SpaNN: Spatial Transcriptomic Data Enhancement Using Deep Neural Network

Spatial transcriptomic sequencing technology is a powerful tool that combines gene expression data with their physical locations in tissues or organs, providing researchers with unprecedented spatial resolution of cellular molecular functions. Currently, spatial transcriptomic sequencing based on in situ hybridization and imaging can obtain cell location information and transcriptome profiles at single-cell resolution, but it only detects a limited number of genes, which restricts its application in exploring whole-genome expression patterns. Therefore, it is essential to predict the spatial distribution of undetected genes in their spatial transcriptomic data. Here, we introduce a novel data enhancement technique, named SpaNN, which predicts transcriptome expression levels in spatial context. SpaNN employs a custom-designed similarity loss that leverages location information from spatial transcriptomic data to train a deep neural network. This network captures joint embeddings and uses a weighted k-nearest-neighbor approach to predict the unmeasured genes spatial expression levels. Our experiments show that SpaNN not only recovers the expression levels of unmeasured genes but also enhances cell clustering and visualization. Additionally, sensitivity and scalability analyses confirm that SpaNN is robust to parameter variations and can handle large-scale datasets effectively.