CASPER: Cross-modal Alignment of Spatial and single-cell Profiles for Expression Recovery

TL;DR

CASPER is a novel computational framework that enhances spatial transcriptomics data by accurately predicting unmeasured gene expressions through cross-modal alignment with single-cell RNA sequencing data, improving analysis capabilities.

Contribution

Introduces CASPER, a cross-attention based model that effectively predicts unmeasured gene expressions in spatial transcriptomics by leveraging single-cell data, outperforming existing methods.

Findings

CASPER significantly improves prediction accuracy across multiple metrics.

The model demonstrates robust performance across diverse datasets.

It advances the integration of spatial and single-cell transcriptomics data.

Abstract

Spatial Transcriptomics enables mapping of gene expression within its native tissue context, but current platforms measure only a limited set of genes due to experimental constraints and excessive costs. To overcome this, computational models integrate Single-Cell RNA Sequencing data with Spatial Transcriptomics to predict unmeasured genes. We propose CASPER, a cross-attention based framework that predicts unmeasured gene expression in Spatial Transcriptomics by leveraging centroid-level representations from Single-Cell RNA Sequencing. We performed rigorous testing over four state-of-the-art Spatial Transcriptomics/Single-Cell RNA Sequencing dataset pairs across four existing baseline models. CASPER shows significant improvement in nine out of the twelve metrics for our experiments. This work paves the way for further work in Spatial Transcriptomics to Single-Cell RNA Sequencing modality translation. The code for CASPER is available at https://github.com/AI4Med-Lab/CASPER.