Application of Volcano Plots in Analyses of mRNA Differential Expressions with Microarrays

TL;DR

This paper reviews the use of volcano plots in microarray analysis, highlighting their role in visualizing differential gene expression and discussing their potential applications beyond microarrays.

Contribution

It provides a unifying framework for understanding volcano plots, regularized statistics, and their applications in gene expression analysis and beyond.

Findings

Volcano plots help visualize differential expression with regularized statistics.

Joint filtering criteria create curved discriminant lines in volcano plots.

Potential to apply volcano plots to fields beyond microarray analysis.

Abstract

Volcano plot displays unstandardized signal (e.g. log-fold-change) against noise-adjusted/standardized signal (e.g. t-statistic or -log10(p-value) from the t test). We review the basic and an interactive use of the volcano plot, and its crucial role in understanding the regularized t-statistic. The joint filtering gene selection criterion based on regularized statistics has a curved discriminant line in the volcano plot, as compared to the two perpendicular lines for the "double filtering" criterion. This review attempts to provide an unifying framework for discussions on alternative measures of differential expression, improved methods for estimating variance, and visual display of a microarray analysis result. We also discuss the possibility to apply volcano plots to other fields beyond microarray.