SpaOTsc: Inferring spatial and signaling relationships between cells from single cell transcriptomic data

Investigators

Qing Nie

Contact info (email)

qnie@uci.edu

1. Define context(s)

reveal new biological insights

Current Conformance Level / Target Conformance Level

Extensive

Primary goal of the model/tool/database

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.

Biological domain of the model

scRNA-seq data of various tissues

Structure(s) of interest in the model

Spatial arrangement of scRNA-seq data, cell-cell communications

Spatial scales included in the model

cellular to tissue

Time scales included in the model

seconds to weeks

2. Data for building and validating the model

Data for building the model	Published?	Private?	How is credibility checked?	Current Conformance Level / Target Conformance Level
in vitro (primary cells cell, lines, etc.)
ex vivo (excised tissues)
in vivo pre-clinical (lower-level organism or small animal)	Yes	No	The model was built in an unsupervised way on unbiased single-cell RNA sequencing data and spatial data.	Extensive
in vivo pre-clinical (large animal)	Yes	No	The model was built in an unsupervised way on unbiased single-cell RNA sequencing data and spatial data.	Extensive
Human subjects/clinical
Other: ________________________

Data for validating the model	Published?	Private?	How is credibility checked?	Current Conformance Level / Target Conformance Level
in vitro (primary cells cell, lines, etc.)
ex vivo (excised tissues)
in vivo pre-clinical (lower-level organism or small animal)	Yes	No	Cross validation using spatial data. By comparing the model determined cell-cell communications to knowledge	Adequate
in vivo pre-clinical (large animal)	Yes	No	Cross validation using spatial data. By comparing the model determined cell-cell communications to knowledge	Adequate
Human subjects/clinical
Other: ________________________

3. Validate within context(s)

	Who does it?	When does it happen?	How is it done?	Current Conformance Level / Target Conformance Level
Verification	Postdocs/investigators	Throughout the project	1) The convergence of algorithm is guaranteed. 2) The computational results for projecting scRNA-seq data to space are consistent with expectations.	Extensive
Validation	Postdocs/investigators	As the unsupervised model was established	1) The spatial projection of scRNA-seq data is cross-validated in predicting known spatial gene expression. 2) The inferred spatial origins of scRNA-seq data is validated by comparing to known spatial locations of specific cell types. 3) The inferred cell-cell communication networks agree with available knowledge.	Extensive
Uncertainty quantification
Sensitivity analysis	Postdocs/investigators	As the unsupervised model was established	By tuning key parameters and comparing to annotated data.	Adequate
Other:__________
Additional Comments

4. Limitations

Disclaimer statement (explain key limitations)	Who needs to know about this disclaimer?	How is this disclaimer shared with that audience?	Current Conformance Level / Target Conformance Level
The technical noise of and the difference between single-cell RNA sequencing data and spatial data might cause inaccuracy.	Scientific community who intends to apply this method to raw scRNA-seq data.	In discussion of the paper.	Adequate

5. Version control

Current Conformance Level / Target Conformance Level
Extensive

	Naming Conventions?	Repository?	Code Review?
individual modeler	Yes	Yes	Peer
within the lab	Yes	Yes	Peer
collaborators

6. Documentation

	Current Conformance Level / Target Conformance Level
Code commented?	Extensive
Scope and intended use described?	Extensive
User’s guide?	Extensive
Developer’s guide?	Partial

7. Dissemination

Current Conformance Level / Target Conformance Level
Extensive

Target Audience(s):	“Inner circle”	Scientific community
Simulations
Models
Software	Python package: https://github.com/zcang/SpaOTsc	Python package: https://github.com/zcang/SpaOTsc
Results	Shared folders	Paper and tutorials
Implications of results

8. Independent reviews

Current Conformance Level / Target Conformance Level
Insufficient

Reviewer(s) name & affiliation:
When was review performed?
How was review performed and outcomes of the review?

9. Test competing implementations

Current Conformance Level / Target Conformance Level
Adequate

	Yes or No (briefly summarize)
Were competing implementations tested?	Yes. The method has been compared to several other commonly used methods on benchmark datasets.
Did this lead to model refinement or improvement?	No

10. Conform to standards

Current Conformance Level / Target Conformance Level
Adequate

	Yes or No (briefly summarize)
Are there operating procedures, guidelines, or standards for this type of multiscale modeling?	Yes. There are several standard procedures for preprocessing scRNA-seq data.
How do your modeling efforts conform?	Common data preprocessing procedures are followed.