Yongtian Wang, Jintian Luo, Shaoqing Jiao, Xiaohan Xie, Tao Wang, Jie Liu, Xuequn Shang, Jiajie Peng
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引用次数: 0
Abstract
Spatial transcriptomics revolutionizes the understanding of tissue organization and cellular interactions by combining high-resolution spatial information with gene expression profiles. Existing spatial transcriptomics analysis platforms face challenges in accommodating diverse techniques, integrating multi-omics data, and providing comprehensive analytical workflows. STExplore, an advanced online platform, is developed to address these limitations. STExplore supports a wide range of technologies, including sequencing-based and image-based methods, and offers a complete analysis workflow encompassing preprocessing, integration with single-cell RNA sequencing (scRNA-seq), cluster-level and gene-level analyses, and cell-cell communication studies. The platform features dynamic parameter adjustments and interactive visualizations at each analytical stage, enabling users to gain deeper insights into the spatial transcriptomic landscape. Case studies on neurogenesis in embryonic brain development, Alzheimer's disease, and brain tissue architecture demonstrate STExplore's capabilities in enhancing gene expression analysis, revealing cellular spatial organizations, and uncovering intercellular communication patterns. STExplore provides a comprehensive and user-friendly solution for the expanding demands of spatial transcriptomics research. The platform is accessible at http://120.77.47.2:3000/.
Small MethodsMaterials Science-General Materials Science
CiteScore
17.40
自引率
1.60%
发文量
347
期刊介绍:
Small Methods is a multidisciplinary journal that publishes groundbreaking research on methods relevant to nano- and microscale research. It welcomes contributions from the fields of materials science, biomedical science, chemistry, and physics, showcasing the latest advancements in experimental techniques.
With a notable 2022 Impact Factor of 12.4 (Journal Citation Reports, Clarivate Analytics, 2023), Small Methods is recognized for its significant impact on the scientific community.
The online ISSN for Small Methods is 2366-9608.