scEpath: energy landscape-based inference of transition probabilities and cellular trajectories 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) offers unprecedented resolution for studying cellular decision-making processes. Robust inference of cell state transition paths and probabilities is an important yet challenging step in the analysis of these data. Here we present scEpath, an algorithm that calculates energy landscapes and probabilistic directed graphs in order to reconstruct developmental trajectories. We quantify the energy landscape using ‘single-cell energy’ and distance-based measures, and find that the combination of these enables robust inference of the transition probabilities and lineage relationships between cell states. We also identify marker genes and gene expression patterns associated with cell state transitions. Our approach produces pseudotemporal orderings that are—in combination—more robust and accurate than current methods, and offers higher resolution dynamics of the cell state transitions, leading to new insight into key transition events during differentiation and development. Moreover, scEpath is robust to variation in the size of the input gene set, and is broadly unsupervised, requiring few parameters to be set by the user. 

Biological domain of the model
scRNA-seq data of various tissues
Structure(s) of interest in the model
celluar trajectories, cell state landscape
Spatial scales included in the model
cells to tissues
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 1) comparing the pseudotime derived by the model to known temporal sequence and 2) by comparing the developmental events identified by the model to known developmental branches. 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 generated cell state landscape agrees with known overall structure. 2) The pseudotime ordering agrees well with known temporal sequence of cell states. Extensive
Validation Students/postdocs/investigators As the unsupervised model was established 1) The inferred cell state landscape and the cell state transition are validated by known cell types and their developmental relationships. 2) The pseudotime ordering is validated by known temporal ordering of cell types and known key regulatory events are recovered 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 and sufficient number of cells might by needed for accurate energy landscape estimation. 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? Adequate
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 package: https://github.com/sqjin/scEpath package: https://github.com/sqjin/scEpath
Results Shared folders Paper and tutorials
Implications of results
8. Independent reviews
Current Conformance Level / Target Conformance Level
To be done
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? 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.