SAW: An efficient and accurate data analysis workflow for Stereo-seq spatial transcriptomics
Curation statements for this article:-
Curated by GigaByte
Editors Assessment:
One limiting factor in the adoption of spatial omics research are workflow systems for data preprocessing, and to address these authors developed the SAW tool to process Stereo-seq data. The analysis steps of spatial transcriptomics involve obtaining gene expression information from space and cells. Existing tools face issues with large data sets, such as intensive spatial localization, RNA alignment, and excessive memory usage. These issues affect the process's applicability and efficiency. To address this, this paper presents a high-performance open-source workflow called SAW for Stereo-Seq. This includes mRNA position reconstruction, genome alignment, matrix generation, clustering, and result file generation for personalized analysis. During review the authors have added examples of MID correction in the article to make the process easier to understand. And In the future, more accurate algorithms or deep learning models may further improve the accuracy of this pipeline.
*This evaluation refers to version 1 of the preprint
This article has been Reviewed by the following groups
Listed in
- Evaluated articles (GigaByte)
Abstract
The basic analysis steps of spatial transcriptomics involve obtaining gene expression information from both space and cells. This process requires a set of tools to be completed, and existing tools face performance issues when dealing with large data sets. These issues include computationally intensive spatial localization, RNA genome alignment, and excessive memory usage in large chip scenarios. These problems affect the applicability and efficiency of the process. To address these issues, a high-performance and accurate spatial transcriptomics data analysis workflow called Stereo-Seq Analysis Workflow (SAW) has been developed for the Stereo-Seq technology developed by BGI. This workflow includes mRNA spatial position reconstruction, genome alignment, gene expression matrix generation and clustering, and generate results files in a universal format for subsequent personalized analysis. The excutation time for the entire analysis process is ∼148 minutes on 1G reads 1*1 cm chip test data, 1.8 times faster than unoptimized workflow.
Article activity feed
-
Editors Assessment:
One limiting factor in the adoption of spatial omics research are workflow systems for data preprocessing, and to address these authors developed the SAW tool to process Stereo-seq data. The analysis steps of spatial transcriptomics involve obtaining gene expression information from space and cells. Existing tools face issues with large data sets, such as intensive spatial localization, RNA alignment, and excessive memory usage. These issues affect the process's applicability and efficiency. To address this, this paper presents a high-performance open-source workflow called SAW for Stereo-Seq. This includes mRNA position reconstruction, genome alignment, matrix generation, clustering, and result file generation for personalized analysis. During review the authors have added examples of MID correction in the article …
Editors Assessment:
One limiting factor in the adoption of spatial omics research are workflow systems for data preprocessing, and to address these authors developed the SAW tool to process Stereo-seq data. The analysis steps of spatial transcriptomics involve obtaining gene expression information from space and cells. Existing tools face issues with large data sets, such as intensive spatial localization, RNA alignment, and excessive memory usage. These issues affect the process's applicability and efficiency. To address this, this paper presents a high-performance open-source workflow called SAW for Stereo-Seq. This includes mRNA position reconstruction, genome alignment, matrix generation, clustering, and result file generation for personalized analysis. During review the authors have added examples of MID correction in the article to make the process easier to understand. And In the future, more accurate algorithms or deep learning models may further improve the accuracy of this pipeline.
*This evaluation refers to version 1 of the preprint
-
AbstractThe basic analysis steps of spatial transcriptomics involve obtaining gene expression information from both space and cells. This process requires a set of tools to be completed, and existing tools face performance issues when dealing with large data sets. These issues include computationally intensive spatial localization, RNA genome alignment, and excessive memory usage in large chip scenarios. These problems affect the applicability and efficiency of the process. To address these issues, a high-performance and accurate spatial transcriptomics data analysis workflow called Stereo-Seq Analysis Workflow (SAW) has been developed for the Stereo-Seq technology developed by BGI. This workflow includes mRNA spatial position reconstruction, genome alignment, gene expression matrix generation and clustering, and generate results files …
AbstractThe basic analysis steps of spatial transcriptomics involve obtaining gene expression information from both space and cells. This process requires a set of tools to be completed, and existing tools face performance issues when dealing with large data sets. These issues include computationally intensive spatial localization, RNA genome alignment, and excessive memory usage in large chip scenarios. These problems affect the applicability and efficiency of the process. To address these issues, a high-performance and accurate spatial transcriptomics data analysis workflow called Stereo-Seq Analysis Workflow (SAW) has been developed for the Stereo-Seq technology developed by BGI. This workflow includes mRNA spatial position reconstruction, genome alignment, gene expression matrix generation and clustering, and generate results files in a universal format for subsequent personalized analysis. The excutation time for the entire analysis process is ∼148 minutes on 1G reads 1*1 cm chip test data, 1.8 times faster than unoptimized workflow.
This work has been published in GigaByte Journal under a CC-BY 4.0 license (https://doi.org/10.46471/gigabyte.111) as part of our Spatial Omics Methods and Applications series (https://doi.org/10.46471/GIGABYTE_SERIES_0005), and has published the reviews under the same license as follows:
Reviewer 1. Zexuan Zhu
It would be helpful if some examples can be provided to illustrate the key steps, e.g., the gene region annotation process and MID correction. Some information of the references is missing. Please carefully check the format of the references.
Decision: Minor Revision
Reviewer 2. Yanjie Wei
In this manuscript, the authors introduce a comprehensive Stereo-seq spatial transcriptomics analysis workflow, termed SAW. This workflow encompasses mRNA spatial position reconstruction, genome alignment, gene expression matrix generation, and clustering, culminating in the production of universally formatted results files for subsequent personalized analysis. SAW is particularly optimized for large field Stereo-seq spatial transcriptomics.The authors provide an in-depth elucidation of SAW's workflow and the optimization techniques employed for each module. However, several aspects warrant further discussion:
The authors outline a strategy to reduce memory consumption during the mapping of CID tagged reads to corresponding coordinates by partitioning the mask file and fastq files. The manuscript, however, lacks a detailed description of how these files are divided. It would be beneficial if the authors could furnish additional information regarding this partitioning method.
The gene expression matrix, a crucial output of the SAW process, lacks sufficient evaluation to substantiate its accuracy. The count tool generates this matrix through three primary steps: gene region annotation, MID correction, and MID deduplication. During the gene annotation phase, a hard threshold (50% of the read overlapping with exon) is used to determine if a read is exonic. The basis for this threshold, however, remains unclear.
In the testing section, the authors evaluated the workflow on 2 S1 chips with approximately 1 million reads. The optimized workflow demonstrated a 1.8-fold speed increase compared to the non-optimized version. Table 2 only presents the total runtime before and after optimization. It would be advantageous if the authors could enrich this table by including the runtime of critical modules, such as read mapping, which accounts for 70% of the total runtime.
-
