Interpretable and context-free deconvolution of multi-scale whole transcriptomic data with UniCell deconvolve
oleh: Daniel Charytonowicz, Rachel Brody, Robert Sebra
| Format: | Article |
|---|---|
| Diterbitkan: | Nature Portfolio 2023-03-01 |
Deskripsi
There is interest in measuring the influence of spatial cellular organization on pathophysiology, which is being accomplished through spatial transcriptomics. There the authors present UniCell Deconvolve, a pre-trained deep learning model that predicts cell identity and deconvolves cell type fractions using a 28 M cell database.