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Open Problems in Single-Cell Analysis

Open Problems in Single‑Cell Analysis

Define and benchmark challenges in single-cell data science via open standards and continuous leaderboards to promote method development and guide users in method selection. This organization hosts the code, tasks, datasets, and docs behind the Open Problems platform.


What lives in this org

  • Core platform - openproblems: the living, extensible, community‑guided benchmarking framework.
  • Common datasets - datasets: workflows for managing and processing common datasets.
  • Benchmarks as repositories: task‑specific repos named task_* (e.g., task_batch_integration, task_label_projection, task_spatially_variable_genes, etc.).
  • Task template - task_template: scaffolding to start a new benchmark with the correct structure.
  • Shared libraries & images - core: helper R/Python packages and base Docker images used across tasks.

How the platform works (in short)

More detailed documentation below.

  • Tasks define an API, reference datasets, and quantitative metrics.
  • Methods implement that task API.
  • Continuous evaluation runs standardized workflows to score methods and update leaderboards.
  • Reproducibility is enforced via containers and declarative workflows.

Tech stack highlights:

  • Nextflow for portable workflows; Viash for modular components; AnnData for standardized I/O; GitHub Actions for CI; deployment via Nextflow Tower with cloud backends (e.g., AWS Batch/S3). See the Technology stack for details.

Quick start

  1. Explore current leaderboards Check out live tasks and results on the Benchmarks page.

  2. Run a benchmark locally Read the platform Documentation for install requirements and common commands. Components are containerized; workflows run on laptop, HPC, or cloud.

  3. Add your method to a task Follow the docs ("Create component → Add a method") and open a PR in the corresponding task_* repo. See repo READMEs for task‑specific APIs.

  4. Propose or start a new task Start from task_template and the docs ("Create a new task"). Open an issue to coordinate scope and maintainership.

  5. Join the community


Governance & community expectations

  • Governance: Open, consensus‑seeking model with defined roles (Core team, Task teams, Infrastructure, etc.). Read the current Governance.
  • Code of Conduct: We follow the Contributor Covenant. Report issues to [email protected]. See the full Code of Conduct.

Citation

If you use Open Problems, please cite:

Luecken, M.D., Gigante, S., Burkhardt, D.B. et al. Defining and benchmarking open problems in single‑cell analysis. Nature Biotechnology (2025). https://doi.org/10.1038/s41587-025-02694-w

To reference specific tasks or datasets, please cite the corresponding task or dataset publications mentioned in the task descriptions and dataset pages. For example, to reference our Open Problems multimodal BMMC datasets, please cite Luecken et al., NeurIPS 2021.

Also see earlier NeurIPS challenge reports and proceedings referenced on the Events page.


Licensing

Open Problems is free open source software and distributed under the MIT License. However, Open Problems tasks may include references to data or code distributed by a third party under a different license. If any question about license arises, please consult the specific repository in which a particular asset is hosted. If further clarification is needed, please open a GitHub Issue referencing the asset if any clarification is needed.

  • Code in this org is MIT unless stated otherwise in the repo.
  • The website repo uses mixed licensing: Markdown/JSON content under CC‑BY‑4.0 and code under MIT (see that repo's LICENSE files).
  • Datasets retain their original source licenses/terms; check dataset pages before downstream use.

Acknowledgments

Open Problems is supported by a growing community and sponsors including the Chan Zuckerberg Initiative, Data Intuitive, Helmholtz Munich, Saturn Cloud, and Seqera. See the website for the latest list.


Development notes

  • Primary languages: Python and R (task repos may include Bash/Nextflow/TeX for workflows and docs).
  • CI builds and unit tests run via GitHub Actions; component images are maintained centrally in core.

Maintainers & contact

  • Issues and PRs: use the relevant repo (openproblems or task_*).
  • Community and governance questions: [email protected].
  • Real‑time chat: Discord.

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  1. openproblems openproblems Public

    Formalizing and benchmarking open problems in single-cell genomics

    TeX 420 88

  2. website website Public

    Legacy website

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Repositories

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  • openproblems-bio/task_grn_inference’s past year of commit activity
    Jupyter Notebook 16 MIT 5 2 (1 issue needs help) 2 Updated Dec 30, 2025
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    R 2 MIT 1 13 1 Updated Dec 19, 2025
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    Python 9 MIT 4 9 6 Updated Dec 13, 2025
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    The Open Problems logo in different formats

    openproblems-bio/logos’s past year of commit activity
    Shell 0 CC-BY-SA-4.0 0 0 0 Updated Nov 28, 2025
  • openproblems Public

    Formalizing and benchmarking open problems in single-cell genomics

    openproblems-bio/openproblems’s past year of commit activity
    TeX 420 MIT 88 13 (1 issue needs help) 0 Updated Nov 26, 2025
  • task_predict_modality Public

    Predicting the profiles of one modality (e.g. protein abundance) from another (e.g. mRNA expression).

    openproblems-bio/task_predict_modality’s past year of commit activity
    Python 4 MIT 2 1 2 Updated Nov 26, 2025
  • core Public
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    R 0 MIT 0 0 2 Updated Nov 21, 2025
  • openproblems-bio/task_batch_integration’s past year of commit activity
    Python 10 MIT 15 11 22 Updated Nov 7, 2025
  • task_denoising Public

    Removing noise in sparse single-cell RNA-sequencing count data

    openproblems-bio/task_denoising’s past year of commit activity
    Python 2 MIT 4 3 1 Updated Nov 6, 2025
  • task_label_projection Public

    Automated cell type annotation from rich, labeled reference data

    openproblems-bio/task_label_projection’s past year of commit activity
    Python 4 MIT 4 3 4 Updated Oct 19, 2025

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