Evaluation of spatial transcriptomics (ST) tools across various metrics and conditions.

(A) Bar plots showing Pearson correlation coefficients (PCCs) and Root Mean Square Errors (RMSEs) between predicted and true proportions in synthetic ST datasets under different spot resolution levels (Lambda = 5, 10, 15, and 20). The Lambda<...

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Bibliographic Details
Main Author:	Huamei Li (8815955) (author)
Other Authors:	Jingchao Liu (2051245) (author), Guige Wang (22634210) (author), Zhenyu Liu (179092) (author), Meng Cao (105914) (author), Lingyun Sun (255929) (author), Cheng Peng (118834) (author), Yiyao Liu (554854) (author), Liang Ma (37793) (author), Qing Xiong (136442) (author)
Published:	2025
Subjects:	Cell Biology Molecular Biology Neuroscience Biotechnology Developmental Biology Cancer Plant Biology Environmental Sciences not elsewhere classified Biological Sciences not elsewhere classified Information Systems not elsewhere classified synthetic data demonstrated reconstructing tissue architectures minimizing assignment algorithm method integrates information improve signal fidelity effectively delineating fine 10 &# 215 theoretic gene selection incomplete gene capture spatial transcriptomic spots spatial hotspot detection segments spatial spots scale spatial patterns lacks spatial context handling unmatched datasets balances transcriptional similarity xlink "> single maps single cells results highlight cell2spatial cell2spatial incorporates neural gene complexity spatial transcriptomics spatial proximity spatial organization spatial coherence transcriptional heterogeneity developed cell2spatial st datasets visium hd versatile framework powerful tool particular strength mouse kidney human thymus guided clustering fully matched enhance scalability developmental trajectories computational framework complex tissues cellular function cellular compositions analytical scope
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Evaluation of spatial transcriptomics (ST) tools across various metrics and conditions.

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