Towards better understanding of developmental disorders from integration of spatial single-cell transcriptomics and epigenomics

The recent emerging techniques of single cell spatial RNA seq makes it possible to profile the transcriptomics data at single cell resolution without loss of the spatial information. However, it is still a challenge to measure epigenomics profiles at spatial levels. In this project, we developed an autoencoder based multi-omics integration method and applied it on spatial mouse fetal brain data to reconstruct the spatial epigenomics profiles. We compared our method with LIGER and showed its better performance on a public dataset measured by latent mixing metrics. We further developed a CNN model to predict autism risk genes based on the spatial RNA seq data. Our model is able to prioritize autism risk genes from whole genome level.