Cumulus helps researchers tackle one of single-cell genomics’ biggest challenges: analyzing datasets too large for a laptop. It’s a cloud-based framework for large-scale scRNA-seq and snRNA-seq analysis, handling everything from cell filtering and clustering to UMAP visualization, making it a natural fit for cancer cell atlas projects and tumor microenvironment studies.

Sustainability Snapshot via Cancer Complexity Toolkit:

📚Strong documentation with README and dependency tracking
🔧 Active development on GitHub (lilab-bcb/cumulus)
✅Sustainability indicators: JOSS Score 0.62 | Almanack 0.64
🌱Opportunities for growth: Test coverage and expanded CI/CD

Why it matters: Cumulus is a cloud-native, scalable solution with a Nature Methods publication and a “Developing” Almanack grade (0.64). For labs working with large single-cell datasets in cancer research, it provides a reproducible, well-documented path from raw counts to biological insight.

🔗 Check out Cumulus on the CCKP

Attribution: Created by Bo Li, Aviv Regev, and Orit Rozenblatt-Rosen at the Broad Institute’s Klarman Cell Observatory.

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