RFECS: A Random-Forest Based Algorithm for Enhancer Identification from Chromatin State
Figure 2
Performance of RFECS for enhancer predictions in H1 and IMR90 cells.
Area under the 5-fold cross-validated ROC curve decreases with increase in number of trees stabilizing gradually in A.)H1 and B.)IMR90 cells. C.)Validation Rate of enhancer predicted in H1 cells, as measured by overlap with DNase-I HS and binding sites of p300, NANOG, OCT4 and SOX2. D.)Misclassification Rate of enhancer predicted using RFECS in H1 as measured as overlap of UCSC TSS, E.)Validation Rate of enhancers predicted by RFECS in IMR90 as measured by overlap with DNase-I HS or p300 binding sites in the same cells. F.)Misclassification Rate of enhancers predicted by RFECS in IMR90 as measured by overlap with UCSC TSS, versus total number of enhancers (upto 40000 enhancers) determined by taking different enrichment cutoffs, are shown for forest trained in the same cell type (⋅red), forest trained in other cell type and predictions made on modifications with averaged RPKM (⋅black), replicate 1 only (⋅blue), and replicate 2 only (⋅green). Training on one replicate and prediction on the other replicate of the same cell-type are indicated by asterisks.