Peer review process
Revised: This Reviewed Preprint has been revised by the authors in response to the previous round of peer review; the eLife assessment and the public reviews have been updated where necessary by the editors and peer reviewers.
Read more about eLife’s peer review process.Editors
- Reviewing EditorJungmin ChoiKorea University, Seoul, Republic of Korea
- Senior EditorMurim ChoiSeoul National University, Seoul, Republic of Korea
Reviewer #2 (Public review):
Summary:
The author proposes a novel method for mapping single-cell data to specific locations with higher resolution than several existing tools.
Strengths:
The spatial mapping tests were conducted on various tissues, including the mouse cortex, human PDAC, and intestinal villus.
Comments on revised version:
I have no additional comments regarding the current version of the manuscript.
Reviewer #3 (Public review):
Summary:
The authors have provided a thorough and constructive response to the comments. They effectively addressed concerns regarding the dependence on marker gene selection by detailing the incorporation of multiple feature selection strategies, such as highly variable genes and spatially informative markers (e.g., via Moran's I), which enhance glmSMA's robustness even when using gene-limited reference atlases.
Furthermore, the authors thoughtfully acknowledged the assumption underlying glmSMA-that transcriptionally similar cells are spatially proximal-and discussed both its limitations and empirical robustness in heterogeneous tissues such as human PDAC. Their use of real-world, heterogeneous datasets to validate this assumption demonstrates the method's practical utility and adaptability.
Overall, the response appropriately contextualizes the limitations while reinforcing the generalizability and performance of glmSMA. The authors' clarifications and experimental justifications strengthen the manuscript and address the reviewer's concerns in a scientifically sound and transparent manner.
Comments on revised version:
Figure 1 does not yet clearly convey what the glmSMA algorithm actually does. I recommend revising or redesigning the figure so that the workflow, main inputs, and outputs of the algorithm are more intuitively presented. A clearer visual explanation would help readers quickly grasp the core concept and contribution of glmSMA.
