Recovering single-cell expression profiles from spatial transcriptomics with scResolve.

Saved in:
Bibliographic Details
Title: Recovering single-cell expression profiles from spatial transcriptomics with scResolve.
Authors: Chen H; Ray and Stephanie Lane Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213, USA., Lee YJ; Ray and Stephanie Lane Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213, USA., Ovando-Ricardez JA; Dorothy M. Davis Heart and Lung Research Institute, Division of Pulmonary, Critical Care and Sleep Medicine, Department of Internal Medicine, The Ohio State University, Columbus, OH 43210, USA., Rosas L; Dorothy M. Davis Heart and Lung Research Institute, Division of Pulmonary, Critical Care and Sleep Medicine, Department of Internal Medicine, The Ohio State University, Columbus, OH 43210, USA., Rojas M; Dorothy M. Davis Heart and Lung Research Institute, Division of Pulmonary, Critical Care and Sleep Medicine, Department of Internal Medicine, The Ohio State University, Columbus, OH 43210, USA., Mora AL; Dorothy M. Davis Heart and Lung Research Institute, Division of Pulmonary, Critical Care and Sleep Medicine, Department of Internal Medicine, The Ohio State University, Columbus, OH 43210, USA., Bar-Joseph Z; Ray and Stephanie Lane Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213, USA; Machine Learning Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213, USA., Lugo-Martinez J; Ray and Stephanie Lane Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213, USA. Electronic address: jlugomar@andrew.cmu.edu.
Source: Cell reports methods [Cell Rep Methods] 2024 Oct 21; Vol. 4 (10), pp. 100864. Date of Electronic Publication: 2024 Sep 25.
Publication Type: Journal Article
Journal Info: Publisher: Elsevier Inc Country of Publication: United States NLM ID: 9918227360606676 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 2667-2375 (Electronic) Linking ISSN: 26672375 NLM ISO Abbreviation: Cell Rep Methods Subsets: MEDLINE
Database: MEDLINE Ultimate
Description
ISSN:2667-2375
DOI:10.1016/j.crmeth.2024.100864