DESeq2 visualizations - MA and volcano plots

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To explore the results, visualizations can be helpful to see a global view of the data, as well as, characteristics of the significant genes. Usually, we expect to see significant genes identified across the range of mean values, which we can plot using the MA plot. If we only see significant genes with high mean values, then it could indicate an issue with our data. The volcano plot helps us to get an idea of the range of fold changes needed to identify significance in our data.

Let's explore our results using MA plots and volcano plots.

Cet exercice fait partie du cours

RNA-Seq with Bioconductor in R

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Instructions

Create an MA plot using the plotMA() function and using the results object, smoc2_res as input.
Create a new column as a logical vector regarding whether padj values are less than 0.05 for the results using the mutate() function.
Create a volcano plot of the log2 foldchange values versus the -log10 adjusted p-value using ggplot() and coloring the points for the genes by whether or not they are significant.

Exercice interactif pratique

Essayez cet exercice en complétant cet exemple de code.

# Create MA plot
___

# Generate logical column 
smoc2_res_all <- data.frame(smoc2_res) %>% mutate(threshold = padj < 0.05)
              
# Create the volcano plot
ggplot(___) + 
        geom_point(aes(x = ___, y = -log10(___), color = ___)) + 
        xlab("log2 fold change") + 
        ylab("-log10 adjusted p-value") + 
        theme(legend.position = "none", 
              plot.title = element_text(size = rel(1.5), hjust = 0.5), 
              axis.title = element_text(size = rel(1.25)))

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