SoptSc: Cell lineage and communication network inference via optimization for single-cell transcriptomics

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

The use of single-cell transcriptomics has become a major approach to delineate cell subpopulations and the transitions between them. While various computational tools using different mathematical methods have been developed to infer clusters, marker genes, and cell lineage, none yet integrate these within a mathematical framework to perform multiple tasks coherently. Such coherence is critical for the inference of cell–cell communication, a major remaining challenge. Here, we present similarity matrix-based optimization for single-cell data analysis (SoptSC), in which unsupervised clustering, pseudotemporal ordering, lineage inference, and marker gene identification are inferred via a structured cell-to-cell similarity matrix. SoptSC then predicts cell–cell communication networks, enabling reconstruction of complex cell lineages that include feedback or feedforward interactions. Application of SoptSC to early embryonic development, epidermal regeneration, and hematopoiesis demonstrates robust identification of subpopulations, lineage relationships, and pseudotime, and prediction of pathway-specific cell communication patterns regulating processes of development and differentiation.

Biological domain of the model
scRNA-seq data of various tissues
Structure(s) of interest in the model
celluar trajectories, 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)
in vivo pre-clinical (large animal) Yes No The model was built in an unsupervised way on unbiased single-cell RNA sequencing 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)
in vivo pre-clinical (large animal) Yes No By comparing the model determined pseudotime and clustering to knowledge based cell type annotation and real temporal points in data. 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 Students/postdocs/investigators Throughout the project 1) The convergence of solution is guaranteed by formal theoretical analysis. 2) The spectrum of clustering agrees with prior knowledge of number of cell types. 3) The inferred cellular trajectories agree with known developmental paths. Extensive
Validation Students/postdocs/investigators As the unsupervised model was established 1) The clustering results are validated using cell types annotated based on knowledge. 2) The temporal ordering is validated using datasets with multiple real temporal points. 3) The method was compared to several other popular methods on various benchmarks and achieved top performance. 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 in single-cell RNA sequencing data might cause inaccuracy. Scientific community who intends to apply this method to raw scRNA-seq data. Adequate
5. Version control
Current Conformance Level / Target Conformance Level
Extensive
Naming Conventions? Repository? Code Review?
individual modeler Yes Yes peers
within the lab Yes Yes peers
collaborators Yes Yes via regular meetings
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 R package: https://mkarikom.github.io/RSoptSC/; MATLAB package: https://github.com/WangShuxiong/SoptSC R package: https://mkarikom.github.io/RSoptSC/; MATLAB package: https://github.com/WangShuxiong/SoptSC
Results Shared folders Paper and tutorials
Implications of results
8. Independent reviews
Current Conformance Level / Target Conformance Level
Partial
Reviewer(s) name & affiliation: Ruan, H., Liao, Y., Ren, Z. et al. Single-cell reconstruction of differentiation trajectory reveals a critical role of ETS1 in human cardiac lineage commitment. BMC Biol 17, 89 (2019). https://doi.org/10.1186/s12915-019-0709-6
When was review performed? 2019
How was review performed and outcomes of the review? The tool has been used by the scientific community for the analysis of single-cell RNA sequencing datasets.
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? Yes.
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.