Context-based intra- and inter-cell type chromatin factor profile predictions achieve high overall performance.

<p>(A) Prediction performances on hold-out chromatin factor profiles based on partial data and chromatin model. Chromatin factor profile predictions are compared with observed chromatin profiles using receiver operating characteristics (ROC) that shows true positive rate (y-axis) and false positive rate (x-axis) at full range of prediction thresholds. The diagonal line (dashed) shows expected performance of random classifier. The histogram shows frequency distribution of area under ROC curves (AUC). (B) Comparison of predicted and observed S2 cell H3K18ac chromatin profile. ChIP profile is visualized as the space-filling Hilbert curve as in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003525#pcbi.1003525-Kharchenko1" target="_blank">[11]</a>, therefore adjacent genomic locations are also close to each other in this 2D representation. Predicted profile based on BG3 cell model is colored yellow, with darker color showing higher probability; Observed binarized profile is colored blue; Overlap between predicted and observed profile is therefore green. H3K18ac is an example chromatin factor which cannot be accurately inferred from any other single chromatin profile (the highest correlation coefficient with H3K18ac is 0.37). (C, D) Comparison of inter-cell type versus intra-cell type chromatin profile prediction performances. Performance is measured by AUC. ‘->’ indicates which cell lines the model is trained for and tested on, e.g. S2->BG3 represents predicting BG3 cell data with model trained on S2 data.