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Ecosystem

We are no longer accepting new tools on this page.
Instead, please submit your tool to the [scverse ecosystem package listing](https://scverse.org/packages/#ecosystem).

Viewers

Interactive manifold viewers.

  • cellxgene via direct reading of .h5ad {small}CZI
  • cirrocumulus via direct reading of .h5ad {small}Broad Inst.
  • cell browser via exporing through {func}~scanpy.external.exporting.cellbrowser {small}UCSC
  • SPRING via exporting through {func}~scanpy.external.exporting.spring_project {small}Harvard Med
  • vitessce for purely browser based viewing of zarr formatted AnnData files {smaller}Harvard Med

Portals

Modalities

RNA velocity

  • scVelo {small}Helmholtz Munich

Spatial Transcriptomics Tools

  • squidpy {small}Helmholtz Munich

    Squidpy is a comprehensive toolkit for working with spatial single cell omics data.

  • PASTE {small}Princeton

    PASTE is a computational method to align and integrate spatial transcriptomics data across adjacent tissue slices by leveraging both gene expression similarity and spatial distances between spots.

  • bento 🍱 {small}UC San Diego

    Bento is an accessible Python toolkit for performing subcellular analysis of spatial transcriptomics data.

Multimodal integration

  • MUON and MuData {small}EMBL/ DKFZ

    MUON, and it's associated data structure MuData are designed to organise, analyse, visualise, and exchange multimodal data. MUON enables a range of analyses for ATAC and CITE-seq, from data preprocessing to flexible multi-omics alignment.

Adaptive immune receptor repertoire (AIRR)

  • scirpy {small}Medical University of Innsbruck

    scirpy is a scanpy extension to expore single-cell T-cell receptor (TCR) and B-cell receptor (BCR) repertoires.

  • dandelion {small}University of Cambridge

    dandelion is a single-cell BCR-seq network analysis package that integrates with transcriptomic data analyzed via scanpy.

Long reads

  • Swan {small}UC Irvine

    Swan is a Python library designed for the analysis and visualization of transcriptomes, especially with long-read transcriptomes in mind. Users can add transcriptomes from different datasets and explore distinct splicing and expression patterns across datasets.

Analysis methods

scvi-tools

  • scvi-tools {small}Berkeley

    scvi-tools hosts deep generative models (DGM) for end-to-end analysis of single-cell omics data (e.g., scVI, scANVI, totalVI). It also contains several primitives to build novel DGMs.

Fate mapping

  • CellRank {small}Helmholtz Munich

    CellRank is a framework to uncover cellular dynamics based on single-cell data. It incorporates modalities such as RNA velocity, pseudotime, developmental potential, real-time information, etc.

Differential expression

(eco-data-integration)=

Data integration

Modeling perturbations

Feature selection

  • triku 🦔 {small}Biodonostia Health Research Institute

  • CIARA {small}Helmholtz Munich

    CIARA is an algorithm for feature selection, that aims for the identification of rare cell types via scRNA-Seq data in scanpy.

Annotation/ Enrichment Analysis

Analyses using curated prior knowledge

  • decoupler is a collection of footprint enrichment methods that allows to infer transcription factor or pathway activities. {small}Institute for Computational Biomedicine, Heidelberg University

  • Cubé {small}Harvard University

    Intuitive Nonparametric Gene Network Search Algorithm that learns from existing biological pathways & multiplicative gene interference patterns.