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Figure 1 | BMC Medical Genomics

Figure 1

From: Discovering functional modules by identifying recurrent and mutually exclusive mutational patterns in tumors

Figure 1

Overview of RME Module Detection. a) An example of a structural reliability model of progression of a particular tumor type. Cancer progression in this example requires aberrations in each of the three distinct functional modules (three horizontal lines). If mutated genes (crossed out in red) occur in all three modules, the connection between the left and right part of the structural model will be lost, indicating failure (cancer). b) A module may be disrupted by different aberrations in distinct tumor samples. One measure of an RME pattern is coverage, defined as the percentage of samples that contain at least one aberration within the module. Another measure of the pattern is exclusivity, defined as the percentage of covered samples that contain exactly one aberration within the module. An aberration in one of the genes within a specific RME module removes selective pressure of aberrations in other genes within the same module, giving rise to the exclusivity. c) Example network where nodes represent genes and edge thickness represents the level of exclusivity. The search for RME patterns starts by constructing such a graph using the Winnow algorithm. This graph indicates three potential RME modules. The node colors and numbers correspond to those in panel a. d) The significance score for RME patterns is dependent on both exclusivity (y-axis) and coverage (x-axis). Shown is the RME algorithmic compression score, d, for a three-gene RME module across 100 samples with aberrations equally distributed, assuming background frequency of 13.38 aberrant genes per sample (see section 2.3 andAdditional file 1). According to the algorithmic significance test, the significance of an RME pattern is 2-d.

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