PCOMPBIOL-D-26-00028

Analysis of anatomical multi-cellular structures from spatial omics data using sosta

PLOS Computational Biology

Dear Dr. Robinson,

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Boyang Ji, Ph.D.

Academic Editor

PLOS Computational Biology

Ilya Ioshikhes

Section Editor

PLOS Computational Biology

Additional Editor Comments:

Dear Prof. Robinson, and co-authors,

Thank you for submitting your manuscript and patience while awaiting peer review. In your study, a structure-based analysis framework was developed for spatial omics data. Reviewers recognized the potential importance and utility of this work. However, reviewers had raised substantive concerns that need be addressed before the manuscript can be considered further.

A few of major concerns - aggregated from reviewers comments below - are:

- The lack of quantitative benchmarking against existing tools.

- The lack of clear guidelines for optimal parameter (intensity threshold) selection, as well as insufficient evaluation of parameter sensitivity. An automated or adaptive thresholding strategy may help address this issue.

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Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Authors:

Please note here if the review is uploaded as an attachment.

Reviewer #1: This manuscript introduces sosta, a Bioconductor package designed for "structure-based analysis" of spatial omics data. By shifting the focus from individual cells to multicellular anatomical structures using point pattern density estimation, the authors address a critical gap between classical histopathology and high-resolution molecular profiling. The utility of the framework is demonstrated through compelling case studies on colorectal cancer progression and human tonsil germinal centers. While the proposed paradigm is promising, there are several issues regarding automation, benchmarking, and biological interpretation that need to be addressed to strengthen the paper.

1. The current reliance on manually adjusting the density cut-off threshold introduces subjectivity and significantly hinders the method’s potential for fully automated and reproducible workflows. This dependence creates a barrier for users without prior histological knowledge or ground truth, as determining the optimal parameters for structure definition remains ambiguous. I strongly recommend implementing a more automated adaptive thresholding strategy (e.g., based on morphological stability) or, at minimum, providing comprehensive guidelines for selecting bandwidth and threshold values across diverse tissue architectures to ensure robustness.

2. While the authors present structure-based analysis as a novel approach, the manuscript lacks a direct comparison with established spatial domain identification methods like CellCharter or Banksy. I recommend that the authors include a quantitative benchmark comparing sosta against these existing tools.

3. The authors observe that 'Fibre Width' increases with pathological progression and correlates with pseudo-time. However, the biological substance underlying these geometric metrics needs to be more explicitly defined. Specifically, the authors should clarify whether the increase in Fibre Width serves as a proxy for biological processes such as epithelial hyperplasia or loss of cell polarity, rather than presenting it solely as a mathematical observation.

4. In the CRC analysis, the authors identify 'transition crypts' using algorithmic thresholds. To validate the reliability of this strategy, I recommend providing a confusion matrix (along with Precision and Recall metrics) comparing the algorithmically detected crypts against expert pathologist manual annotations.

Reviewer #2: The manuscript introduces sosta, a structure-based analysis framework and an R/Bioconductor package designed for spatial omics data. The authors correctly identify a critical gap in the field: while most current methods prioritize single-cell spatial distributions, biological functions are often driven by higher-order multicellular anatomical structures. My comments are as follows:

1 While the results presented are quite compelling, the manuscript does not include a comparative analysis with any existing methodologies. Consequently, it is difficult to assess the actual impact and standing of the proposed method within the field.

2 For instance, in the area of cell-type annotation, a significant number of methods have already been established; these warrant a thorough discussion and comparison.

3 Furthermore, the authors should contextualize their work within the broader landscape of spatial omics computational research. Specifically, it would be beneficial to discuss and compare the proposed approach with relevant methods such as STAGATE, SpaMask, mclSTExp, SpaBatch, SpaCross, and SpaDAMA.

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Reviewer #1: None

Reviewer #2: Yes

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Reviewer #1: No

Reviewer #2: No

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Dear Prof Robinson,

We are pleased to inform you that your manuscript 'Analysis of multicellular anatomical structures from spatial omics data using sosta' has been provisionally accepted for publication in PLOS Computational Biology.

Before your manuscript can be formally accepted you will need to complete some formatting changes, which you will receive in a follow up email. A member of our team will be in touch with a set of requests.

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Thank you again for supporting Open Access publishing; we are looking forward to publishing your work in PLOS Computational Biology.

Best regards,

Chongzhi Zang

Academic Editor

PLOS Computational Biology

Ilya Ioshikhes

Section Editor

PLOS Computational Biology

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I apologize for the delay in the editorial process for this manuscript, as I was only recently assigned as the editor. One of the reviewers has been non-responsive; however, after reviewing the manuscript, the previous reviews, and the authors' response to the reviewers' comments, I am convinced that the reviewers' concerns have been well addressed. I am therefore pleased to accept the revised manuscript for publication in PLOS Computational Biology.

Reviewer's Responses to Questions

Comments to the Authors:

Please note here if the review is uploaded as an attachment.

Reviewer #2: In this version of the manuscript, the authors have addressed my questions satisfactorily, and I have no further comments.

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Have the authors made all data and (if applicable) computational code underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data and code underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data and code should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data or code —e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #2: Yes

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Reviewer #2: No