MUSTANG: Multi-sample spatial transcriptomics data analysis with cross-sample transcriptional similarity guidance

Abstract

Summary: Spatially resolved transcriptomics has revolutionized genome-scale transcriptomic profiling by providing high-resolution characterization of transcriptional patterns. Here, we present our spatial transcriptomics analysis framework, MUSTANG (MUlti-sample Spatial Transcriptomics data ANalysis with cross-sample transcriptional similarity Guidance), which is capable of performing multi-sample spatial transcriptomics spot cellular deconvolution by allowing both cross-sample expression-based similarity information sharing as well as spatial correlation in gene expression patterns within samples. Experiments on a semi-synthetic spatial transcriptomics dataset and three real-world spatial transcriptomics datasets demonstrate the effectiveness of MUSTANG in revealing biological insights inherent in the cellular characterization of tissue samples under study. The bigger picture: Spatial transcriptomics (ST) enables the localization of cell types and their associated gene expression within tissue samples. In multi-cellular resolution ST, a tissue is divided into spots consisting of several cells, and this sometimes creates difficulties for cell characterization and identification in complex tissue samples. There are several methods for spot deconvolution, but most are limited to single-sample analysis and require a reference cellular profile. Here, we present MUSTANG (MUlti-sample Spatial Transcriptomics data ANalysis with cross-sample transcriptional similarity Guidance), a data analysis framework that permits multi-sample spot cellular deconvolution without a reference expression profile.

Keywords

• spatial transcriptomics
• multi-sample analysis
• cellular deconvolution
• gene expression
• Bayesian modeling