Software

Single Cell Resources

  • TISCH   (Tumor Immune Single-cell Hub) is a scRNA-seq database that includes over 6M human and mouse tumor scRNA-seq cells, and provides detailed cell-type annotation and gene expression visualization across different cancer types.
  • HUSCH   (Human Universal Single-cell Hub) is a scRNA-seq database that includes over 3M human normal scRNA-seq cells from 41 tissue/organs, and provides detailed cell-type annotation and gene expression visualization across different tissue/organs.
  • TabulaTIME   (Tabula of pan-cancer Tumor Immune Microenvironment) is a comprehensive tumor microenvironment landscape by integrating single-cell and spatial transcriptomic data.
  • MetroTIME   (Metabolic Regulome of the pan-cancer Tumor Immune Microenvironment) serves as a comprehensive resource detailing the metabolic heterogeneities and their associated regulomes in pan-cancer fibroblasts and myeloid cells.

Single Cell Multiomics

  • SCRIPro   (Single Cell Regulatory network Inference for Spatial Multiomics) is a tool for infering the gene regulatory networks for both single-cell and spatial transcriptomics and multiomics.
  • SCRIP   (Single Cell Regulatory network Inference using ChIP-seq and motif) is a tool for evaluating the binding enrichment of transcription regulators at the single-cell resolution based on integration of scATAC-seq and bulk ChIP-seq reference.
  • SELINA   (Single-cELl Identity NAvigator) is a deep learning-based framework for cell-type annotation of human scRNA-seq data using large-scale curated references.
  • SCREE   (Single-cell CRISPR screen data analysEs and perturbation modEling) is a workflow to perform quality control and analyses of multimodal single-cell CRISPR screen datasets.

Spatial Multiomics

  • Cellist   (Cell identification in high-resolution Spatial Transcriptomics) Cellist is a computational method for accurate cell segmentation and signal enhancement on high-resolution spatial transcriptomics.
  • STRIDE   (Spatial TRanscrIptomics DEconvolution by topic modeling) is a topic-model-based deconvolution method for low-resolution spatial transcriptomics by integrating with scRNA-seq.
  • CCI   Benchmarking of CCI prediction tools based on the integration of single-cell and spatial transcriptomic data.