Omicverse Single Cell Clustering Backends

OmicVerse Single-Cell Clustering Backends

Goal

Turn the notebook's clustering comparison into one reusable job: choose and run a clustering backend on a preprocessed single-cell AnnData. Keep this skill focused on backend selection and output contracts after PCA and, when needed, graph construction.

Quick Workflow

1. Inspect whether the input already has PCA or another embedding, and whether a neighbor graph already exists.
2. Choose a backend explicitly: leiden, louvain, scICE, or GMM for the notebook-covered paths.
3. Build a neighbor graph first for leiden and louvain.
4. Pass use_rep explicitly for embedding-based methods such as GMM and scICE.
5. Validate which obs columns were added, and treat ARI or figure rendering as optional downstream reporting rather than the core contract.

Interface Summary

• ov.utils.cluster(adata, method='leiden', use_rep='X_pca', random_state=1024, n_components=None, **kwargs) dispatches to a clustering backend.
• Live source exposes more method values than the notebook covers: leiden, louvain, kmeans, GMM, mclust, mclust_R, schist, and scICE.
• leiden and louvain forward **kwargs to scanpy clustering on the current graph.
• GMM and mclust use the embedding named by use_rep, require n_components, and write both mclust and gmm_cluster.
• scICE returns a fitted model object and adds one or more scICE_k* columns to adata.obs through add_to_adata(...).
• scICE runtime behavior depends on resolution_range, n_steps, n_trials, and n_boot.

Stage Selection

• Use leiden for the default graph-based clustering path.
• Use louvain only when Louvain compatibility is specifically requested.
• Use GMM when the user wants soft-assignment-style Gaussian mixture clustering on an embedding instead of graph partitioning.
• Use scICE when the user wants consistency-guided cluster-count selection and is willing to pay the extra bootstrap cost.
• Keep the notebook's UMAP and ARI cells optional; they are reporting on top of clustering outputs, not the reusable skill boundary.

Input Contract

• Start from an AnnData object that already has PCA or another usable embedding for embedding-based methods.
• Ensure a neighbor graph exists before leiden or louvain.
• Ensure the embedding named by use_rep exists before GMM or scICE.
• Pass n_components when using GMM or mclust.

Minimal Execution Patterns

import omicverse as ov

ov.pp.neighbors(adata, n_neighbors=15, n_pcs=50, use_rep="scaled|original|X_pca")
ov.utils.cluster(adata, method="leiden", resolution=1.0)
ov.utils.cluster(adata, method="louvain", resolution=1.0)

ov.utils.cluster(
    adata,
    method="GMM",
    use_rep="scaled|original|X_pca",
    n_components=21,
)

scice_model = ov.utils.cluster(
    adata,
    method="scICE",
    use_rep="scaled|original|X_pca",
    resolution_range=(4, 20),
    n_boot=50,
    n_steps=11,
)

Constraints

• Do not use this skill as a preprocessing skill; it assumes the object is already prepared enough to cluster.
• Do not leave method implicit.
• Do not assume the notebook's cluster counts are stable defaults for other datasets.
• Treat scICE_k* outputs as data-dependent candidate solutions, not a fixed schema with one guaranteed suffix.
• Keep smoke and acceptance commands shell-agnostic.

Validation

• Check that the intended obs column was added for the chosen backend.
• For GMM, check both mclust and gmm_cluster.
• For scICE, check that a model object was returned and that at least one scICE_k* column was added.
• If comparing backends, compute metrics such as ARI only after all requested label columns exist.
• If only a bounded smoke path was run, say which backends were executed and which were source-grounded only.

Resource Map

• Use the branch selection notes when choosing a backend.
• Use the source grounding notes for current method branches and output columns.
• Use the notebook mapping notes to trace the notebook's sections into this reusable skill.
• Use the compatibility notes for dependency-sensitive or backend-sensitive paths.