Add new core packages blog post

content/blog/2025-core-expansion.md

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+title = "Four new scverse core packages"
+date = 2025-07-01T00:00:05+01:00
+description = "scverse expands with new core packages."
+author = "Lukas Heumos"
+draft = false
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+
+# Four new core packages in scverse
+
+We're happy to announce that four new packages have joined the scverse core ecosystem: [SnapATAC2](https://github.com/scverse/snapatac2), [rapids-singlecell](https://github.com/scverse/rapids_singlecell), [pertpy](https://github.com/scverse/pertpy), and [decoupler](https://github.com/scverse/decoupler).
+This broadens the scope of scverse beyond our so far supported modalities and brings in new functionality for epigenomics, perturbation screens, GPU acceleration, and functional inference.
+
+Single-cell analysis is evolving rapidly, with new experimental modalities and larger datasets becoming the norm.
+To keep up with this, we’re expanding scverse to support more domains, more data types, and more scalable computing backends.
+By bringing these mature, well-maintained tools into the core, we aim to provide a consistent and interoperable foundation across all stages of single-cell analysis.
+
+We're also welcoming the following lead maintainers to the scverse core developer team:  
+- **Kai Zhang** for **snapatac2**
+- **Pau Badia i Mompel** for **decoupler**
+
+## SnapATAC2
+
+[SnapATAC2](https://github.com/scverse/snapatac2) enables fast, scalable analysis of single-cell ATAC-seq and related epigenomic data.  
+Built in Rust with a Python front end, it handles millions of cells efficiently.  
+The package offers preprocessing, dimensionality reduction, clustering, and visualization methods.  
+All outputs are stored in AnnData and integrate seamlessly with scanpy and other scverse frameworks.
+
+<img src="/img/blog/snapatac2_overview.png" style="max-width: 100%;" alt="SnapATAC2 overview" />
+
+## rapids-singlecell
+
+[rapids-singlecell](https://github.com/scverse/rapids_singlecell) accelerates the full single-cell analysis pipeline through GPU acceleration with CuPy and NVIDIA RAPIDS.
+Core steps—including PCA, neighborhood graph construction, and clustering—are executed on GPU using cuML, cuGraph, and custom CUDA/CuPy kernels for peak performance.
+
+RSC integrates directly with AnnData and offers near drop-in replacements not only for scanpy, but also for selected functions from decoupler and squidpy.
+By preserving familiar APIs and data structures, it enables seamless GPU acceleration of existing workflows—scaling to millions of cells without the computational bottlenecks of CPU-based analysis.
+
+<img src="/img/blog/rsc_overview.png" style="max-width: 100%;" alt="RSC overview" />
+
+For more details, we refer to a recent blog post by NVIDIA: [Driving Toward Billion-Cell Analysis and Biological Breakthroughs with RAPIDS-singlecell](https://developer.nvidia.com/blog/driving-toward-billion-cell-analysis-and-biological-breakthroughs-with-rapids-singlecell)
+
+## pertpy
+
+[pertpy](https://github.com/scverse/pertpy) focuses on single-cell perturbation screens, including CRISPR and compound treatments.  
+It supports differential analysis, signature scoring, and dose-response modeling.  
+Metadata handling and visualization are tailored for perturbation-specific use cases.  
+Built on AnnData and scverse libraries, pertpy fits smoothly into existing pipelines.
+
+<img src="https://github.com/user-attachments/assets/d2e32d69-b767-4be3-a938-77a9dce45d3f" alt="pertpy overview" style="max-width: 100%;" />
+
+## decoupler
+
+[decoupler](https://github.com/scverse/decoupler) enables the inference of enrichment scores from omics data using prior knowledge.  
+It maps omics profiles to annotated biological sets, such as transcription factors, pathways, or kinases, using methods like GSEA, GSVA, and linear models.
+Designed for bulk, single-cell and spatial data, decoupler works directly with our scverse core data structures.
+
+<img src="/img/blog/decoupler_overview.png" style="max-width: 100%;" alt="decoupler overview" />
+
+## What this means
+
+We're continuing to support a modular but coherent ecosystem where high-quality tools can interoperate.
+These additions bring coverage of new data types and analysis goals while staying within the same technical foundations.
+All packages use our scverse core data structures, follow shared conventions, and benefit from a growing set of shared infrastructure and community practices.
+We aim to keep development decentralized and open while improving alignment across projects.
+
+## Get involved
+
+If you're building something new, we'd love to have your work be a part of the [scverse ecosystem](https://github.com/scverse/ecosystem-packages).
+If you're our tools, please share feedback or ideas via the respective issue trackers.
+The best places to start are [scverse.org](https://scverse.org), [github.com/scverse](https://github.com/scverse), and our [scverse zulip chat](https://scverse.zulipchat.com/).
+We're always looking for more contributors to our packages but especially also for community related work.
+Please reach out!
+
+## Thank you
+
+We’re grateful to the maintainers of these packages for their work and commitment to open, reusable tools.  
+Their contributions help make the scverse ecosystem more useful, inclusive, and sustainable.
+We’re also thankful to the community for using, testing, and contributing to these tools — your feedback drives everything we do.
+
+*— The scverse core team*