Introduction

Spatial gene co-expression analysis aims to identify gene modules with similar expression patterns in space, helping to understand gene interactions, functional groupings, and the mining of core genes. Through in-depth mining of spatial transcriptomics data, the spatial heterogeneity of tissue structure and function can be revealed. It provides a geneset basis for various downstream analyses, including gene set enrichment analysis, protein interaction network analysis and public bulk database validation.

Algorithm recommendation

If you want to quickly obtain results, it is recommended to try hdWGCNA and NeST first. hdWGCNA shows higher consistency in functional analysis of co-expressed gene sets, while NeST demonstrates more accurate spatial pattern recognition. In addition, if your data matrix is too sparse, you can consider using Hotspot for processing.For detailed algorithm evaluation information, please refer to this website.

Algorithm	hdWGCNA	NeST	Hotspot
Result scale	⭐⭐	⭐⭐	⭐⭐⭐
Spatial pattern recognition	⭐⭐	⭐⭐⭐	⭐
Gene function consistency	⭐⭐⭐	⭐	⭐⭐
Running time	⭐⭐⭐	⭐⭐⭐	⭐

Reference

• DeTomaso, D., & Yosef, N. (2021). Hotspot identifies informative gene modules across modalities of single-cell genomics. Cell systems, 12(5), 446-456.

• Morabito, S., Reese, F., Rahimzadeh, N., Miyoshi, E., & Swarup, V. (2023). hdWGCNA identifies co-expression networks in high-dimensional transcriptomics data. Cell reports methods, 3(6).

• Walker, B. L., & Nie, Q. (2023). NeST: nested hierarchical structure identification in spatial transcriptomic data. Nature communications, 14(1), 6554.