The 'volcanoplot' function provides an efficient visualization of hypothesis test outcomes, common in genomics and statistical analysis. It generates a distinct scatter plot, illustrating the relationship between significance (negative logarithm of p-values) and effect size (logarithmic fold changes). This volcano-like pattern highlights relevant variables at the plot's edges, making it easy to gain quick insights within complex datasets. It can be a valuable tool for researchers to analyze high-throughput experimental data, particularly in bioinformatics context.
Example Use:
% Load example data and compute pvalues & log fold changes
load('sampleproteomicsdata.mat');
[~, p] = ttest2(dependentData, independentData, 'Dim', 2);
log2fc = mean(dependentData, 2, 'omitnan') - mean(independentData, 2, 'omitnan');
% Prepare the figure and plot
figure(1); clf();
volcanoplot(log2fc, p, 'Labels', proteinNames);
Plot with customized cutoffs
% Set PValue cutoff to 0.01 and fold change cutoff to 3
figure(1); clf();
volcanoplot(log2fc, p, 'Labels', proteinNames, ...
'PCutoff', 0.01, 'XCutoff', log2(3));
引用格式
Serhan Yilmaz (2026). Volcano plot (https://ww2.mathworks.cn/matlabcentral/fileexchange/133987-volcano-plot), MATLAB Central File Exchange. 检索时间: .
