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49 | 49 | <span class=package-details>decoupler is a framework containing different enrichment statistical methods to extract biologically driven scores from omics data within a unified framework.</span></div></div><div class=package-links><a href=https://github.com/scverse/decoupler target=_blank>GitHub</a> |
50 | 50 | <a href=https://decoupler.readthedocs.io/en/latest/ target=_blank>Documentation and tutorials</a> |
51 | 51 | <a href=https://pypi.org/project/decoupler/ target=_blank>PyPI</a> |
52 | | -<a href=https://anaconda.org/conda-forge/decoupler-py target=_blank>Conda</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 74 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/quadbio/cellmapper target=_blank>CellMapper</a></td><td>CellMapper is a leightweight tool to transfer labels, expression values and embeddings from reference to query datasets using k-NN mapping. It’s fast and versatile, applicable to mapping scenarios in space, across modalities, or from an atlas to a new query dataset.</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 |
| 52 | +<a href=https://anaconda.org/conda-forge/decoupler-py target=_blank>Conda</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 75 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/quadbio/cellmapper target=_blank>CellMapper</a></td><td>CellMapper is a leightweight tool to transfer labels, expression values and embeddings from reference to query datasets using k-NN mapping. It’s fast and versatile, applicable to mapping scenarios in space, across modalities, or from an atlas to a new query dataset.</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 |
53 | 53 | 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. |
54 | 54 | It contains two main modules - kernels compute cell-cell transition probabilities and estimators generate |
55 | 55 | 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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71 | 71 | such as those coming from the Human Cell Atlas.</td></tr><tr class="package-links eco-table-row"><td><a href=https://github.com/zktuong/dandelion target=_blank>dandelion</a></td><td>dandelion - A single cell BCR/TCR V(D)J-seq analysis package for 10X Chromium 5’ data. |
72 | 72 | It streamlines the pre-processing, leveraging some tools from immcantation suite, and |
73 | 73 | integrates with scanpy/anndata for single-cell BCR/TCR analysis. It also includes a |
74 | | -couple of functions for visualization.</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, |
| 74 | +couple of functions for visualization.</td></tr><tr class="package-links eco-table-row"><td><a href=https://github.com/joschif/delnx target=_blank>delnx</a></td><td>delnx is a python package for differential expression analysis of (single-cell) genomics data. It enables scalable analyses of atlas-level datasets through GPU-accelerated regression models and statistical tests implemented in JAX and provides a consistent interface to perform DE analysis with other methods, such as statsmodels and PyDESeq2.</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, |
75 | 75 | vector field reconstruction, potential landscape mapping, differential geometry analyses, |
76 | 76 | 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 |
77 | 77 | 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 |
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