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

TL;DR

OmicsMapNet transforms high-dimensional omics data into 2D images to leverage deep CNNs for phenotype classification and feature identification, demonstrating high accuracy in glioma grading.

Contribution

The paper introduces OmicsMapNet, a novel method that converts omics data into images for deep learning analysis, enabling functional feature discovery.

Findings

High classification accuracy for glioma grades

Identification of biologically relevant features

Effective use of hierarchical gene organization

Abstract

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.