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scvi-tools

Skill1 starsChanged 4 months agoNo licenceView on GitHub

Probabilistic single-cell RNA-seq with scvi-tools — scVI for a batch-corrected latent space, scANVI for semi-supervised label transfer, and Bayesian differential expression. Reach for this skill to integrate scRNA-seq batches, embed cells for clustering, transfer annotations from a reference onto a query, or score differentially expressed genes per cluster. For spatial deconvolution / mapping use the cell2location, DestVI, or Tangram methods instead.

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17 other projects use a version of this, 2 of them edited.

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Projects with it also use esmfold2, fair-esm2 and 5 more.

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Files found in the same public projects as a version of this one, more often than they turn up anywhere else. Each project counts once, large collections count for less, and this file's own repository is left out.
  • JimLiuSkill

    esmfold2

    Biohub ESMFold2 / ESMFold2-Fast all-atom co-folding (Candido et al. 2026, github.com/Biohub/esm). Single-sequence and MSA modes; protein, DNA, RNA, ligand (CCD/SMILES), modified residues. FoldBench Ab-Ag 50-55%, PPI 70-77% DockQ-pass. Also covers the ESMC-{300M,600M,6B} protein language models from the same release: masked-LM logits, hidden states, mutation scoring, contact prediction, and the SAE interpretability head. MIT-licensed weights on HuggingFace org `biohub`. Use this skill when: (1) Predicting complex structures with single-sequence input, (2) Validating designed binders with ESMFold2-Fast, (3) Running ESMFold2 with MSA input, (4) Getting ESMC embeddings or per-residue mutation scores, (5) Choosing kernel backend and sampling-step settings for paper-faithful throughput.

    Also in 3 projects that use this

  • JimLiuSkill

    fair-esm2

    Embed proteins with Meta AI's ESM-2 (`fair-esm` package). Use this skill when: (1) Extracting per-residue or per-sequence embeddings for downstream ML, (2) Masked-LM likelihood / mutation effect scoring, (3) Contact prediction from a sequence.

    Also in 3 projects that use this

  • naitySkill

    chai1

    Skill for biomolecular structure prediction with Chai-1 from Chai Discovery. Use this skill when a user wants to predict protein structures, protein-ligand complexes, protein-nucleic acid complexes, or multi-chain biomolecular assemblies. Also trigger when the user mentions Chai-1, chai-lab, or wants an AlphaFold3/Boltz-2 alternative that supports proteins, small molecules, RNA, and DNA. Chai-1 is Apache 2.0 licensed (commercial use allowed).

    Also in 3 projects that use this

  • naitySkill

    evo2

    Skill for genomic sequence modeling and design with Evo2 from Arc Institute. Use this skill when a user wants to model or generate DNA sequences, score variant effects at single-nucleotide resolution, extract genomic embeddings, analyze mutations in non-coding or coding regions, design synthetic genomic elements, compute positional entropy across a sequence, or work with genome-scale context (up to 1M base pairs). Also trigger when the user mentions Evo2, Evo 2, Arc Institute DNA model, genomic language models, DNA variant scoring, or genome-scale foundation models. NOTE: Evo2 is a DNA model — it is NOT a protein language model. For protein embeddings or protein variant scoring use ESM2, ESM-C, or ProtT5 instead.

    Also in 3 projects that use this

  • +3 more from JimLiu

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