SSA: a novel method for Single-cell and Spatial transcriptomics Alignment

Abstract

Single-cell RNA sequencing (scRNA-seq) provides expression profiles of individual cells but fails to preserve crucial spatial information. On the other hand, Spatial Transcrip- tomics technologies are able to analyze specific regions within tissue sections, but lack of the capability to examine in single-cell resolution. To overcome these issues, we present Single-cell and Spatial transcriptomics Alignment (SSA), a novel technique that employs an optimal transport algorithm to assign individual cells from a scRNA-seq atlas to their spa- tial locations in actual tissue based on their expression profiles. SSA has demonstrated su- perior performance compared to existing methods SpaOTsc, Tangram, Seurat and DistMap using 10 semi-simulated datasets generated from a high-resolution spatial transcriptomics human breast cancer dataset with 100,064 cells. This advancement provides a refined tool for researchers to delve deeper in understanding of the relationship between cellular spatial organization and gene expression.

Bang Tran

Assistant Professor

My research interests include single-cell imputation, single-cell analysis.