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 <a href=https://anaconda.org/bioconda/scirpy target=_blank>Conda</a></div></div></div><div class=package-section><div class=package-img><img src=../img/icons/squidpy.svg></div><div class=package-info><div class=package-tile id=squidpy-tile><div class=package-text><span class=package-name>squidpy</span>
 <span class=package-details>Squidpy is a tool for the analysis and visualization of spatial molecular data. It builds on top of scanpy and anndata, from which it inherits modularity and scalability. It provides analysis tools that leverages the spatial coordinates of the data, as well as tissue images if available.</span></div></div><div class=package-links><a href=https://github.com/theislab/squidpy target=_blank>GitHub</a>
 <a href=https://squidpy.readthedocs.io/ target=_blank>Documentation and tutorials</a>
-<a href=https://pypi.org/project/squidpy/ target=_blank>PyPI</a></div></div></div></div><h2 id=ecosystem>Ecosystem packages maintained by scverse community</h2><div><p><p>Many popular packages rely on scverse functionality. For instance, they take advantage of established data format standards such as AnnData and MuData, or are designed to be integrated into the workflow of analysis frameworks. Here, we list ecosystem packages following development best practices (continuous testing, documented, available through standard distribution tools).</p><p><em>This listing is a work in progress. See <a href=https://github.com/scverse/ecosystem-packages>scverse/ecosystem-packages</a> for inclusion criteria, and to submit more packages.</em></p></p></div><div id=ecosystem-packages><input type=text class=form-control id=eco-filter onkeyup=filterPackages() placeholder="Search through 41 packages" title="Type in your search query"><table class=table id=eco-table><thead><tr><th scope=col>Package</th><th scope=col>Description</th></tr></thead><tbody><tr class="package-links eco-table-row"><td><a href=https://github.com/morris-lab/CellOracle target=_blank>CellOracle</a></td><td>A computational tool that integrates single-cell transcriptome and epigenome profiles
+<a href=https://pypi.org/project/squidpy/ target=_blank>PyPI</a></div></div></div></div><h2 id=ecosystem>Ecosystem packages maintained by scverse community</h2><div><p><p>Many popular packages rely on scverse functionality. For instance, they take advantage of established data format standards such as AnnData and MuData, or are designed to be integrated into the workflow of analysis frameworks. Here, we list ecosystem packages following development best practices (continuous testing, documented, available through standard distribution tools).</p><p><em>This listing is a work in progress. See <a href=https://github.com/scverse/ecosystem-packages>scverse/ecosystem-packages</a> for inclusion criteria, and to submit more packages.</em></p></p></div><div id=ecosystem-packages><input type=text class=form-control id=eco-filter onkeyup=filterPackages() placeholder="Search through 42 packages" title="Type in your search query"><table class=table id=eco-table><thead><tr><th scope=col>Package</th><th scope=col>Description</th></tr></thead><tbody><tr class="package-links eco-table-row"><td><a href=https://github.com/morris-lab/CellOracle target=_blank>CellOracle</a></td><td>A computational tool that integrates single-cell transcriptome and epigenome profiles
 to infer gene regulatory networks (GRNs), critical regulators of cell identity.</td></tr><tr class="package-links eco-table-row"><td><a href=https://github.com/theislab/cellrank target=_blank>CellRank</a></td><td>CellRank is a toolkit to uncover cellular dynamics based on Markov state modeling of single-cell data.
