Ecosystem

Note

If you’d like to see your tool included here, please open a pull request!

With ecosystem, we mean the broader single-cell related tools that operate on AnnData. If your tool doesn’t do this, but is useful for analysing single cell data we also accept light wrappers for some tools in scanpy.external.

Viewers

Interactive manifold viewers.

• cellxgene via direct reading of .h5ad CZI

• cirrocumulus via direct reading of .h5ad Broad Inst.

• cell browser via exporing through cellbrowser() UCSC

• SPRING via exporting through spring_project() Harvard Med

• vitessce for purely browser based viewing of zarr formatted AnnData files Harvard Med

Portals

• the Gene Expression Analysis Resource U Maryland

• the Galaxy Project for the Human Cell Atlas [tweet] U Freiburg

• the Expression Atlas EMBL-EBI

Modalities

RNA velocity

• scVelo Helmholtz Munich

Spatial Transcriptomics Tools

• squidpy Helmholtz Munich

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

• PASTE 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.

Multimodal integration

• MUON and MuData 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 Medical University of Innsbruck

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

• dandelion University of Cambridge

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

Long reads

• Swan 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 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 Helmholtz Munich

  CellRank is a toolkit to uncover cellular dynamics based on scRNA-seq data with RNA velocity annotation by detecting initial and terminal populations, inferring fate potentials and uncovering gene expression trends towards specific terminal populations.

Differential expression

• diffxpy Helmholtz Munich

Data integration

• scanaroma MIT

Modeling perturbations

• scGen / trVAE Helmholtz Munich

Feature selection

• triku 🦔 Biodonostia Health Research Institute

• CIARA 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. Institute for Computational Biomedicine, Heidelberg University

• Cubé Harvard University

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