Rather than using significance (or a given arbitrary threshold for it), you might want to use effect size (after ensuring that you only plot genes which are well in the regime of your detection method). This might be particularly the case if you needed to justify "biological relevance" or find genes for well-doable follow-up experiments.
If C1 and C2 should be biologically different (e.g.: different cell lines,...) a 2D scatter plot might be a better argument than p-values.
If you plot log(T1/C1) on X, and log(T2/C2) on Y, you will immediately see if most genes share a trend TvsC, and whether there would be subsets of genes that would clearly stand out from the rest of the genes, and populate the X or Y axis. If the general trend does not follow the diagonal, you might conclude that 1 and 2 would affect the same genes, but that in 1 or 2 the response is stronger / faster. If you do not see evidence for either, differences in the p-values might result from chance, or slight variation in the experimental settings.
A scatter plot will also tell you if it changes in T1/C1 and T2/C2 affect induced and repressed genes similarly.
(Also note that, if you had arbitrarily precise measurement method, all genes would appear differentially expressed.)
modified 3.1 years ago
3.1 years ago by
unksci • 160