Omics Imagification: Transforming High-throughput Molecular Representation of a Cell into an Image

Different omics profiles, depending on the underlying technology, encompass measurements of several hundred to several thousands of molecules in a biological sample or a cell. This study develops upon the concept of "omics imagification" as a process of transforming a vector representing these numerical measurements into an image with a one-to-one relationship with the corresponding sample. The proposed imagification process transforms a high-dimensional vector of molecular measurements into a two-dimensional RGB image to enable holistic molecular representation of a biological sample and to improve the classification of different biological phenotypes using automated image recognition methods in computer vision. A transformed image represents 2D-coordinates of molecules in a neighbour embedded space representing molecular abundance and gene intensity. The proposed method was applied to single-cell RNA sequencing (scRNA-seq) data to "imagify" gene expression profiles of individual cells. Our results show that a simple convolutional neural network trained on these single-cell transcriptomics images accurately classifies diverse cell types outperforming the best-performing scRNA-seq classifiers such as Support Vector Machine and Random Forrest.