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.

Publication
Proceedings of the 16th International Conference on Bioinformatics and Computational Biology (BICOB-2024)
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Bang Tran
Assistant Professor

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