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We developed OmicsMapNet approach to take advantage of existing deep leaningframeworks to analyze high-dimensional omics data as 2-dimensional images. Theomics data of individual samples were first rearranged into 2D images in whichmolecular features related in functions, ontologies, or other relationshipswere organized in spatially adjacent and patterned locations. Deep learningneural networks were trained to classify the images. Molecular featuresinformative of classes of different phenotypes were subsequently identified. Asan example, we used the KEGG BRITE database to rearrange RNA-Seq expressiondata of TCGA diffuse glioma samples as treemaps to capture the functionalhierarchical structure of genes in 2D images. Deep Convolutional NeuralNetworks (CNN) were derived using tools from TensorFlow to learn the grade ofTCGA LGG and GBM samples with relatively high accuracy. The most contributoryfeatures in the trained CNN were confirmed in pathway analysis for theirplausible functional involvement.

OmicsMapNet: Transforming omics data to take advantage of Deep Convolutional Neural Network for discovery