13.1 Exercise 2 - Barplots

1. Import GSE277039/gencode.vM38.annotation_sample.csv in an object that you will call gtf. You can check the first 20 rows of gtf using the head() function: check the help page to see how it works.

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gtf <- read_csv("GSE277039/gencode.vM38.annotation_sample.csv")

The data in gtf represents a small subset of the gencode vM38 mouse gene annotation.

head(gtf, 20)

## # A tibble: 20 × 7
##    chr       start       end strand gene_id               gene_type      gene_symbol
##    <chr>     <dbl>     <dbl> <chr>  <chr>                 <chr>          <chr>      
##  1 chr10  41361638  41366365 -      ENSMUSG00000019822.13 protein_coding Smpd2      
##  2 chr19   3901773   3944369 +      ENSMUSG00000024843.16 protein_coding Chka       
##  3 chr4   37297905  37298025 +      ENSMUSG00000088718.4  snoRNA         Gm22732    
##  4 chr9   99125420  99182457 +      ENSMUSG00000056267.15 protein_coding Cep70      
##  5 chr14  18063426  18072247 -      ENSMUSG00000095056.8  protein_coding Gm3159     
##  6 chr7  135886020 135901318 -      ENSMUSG00000127332.1  lncRNA         Gm72246    
##  7 chr16  34470291  34498988 +      ENSMUSG00000022832.12 protein_coding Ropn1      
##  8 chr11  46345762  46372082 +      ENSMUSG00000020399.15 protein_coding Havcr2     
##  9 chr6    5767578   5768992 -      ENSMUSG00000136477.1  lncRNA         Gm67521    
## 10 chr7   42258950  42292012 -      ENSMUSG00000074158.10 protein_coding Zfp976     
## 11 chr15 102426627 102432111 +      ENSMUSG00000023051.13 protein_coding Tarbp2     
## 12 chr12  85253391  85255602 -      ENSMUSG00000126100.1  lncRNA         Gm59518    
## 13 chr9  109946776 110069246 +      ENSMUSG00000032481.18 protein_coding Smarcc1    
## 14 chr3   94882042  94914154 +      ENSMUSG00000038861.15 protein_coding Pi4kb      
## 15 chr15  83960298  83989955 -      ENSMUSG00000018865.10 protein_coding Sult4a1    
## 16 chr13  48298781  48310585 +      ENSMUSG00000127329.1  lncRNA         Gm52065    
## 17 chr17  46875397  46940430 -      ENSMUSG00000023972.11 protein_coding Ptk7       
## 18 chr5  143950816 143950919 +      ENSMUSG00000119602.1  snRNA          Gm24311    
## 19 chr7   41485238  41522136 -      ENSMUSG00000090383.3  protein_coding Vmn2r58    
## 20 chr3   89344013  89358259 +      ENSMUSG00000042613.10 protein_coding Pbxip1

2. Create a simple barplot displaying the number of genes per chromosome (tip: you want to visualize each chromosome on the x axis):

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ggplot(data=gtf, mapping=aes(x=chr)) + 
  geom_bar()

3. Keep chromosomes on the x axis, and split the barplot per gene type.

TIP: remember how we set color= in mapping=aes() function in the scatter plot section? Give it a try here!

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ggplot(data=gtf, mapping=aes(x=chr, color=gene_type)) + 
  geom_bar()

4. Change color= with fill= in aes(). What changes?

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ggplot(data=gtf, mapping=aes(x=chr, fill=gene_type)) + 
  geom_bar()

5. Add a title to the graph:

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ggplot(data=gtf, mapping=aes(x=chr, fill=gene_type)) + 
  geom_bar() +
  ggtitle(label = "Number of genes per chromosome, split by gene type")

6. Change the default theme:

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ggplot(data=gtf, mapping=aes(x=chr, fill=gene_type)) + 
  geom_bar() +
  ggtitle(label = "Number of genes per chromosome, split by gene type") +
  theme_bw()

7. Save the graph in PNG format in the workshop’s directory.

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# save plot in an object
gtfbars <- ggplot(data=gtf, mapping=aes(x=chr, fill=gene_type)) + 
  geom_bar() +
  ggtitle(label = "Number of genes per chromosome, split by gene type") +
  theme_bw()

# save as PNG file
ggsave(filename="gtfbarplot.png", plot=gtfbars, 
       device="png")