Peer review process
Not revised: This Reviewed Preprint includes the authors’ original preprint (without revision), an eLife assessment, public reviews, and a provisional response from the authors.
Read more about eLife’s peer review process.Editors
- Reviewing EditorMohammad KarimiKing's College London, London, United Kingdom
- Senior EditorAlan MosesUniversity of Toronto, Toronto, Canada
Reviewer #1 (Public Review):
Summary:
Ren et al developed a novel computational method to investigate cell evolutionary trajectory for scRNA-seq samples. This method, MGPfact, estimates pseudotime and potential branches in the evolutionary path by explicitly modeling the bifurcations in a Gaussian process. They benchmarked this method using synthetic as well as real-world samples and showed superior performance for some of the tasks in cell trajectory analysis. They further demonstrated the utilities of MGPfact using single-cell RNA-seq samples derived from microglia or T cells and showed that it can accurately identify the differentiation timepoint and uncover biologically relevant gene signatures.
Strengths:
Overall I think this is a useful new tool that could deliver novel insights for the large body of scRNA-seq data generated in the public domain. The manuscript is written in a logical way and most parts of the method are well described.
Weaknesses:
Some parts of the methods are not clear.
It should be outlined in detail how pseudo time T is updated in Methods. It is currently unclear either in the description or Algorithm 1.
There should be a brief description in the main text of how synthetic data were generated, under what hypothesis, and specifically how bifurcation is embedded in the simulation.
Please explain what the abbreviations mean at their first occurrence.
In the benchmark analysis (Figures 2/3), it would be helpful to include a few trajectory plots of the real-world data to visualize the results and to evaluate the accuracy.
It is not clear how this method selects important genes/features at bifurcation. This should be elaborated on in the main text.
It is not clear how survival analysis was performed in Figure 5. Specifically, were critical confounders, such as age, clinical stage, and tumor purity controlled?
I recommend that the authors perform some sort of 'robustness' analysis for the consensus tree built from the bifurcation Gaussian process. For example, subsample 80% of the cells to see if the bifurcations are similar between each bootstrap.
Reviewer #2 (Public Review):
Summary of the manuscript:
The authors present MGPfactXMBD, a novel model-based manifold-learning framework designed to address the challenges of interpreting complex cellular state spaces from single-cell RNA sequences. To overcome current limitations, MGPfactXMBD factorizes complex development trajectories into independent bifurcation processes of gene sets, enabling trajectory inference based on relevant features. As a result, it is expected that the method provides a deeper understanding of the biological processes underlying cellular trajectories and their potential determinants.
MGPfactXMBD was tested across 239 datasets, and the method demonstrated similar to slightly superior performance in key quality-control metrics to state-of-the-art methods. When applied to case studies, MGPfactXMBD successfully identified critical pathways and cell types in microglia development, validating experimentally identified regulons and markers. Additionally, it uncovered evolutionary trajectories of tumor-associated CD8+ T cells, revealing new subtypes with gene expression signatures that predict responses to immune checkpoint inhibitors in independent cohorts.
Overall, MGPfactXMBD represents a relevant tool in manifold learning for scRNA-seq data, enabling feature selection for specific biological processes and enhancing our understanding of the biological determinants of cell fate.
Summary of the outcome:
The novel method addresses core state-of-the-art questions in biology related to trajectory identification. The design and the case studies are of relevance.
However, in my opinion, the manuscript requires several clarifications and updates.
Also, how the methods compare with existing Deep Learning based approaches such as TIGON is a question mark. If a comparison would be possible, it should be conducted; if not, it should be clarified why.
Strengths:
(1) Relevant methodology for a current field of research.
(2) Relevant case studies with relevant outcomes.
Weaknesses:
(1) In general, the manuscript may be improved by making the text more accessible to the Journal's audience: (i) intuitive explanation of some concepts; (ii) review the flow of some explanations.
(2) Additionally, several parts require more details on how the methods work, especially the case studies.
(3) Finally, there are missing references to published work and possibly some additional comparisons to make.