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name: downstream-analysis description: "Use trained scvi-tools models for latent embeddings, normalized expression/protein/accessibility, imputation-like posterior outputs, differential expression/abundance/accessibility/methylation, feature correlations, posterior predictive checks, criticism, and simulation outputs." disable-model-invocation: true metadata: disco-role: operating license: BSD 3-Clause

downstream-analysis

Use this sub-skill after a scvi-tools model is constructed and trained, and the task is to extract biological results or quality checks from the fitted posterior.

Fast Routing

• Need embeddings, denoised values, imputation-like predictions, counterfactual batch conditioning, or posterior predictive samples: read workflows.
• Need exact method names, key parameters, return shapes, or model-family support: read API reference.
• Need differential expression, abundance, accessibility, methylation, or group/boolean selection behavior: read workflows and API reference.
• Need posterior predictive checks or a criticism report for one or more trained models: read workflows.
• Need to debug untrained models, wrong model families, missing categories, invalid gene/region/protein lists, batch conditioning, or memory issues: read troubleshooting.

Boundaries

• Use data-setup for AnnData/MuData construction, setup_anndata, setup_mudata, count layers, and registry validation before model creation.
• Use core-models or multimodal-and-spatial for choosing model families and modality-specific setup.
• Use training-and-inference for .train(...), accelerators, callbacks, checkpoints, and long-running inference settings.
• Use model-io-and-hub for saving/loading models or publishing artifacts.

Minimal Patterns

latent = model.get_latent_representation()
adata.obsm["X_scvi"] = latent

normalized = model.get_normalized_expression(gene_list=["MALAT1"], return_numpy=False)
de = model.differential_expression(groupby="cell_type", group1="B cells", group2="T cells")

Validate that the model is trained, the requested method exists on that model family, all groupby/sample_key categories exist in .obs, and feature lists match adata.var_names, region names, or protein names before running expensive posterior sampling.