Fig. 3

Comparison of Poisson regression model, site-specific binomial logistic regression model and site-specific multinomial logistic regression model. a Motivation (site-specificity) and conceptual explanation of the different models. Consider a 1.2 Mb region on Chromosome 3. We observe a number of mutations and the value of the explanatory variables replication timing, GC content and phyloP score. Given the values of the explanatory variables we use Poisson, site-specific binomial logistic regression or site-specific multinomial logistic regression to predict the number of mutations in a region (Poisson), the probability of a mutation in a single site (binomial) or even the probability of the three types of mutation in a single site (multinomial). b Predicted versus observed number of mutations for the three models for 100 kb regions. c Site-specific models perform substantially better in 1000 randomly selected sites. d The prediction for different mutation types with binomial logistic regression model in 1000 randomly selected sites. e The prediction for different mutation types with multinomial logistic regression model in 1000 randomly selected sites