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stSCI: A multi-task learning framework for integrative analysis of single-cell and spatial transcriptomics data
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DOI：10.1016/j.xinn.2025.101220.png)
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Abstract
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Spatial transcriptomics (ST) preserves spatial context in gene expression analysis yet faces limitations like low resolution and RNA capture inefficiency. To address these, we present stSCI, a computational method integrating single-cell (SC) and ST data into a unified, batch-corrected embedding space. stSCI employs a fusion module with three specialized optimization tasks to generate biologically preserved joint latent representations, enabling five key analyses: spatial domain identification in single/multi-slice ST data; ST deconvolution predicting cell type proportions in low-resolution spots; SC spatial coordinate reconstruction using ST references; and cross-modality batch correction. Evaluated on 13 set of different ST datasets spanning sequencing- and imaging-based platforms, and benchmarked against 27 state-of-the-art methods, stSCI improves spatial domain identification, maps cell type proportions in ST data, accurately reconstructs tissue architecture and regional structures, and integrates SC/ST datasets by removing batch effects without compromising biological signals. In a key application, stSCI successfully resolves the dynamic spatiotemporal response of a lymphatic niche during Salmonella infection, demonstrating its power to generate novel biological insights from complex disease models. stSCI’s robustness and versatility make it a powerful tool for uncovering tissue organization and molecular functions.
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