Volcano plot (statistics)

Volcano eg

Volcano Plot (Statistics)

A Volcano plot is a graphical method commonly used in statistics to visualize the results of statistical tests, such as t-tests or ANOVA, applied to high-throughput data sets. The plot displays the relationship between statistical significance (usually represented as the negative logarithm of the p-value) on the y-axis and the magnitude of the effect size (often represented as the log2 fold change) on the x-axis.

Overview

Volcano plots are particularly useful in identifying differentially expressed genes, proteins, or other features between two or more conditions in experiments such as microarray or RNA-sequencing studies. The plot allows researchers to quickly identify which features are both statistically significant and biologically relevant.

Construction

To construct a volcano plot, the statistical significance of each feature is plotted against its effect size. Features that are statistically significant are typically represented by points that fall above a certain threshold on the y-axis (e.g., a p-value cutoff), while features with a large effect size are represented by points that fall far from the center on the x-axis.

Interpretation

In a volcano plot, points that are located in the upper-right or upper-left corners of the plot are of particular interest. Points in the upper-right corner represent features that are both statistically significant and have a large effect size, while points in the upper-left corner represent features that are statistically significant but have a small effect size.

Applications

Volcano plots are widely used in various fields, including genomics, proteomics, and metabolomics, to identify key features that are differentially expressed or regulated under different experimental conditions. They are also used in drug discovery and clinical research to identify potential biomarkers or therapeutic targets.

Example

As an example, a volcano plot generated from a gene expression microarray experiment might show genes that are significantly upregulated in a disease condition compared to a control condition, providing insights into the molecular mechanisms underlying the disease.

See also

• Microarray analysis
• RNA-sequencing
• Differential expression analysis

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