HE2Gene: image-to-RNA translation via multi-task learning for spatial transcriptomics data

Xingjian Chen, Jiecong Lin, Yuchen Wang, Weitong Zhang, Weidun Xie, Zetian Zheng, Ka-Chun Wong

Abstract: Tissue context and molecular profiling are commonly used measures in understanding normal development and disease pathology. In recent years, the development of spatial molecular profiling technologies (e.g. spatial resolved transcriptomics) has enabled the exploration of quantitative links between tissue morphology and gene expression. However, these technologies remain expensive and time-consuming, with subsequent analyses necessitating high-throughput pathological annotations. On the other hand, existing computational tools are limited to predicting only a few dozen to several hundred genes, and the majority of the methods are designed for bulk RNA-seq.