Batch correcting single-cell spatial transcriptomics count data with Crescendo improves visualization and detection of spatial gene patterns

Abstract

Spatial transcriptomics facilitates gene expression analysis of cells in their spatial anatomical context. Batch effects hinder visualization of gene spatial patterns across samples. We present the Crescendo algorithm to correct for batch effects at the gene expression level and enable accurate visualization of gene expression patterns across multiple samples. We show Crescendo’s utility and scalability across three datasets ranging from 170,000 to 7 million single cells across spatial and single-cell RNA sequencing technologies. By correcting for batch effects, Crescendo enhances spatial transcriptomics analyses to detect gene colocalization and ligand-receptor interactions and enables cross-technology information transfer.