Volcano Plots

In this article, I will cover a well-known plot used mainly in genomics called the volcano plot. It is used to visualize the outcomes of statistical tests, often in high-throughput experiments like gene expression microarrays or RNA-seq (Don't worry, I will not bring COVID-19 into the conversation ??), in that case the volcano plot helps in pinpointing genes or features that show significant differential expression between two conditions

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In a volcano plot ??? , the x-axis typically shows the magnitude of change (fold change) between two conditions, while the y-axis indicates the statistical significance of that change (p-value or adjusted p-value ; maybe I will cover that next week).

In the example above each point on the plot corresponds to a gene or feature, with its location determined by both its fold change and statistical significance (x and y axis)

Understanding the Anatomy of a Volcano Plot

In the example that I am showing below, the genes that are most statistically significant are located towards the top because they have lower p-values (indicated by the points above the red line)

• The x-axis illustrates the fold change (FC), which represents the ratio between the expression of a gene in two groups (example: expression of gene X in group B divided by expression of gene X in group A)
• Towards the right side of the plot (where FC > 0; red dots), indicates upregulation in group B compared to group A. Conversely, a negative FC (FC < 0 ; green dots ) suggests downregulation in group B compared to group A
• When the FC is near 0, it signifies no significant change in group B compared to group A

Why do we transform the result with Log ?

We use logarithmic transformations to make the results easier to interpret and visualize (The fold change (FC) is typically converted to a log2 scale)

We also apply a negative log10 transformation to the p-values, which typically range from 0 to 1 (since we have very small p-value, making their significance more apparent)

Note : You can use -log10(p-values), which in this plot can range from 0 to 2, depending on your data.

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