Welcome!
What is webSCST?
webSCST is the first web tool for single-cell RNA-seq data and spatial transcriptome integration. The user-friendly interactive interface provides three main functions: single-cell data uploading and processing , spatial transcriptome database and integration . Users could upload their raw single-cell RNA-seq data, after processing and automatically matching with the spatial transcriptome datasets we manually collected, finally got the predicted spatial information for each cell type.
How does webSCST work?
Cite webSCST
Zilong Zhang, Feifei Cui, Wei Su, Chen Cao, Quan Zou*. webSCST: an interactive web application for single-cell RNA-seq data and spatial transcriptome data integration.
Contact
For questions and suggestions, please contact:
Zilong Zhang (zhangzilong@bi.a.u-tokyo.ac.jp) or Quan Zou (quanzou@nclab.net)
Single-cell Sequencing Data Upload
Don't know how to get started? You can load our demo to experience it first.
Data Preview
1. Violin Plot and Feature Scatter
2. Normailization and Scaling
Export Data
The demo here is obtained after the Quality Control of the previous steps using the original dataset in File Upload Tab.
The DEMO data here is a matched spatail data as an example.
You could match your own spatial data automatically by choose species and organs or mannually select any replicate you liked in our database .
Database
About
Integration Methods Used in webSCST
AddModuleScore: AddModuleScore is a function in R package {Seurat}
, which is a scoring-based method. Since AddModuleScore function aims to find average expression levels of each cluster, it has been widely used for finding similar gene expression patterns between single-cell clusters and spatial transcriptome clusters.
Citation: Stuart T, Butler A, Hoffman P, Hafemeister C, Papalexi E, Mauck WM, 3rd, Hao Y, Stoeckius M, Smibert P, Satija R: Comprehensive Integration of Single-Cell Data. Cell 2019, 177(7):1888-1902.e1821.
MIA: MIA is a mapping integration method which is short for “Multimodal Intersection Analysis”. MIA first identifies cell type-specific genes in single-cell data and region-specific genes in spatial data, and then performs the integration by hypergeometric distribution of these two types of genes.
Citation: Moncada R, Barkley D, Wagner F, Chiodin M, Devlin JC, Baron M, Hajdu CH, Simeone DM, Yanai I: Integrating microarray-based spatial transcriptomics and single-cell RNA-seq reveals tissue architecture in pancreatic ductal adenocarcinomas. Nature biotechnology 2020, 38(3):333-342.
ssGSEA: ssGSEA is a function in R package {GSVA}
, which is an extension of GSEA (Gene Set Enrichment Analysis). Utilizing the maker genes for each cell types (obtained from single-cell data), ssGSEA could score the spatial location with spatial gene expression patterns.
Citation: Hänzelmann S, Castelo R, Guinney J: GSVA: gene set variation analysis for microarray and RNA-seq data. BMC bioinformatics 2013, 14:7.
RCTD: RCTD is a deconvolution integration method by statistical models. Utilizing a Possion distribution to model the genes for each pixel, RCTD try to obtain average expression for each gene per cell-type. A random platform parameter is also included makes RCTD more robust for cross-platform spatial data decomposition.
Citation: Cable DM, Murray E, Zou LS, Goeva A, Macosko EZ, Chen F, Irizarry RA: Robust decomposition of cell type mixtures in spatial transcriptomics. Nature biotechnology 2021.
Addresses
1.Institute of Fundamental and Frontier Sciences, University of Electronic Science and
Technology of China. No.4, Section2, North Jianshe Road, Chengdu, China.
2.Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China. Building 1, Qu Shidai Innovation Building, No. 288, Qinjiang East Road, Kecheng District, Quzhou, China.
3.Yahoo Japan Corporation, Kioi Tower, 1-3 Kioicho, Chiyoda-ku, Tokyo, 102-8282, Japan.