deploy: 1f52ac3

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 106 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 107 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.
@@ -102,7 +102,7 @@
 inference with calibrated uncertainty estimates and capturing both global and cell-state-specific effects of perturbation.</td></tr><tr class="package-links eco-table-row"><td><a href=https://github.com/saezlab/liana-py target=_blank>liana</a></td><td>Python package to infer cell-cell communication events from omics data using a collection of methods.</td></tr><tr class="package-links eco-table-row"><td><a href=https://github.com/dcjones/maxspin target=_blank>maxspin</a></td><td>An information theoretic approach to detecting spatially varying genes</td></tr><tr class="package-links eco-table-row"><td><a href=https://github.com/theislab/moscot target=_blank>moscot</a></td><td>moscot is a scalable toolbox for multiomics single-cell optimal transport applications.</td></tr><tr class="package-links eco-table-row"><td><a href=https://github.com/scverse/mudata target=_blank>MuData</a></td><td>MuData is a format for annotated multimodal datasets where each modality is represented
 by an AnnData object. MuData&rsquo;s reference implementation is in Python, and the cross-language
 functionality is achieved via HDF5-based .h5mu files with libraries in R and Julia.</td></tr><tr class="package-links eco-table-row"><td><a href=https://github.com/scverse/muon target=_blank>Muon</a></td><td>muon is a Python framework for multimodal omics analysis. It provides functionality
-for working with multimodal data, including preprocessing, integration, and visualization.</td></tr><tr class="package-links eco-table-row"><td><a href=https://github.com/MICS-Lab/novae target=_blank>novae</a></td><td>Graph-based foundation model for spatial transcriptomics data.
+for working with multimodal data, including preprocessing, integration, and visualization.</td></tr><tr class="package-links eco-table-row"><td><a href=https://github.com/imsb-uke/nichepca target=_blank>nichepca</a></td><td>A Python package for PCA-based spatial domain identification in single-cell spatial transcriptomics data.</td></tr><tr class="package-links eco-table-row"><td><a href=https://github.com/MICS-Lab/novae target=_blank>novae</a></td><td>Graph-based foundation model for spatial transcriptomics data.
 Zero-shot spatial domain inference, batch-effect correction, and many other features.</td></tr><tr class="package-links eco-table-row"><td><a href=https://github.com/Starlitnightly/omicverse target=_blank>omicverse</a></td><td>OmicVerse is the fundamental package for multi omics included bulk and single cell analysis with Python.
 The original name of the omicverse was Pyomic, but we wanted to address a whole universe of transcriptomics,
 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/dpeerlab/Palantir target=_blank>Palantir</a></td><td>Palantir is an algorithm to align cells along differentiation trajectories.