DEEPsc: A Deep Learning-Based Map Connecting Single-Cell Transcriptomics and Spatial Imaging 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) data provides unprecedented information on cell fate decisions; however, the spatial arrangement of cells is often lost. Several recent computational methods have been developed to impute spatial information onto a scRNA-seq dataset through analyzing known spatial expression patterns of a small subset of genes known as a reference atlas. However, there is a lack of comprehensive analysis of the accuracy, precision, and robustness of the mappings, along with the generalizability of these methods, which are often designed for specific systems. We present a system-adaptive deep learning-based method (DEEPsc) to impute spatial information onto a scRNA-seq dataset from a given spatial reference atlas. By introducing a comprehensive set of metrics that evaluate the spatial mapping methods, we compare DEEPsc with four existing methods on four biological systems. We find that while DEEPsc has comparable accuracy to other methods, an improved balance between precision and robustness is achieved. DEEPsc provides a data-adaptive tool to connect scRNA-seq datasets and spatial imaging datasets to analyze cell fate decisions. Our implementation with a uniform API can serve as a portal with access to all the methods investigated in this work for spatial exploration of cell fate decisions in scRNA-seq data. All methods evaluated in this work are implemented as an open-source software with a uniform interface.

Biological domain of the model
scRNA-seq and spatial data of various tissues
Structure(s) of interest in the model
Spatial origin of scRNA-seq data
Spatial scales included in the model
cellular to tissue
Time scales included in the model
N/A
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)
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. Adequate
in vivo pre-clinical (large animal)
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 Students/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 Students/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. Extensive
Uncertainty quantification
Sensitivity analysis Students/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 Public
Simulations
Models
Software Matlab package: https://github.com/fmaseda/DEEPsc Matlab package: https://github.com/fmaseda/DEEPsc Matlab package: https://github.com/fmaseda/DEEPsc
Results https://github.com/fmaseda/DEEPsc Paper and Github repo: https://github.com/fmaseda/DEEPsc Paper and Github repo: https://github.com/fmaseda/DEEPsc
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.