Volcano plots in analyzing differential expressions with mRNA microarrays

Area of Science:

• Bioinformatics
• Statistical Visualization
• Genomics

Background:

• Volcano plots are essential tools for visualizing differential gene expression analysis in high-throughput studies.
• They plot unstandardized signal against standardized signal, aiding in the interpretation of statistical metrics like the t-statistic.
• Understanding these plots is crucial for interpreting microarray analysis results.

Purpose of the Study:

• To review the fundamental and interactive applications of volcano plots.
• To emphasize the role of volcano plots in comprehending the regularized t-statistic.
• To propose a unified framework for discussing differential expression measures, variance estimation, and visualization in microarray analysis.

Main Methods:

• Review of existing literature on volcano plot applications and statistical methods.
• Comparison of joint filtering (regularized statistics) with double filtering criteria using discriminant lines in volcano plots.
• Exploration of the potential extension of volcano plot utility to fields outside of microarray analysis.

Main Results:

• The joint filtering gene selection criterion, based on regularized statistics, is characterized by a curved discriminant line in volcano plots.
• This contrasts with the perpendicular lines observed for the traditional "double filtering" criterion.
• Volcano plots offer a powerful visual display for microarray analysis results.

Conclusions:

• Volcano plots are versatile visualization tools with significant implications for statistical analysis in genomics.
• The review provides a framework for advancing methods in differential expression analysis and variance estimation.
• The utility of volcano plots may extend to diverse scientific fields beyond their initial application in microarrays.