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Author: scverse-bot

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 <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 110 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 lightweight 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 111 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 lightweight 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/theislab/DRVI target=_blank>DRVI</a></td><td>DRVI is a tool for the unsupervised disentanglement and integration of single-cell omics.
+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/davidrm-bio/DOTools_py target=_blank>DOTools_py</a></td><td>Convenient and user-friendly package to streamline common workflows in single-cell RNA sequencing data analysis with improved visualisation</td></tr><tr class="package-links eco-table-row"><td><a href=https://github.com/theislab/DRVI target=_blank>DRVI</a></td><td>DRVI is a tool for the unsupervised disentanglement and integration of single-cell omics.
 By providing interpretable latent dimensions, it allows users to identify cellular
 heterogeneity and biological processes beyond traditional cell types, identify rare cell types,
 and highlight developmental stages. DRVI is implemented using scvi-tools and includes a
@@ -81,7 +81,7 @@
 and create comprehensive cellular maps of diverse tissues. Cell2location accounts for technical sources
 of variation and borrows statistical strength across locations, thereby enabling the integration of single-cell
 and spatial transcriptomics with higher sensitivity and resolution than existing tools.</td></tr><tr class="package-links eco-table-row"><td><a href=https://github.com/chanzuckerberg/cellxgene target=_blank>cellxgene</a></td><td>CZ CELLxGENE Annotate (pronounced &ldquo;cell-by-gene&rdquo;) is an interactive data explorer for single-cell datasets,
-such as those coming from the Human Cell Atlas.</td></tr><tr class="package-links eco-table-row"><td><a href=https://github.com/scverse/cookiecutter-scverse target=_blank>cookiecutter-scverse</a></td><td>Cookiecutter template for scverse packages offering automated template sync</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&rsquo; data.
+such as those coming from the Human Cell Atlas.</td></tr><tr class="package-links eco-table-row"><td><a href=https://github.com/kharchenkolab/clone2vec target=_blank>clone2vec</a></td><td>clone2vec is a Python package for analysis of lineage tracing coupled with single-cell RNA-Seq.</td></tr><tr class="package-links eco-table-row"><td><a href=https://github.com/scverse/cookiecutter-scverse target=_blank>cookiecutter-scverse</a></td><td>Cookiecutter template for scverse packages offering automated template sync</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&rsquo; data.
 It streamlines the pre-processing, leveraging some tools from immcantation suite, and
 integrates with scanpy/anndata for single-cell BCR/TCR analysis. It also includes a
 couple of functions for visualization.</td></tr><tr class="package-links eco-table-row"><td><a href=https://github.com/scverse/decoupler target=_blank>decoupler</a></td><td>decoupler is a framework containing different enrichment statistical methods to extract
@@ -128,8 +128,7 @@
 drop-in replacement for scanpy, squidpy, and decoupler.</td></tr><tr class="package-links eco-table-row"><td><a href=https://github.com/earmingol/scCellFie target=_blank>scCellFie</a></td><td>scCellFie infers metabolic activities from single-cell and spatial transcriptomics and offers a variety of downstream analyses.</td></tr><tr class="package-links eco-table-row"><td><a href=https://github.com/jkobject/scDataLoader target=_blank>scDataLoader</a></td><td>A dataloader for large single cell databases like cellxgene.
 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/yizhak-lab-ccg/scXpand target=_blank>scXpand</a></td><td>scXpand is a machine learning framework for pan-cancer detection of T-cell clonal expansion
+across cell types, studies and species.</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/yizhak-lab-ccg/scXpand target=_blank>scXpand</a></td><td>scXpand is a machine learning framework for pan-cancer detection of T-cell clonal expansion
 directly from single-cell RNA sequencing (scRNA-seq), without paired T-cell receptor (TCR) sequencing.</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/scverse/scanpy target=_blank>Scanpy</a></td><td>Scanpy is a scalable toolkit for analyzing single-cell gene expression data built jointly
 with anndata. It includes preprocessing, visualization, clustering, trajectory inference
 and differential expression testing. The Python-based implementation efficiently deals