![]() ![]() ![]() In the wild-type, the abundance values for Q9M0A7 were 258, 310, and 297 in our three replicates. Let’s consider the data for just one of those proteins, called Q9M0A7. In this example we have two conditions (wild-type and mutant), replicate data (×3 replicates for wild-type and ×3 replicates for the mutant), and many data points (for around 1300 proteins). But this example is applicable for any situation where you are comparing two conditions, have replicate data, and many data points. Here we’ll use data from a proteomics experiment comparing wild-type plants versus mutant plants, with the aim of quickly identifying those proteins that have a very different abundance under these conditions. Those data points in the top-right and top-left sectors are those of most interest because they are the most different between the two conditions and with high statistical confidence about that difference. Data sets plotted in this way often resemble an erupting volcano, which accounts for the name. The x-axis displays the fold-change between the two conditions this is plotted as the log of the fold-change so that changes in both directions appear equidistant from the centre. This results in data points with low p-values (highly significant) appearing toward the top of the plot. How are volcano plots made?Ī volcano plot is constructed by plotting the negative log of the p-value on the y-axis (usually base 10). By separating these data by the magnitude of the difference between the two conditions (on the x-axis) and the statistical significance of that difference (on the y-axis), it’s possible to quickly pick out those data points (e.g., genes or proteins) that display a large magnitude change but are also statistically significant. disease) and involve many thousands of replicate data points. Volcano plots are increasingly popular in ‘omics’ type experiments (e.g., genomics, proteomics, and metabolomics) that typically compare two conditions (e.g., wild-type vs. Volcano plots do this by plotting a measure of the statistical significance of a change (e.g., p-value) on the y-axis, versus the magnitude of the change (fold-change) on the x-axis. A volcano plot is a type of scatter-plot that can be used to quickly identify meaningful changes from within a very large data set. ![]()
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