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35 | 35 | <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 alt="Logo for squidpy"></div><div class=package-info><div class=package-tile id=squidpy-tile><div class=package-text><span class=package-name>squidpy</span> |
36 | 36 | <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> |
37 | 37 | <a href=https://squidpy.readthedocs.io/ target=_blank>Documentation and tutorials</a> |
38 | | -<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 70 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/quadbio/cell-annotator target=_blank>CellAnnotator</a></td><td>CellAnnotator is a leightweight tool to query large language models for cell type labels in scRNA-seq data. It can incorporate prior knowledge, and it creates consistent labels across samples in your study.</td></tr><tr class="package-links eco-table-row"><td><a href=https://github.com/CSOgroup/cellcharter target=_blank>CellCharter</a></td><td>CellCharter is a framework to identify, characterize and compare spatial domains from spatial omics and multi-omics data.</td></tr><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 |
| 38 | +<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 71 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/quadbio/cell-annotator target=_blank>CellAnnotator</a></td><td>CellAnnotator is a leightweight tool to query large language models for cell type labels in scRNA-seq data. It can incorporate prior knowledge, and it creates consistent labels across samples in your study.</td></tr><tr class="package-links eco-table-row"><td><a href=https://github.com/CSOgroup/cellcharter target=_blank>CellCharter</a></td><td>CellCharter is a framework to identify, characterize and compare spatial domains from spatial omics and multi-omics data.</td></tr><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 |
39 | 39 | 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. |
40 | 40 | It contains two main modules - kernels compute cell-cell transition probabilities and estimators generate |
41 | 41 | 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/ventolab/CellphoneDB target=_blank>CellphoneDB</a></td><td>CellphoneDB is a publicly available repository of HUMAN curated receptors, ligands and their interactions paired with a tool to interrogate your own single-cell transcriptomics data (or even bulk transcriptomics data if your samples represent pure populations!). A distinctive feature of CellphoneDB is that the subunit architecture of either ligands and receptors is taken into account, representing heteromeric complexes accurately. This is crucial, as cell communication relies on multi-subunit protein complexes that go beyond the binary representation used in most databases and studies. CellphoneDB also incorporates biosynthetic pathways in which we use the last representative enzyme as a proxy of ligand abundance, by doing so, we include interactions involving non-peptidic molecules. CellphoneDB includes only manually curated and reviewed molecular interactions with evidenced role in cellular communication.</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 |
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60 | 60 | couple of functions for visualization.</td></tr><tr class="package-links eco-table-row"><td><a href=https://github.com/saezlab/decoupler-py target=_blank>decoupler</a></td><td>Python package to infer biological activities from omics data using a collection of methods.</td></tr><tr class="package-links eco-table-row"><td><a href=https://github.com/aristoteleo/dynamo-release target=_blank>dynamo-release</a></td><td>Inclusive model of expression dynamics with metabolic labeling based scRNA-seq / multiomics, |
61 | 61 | vector field reconstruction, potential landscape mapping, differential geometry analyses, |
62 | 62 | and most probably paths / in silico perturbation predictions.</td></tr><tr class="package-links eco-table-row"><td><a href=https://github.com/colomemaria/epiScanpy target=_blank>epiScanpy</a></td><td>EpiScanpy is a toolkit to analyse single-cell open chromatin (scATAC-seq) and single-cell |
63 | | -DNA methylation (for example scBS-seq) data.</td></tr><tr class="package-links eco-table-row"><td><a href=https://github.com/mikelkou/fava target=_blank>fava</a></td><td>FAVA uses Variational Autoencoders to infer functional associations from |
| 63 | +DNA methylation (for example scBS-seq) data.</td></tr><tr class="package-links eco-table-row"><td><a href=https://github.com/zunderlab/eschr target=_blank>eschr</a></td><td>ESCHR is an ensemble clustering method that provides hard clustering along with |
| 64 | +uncertainty scores and soft clustering outputs for enhanced interpretability.</td></tr><tr class="package-links eco-table-row"><td><a href=https://github.com/mikelkou/fava target=_blank>fava</a></td><td>FAVA uses Variational Autoencoders to infer functional associations from |
64 | 65 | large-scale scRNA-seq (and proteomics) data.</td></tr><tr class="package-links eco-table-row"><td><a href=https://github.com/saeyslab/FlowSOM_Python target=_blank>flowsom</a></td><td>The complete FlowSOM package known from R, now available in Python! |
65 | 66 | Analyze high-dimensional cytometry data using FlowSOM, |
66 | 67 | a clustering and visualization algorithm based on a self-organizing map (SOM). |
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