scMMO-atlas: single-cell multimodal omics atlas

A platform for displaying multiple omics of single-cell multimodal omics data

Welcome to scMMO-atlas

scMMO-atlas, accessible via https://www.biosino.org/scMMO-atlas/ , serves as a comprehensive repository for single-cell multimodal omics data, covering gene expression, chromatin accessibility, proteins and a variety of other omics data. This extensive resource comprises information from 3,168,824 human and mouse cells, spanning 27 cell lines/organs/tissues across 70 datasets.

Data summary

Distribution of species

Distribution of doners condition and omics approach

Distribution of tissues/organs

Database framework


Data processing workflow (scRNA & scATAC data)


Protocol for data sharing in scMMO-atlas

If you would like to share your experimental data, please contact us via email: 12131301(a)mail.sustech.edu.cn. Please submit data in the following format:

1. The data needs to be stored in Rdata/Rds format.

2. For scRNA+scATAC data, please be sure to provide ATAC fragment and peak files.





Reference

  • Cheng W, Yin C, Yu S, Chen X, Hong N, Jin W. scMMO-atlas: a single cell multimodal omics atlas and portal for exploring fine cell heterogeneity and cell dynamics. Nucleic Acids Res. 2024 Sep 24:gkae821. DOI:10.1093/nar/gkae821.
  • Source codes of scMMO-atlas: https://github.com/Chengwenwen1/scMMO-atlas/.





  • Latest database update

  • Version 1.0 was released with 70 data sets. 2024-01-26












  • Gene module

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    Dataset module

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    The scRNA & scATAC page offers comprehensive outcomes for annotated scRNA+scATAC data. Users begin by selecting their desired dataset through Dropdown , displaying sequencing approach, species and health, and cell number on the right. The Integration section showcases dataset specifics, an integrated UMAP plot with subgroup labels, highly expressed markers, and a corrlation heatmap. RNA and ATAC highlight data quality and subset-specific expressed genes/peaks. The Search section supports personalized exploration of gene expression in each cluster. The Peak browser allows gene-based or location-based enrichment per cell type. Annotated Rdata and metadata are accessible on the Download page.

    Data summary

    Species of scRNA & scATAC data

    Tissues of scRNA & scATAC data

    Summary of cells, genes and peaks

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    Information

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    Data Quality

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    Variable Features

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    Cell type DEGs


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    Data Quality


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    Cell type peaks


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    Peak browser


  • Recommended download Metadata
  • Download meta data that includes detailed annotation information for each cell, including orig.ident, nCount_RNA, nFeature_RNA, nCount_ATAC, nFeature_ATAC, nucleosome_signal, nucleosome_percentile, TSS.enrichment, TSS.percentile and celltype.


    Download Metadata


  • Unstable download for large Rdata
  • Download the fully annotated Seurat data. This Rdata includes the data of RNA, ATAC and WNN integration, which can be used to display the UMAP distribution of cellular multiomics.


    Download RDS


    scRNA and scProtein


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    Cell type genes

    Cell type proteins

    Other single cell multimodal data types

    Data set list

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    Submission data and Prediction based on single-cell multimodal atlas


    Example input dataset could download HERE Rds objects should include at least RNA and ACTIVITY count matrix Be sure to select omics, species, and tissue types that match the data

    ***Once submitted, your data will be saved on our server. We guarantee that the data will be used solely for research related to scMMO-atlas and will not be disclosed or made public.


    Contact Us


    We are dedicated to maintaining and enhancing this website, ensuring ongoing data updates. If you have any questions or suggestions, please don't hesitate to reach out to us. Your input is valuable to us.



    Contact: Wenwen Cheng



    Email: 12131301(a)mail.sustech.edu.cn



    Adress: 1088 Xueyuan Blvd., Nanshan District, Shenzhen, China



    Our lab: http://www.sinh.cas.cn/rcdw/qtyjzz/202312/t20231211_6908461.html




    The information provided is subject to ongoing review. The University reserves the right to amend the information from time to time. Best Viewed with resolution of 1024 x 768 (or higher) and supports Internet Explorer 8+, Mozilla Firefox 3.5+, Google Chrome and Safari. © 2023 South University of Science and Technology of China. All Rights Reserved.










    Copyright @ Lab of Dr. Wenfei Jin of CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences.