Reviewer name and names of any other individual's who aided in reviewer	chunquan Li
Do you understand and agree to our policy of having open and named reviews, and having your review included with the published manuscript. (If no, please inform the editor that you cannot review this manuscript.)	Yes
Is the language of sufficient quality?	Yes
Please add additional comments on language quality to clarify if needed
Is there a clear statement of need explaining what problems the software is designed to solve and who the target audience is?	Yes
Additional Comments
Is the source code available, and has an appropriate Open Source Initiative license <a href="https://opensource.org/licenses" target="_blank">(https://opensource.org/licenses)</a> been assigned to the code?	Yes
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As Open Source Software are there guidelines on how to contribute, report issues or seek support on the code?	Yes
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Is the code executable?	Yes
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Is installation/deployment sufficiently outlined in the paper and documentation, and does it proceed as outlined?	Unable to test
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Is the documentation provided clear and user friendly?	Yes
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Is there a clearly-stated list of dependencies, and is the core functionality of the software documented to a satisfactory level?	Yes
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Have any claims of performance been sufficiently tested and compared to other commonly-used packages?	Yes
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Are there (ideally real world) examples demonstrating use of the software?	Yes
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Additional Comments
Any Additional Overall Comments to the Author	Stereo-seq, an advanced spatial transcriptomics technique, allows detailed analysis of large tissues at the single-cell level with precise subcellular resolution. Author's prior software was groundbreaking, generating robust single-cell spatial gene expression profiles using cell nuclei staining images and statistical methods. They've enhanced their software to STCellbin, using cell nuclei images to align cell membrane/wall staining images. This update employs improved cell segmentation, ensuring accurate boundaries and more dependable single-cell spatial gene expression profiles. Successful tests on mouse liver and Arabidopsis seed datasets demonstrate STCellbin's effectiveness, enabling a deeper insight into the role of single-cell characteristics in tissue biology. However, I do have some suggestions and questions about certain parts of the manuscript. 1. The authors should show the advantages and performance of STCellbin compared to other methods, such as its computational efficiency, accuracy, and suitability for various image types. 2. To comprehensively assess the performance of STCellbin, the authors should consider integrating other commonly used cell segmentation evaluation metrics, such as F1-score, Dice coefficient, and so forth. 3. To ensure the completeness and reproducibility of the data analysis, more detailed information regarding the clustering analysis of the single-cell spatial gene expression maps generated through STCellbin is requested. This information should encompass methods, parameters, and results such as cluster type annotations. 4. The authors can use simpler and clearer language and terminology to describe the image registration process in the methods section, ensuring that readers can easily understand the workflow and principles of image registration.
Recommendation	Major Revisions