Taking spatial omics into the next dimension

Spatial Omics is a new field that is taking large-scale data-rich biological and biomedical research into new dimensions. This is having a significant impact on the fundamental fields of biology and biomedicine. Spatial omics technologies are high-throughput methods for analyzing biological data-based spatial information. It allows researchers to uncover the spatial distribution characteristics in cells, tissues, and organs, which provides a fresh perspective for studying the structure and function of biological systems. For research in this field to continue to progress, new algorithms, methods and tools for spatial omics technology are essential. To address these needs, the first articles in a new Spacial Omics series have just been published in GigaScience Press’ open-science journals GigaScience and GigaByte. 

The huge potential of Spatial Omics technology is being held back due to the challenges in handling enormous multi-dimensional datasets. Scientists currently lack available techniques and computational tools for using this novel spatial information even with the ability to re-use existing single-cell data-analysis algorithms. It is therefore crucial to have new customized algorithms and tools specifically created to analyze and interpret spatial omics data. More, to allow the community to take full advantage of these tools, they must be open-source, easy-to-use, and designed to handle the enormous data volumes produced in these experiments.

To begin to establish a place for the community to find multiple new spatial omics analysis methods, GigaScience Press has just published the first batch of articles in a new cross-journal thematic series in GigaScience and GigaByte. These series provide a home for novel spatial omics algorithms, tools, and applications. The openly available pipelines and tools include data preprocessing methods, data quality assessment and improvement, basic analyses, downstream analysis mining, and more. Together, these articles in ongoing series help roll-out and democratize use of this technology by streamlining analyses and providing a toolkit of adaptable open-source tools for others to use and build on.

One of these just released articles is published in GigaScience Press’ headline journal, GigaScience, provides a new analysis tool called Siamese Graph Autoencoder (SGAE) [1], an algorithm for detecting spatial domains. SGAE outperforms other methods in terms of capturing spatial patterns and generating high-quality clusters. This enables researchers to resolve anatomical structures such as cortex structures of the brain or gastrulation during mouse embryonic development, with better clarity than other current methods. This groundbreaking new technology pushes the boundaries of what can be studied and discovered.

Articles published in the co-series in the journal GigaByte address major limiting factors in the adoption of spatial omics research: the availability of workflow systems for data preprocessing. One of articles presents SAW, a tool that processes Stereo-seq data [2], which allows better data quality assessment of large spatial transcriptomics datasets. Another paper presents the BatchEval tool [3], which helps researchers identify and remove batch effects, ensuring reliable and meaningful insights from integrated datasets. In addition to tools for improving data processing, a number of new analysis tool articles are also released in the launch of this new series. These include articles that present the imputation algorithm Efficient and Adaptive Gaussian smoothing (EAGS) tool [4], which improves data quality in highly resolved spatial transcriptomics; the Variable Neighborhood Search (VNS) method [5], which serves to better cluster cells based on both gene expression and spatial coordinates; and the STCellbin tool [6], which uses cell nuclei staining images as a bridge to align cell membrane/wall staining images with spatial gene expression maps.

This cross-journal thematic series will continue to publish a host of other papers in the coming months, and remains open for submissions of similar open-source, reproducible algorithms, tools and applications. Both GigaScience and Gigabyte aid authors to share due to having in-house data hosting and curation support to provide open-science  articles. This encourages others to complement the development of the spatial omics data tool community and continue to promote rapid scientific research progress in this new and growing field.
 

GigaScience Journal Series page: https://academic.oup.com/gigascience/pages/spatial-omics-methods-and-applications

GigaByte Journal series page: https://doi.org/10.46471/GIGABYTE_SERIES_0005  

Further Reading

1. Cao L et al. Deciphering spatial domains from spatially resolved transcriptomics with Siamese Graph Autoencoder. GigaScience 2024 doi:10.1093/gigascience/giae003

2. Gong C et al. SAW: An efficient and accurate data analysis workflow for Stereo-seq spatial transcriptomics. GigaByte 2024 doi:10.46471/gigabyte111 

3. Zhang C. et al. BatchEval Pipeline: Batch Effects Evaluation Workflow for Multi-batch Dataset Joint Analysis. GigaByte 2024 doi:10.46471/gigabyte108

4. Lv T et al. EAGS: efficient and adaptive Gaussian smoothing applied to high-resolved spatial transcriptomics. GigaScience 2024. doi:10.1093/gigascience/giad097

5. Ivanovic M et al. A Novel Variable Neighborhood Search Approach for Cell Clustering for Spatial Transcriptomics. GigaByte 2024 doi:10.46471/gigabyte109

6. Kang Q et al. Generating single-cell gene expression profiles for high-resolution spatial transcriptomics based on cell boundary images. GigaByte 2024 doi:10.46471/gigabyte110

Contact Information:

Scott Edmunds, Editor-in-Chief

Email: Scott@gigasciencejournal.com Cell: +852 92490853

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About GigaScience Press

GigaScience Press is BGI's Open Access Publishing division, which publishes scientific journals and data. Its publishing projects are carried out with international publishing partners and infrastructure providers, including Oxford University Press and River Valley Technologies. It currently publishes two data-centric journals: its premier journal GigaScience (launched 2012) and its new journal GigaByte (launched 2020). It also publishes data, software, and other research objects via its GigaDB.org database. To encourage transparent reporting of scientific research as well as enable future access and analyses, it is a requirement of manuscript submission to all GigaScience Press journals that all supporting data and source code be made available in GigaDB or in a community approved, publicly available repository. See GigaSciencePress.com

About GigaScience

GigaScience is co-published by GigaScience Press and Oxford University Press. Winner of the 2018 PROSE award for Innovation in Journal Publishing (Multidisciplinary), the journal covers research that uses or produces 'big data' from the full spectrum of the biological and biomedical sciences. It also serves as a forum for discussing the difficulties of and unique needs for handling large-scale data from all areas of the life and medical sciences. The journal has a completely novel publication format -- one that integrates manuscript publication with complete data hosting, and analyses tool incorporation. To encourage transparent reporting of scientific research as well as enable future access and analyses, it is a requirement of manuscript submission to GigaScience that all supporting data and source code be made available in the GigaScience database, GigaDB, as well as in publicly available repositories. GigaScience will provide users access to associated online tools and workflows, and has integrated a data analysis platform, maximizing the potential utility and re-use of data.\

About GigaByte:

GigaByte, published by GigaScience Press, focuses on publishing short studies with data or bioinformatic tools at the center of the study. With its  novel, end-to-end XML publishing platform, article publication can be done in a quicker and more interactive manner than traditional scientific publications. The papers from this vector-borne disease series include embedded dynamics, such as  interactive maps and embedded protocols. Additionally, ther are multilingual options for many papers that allow Portuguese and Spanish speakers to better comprehend the implications of important work relating to the public health of their communities.

About GigaDB:

GigaDB is a data repository supporting scientific publications in the Life/Biomedical Sciences domain. GigaDB organizes and curates data from individually publishable units into datasets, which are openly available as  FAIR data. GigaDB primarily serves as a repository to host data, tools, and other research objects that underlie the research in the article, and also data from articles from other journals in approved cases. Through association with DataCite, each dataset in GigaDB is assigned a DOI that can be used as a standard citation for future use of these data in other articles by the authors and other researchers. To maximize utility for the research community, all datasets in GigaDB are placed under a CC0 waiver. However, for data that needs to be protected, GigaDB will include the contact information for access, the restrictions for use, and host the application form needed to gain permission for use. The protected databases must be persistent and internationally accessible.