  We developed OmicsMapNet approach to take advantage of existing deep leaning
frameworks to analyze high-dimensional omics data as 2-dimensional images. The
omics data of individual samples were first rearranged into 2D images in which
molecular features related in functions, ontologies, or other relationships
were organized in spatially adjacent and patterned locations. Deep learning
neural networks were trained to classify the images. Molecular features
informative of classes of different phenotypes were subsequently identified. As
an example, we used the KEGG BRITE database to rearrange RNA-Seq expression
data of TCGA diffuse glioma samples as treemaps to capture the functional
hierarchical structure of genes in 2D images. Deep Convolutional Neural
Networks (CNN) were derived using tools from TensorFlow to learn the grade of
TCGA LGG and GBM samples with relatively high accuracy. The most contributory
features in the trained CNN were confirmed in pathway analysis for their
plausible functional involvement.
