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. 2019 Jul 5;35(14):i389–i397. doi: 10.1093/bioinformatics/btz332

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© The Author(s) 2019. Published by Oxford University Press.

This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com

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Fig. 4. — Comparison of evolutionary predictability among different cancer types. The vertical axis shows the CBN-based predictability $ϕ_{⪯}$ , computed based on Equation (12) for each given cancer type. The error bars indicate the standard deviation of $ϕ_{⪯}$ calculated from 100 bootstrap samples of equal size as the original genotype data. The genotypes for each cancer type are defined based on the mutational data of the corresponding n=10 most frequently mutated driver genes from TCGA (blue bars) or MSK-IMPACT (red bars). The cancer types are arranged from left to right in ascending order of their $ϕ_{⪯}$ quantified based on TCGA data