sc_reconstruction.metrics.metric_knn_purity#
- sc_reconstruction.metrics.metric_knn_purity(adata_pred, adata_pert_true, adata_ctrl, *, k=20, use_rep=None)[source]#
KNN purity of predicted perturbation in a (true-perturbed, control) pool.
Wraps
sc_reconstruction.metrics.utils.knn_purity().- Parameters:
adata_pred (anndata.AnnData) – Predicted perturbed cells (the query).
adata_pert_true (anndata.AnnData) – Ground-truth perturbed cells (the positive pool).
adata_ctrl (anndata.AnnData) – Control cells (the negative pool).
k (int) – Number of neighbors. Returns a float in
[0, 1]: 1.0 = all neighbors are true-perturbed, 0.5 = random baseline.use_rep (str | None) – If given, use
.obsm[use_rep]instead of.X(recommended; KNN in a learned latent space is far more meaningful than in raw expression).
- Return type: