Article

Spatial Deconvolution of Cell Types and Cell States at Scale Utilizing TACIT

2024-06-03

Abstract excerpt

<h4>ABSTRACT</h4> Identifying cell types and states remains a time-consuming and error-prone challenge for spatial biology. While deep learning is increasingly used, it is difficult to generalize due to variability at the level of cells, neighborhoods, and niches in health and disease. To address this, we developed TACIT, an unsupervised algorithm for cell annotation using predefined signatures that operates with...

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Literature Corpus work: 0cd76f37-535f-5d0c-a3c4-fa4adbdab9bd
DOI: 10.1101/2024.05.31.596861

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