PUBLICATION
Inferring spatial and signaling relationships between cells from single cell transcriptomic data
- Authors
- Cang, Z., Nie, Q.
- ID
- ZDB-PUB-200731-1
- Date
- 2020
- Source
- Nature communications 11: 2084 (Journal)
- Registered Authors
- Keywords
- none
- MeSH Terms
-
- Animals
- Cell Communication
- Cluster Analysis
- Databases, Genetic
- Drosophila/embryology
- Drosophila/genetics
- Gene Expression Regulation, Developmental
- Reproducibility of Results
- Sequence Analysis, RNA
- Signal Transduction/genetics*
- Single-Cell Analysis*
- Transcriptome/genetics*
- Visual Cortex/metabolism
- Zebrafish/embryology
- Zebrafish/genetics
- PubMed
- 32350282 Full text @ Nat. Commun.
Citation
Cang, Z., Nie, Q. (2020) Inferring spatial and signaling relationships between cells from single cell transcriptomic data. Nature communications. 11:2084.
Abstract
Single-cell RNA sequencing (scRNA-seq) provides details for individual cells; however, crucial spatial information is often lost. We present SpaOTsc, a method relying on structured optimal transport to recover spatial properties of scRNA-seq data by utilizing spatial measurements of a relatively small number of genes. A spatial metric for individual cells in scRNA-seq data is first established based on a map connecting it with the spatial measurements. The cell-cell communications are then obtained by "optimally transporting" signal senders to target signal receivers in space. Using partial information decomposition, we next compute the intercellular gene-gene information flow to estimate the spatial regulations between genes across cells. Four datasets are employed for cross-validation of spatial gene expression prediction and comparison to known cell-cell communications. SpaOTsc has broader applications, both in integrating non-spatial single-cell measurements with spatial data, and directly in spatial single-cell transcriptomics data to reconstruct spatial cellular dynamics in tissues.
Genes / Markers
Expression
Phenotype
Mutations / Transgenics
Human Disease / Model
Sequence Targeting Reagents
Fish
Orthology
Engineered Foreign Genes
Mapping