Recent advances in spatially variable gene detection in spatial transcriptomics

Sikta Das Adhikari; Jiaxin Yang; Jianrong Wang; Yuehua Cui

doi:10.1016/j.csbj.2024.01.016

Recent advances in spatially variable gene detection in spatial transcriptomics

Comput Struct Biotechnol J. 2024 Feb 2:23:883-891. doi: 10.1016/j.csbj.2024.01.016. eCollection 2024 Dec.

Authors

Sikta Das Adhikari^{1

2}, Jiaxin Yang¹, Jianrong Wang¹, Yuehua Cui²

Affiliations

¹ Department of Computational Mathematics, Science and Engineering, Michigan State University, East Lansing, MI 48824, USA.
² Department of Statistics and Probability, Michigan State University, East Lansing, MI 48824, USA.

Abstract

With the emergence of advanced spatial transcriptomic technologies, there has been a surge in research papers dedicated to analyzing spatial transcriptomics data, resulting in significant contributions to our understanding of biology. The initial stage of downstream analysis of spatial transcriptomic data has centered on identifying spatially variable genes (SVGs) or genes expressed with specific spatial patterns across the tissue. SVG detection is an important task since many downstream analyses depend on these selected SVGs. Over the past few years, a plethora of new methods have been proposed for the detection of SVGs, accompanied by numerous innovative concepts and discussions. This article provides a selective review of methods and their practical implementations, offering valuable insights into the current literature in this field.

Keywords: Single cell RNA sequencing; Spatial transcriptomics; Spatially resolved transcriptomics; Spatially variable genes.

Publication types

Review

Grants and funding

R01 GM131398/GM/NIGMS NIH HHS/United States