Improved reconstruction of single-cell developmental potential with CytoTRACE 2
Minji Kang(California Institute for Regenerative Medicine), Gunsagar S. Gulati(Dana-Farber Cancer Institute), Erin L. Brown(California Institute for Regenerative Medicine), Zhen Qi(California Institute for Regenerative Medicine), Susanna Avagyan(Stanford University), José Juan Almagro Armenteros(California Institute for Regenerative Medicine), Rachel Gleyzer(California Institute for Regenerative Medicine), Wubing Zhang(California Institute for Regenerative Medicine), Chloé B. Steen(Oslo University Hospital), Jeremy Philip D’Silva(California Institute for Regenerative Medicine), Janella C. Schwab(California Institute for Regenerative Medicine), Michael F. Clarke(California Institute for Regenerative Medicine), Aadel A. Chaudhuri(Mayo Clinic), Aaron M. Newman(California Institute for Regenerative Medicine)
Cited by 47Open Access
Abstract
While single-cell RNA sequencing has advanced our understanding of cell fate, identifying molecular hallmarks of potency-a cell's ability to differentiate into other cell types-remains a challenge. Here we introduce CytoTRACE 2, an interpretable deep learning framework for predicting absolute developmental potential from single-cell RNA sequencing data. Across diverse platforms and tissues, CytoTRACE 2 outperformed previous methods in predicting developmental hierarchies, enabling detailed mapping of single-cell differentiation landscapes and expanding insights into cell potency.
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