Upload additional files	TRR-202604-01R02/stage_files/TRR-202604-01/Review MS/comments.docx
Reviewer name and names of any other individual's who aided in reviewer	Haojing Shao
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?	No
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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?	Yes
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Is the documentation provided clear and user friendly?	No
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Is there enough clear information in the documentation to install, run and test this tool, including information on where to seek help if required?	No
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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?	No
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Is test data available, either included with the submission or openly available via cited third party sources (e.g. accession numbers, data DOIs)?	Yes
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Are there (ideally real world) examples demonstrating use of the software?	No
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Is automated testing used or are there manual steps described so that the functionality of the software can be verified?	Yes
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Any Additional Overall Comments to the Author	This paper presents Poly Pipeline, a universal bioinformatics workflow for spatial transcriptomics optimized for polyploid organisms. It solves key issues including inconsistent proprietary data formats, severe data sparsity and duplicated gene bias in polyploid genomes. The pipeline contains multi-step modules: format conversion, quality filtering, PCA/UMAP dimensional reduction, multi-mode spatial clustering, marker gene screening and hdWGCNA co-expression network construction to reduce dropout noise. Benchmarks on wheat, rice, Arabidopsis and mouse datasets prove its efficiency and wide applicability. Spatial Leiden clustering delivers clearer tissue domain segmentation. This open-source Linux pipeline follows FAIR data principles and works for both plants and animals. Comments: Major comments: 1. What is the major novelty and advantage of the Poly Pipeline software? Is the main contribution the development of a Snakemake-based framework to integrate these analyses? Most of these analyses can also be performed using existing tools without this pipeline. The authors should clearly explain the unique value and advantages of Poly Pipeline from the perspective of the analysis workflow and biological applications. 2. The amount of presented work appears insufficient. In Figure 1, the authors mention multiple analytical modules, including PCA/UMAP visualization, spatial clustering, highly variable gene (HVG) identification, hdWGCNA analysis, and network generation. However, the current results mainly focus on spatial clustering and HVG analysis. Where are the results from the other modules? I suggest adding additional results and figures, such as PCA/UMAP visualization, hdWGCNA analysis, and gene regulatory network construction, to better demonstrate the capability of the pipeline. 3. The dataset used for spatial clustering in Figure 2 appears outdated, and the clustering results show obvious granularity artifacts. I recommend using a more recent high-resolution spatial transcriptomics dataset for evaluation. For example, datasets from the following resource could be considered: https://en.stomics.tech/resources/demo-data/list.html 4. In Table 2, statistical analyses such as skewness, kurtosis, and the Shapiro–Wilk test should include detailed statistical values, especially the corresponding P-values. In addition, these results should be compared with existing tools. Do the results demonstrate better performance compared with other methods, or are they generally consistent with existing approaches? This comparison is important for demonstrating the advantages and reliability of Poly Pipeline. 5. The title emphasizes “Poly Pipeline”; however, I did not observe any specific analyses or methodological designs targeting the unique characteristics of polyploid genomes or polyploid species. The current manuscript mainly demonstrates the application of the pipeline to polyploid species. I suggest either revising the title to better match the actual content or adding polyploid-specific analyses to highlight the unique advantages of this software. Minor comments: 1. The Introduction section does not align well with the actual analyses performed in the manuscript. I suggest revising the Introduction to focus more specifically on the problems addressed and functions implemented by Poly Pipeline. 2. Is the GEF format only supported by Stereo-seq data? Can Poly Pipeline process other widely used spatial transcriptomics data formats, such as 10x Genomics Visium, Xenium datasets, h5ad files, or Seurat objects? If these formats are not currently supported, the authors should discuss this limitation in the Discussion section.
Recommendation	Major Revisions