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Author: scverse-bot

packages/index.html

@@ -49,7 +49,7 @@
 <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>
 <a href=https://decoupler.readthedocs.io/en/latest/ target=_blank>Documentation and tutorials</a>
 <a href=https://pypi.org/project/decoupler/ target=_blank>PyPI</a>
-<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 73 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&rsquo;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
+<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&rsquo;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
 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/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
@@ -96,7 +96,8 @@
 Does weighted random sampling, downloading and preprocessing.
 works with anndata, zarr, and h5ad files.</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/kharchenkolab/scLiTr target=_blank>scLiTr</a></td><td>scLiTr (single-cell Lineage Tracing) is a python package for exploratory analysis of
-barcoding-based scRNA-Seq lineage tracing experiments</td></tr><tr class="package-links eco-table-row"><td><a href=https://github.com/cantinilab/scPRINT target=_blank>scPRINT</a></td><td>A single cell foundation model for Gene network inference and more&mldr;</td></tr><tr class="package-links eco-table-row"><td><a href=https://github.com/loosolab/scanpro target=_blank>scanpro</a></td><td>robust cell proportion analysis for single cell data</td></tr><tr class="package-links eco-table-row"><td><a href=https://github.com/dawe/schist target=_blank>schist</a></td><td>schist applies Stochastic Block Models (SBM) to the analysis of single cell data, in particular to identify cell populations</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/scmcphub target=_blank>scmcp</a></td><td>A MCP server hub for scRNA-Seq analysis software.</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.
+barcoding-based scRNA-Seq lineage tracing experiments</td></tr><tr class="package-links eco-table-row"><td><a href=https://github.com/cantinilab/scPRINT target=_blank>scPRINT</a></td><td>A single cell foundation model for Gene network inference and more&mldr;</td></tr><tr class="package-links eco-table-row"><td><a href=https://github.com/loosolab/scanpro target=_blank>scanpro</a></td><td>robust cell proportion analysis for single cell data</td></tr><tr class="package-links eco-table-row"><td><a href=https://github.com/dawe/schist target=_blank>schist</a></td><td>schist applies Stochastic Block Models (SBM) to the analysis of single cell data, in particular to identify cell populations</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/scmcphub target=_blank>scmcp</a></td><td>A MCP server hub for scRNA-Seq analysis software.</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/bionetslab/scxmatch target=_blank>scxmatch</a></td><td>Single-cell Cross Match (scxmatch) is a is a Python package that implements Rosenbaum&rsquo;s cross-match test using distance-based matching to assess distribution shifts between two groups of high-dimensional data.
+This is particularly useful in analyzing multivariate distributions in structured data, such as single-cell RNA-seq or ATAC-seq.</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.
 SiFT can be applied to a wide range of tasks, from (i) the removal of unwanted variation as a pre-processing step, through (ii) revealing hidden biological structure by utilizing prior knowledge with respect to existing signal, to (iii) uncovering trajectories of interest using reference data to remove unwanted variation.</td></tr><tr class="package-links eco-table-row"><td><a href=https://github.com/NKI-CCB/sobolev_alignment target=_blank>Sobolev Alignment</a></td><td>Sobolev alignment of deep probabilistic models for comparing single cell profiles from pre-clinical models and patients</td></tr><tr class="package-links eco-table-row"><td><a href=https://github.com/gustaveroussy/sopa target=_blank>sopa</a></td><td>Technology-invariant pipeline for spatial-omics analysis that