This simple example shows that the Normalized Shannon Entropy variations of the gene expression profile convey information about global transcriptomic changes; however, this measure alone is not enough to characterize the deviations from normal tissue profiles. For example, assume that Sample 1 is the normal profile of a particular tissue type. Assume that Sample 3 is the profile of a cancer cell that originated from that tissue type, the variation of Normalized Shannon Entropy can be related to this malignant change. However, as Sample 2 illustrates, Normalized Shannon Entropy is not enough to let us to measure the variation from a profile and at least another Information Theory quantifier is needed. We resort to Statistical Complexity quantifiers, which in turn use the Jensen-Shannon divergence [798] to provide this complementary dimension [800] (refer to the 'Materials and Methods' section for a mathematical definition of the Jensen-Shannon divergence). Figure 21 shows how the Jensen-Shannon divergence helps us to evaluate the variation between profiles. Samples 1 and 2, as perhaps intuitively expected, have the largest divergence between them, their Jensen Shannon divergence is 0.286636 (JS(1,2) = Table 3 . An example dataset to illustrate the principles of Shannon Entropy and the Information Theory quantifiers used in this work. Table 3 . Sample 4 has the largest attainable value since the expression of all probes is the same. Samples 1 and 2, which have the same set of expression values, although in different probes, have the same value of Normalized Shannon Entropy. As a consequence, there is a need for another quantifier of gene expression to address the permutational indistinguishability of these two expression profiles. The Jensen-Shannon divergence provides a natural alternative (see Table 4 ). doi:10.1371/journal.pone.0012262.g021 Table 4 . Jensen-Shannon divergence values using the example introduced in Table 3 . Table 4 . Let H P (j) Â Ã be the Normalized Shannon Entropy of a transcriptional sample profile, then the MPR-Statistical Complexity C (MPR) P (j) Â Ã is defined as being proportional to the product of the Normalized Shannon Entropy times the Jensen-Shannon divergence of the profile with the equiprobable distribution (in the example above the equiprobable distribution is that of Sample 4). Then we have