The YellowPages Module provides users with a collection of valuable external resources and databases relevant to single-cell, spatial transcriptomics, and multi-omics research.
Established in 2020, STOmics offers the most advanced spatiotemporal multi-omics platform, enabling unbiased discovery to answer biological questions in scientific research as clinical applications. Powered by Stereo-seq technology, STOmics is the only spatial technology across the globe capable of accessing the whole transcriptome at true single-cell resolution, with subcellular data achieved through Ultra-HD resolution, and field of view options over 160 square cm. In addition to several impactful publications in Cell, Nature, and Science, STOmics continues to forge international collaborations and advance scientific discoveries at a global scale.
In recent years, the explosive growth of spatial technologies has enabled the characterization of spatial heterogeneity of tissue architectures. Compared to traditional sequencing, spatial transcriptomics reserves the spatial information of each captured location, and has provided novel insights into diverse spatially related biological contexts. We established SPASCER database, which stand for spatial transcriptomics annotation at single-cell resolution, aims to provide spatial transcriptome analysis results in single-cell resolution by integration of single-cell RNA sequencing (scRNA-seq) data. SPASCER database includes 1 082 datasets from 43 studies that across 16 organs. scRNA-seq was integrated to deconvolve/map spatial transcriptomics, and processed with spatial cell-cell interaction, gene pattern and pathway enrichment analysis. Cell-cell interactions and gene regulation network of scRNA-seq from matched spatial transcriptomics were performed as well. The application of SPASCER will provide new insights into tissue architecture and a solid foundation for the mechanistic understanding of many biological processes in healthy and diseased tissues.
Spatially resolved transcriptomics providing gene expression profiles with positional information is key to tissue function and fundamental to disease pathology. SpatialDB is the first public database that specifically curates spatially resolved transcriptomic data from published papers, aiming to provide a comprehensive and accurate resource of spatial gene expression profiles in tissues. Currently, SpatialDB contains detailed information of datasets generated by 8 spatially resolved transcriptomic techniques (Spatial Transcriptomics, Slide-seq, LCM-seq, seqFISH, MERFISH, Liver single cell zonation,Geo-seq and Tomo-seq) from 24 studies. SpatialDB allows users to browse the spatial gene expression profile of all the 8 techniques online and compare the spatial gene expression profile of any two datasets generated by the same or different techniques side by side. It also provides spatially variable (SV) genes identified by SpatialDE and trendsceek, as well as the functional enrichment results of SV genes.
SCAR is the first cancer database combining single-cell transcriptome and spatial transcriptomic datasets covering the most cancer types, with 348 cancer subtypes available to date. Furthermore, SCAR includes the spatial transcriptomic data of 21 organs and 34 types of single-cell omics techniques. On the other hand, SCAR also provides a wide range of analytical and visualization tools in tumor cell types classification, biomarker selection, survival curve prediction etc. It will allow users to comprehensively evaluate the tumor microenvironment and immunological response for cancer studies.
SCAN is the first neural database that combines single cell transcriptome and spatial transcriptome data, covering 10,679,684 cells from 960 species and 880 data sets generated by 26 single cell sequencing techniques. In addition, SCAN also covers 477 spatial transcriptome datasets including brain, retina, spinal cord, and embryos, and over 100 neurological diseases. On the other hand, SCAN also provides a wide range of analysis and visualization tools in the classification of neural cell types, evolution and development, tissue microenvironment, and disease development. It will enable users to comprehensively evaluate the composition of the nervous system in physiological and pathological conditions, and provide rich resources for research in the field of neuroscience.
SPEED is the largest pan-species single cell database at present, covering 127 species to date. SPEED also collects various scRNA-seq atlases in evolution, development and disease, providing an ecological and evolutionary perspective for the study of development and disease.
CELLxGENE is a suite of tools that help scientists to find, download, explore, analyze, annotate, and publish single cell datasets. It includes several powerful tools with various features to help you to engage with single cell data.
