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bioRxiv · 10.1101/2022.12.15.520612

Accurate cell type deconvolution in spatial transcriptomics using a batch effect-free strategy

Wang, L.·Wu, L.·Liu, C.·Wang, W.·Zhang, X. H.- F.·Liu, Z.
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Abstract

Sequencing-based spatial transcriptomics (ST) techniques have been groundbreaking in dissecting cell-cell communications within tissues by profiling positional gene expression. However, the most widely used ST technique, Visium Spatial Gene Expression by 10x Genomics (Visium), does not provide single-cell resolution, making it difficult to profile cell type-level information. Many reference-based deconvolution methods have been developed to increase its resolution, but the platform and batch effects between the reference and ST data compromise their accuracy. Here, we propose a new approach, Region-based cell Sorting (ReSort), that generates a pseudo-internal-reference to reduce these platform effects. By simulating ST datasets under various scenarios, we demonstrate that ReSort significantly improves the accuracy of six state-of-the-art reference-based deconvolution methods. Moreover, applying ReSort to a mouse breast cancer tumor bearing both epithelial and mesenchymal clones identifies the spatial differences of immune cells between the clones, providing important insights for understanding the relationship between epithelial-mesenchymal transition and immune infiltration in breast cancer.

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Wang, L., Wu, L., Liu, C., Wang, W., Zhang, X. H.- F., Liu, Z.. 2022-12-19. Accurate cell type deconvolution in spatial transcriptomics using a batch effect-free strategy. https://doi.org/10.1101/2022.12.15.520612

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