Scalable Condition-relevant Cell Niche Analysis of Spatial Omics Data with Taichi

Cui, Y.; Yuan, Z.

doi:10.1101/2024.05.30.596656

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bioRxiv · 10.1101/2024.05.30.596656

Scalable Condition-relevant Cell Niche Analysis of Spatial Omics Data with Taichi

Cui, Y.·Yuan, Z.

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Abstract

Tissues are composed of heterogeneous cell niches, which can be investigated using spatial omics technologies. Large consortia have accumulated vast amounts of spatially resolved data, which typically assign slice-level condition labels without considering intra-slice heterogeneity, particularly differential cell niches that respond to certain perturbations. Here, we present Taichi, an efficient and scalable method for condition-relevant cell niche analysis that does not rely on pre-defined discrete spatial clustering. Taichi utilizes a scalable spatial co-embedding approach that effectively accounts for batch effects, incorporating advanced label refinement and graph heat diffusion techniques to explore condition-relevant cell niches across extensive multi-slice and multi-condition spatial omics datasets. Comprehensive benchmarks demonstrate Taichis ability to precisely identify condition-relevant niches under various levels of perturbations. We showcase Taichis effectiveness in accurately delineating major shifts in cell niches in a mouse model of diabetic kidney disease compared to a normal group, revealing disease-specific cell-cell interactions and spatial gene expression patterns. Furthermore, Taichi can identify key subtype-relevant niches between colorectal cancer patient groups with significantly different survival outcomes. Moreover, we demonstrate that Taichi can help discover more fine-grained clinical properties within the originally coarse-defined patient groups in large-scale tumor spatial atlases, reflecting intra-group heterogeneity obscured previously. Additionally, we combine Taichi and tensor decomposition to discover higher-order biomarkers relevant to the immunotherapy response of triple-negative breast cancer. Finally, we highlight Taichis speed and scalability by confirming its unique applicability in large-scale scenarios containing up to 16 million cells in [~] 12 minutes. Taichi provides a powerful tool for mining disease-relevant spatially resolved insights in the era of big data in spatial biology.

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Cui, Y., Yuan, Z.. 2024-06-03. Scalable Condition-relevant Cell Niche Analysis of Spatial Omics Data with Taichi. https://doi.org/10.1101/2024.05.30.596656

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