anno,by="ID") %>% 
  # Remove superfluous information ,select Support to select by column name and column number at the same time 
  select(c(GeneName,2:7)) %>%  
  #· Add a row , The content is the average number of each line 
  mutate(rowMean =rowMeans(.[,-1])) %>% 
  # Rank the average value of expression quantity from the largest to the smallest 
  arrange(desc(rowMean)) %>% 
  #  duplicate removal ,GeneName Leave the first 
  distinct(GeneName,.keep_all = T) %>% 
  #GeneName Convert to row name 
  column_to_rownames(var="GeneName") %>% 
  # Reverse the selection of the column that removes the average 
  select(-rowMean)

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After the pipe symbol . It can represent the data passed in before the pipe symbol , If the tidyverse All functions of should be omitted , Default first parameter , If you call another function , use . Just replace it . give the result as follows ：

Thought comes from the fruit teacher .