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  Indian J Med Microbiol
 

Figure 3: Canonical pointwise mutual information maps depicting various forms of spatial intratumor heterogeneity. (a) Cartoon representation of eight different cellular phenotypes based on high-dimensional biomarker intensity patterns acquired via pattern recognition algorithms [see Figure 4 for more details]. (b) A pointwise mutual information map with strong diagonal entries and weak off-diagonal entries describes a globally heterogeneous but locally homogeneous tumor. In this example, the pointwise mutual information map highlights locally homogeneous tumor microdomains containing cells of only one type each, phenotypes 2, 4, and 8 respectively. (c) On the contrary, a pointwise mutual information map with strong off-diagonal entries describes a tumor that is locally heterogeneous. In this example, locally heterogenous tumor microdomains exist as portrayed by the off-diagonal entries. One domain contains phenotypes 1 and 6, another contains phenotypes 2 and 4, and yet another containing phenotypes 3 and 8. (d) Pointwise mutual information maps can also portray anti-associations [e.g., if phenotype 1 never occurs spatially near phenotype 3, see Figures 5 and 6]. The ensemble of associations and anti-associations of varying intensities along or off the diagonal represents the true complexity of tumor images in a format that can be summarized and interrogated

Figure 3: Canonical pointwise mutual information maps depicting various forms of spatial intratumor heterogeneity. (a) Cartoon representation of eight different cellular phenotypes based on high-dimensional biomarker intensity patterns acquired via pattern recognition algorithms [see Figure 4 for more details]. (b) A pointwise mutual information map with strong diagonal entries and weak off-diagonal entries describes a globally heterogeneous but locally homogeneous tumor. In this example, the pointwise mutual information map highlights locally homogeneous tumor microdomains containing cells of only one type each, phenotypes 2, 4, and 8 respectively. (c) On the contrary, a pointwise mutual information map with strong off-diagonal entries describes a tumor that is locally heterogeneous. In this example, locally heterogenous tumor microdomains exist as portrayed by the off-diagonal entries. One domain contains phenotypes 1 and 6, another contains phenotypes 2 and 4, and yet another containing phenotypes 3 and 8. (d) Pointwise mutual information maps can also portray anti-associations [e.g., if phenotype 1 never occurs spatially near phenotype 3, see Figures 5 and 6]. The ensemble of associations and anti-associations of varying intensities along or off the diagonal represents the true complexity of tumor images in a format that can be summarized and interrogated