Leveraging spatial transcriptomics data to recover cell locations in single-cell RNA-seq with CeLEry

Qihuang Zhang (), Shunzhou Jiang, Amelia Schroeder, Jian Hu, Kejie Li, Baohong Zhang, David Dai, Edward B. Lee, Rui Xiao and Mingyao Li ()
Additional contact information
Qihuang Zhang: McGill University
Shunzhou Jiang: University of Pennsylvania
Amelia Schroeder: University of Pennsylvania
Jian Hu: Emory University
Kejie Li: Biogen, Inc.
Baohong Zhang: Biogen, Inc.
David Dai: University of Pennsylvania
Edward B. Lee: University of Pennsylvania
Rui Xiao: University of Pennsylvania
Mingyao Li: University of Pennsylvania

Nature Communications, 2023, vol. 14, issue 1, 1-19

Abstract: Abstract Single-cell RNA sequencing (scRNA-seq) has revolutionized our understanding of cellular heterogeneity in health and disease. However, the lack of physical relationships among dissociated cells has limited its applications. To address this issue, we present CeLEry (Cell Location recovEry), a supervised deep learning algorithm that leverages gene expression and spatial location relationships learned from spatial transcriptomics to recover the spatial origins of cells in scRNA-seq. CeLEry has an optional data augmentation procedure via a variational autoencoder, which improves the method’s robustness and allows it to overcome noise in scRNA-seq data. We show that CeLEry can infer the spatial origins of cells in scRNA-seq at multiple levels, including 2D location and spatial domain of a cell, while also providing uncertainty estimates for the recovered locations. Our comprehensive benchmarking evaluations on multiple datasets generated from brain and cancer tissues using Visium, MERSCOPE, MERFISH, and Xenium demonstrate that CeLEry can reliably recover the spatial location information for cells using scRNA-seq data.

Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://www.nature.com/articles/s41467-023-39895-3 Abstract (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-023-39895-3

Ordering information: This journal article can be ordered from
https://www.nature.com/ncomms/

DOI: 10.1038/s41467-023-39895-3

Access Statistics for this article

Nature Communications is currently edited by Nathalie Le Bot, Enda Bergin and Fiona Gillespie

More articles in Nature Communications from Nature
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().