Omicverse Single Cell Metatime Annotation

OmicVerse Single-Cell — MetaTiME Tumor Microenvironment Annotation

Goal

Take a batch-corrected, dimension-reduced single-cell AnnData (typically from a tumor scRNA-seq cohort) and annotate it with MetaTiME meta-components (MeCs) — pretrained gene programs derived from millions of single cells across hundreds of tumor scRNA-seq studies. Output is a per-cell obs['MetaTiME'] (fine-grained cell state) and obs['Major_MetaTiME'] (coarse roll-up category). The pipeline is over-cluster → score → write.

MetaTiME's value vs. de-novo annotation is that the meta-components are transferable across cohorts: a TIL-1 (tissue-resident memory CD8+ T cell) annotation in your cohort means the same biology as a TIL-1 annotation in any other cohort that ran MetaTiME.

Quick Workflow

1. Load a batch-corrected AnnData. The canonical MetaTiME demo uses scVI-corrected embedding (adata.obsm['X_scVI']); Harmony / Conos / scanorama corrected embeddings work equivalently.
2. (Optional) Build an MDE projection for downstream visualisation: adata.obsm['X_mde'] = ov.utils.mde(adata.obsm['X_scVI']). MDE is a faster UMAP alternative bundled with OmicVerse.
3. Construct: TiME_object = ov.single.MetaTiME(adata, mode='table'). The mode='table' setting maps meta-component scores to cell-state labels via the bundled lookup table (default; the alternative is hard-cluster mapping but 'table' is canonical).
4. Over-cluster at high resolution: TiME_object.overcluster(resolution=8, clustercol='overcluster'). Writes adata.obs['overcluster']. Resolution=8 (much higher than typical analysis resolutions of 0.5–1.5) is required — MetaTiME's MeC scoring is computed at the cluster level, so over-clustering preserves cell-state granularity.
5. Predict: TiME_object.predictTiME(save_obs_name='MetaTiME'). Scores each cluster against all MeCs, assigns the dominant MeC, and writes both fine-grained (obs['MetaTiME']) and major-category (obs['Major_MetaTiME']) labels.
6. Visualise: TiME_object.plot(cluster_key='MetaTiME', basis='X_mde', dpi=80) — built-in plot with collision-aware label placement, OR fall back to sc.pl.embedding(adata, basis='X_mde', color=['Major_MetaTiME'], frameon=False) for a standard scanpy figure.

Interface Summary

ov.single.MetaTiME(
    adata: AnnData,
    mode: str = 'table',           # 'table' (canonical lookup) or 'cluster' (hard cluster mapping)
)
# Constructor; stores adata reference. No work done yet.

Methods (must run in order):

• TiME_object.overcluster(resolution: float = 8, random_state: int = 0, clustercol: str = 'overcluster') — high-resolution Leiden clustering. Writes adata.obs[clustercol].
• TiME_object.predictTiME(save_obs_name: str = 'MetaTiME') — scores clusters against MeCs, writes adata.obs[save_obs_name] (fine) and adata.obs[save_obs_name + '_Major'] (typically 'Major_MetaTiME').
• TiME_object.plot(basis: str = 'X_umap', cluster_key: str = 'MetaTiME', fontsize: int = 8, min_cell: int = 5, title=None, figsize: tuple = (6, 6), dpi: int = 80, frameon: bool = False, legend_loc=None, palette=None) → (fig, ax) — collision-adjusted labelled scatter on the chosen embedding.

Helper:

• ov.utils.mde(matrix) → ndarray — Minimum Distortion Embedding; faster than UMAP, similar quality. Pass any low-D representation (PCA / scVI / Harmony / scanorama) to project to 2D.

Boundary

Inside scope:

• Three-step MetaTiME annotation on a batch-corrected AnnData.
• Over-clustering + MeC scoring + label writeback.
• Built-in plot with collision-aware labels.
• Major-category roll-up (Tumor / T cell / Myeloid / Stromal / etc.).

Outside scope — separate skill:

• Batch correction itself (scVI / Harmony / Conos / scanorama) — see existing batch-integration skill.
• Other annotation tools (CellTypist, gpt4celltype, scsa) — see existing single-cell-annotation skill.
• Cell Ontology mapping of free-text annotations — see omicverse-single-cell-cellmatch-ontology.
• Multi-annotator consensus voting — see omicverse-single-cell-cellvote-consensus.
• Non-tumor cohorts: MetaTiME's MeCs are derived from tumor data and are most useful in TME context. On normal-tissue cohorts, MeC labels still resolve but the biological interpretation is less direct.

