Machine learning on multiple epigenetic features reveals H3K27Ac as a driver of gene expression prediction across patients with glioblastoma
Fig 8
H3K27Ac only model produced RNA-seq true versus predicted value scatter plots like the prior experimental setup.
A) GSC2, B) Mack-GSC7, C) Mack-GSC14, D) Mack-GSC18, E) Mack-GSC20, F) Mack-GSC25, G) Mack-GSC27, H) Mack-GSC35, I) Mack-GSC36, J) Mack-GSC38, K) Mack-GSC44. Each plot is representative of the model run (out of the 10 runs per dataset) that produced the highest PCC for that dataset and the axes represent RNA-seq count values after log2 transformation. The ranges of both the true and predicted values for each dataset follow the previous testing closely. Additionally, as in the previous testing, the predicted values histograms’ shapes and sizes in each visualization differ from each other but follow the same trends.