Summary of real datasets used for analysis.

<div><p>Accurate determination of cell-type composition in disease-relevant tissues is essential for identifying potential disease targets and understanding tissue heterogeneity. Most current spatial transcriptomics (ST) technologies lack single-cell resolution, which makes precise cell-...

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Bibliographic Details
Main Author:	Lin Huang (288599) (author)
Other Authors:	Xiaofei Liu (72796) (author), Fangfang Zhu (1462585) (author), Wenwen Min (10906372) (author)
Published:	2025
Subjects:	Molecular Biology Immunology Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Information Systems not elsewhere classified unified latent representation thereby reducing discrepancies model &# 8217 methods often overlook extracting reliable features effectively minimize noise mitigate spatial artifacts several deconvolution methods makes precise cell cell rna sequencing adversarial masked autoencoder type composition inference current spatial transcriptomics understanding tissue heterogeneity technologies lack single 32 simulated datasets st data spots st data generated cell type composition real st data spadama leverages domain spadama </ p type composition spatial transcriptomics type deconvolution st data 4 real improving cell data modalities cell resolution adversarial training adversarial learning validated spadama spadama harmonizes world datasets target domain superior performance source domain relevant tissues promising tool inherent differences
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Summary of real datasets used for analysis.

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