PCOMPBIOL-D-25-01571

Randomized Spatial PCA (RASP): a computationally efficient method for dimensionality reduction of high-resolution spatial transcriptomics data

PLOS Computational Biology

Dear Dr. Gingerich,

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Jean Fan

Section Editor

PLOS Computational Biology

Additional Editor Comments:

The authors present a practically useful tool for scalable spatially aware dimensionality reduction RASP. In order to warrant publication, the current paper should be revised to include more comparison with existing tools to better clarify the unique attributes of RASP, application to additional spatial transcriptomic datasets with larger gene panels to demonstrate scalability and applicability to data from newer spatial technologies, as well as additional clarifications on hyperparameters to guide users.

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Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Authors:

Please note that one review is uploaded as an attachment.

Reviewer #1: I uploaded the review as an attachment.

Reviewer #2: This manuscript presents RASP, a scalable spatially aware dimension reduction method that integrates randomized PCA with post-hoc KNN inverse-distance smoothing. The method is computationally efficient, and can easily fit into mainstream ST workflows. This method's novelty is incremental but practically useful. The authors evaluated the results across multiple platforms (Visium, MERFISH, Stereo-seq, Xenium), and the tool appears pretty practical. Below are some comments I have:

1. RASP is a dimensiona reduction method, not a clustering or domain detection method itself. The cell types and spatial regions are obtained from its output spatially smoothed PCs through clustering, and the resulting cluster resolution depends on KNN and β parameters. This is described in the methods and results of the manuscript, but should be made explicit in the Abstract and Introduction to avoid confusion. Currently it reads like this is a method for spatial region detection in abstract.

2. On line 99, BASS is listed as a "spatial domain detection" method. However, BASS performs joint cell type and domain inference. Additionally, in Fig. 4A (mouse brain), where comparisons are made on cell type classification, it would be more appropriate to use the cell-type labels produced by BASS.

3. Gene expression reconstructions are only validated qualitatively. The current manuscript lacks quantitative evaluation on gene expression reconstruction. The authors could consider either predict masked gene expression counts at locations through MSE or correlation, or held-out a subset of genes and quantify reconstruction accuracy.

4. The manuscript shows parameter sensitivity analysis and claims that users can tune KNN and β using metrics like Moran’s I and CHAOS. To improve usability, please provide practical defaults and a recommendation table for typical KNN and β values for cell-type annotation vs. spatial domain detection,

or preferred clustering algorithms (e.g., Louvain, Leiden), and suggestions for choosing cluster number or resolution in unsupervised settings.

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Reviewer #1: Yes

Reviewer #2: Yes

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Reviewer #1: No

Reviewer #2: No

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Attachments

Attachment

Submitted filename: rasp_review.pdf

Dear Mr Gingerich,

We are pleased to inform you that your manuscript 'Randomized Spatial PCA (RASP): a computationally efficient method for dimensionality reduction of high-resolution spatial transcriptomics data' has been provisionally accepted for publication in PLOS Computational Biology.

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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,

Jean Fan

Section Editor

PLOS Computational Biology

Jean Fan

Section Editor

PLOS Computational Biology

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Reviewer's Responses to Questions

Comments to the Authors:

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

Reviewer #1: I am satisfied with the manuscript in its revised form.

Reviewer #2: The reviewers have addressed all my 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 #1: Yes

Reviewer #2: Yes

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Reviewer #1: No

Reviewer #2: No