54: Optimal Gene Panel Selection for Targeted Spatial Transcriptomics Experiments

Music City Center 

Spatial transcriptomics is an emerging and transformative technique that provides high-resolution insights into gene expression patterns across diverse cell populations. However, because most single-cell resolution spatial profiling methods can only measure a limited set of genes, it is crucial to select a gene panel that optimally captures the biological information. Methods for optimal gene panel design are still lacking. Here, we introduce a novel method, optimal reconstruction genes selection for spatial transcriptomics (ReconST), incorporating a specifically designed autoencoder model to identify a minimal yet highly informative set of genes. By training our model on single-cell RNA sequencing (scRNA-seq) data, we show that this selected gene panel optimally reconstructs the full transcriptome. We validate our approach on paired scRNAseq data and MERFISH data, demonstrating improved reconstruction accuracy and a clear representation of spatial patterns. ReconST provides a practical and explainable framework for optimal gene panel selection, advancing the use of spatial transcriptomics to deepen our understanding of gene expression in tissue contexts.

Spatial Transcriptomics

Gene Panel Selection

Self-supervised learning

Deep learning

Regularization 

Section on Statistics in Genomics and Genetics