 It contains two main modules - kernels compute cell-cell transition probabilities and estimators generate
 hypothesis based on these.</td></tr><tr class="package-links eco-table-row"><td><a href=https://github.com/gao-lab/Cell_BLAST target=_blank>Cell_BLAST</a></td><td>Cell BLAST is a cell querying tool for single-cell transcriptomics data.</td></tr><tr class="package-links eco-table-row"><td><a href=https://github.com/lilab-bcb/cirrocumulus target=_blank>Cirrocumulus</a></td><td>Cirrocumulus is an interactive visualization tool for large-scale single-cell genomics data.</td></tr><tr class="package-links eco-table-row"><td><a href=https://github.com/JonathanShor/DoubletDetection target=_blank>DoubletDetection</a></td><td>DoubletDetection is a Python3 package to detect doublets (technical errors) in single-cell
@@ -60,7 +60,7 @@
 so we changed the name to OmicVerse, it aimed to solve all task in RNA-seq.</td></tr><tr class="package-links eco-table-row"><td><a href=https://github.com/lilab-bcb/pegasus target=_blank>pegasus</a></td><td>Pegasus is a tool for analyzing transcriptomes of millions of single cells.</td></tr><tr class="package-links eco-table-row"><td><a href=https://github.com/aertslab/pySCENIC target=_blank>pySCENIC</a></td><td>pySCENIC is a lightning-fast python implementation of the SCENIC pipeline
 (Single-Cell rEgulatory Network Inference and Clustering) which enables
 biologists to infer transcription factors, gene regulatory networks and
-cell types from single-cell RNA-seq data.</td></tr><tr class="package-links eco-table-row"><td><a href=https://github.com/lzj1769/pychromVAR target=_blank>pychromVAR</a></td><td>A python pacakge for chromVAR.</td></tr><tr class="package-links eco-table-row"><td><a href=https://github.com/LouisFaure/scFates target=_blank>scFates</a></td><td>A scalable python package for tree inference and advanced pseudotime analysis from scRNAseq data.</td></tr><tr class="package-links eco-table-row"><td><a href=https://github.com/theislab/scgen target=_blank>scGen</a></td><td>scGen is a generative model to predict single-cell perturbation response
+cell types from single-cell RNA-seq data.</td></tr><tr class="package-links eco-table-row"><td><a href=https://github.com/lzj1769/pychromVAR target=_blank>pychromVAR</a></td><td>A python pacakge for chromVAR.</td></tr><tr class="package-links eco-table-row"><td><a href=https://github.com/scverse/rapids_singlecell target=_blank>rapids-singlecell</a></td><td>A GPU acclerated python package for singlecell data analysis</td></tr><tr class="package-links eco-table-row"><td><a href=https://github.com/LouisFaure/scFates target=_blank>scFates</a></td><td>A scalable python package for tree inference and advanced pseudotime analysis from scRNAseq data.</td></tr><tr class="package-links eco-table-row"><td><a href=https://github.com/theislab/scgen target=_blank>scGen</a></td><td>scGen is a generative model to predict single-cell perturbation response
 across cell types, studies and species.</td></tr><tr class="package-links eco-table-row"><td><a href=https://github.com/theislab/scib target=_blank>scib</a></td><td>Evaluating single-cell data integration methods</td></tr><tr class="package-links eco-table-row"><td><a href=https://github.com/frankligy/scTriangulate target=_blank>scTriangulate</a></td><td>Python package to mix-and-match conflicting clustering results in single cell analysis and generate reconciled clustering solutions</td></tr><tr class="package-links eco-table-row"><td><a href=https://github.com/theislab/scvelo target=_blank>scVelo</a></td><td>scVelo is a scalable toolkit for RNA velocity analysis in single cells, based on <a href=https://doi.org/10.1038/s41587-020-0591-3>Bergen et al., Nature Biotech, 2020</a>.</td></tr><tr class="package-links eco-table-row"><td><a href=https://github.com/MICS-Lab/scyan target=_blank>scyan</a></td><td>Biology-driven deep generative model for cell-type annotation in cytometry.
 Scyan is an interpretable model that also corrects batch-effect and
 can be used for debarcoding or population discovery.</td></tr><tr class="package-links eco-table-row"><td><a href=https://github.com/theislab/sfaira target=_blank>sfaira</a></td><td>sfaira is a model and a data repository in a single python package.</td></tr><tr class="package-links eco-table-row"><td><a href=https://github.com/nitzanlab/sift-sc target=_blank>sift-sc</a></td><td>SiFT is a computational framework which aims to uncover the underlying structure by filtering out previously exposed biological signals.