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?

  • Step 1. Users first upload their raw single-cell RNA-seq (scRNA-seq) data, webSCST could interactively process the dataset. Specially, users need to upload their single-cell gene expression matrix in mtx format, gene names and cell names in tsv format and cell type annotation in txt format. After submission, users could obtain the summary information for the submitted dataset, such as the number of genes, cells and cell types. Users could learn the details of the requirements by downloading the demo file.
  • Step 2. In "Quality Control" page, users could interactively process their submitted dataset. Users could first filter low-quality cells by setting thresholds, such as minimum detected genes numbers in each cell, maximum detected gene numbers in each cell and percentage of mitochondria counts. In "Normalization and Scaling" step, users could select or enter desired parameters like normalize scale factor or utilized feature numbers. Subsequently, users could interactively select principal component numbers they want to use based on the clustering results in "Clustering" step. Lastly, users could download the processed clean dataset and makers genes for each cell type for further analysis.
  • Step 3. In "Database" section, we have manually curated 43 spatial transcriptome datasets (136 sub-datasets), which will be constantly added according to the increasing of publicly available spatial transcriptome datasets. Users can view the spatial expression of any gene of interest after specifying the species and tissue.
  • Step 4. In "Data Integration" page, users need to upload their processed scRNA dataset and makers downloaded from "Quality Control" step. Alternatively, users could also click "Load QC Result" button to automatically upload the dataset they just processed. Processed single-cell data could then be automatically matched with the spatial transcriptome data we curated according to the organ and species. By clicking "Match Spatial Dataset Manually" button, users may also choose any spatial transcriptome datasets manually collected in the database by ID for integration.
  • Step 5. Currently, four popular integration methods (AddModuleScore, MIA, ssGSEA and RCTD) have been included in webSCST. Users could interactively select any method and download the integration results they are interested in. In the future, newly developed integration tools would be added to maintain webSCST as an up-to-date resource.

  • 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.


    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

    Step 1


    2. Normailization and Scaling

    Step 2

    Export Data

    Step 4
    Don't know how to get started? You can load our demo to experience it first.
    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 .




    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.


    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.