Omicverse Single Cell Scenic

OmicVerse Single-Cell SCENIC

Goal

Run a reusable SCENIC analysis spine on single-cell AnnData: verify motif and ranking resources, initialize ov.single.SCENIC, infer a GRN, build regulons and AUCell scores, then optionally continue into RSS scoring, regulon binarization, and TF-centered GRN exploration. Keep this skill centered on one SCENIC result object rather than on the tutorial dataset.

Quick Workflow

1. Inspect the input AnnData, especially the raw-count layer name, gene symbols, and grouping column for downstream interpretation.
2. Confirm that the ranking databases and motif annotation table exist before constructing ov.single.SCENIC(...).
3. Pick the GRN branch up front with method='regdiffusion', method='grnboost2', or method='genie3'.
4. Run SCENIC.cal_grn(...) on a raw-count layer, then run SCENIC.cal_regulons(...) with explicit module-building kwargs when you need the notebook-style pruning behavior.
5. Treat the returned regulon AnnData plus scenic_obj.auc_mtx, scenic_obj.regulons, and scenic_obj.modules as the shared handoff point for all downstream stages.
6. Only if the user asks for interpretation, continue into RSS, binarization, embedding overlays, regulon marker ranking, or TF-target GRN plotting.
7. Validate the expected layers, tables, and object attributes before writing outputs.

Interface Summary

• ov.single.SCENIC(adata, db_glob, motif_path, n_jobs=8) requires an AnnData, a ranking-database glob, and a motif annotation table.
• SCENIC.cal_grn(method='regdiffusion'|'grnboost2'|'genie3', layer='counts', tf_names=None, **kwargs) infers weighted TF-target edges and writes edgelist plus adjacencies.
• SCENIC.cal_regulons(rho_mask_dropouts=True, seed=42, **kwargs) builds modules, prunes them with cisTarget, computes AUCell scores, and returns a regulon AnnData.
• The forwarded module-building kwargs come from pySCENIC modules_from_adjacencies(...), including thresholds, top_n_targets, top_n_regulators, min_genes, absolute_thresholds, rho_dichotomize, keep_only_activating, and rho_threshold.
• regulon_specificity_scores(auc_mtx, cell_type_series) converts the AUCell matrix into a cell-type-by-regulon RSS table.
• binarize(auc_mtx, threshold_overides=None, seed=None, num_workers=1) derives per-regulon thresholds and returns a binary activity matrix plus thresholds.
• plot_rss(rss, cell_type, top_n=5, max_n=None, ax=None) labels the top RSS regulons for one group.
• sc.tl.rank_genes_groups(..., method='t-test'|'wilcoxon'|'logreg'|'t-test_overestim_var', use_raw=..., layer=...) is the notebook's differential-ranking branch for regulon AnnData.
• ov.single.cosg(..., mu=1, remove_lowly_expressed=False, expressed_pct=0.1, calculate_logfoldchanges=True, use_raw=True, layer=None) is the faster marker-style alternative branch on the regulon matrix.
• ov.single.build_correlation_network_umap_layout(...), ov.single.add_tf_regulation(...), and ov.single.plot_grn(...) form the notebook's optional TF-target network visualization stage.

Boundary

• Keep this as one SCENIC skill because the notebook's downstream stages all consume the same SCENIC outputs and are weak on their own without a completed regulon/AUCell result.
• Do not split just because a notebook changes method, threshold choices, dropout masking, or whether it continues into RSS or plotting.
• Split only when the user starts from an already computed regulon object or AUCell matrix and does not need SCENIC inference at all.
• Do not absorb unrelated preprocessing, trajectory inference, or cell-type annotation notebooks here.

