In statistics, a volcano plot is a kind of scatter plot that is applied to quickly seek out changes in large data sets composed of replicate data. It plots fold-change versus significance on the x and y axes, respectively. These plots are increasingly widely used in omic experiments such as genomics, transcriptomics, proteomics, and metabolomics where one usually has a long list of many thousands of replicate data points between two distinct conditions and one wants to quickly identify the most significant changes. A volcano plot combines a measure of statistical significance from a statistical test (e.g. a p value from an ANOVA model) with the magnitude changes, enabling quick visual determination of these data-points that represent large magnitude changes that are also statistically significant.
In a volcano plot, the y axis is the negative log of the p value (usually base 10). This results in highly significant points with low p values appearing toward the top of the plot. The log of the fold change (FC) between the two conditions is plotted on the x axis. The log of the fold change is adopted so that changes in both directions become equidistant from the center. Plotting points in this way form two regions of interest in the plot: those points that are found toward the top of the plot that are far too either the left- or right-hand sides. These represent values that display large magnitude fold changes as well as high statistical significance.
Volcano plots display both noise-level-standardized and unstandardized signals concerning differential expression of mRNA levels. Regularized test statistic and joint filtering have an intuitive geometric interpretation in a volcano plot, and its advantage over the double filter criterion of genes can be easily understood. As a scattering plot, the volcano plot can incorporate other external information, such as gene annotation, to aid the hypothesis generating process concerning a disease or phenotype.
Applications of volcano plot:
- Microarray analysis
- Genetic association studies
- Transcriptomics research
- Proteomics research
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