Differential contribution to gene expression prediction of histone modifications at enhancers or promoters
Fig 2
Performance and variable importance of enhancer and promoter Hi-C-top predictive models in ESCs.
Predicted expression of the test subset of genes calculated by the models versus their measured expression by RNA-seq. Model performances are represented by the Pearson’s correlation (r) between predicted and measured expression values. (A) Left, the model trained on the promoter regions associated to at least one enhancer using the top significant interactions of Hi-C (Hi-C–top promoter model). Right, the performance of the same model after randomizing the expression of the training subset of genes. The color bar represents the density of dots. (B) Left, the model trained on the enhancer regions associated to at least one promoter using the top significant interactions of Hi-C (Hi-C–top enhancer model). Right, the performance of the same model after randomizing the expression of the training subset of genes. The color bar represents the density of dots. (C) Importance of each histone modification used to train the Hi-C–top promoter predictive model. Importance is defined as the contribution of each variable in the linear regression predictive model and corresponds to the absolute value of the t-statistic for each model parameter. (D) As for C, but for the Hi-C–top enhancer predictive model. (E) As for B, but the model is trained without H3K27me3 as predictive variable. (F) As for D, but the model is trained without H3K27me3 as predictive variable.