Branch Selection

• Use method='regdiffusion' when you want the notebook's neural GRN branch and can afford a training-oriented run.
• Use method='grnboost2' when you explicitly want the arboreto gradient-boosting branch, but verify the current wrapper on your runtime before treating it as production-ready.
• Use method='genie3' when you explicitly want that arboreto backend, and apply the same runtime verification caution as grnboost2.
• Use layer='counts' when the object follows the current live default; use layer='raw_count' only when the input object actually stores raw counts under that notebook-era name.
• Keep rho_mask_dropouts=True when reproducing the notebook's regulon path; turn it off only when you intentionally want the newer all-cells correlation behavior.
• Tune thresholds, top_n_targets, and top_n_regulators together because modules_from_adjacencies(...) combines all three module-construction strategies before pruning.
• Keep keep_only_activating=True for the portable path; set it to False only when you explicitly want both activating and repressing modules.
• Use absolute_thresholds=False unless you have a reason to express module thresholds in raw edge-weight units instead of percentiles.
• Use RSS when the user wants cell-type-specific regulons, binarization when they want on/off activity calls, and TF-network plotting only when they want a targeted exploratory view for selected TFs.
• Use sc.tl.rank_genes_groups when statistical testing matters; use ov.single.cosg when you want a fast specificity-style ranking branch on the regulon matrix.

Input Contract

• Start from an AnnData with raw counts in a sparse layer that supports .toarray().
• Ensure gene symbols are appropriate for the chosen motif/ranking resources.
• Provide a grouping column in adata.obs before RSS or marker-style downstream analysis.
• Expect SCENIC.cal_grn(...) to reject missing layers and to reject a layer that already looks log-normalized.
• Expect SCENIC.cal_regulons(...) to use adata.to_df() as the expression matrix for module pruning and AUCell scoring.
• Treat the motif table and ranking databases as external prerequisites, not as something this skill downloads.

Minimal Execution Patterns

import omicverse as ov

scenic = ov.single.SCENIC(
    adata,
    db_glob=db_glob,
    motif_path=motif_path,
    n_jobs=8,
)
edgelist = scenic.cal_grn(
    method="regdiffusion",
    layer="counts",
    n_steps=1,
    batch_size=8,
    device="cpu",
)
regulon_ad = scenic.cal_regulons(
    rho_mask_dropouts=True,
    thresholds=(0.75, 0.90),
    top_n_targets=(50,),
    top_n_regulators=(5, 10, 50),
)

from omicverse.external.pyscenic.rss import regulon_specificity_scores
from omicverse.external.pyscenic.binarization import binarize

rss = regulon_specificity_scores(
    scenic.auc_mtx,
    scenic.adata.obs["cell_type"],
)
binary_mtx, auc_thresholds = binarize(
    scenic.auc_mtx,
    num_workers=1,
)

import scanpy as sc
import omicverse as ov

sc.tl.rank_genes_groups(
    regulon_ad,
    groupby="cell_type",
    method="t-test",
    use_raw=False,
)
ov.single.cosg(
    regulon_ad,
    groupby="cell_type",
    key_added="cell_type_cosg",
    use_raw=False,
)

Validation

• Check that the requested raw-count layer exists before SCENIC.cal_grn(...).
• Check that scenic.edgelist and scenic.adjacencies are non-empty and contain TF, target, and importance.
• Check that scenic.modules, scenic.regulons, scenic.auc_mtx, and the returned regulon AnnData all exist after SCENIC.cal_regulons(...).
• Check that the regulon AnnData obs index matches the source cells before copying embeddings or group labels onto it.
• Check that RSS output uses groups as rows and regulons as columns.
• Check that binarization returns both a binary matrix and threshold series with matching regulon names.
• Check that marker ranking wrote the expected key into uns before plotting.
• If you only validated the constructor plus a bounded regdiffusion smoke, say so explicitly and do not claim a full cisTarget reproduction.

Resource Map

• Read the branch-selection reference when choosing the GRN backend, regulon module settings, or downstream interpretation branch.
• Read the source-grounding reference before extending the skill with more interface-specific claims.
• Read the notebook-map reference when deciding whether a new SCENIC tutorial belongs here or should become a separate downstream-only skill.
• Read the compatibility reference when a notebook uses raw_count, older dropout masking assumptions, or plotting helpers with extra dependencies.