SpaDecon: cell-type deconvolution in spatial transcriptomics with semi-supervised learning

Kyle Coleman; Jian Hu; Amelia Schroeder; Edward B. Lee; Mingyao Li

doi:10.1038/s42003-023-04761-x

Communications Biology (Apr 2023)

SpaDecon: cell-type deconvolution in spatial transcriptomics with semi-supervised learning

Kyle Coleman,
Jian Hu,
Amelia Schroeder,
Edward B. Lee,
Mingyao Li

Affiliations

Kyle Coleman: Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania
Jian Hu: Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania
Amelia Schroeder: Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania
Edward B. Lee: Translational Neuropathology Research Laboratory, Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania
Mingyao Li: Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania

DOI: https://doi.org/10.1038/s42003-023-04761-x
Journal volume & issue: Vol. 6, no. 1
pp. 1 – 13

Abstract

Read online

SpaDecon is a semi-supervised learning-based method for cell-type deconvolution of spatially resolved transcriptomics (SRT) data that is also computationally fast and memory efficient for large-scale SRT studies.

Published in Communications Biology

ISSN: 2399-3642 (Online)
Publisher: Nature Portfolio
Country of publisher: United Kingdom
LCC subjects: Science: Biology (General)
Website: https://www.nature.com/commsbio/

About the journal