webSCST: an interactive web application for single-cell RNA-sequencing data and spatial transcriptomic data integration

Author:

Zhang Zilong123ORCID,Cui Feifei123ORCID,Su Wei4,Dou Lijun23ORCID,Xu Anqi5,Cao Chen6ORCID,Zou Quan23ORCID

Affiliation:

1. School of Computer Science and Technology, Hainan University , Haikou 570228, China

2. Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China , Chengdu 610054, China

3. Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China , Quzhou 324000, China

4. Yahoo Japan Corporation , Tokyo 102-8282, Japan

5. The First School of Clinical Medicine, Shandong University of Traditional Chinese Medicine , Jinan 250014, China

6. School of Biomedical Engineering and Informatics, Nanjing Medical University , Nanjing 211166, China

Abstract

Abstract Summary Integrative analysis of single-cell RNA-sequencing (scRNA-seq) data with spatial data for the same species and organ would provide each cell sample with a predictive spatial location, which would facilitate biological study. However, publicly available spatial sequencing datasets for specific species and organs are rare and are often displayed in different formats. In this study, we introduce a new web-based scRNA-seq analysis tool, webSCST, that integrates well-organized spatial transcriptome sequencing datasets categorized by species and organs, provides a user-friendly interface for raw single-cell processing with popular integration methods and allows users to submit their raw scRNA-seq data once to obtain predicted spatial locations for each cell type. Availability and implementation webSCST implemented in shiny with all major browsers supported is available at http://www.webscst.com. webSCST is also freely available as an R package at https://github.com/swsoyee/webSCST.

Funder

National Natural Science Foundation of China

Special Science Foundation of Quzhou

Publisher

Oxford University Press (OUP)

Subject

Computational Mathematics,Computational Theory and Mathematics,Computer Science Applications,Molecular Biology,Biochemistry,Statistics and Probability

Reference10 articles.

1. Robust decomposition of cell type mixtures in spatial transcriptomics;Cable;Nat. Biotechnol,2021

2. Advances in spatial transcriptomic data analysis;Dries;Genome Res,2021

3. SpatialDB: a database for spatially resolved transcriptomes;Fan;Nucleic Acids Res,2020

4. GSVA: gene set variation analysis for microarray and RNA-seq data;Hänzelmann;BMC Bioinformatics,2013

5. Integrated analysis of multimodal single-cell data;Hao;Cell,2021

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