
==== Front
bioRxiv
BIORXIV
bioRxiv
2692-8205
Cold Spring Harbor Laboratory

10.1101/2024.08.23.609477
preprint
1
Article
SPACE: Spatially variable gene clustering adjusting for cell type effect for improved spatial domain detection
Das Adhikari Sikta http://orcid.org/0009-0006-2433-8200

Steele Nina G. http://orcid.org/0000-0003-1564-3562

Theisen Brian http://orcid.org/0009-0004-1079-8594

Wang Jianrong http://orcid.org/0000-0002-9290-4888

Cui Yuehua http://orcid.org/0000-0001-8099-1753

25 8 2024
2024.08.23.609477https://creativecommons.org/licenses/by/4.0/ This work is licensed under a Creative Commons Attribution 4.0 International License, which allows reusers to distribute, remix, adapt, and build upon the material in any medium or format, so long as attribution is given to the creator. The license allows for commercial use.
http://biorxiv.org/lookup/doi/10.1101/2024.08.23.609477
nihpp-2024.08.23.609477.pdf
Abstract

Recent advances in spatial transcriptomics have significantly deepened our understanding of biology. A primary focus has been identifying spatially variable genes (SVGs) which are crucial for downstream tasks like spatial domain detection. Traditional methods often use all or a set number of top SVGs for this purpose. However, in diverse datasets with many SVGs, this approach may not ensure accurate results. Instead, grouping SVGs by expression patterns and using all SVG groups in downstream analysis can improve accuracy. Furthermore, classifying SVGs in this manner is akin to identifying cell type marker genes, offering valuable biological insights. The challenge lies in accurately categorizing SVGs into relevant clusters, aggravated by the absence of prior knowledge regarding the number and spectrum of spatial gene patterns. Addressing this challenge, we propose SPACE, SPatially variable gene clustering Adjusting for Cell type Effect, a framework that classifies SVGs based on their spatial patterns by adjusting for confounding effects caused by shared cell types, to improve spatial domain detection. This method does not require prior knowledge of gene cluster numbers, spatial patterns, or cell type information. Our comprehensive simulations and real data analyses demonstrate that SPACE is an efficient and promising tool for spatial transcriptomics analysis.

Key Points

SPACE eliminates the need for prior knowledge about the number of gene clusters, known cell types, or the quantity of SVGs to identify clusters for downstream analysis.

SPACE offers a method to effectively leverage SVGs for low-dimensional embedding within each cluster to improve the accuracy of spatial domain detection.

The efficiency and utility of the SPACE algorithm have been validated across multiple datasets and simulations, demonstrating its effectiveness in producing meaningful and interpretable results.
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