Branch Selection

mode='table' vs mode='cluster'

• 'table' (default) — uses the bundled lookup table to map MeC scores to cell-state labels. Canonical and stable across MetaTiME versions.
• 'cluster' — hard cluster-level mapping based on majority MeC; less granular, rarely needed.

resolution for overcluster

• 8 (tutorial default) — appropriate for cohorts of 5k–50k cells.
• 10–12 — for very large cohorts (>100k cells); preserves more sub-states.
• 4–6 — for small cohorts (<5k cells); avoids over-fragmentation.
• Below 4 — too coarse; MeC scoring loses resolution.

basis for plot

• 'X_mde' (recommended) — fast and visually similar to UMAP.
• 'X_umap' — standard.
• 'X_tsne' — when the cohort has clear cluster separation.
• 'X_scVI' (raw 30-D scVI) — won't work; needs a 2-D embedding.

fontsize and min_cell for plot

• fontsize=8 (default) — readable on most figures.
• min_cell=5 — only label clusters with ≥5 cells. Increase to 20–50 for larger cohorts to reduce label clutter.

Major_MetaTiME vs MetaTiME

• Major_MetaTiME — coarse categories (e.g. T_cell, Myeloid, Tumor, Stromal). Use for high-level cohort summaries.
• MetaTiME — fine cell states (e.g. CD8_TIL_1, TAM_M2_C1, Treg_TIM3+). Use for biological interpretation.
• Always report both in figures: major labels as the colour, fine labels as text.

Input Contract

• AnnData with a batch-corrected low-D representation in obsm ('X_scVI', 'X_harmony', 'X_pca_corrected', etc.). MetaTiME expects integrated data — running on a single-batch cohort works but skip step 2.
• Counts in .X should be log-normalised (sc.pp.normalize_total + sc.pp.log1p); MeC scoring is on log-CPM scale.
• Gene symbols (not IDs) in var_names — the MeC lookup is keyed by symbol.
• Cohort must be human (the bundled MeCs are human-derived); mouse cohorts need symbol-translation or a different MeC set (not bundled).

Minimal Execution Patterns

import omicverse as ov
import scanpy as sc

ov.utils.ov_plot_set()

# 1) Load batch-corrected AnnData (scVI corrected here)
adata = sc.read('TiME_adata_scvi.h5ad')

# 2) MDE projection for visualisation
adata.obsm['X_mde'] = ov.utils.mde(adata.obsm['X_scVI'])

# 3) Construct + over-cluster + predict
TiME_object = ov.single.MetaTiME(adata, mode='table')
TiME_object.overcluster(resolution=8, clustercol='overcluster')
TiME_object.predictTiME(save_obs_name='MetaTiME')

# obs now has:  'overcluster', 'MetaTiME', 'Major_MetaTiME'
print(adata.obs[['MetaTiME', 'Major_MetaTiME']].value_counts().head(10))

# 4) Visualise — built-in collision-adjusted labels
fig, ax = TiME_object.plot(
    cluster_key='MetaTiME',
    basis='X_mde',
    dpi=80,
)

# Or a standard scanpy embedding plot for the major categories
sc.pl.embedding(
    adata, basis='X_mde',
    color=['Major_MetaTiME'],
    frameon=False, ncols=1,
)

Validation

• After overcluster(resolution=8): adata.obs['overcluster'].nunique() should be in the dozens–low hundreds. If it's <10, the resolution is too low for MetaTiME (over-clustering is the point).
• After predictTiME: both MetaTiME and Major_MetaTiME columns are populated, no nulls. If many cells get 'Unassigned', the MeC scoring failed — check that gene symbols match human convention and that .X is log-normalised.
• Sanity check: Major_MetaTiME should include the canonical TME categories you expect (Tumor, T_cell, Myeloid, B/Plasma, Stromal, Endothelial, etc.). If the cohort is tumor-only and only Tumor appears as a major, the MeC scoring is wrong (probably input is scaled instead of log).
• The MetaTiME plot is busy by default — clusters smaller than min_cell are unlabelled to reduce clutter.
• Don't run MetaTiME on a non-batch-corrected cohort — batch effects dominate the over-clustering and produce a per-batch fragmentation rather than per-cell-state.

Resource Map

• See reference.md for compact copy-paste snippets.
• See references/source-grounding.md for the verified MetaTiME constructor + method signatures and the bundled MeC lookup behavior.
• For batch correction (scVI / Harmony / Conos / scanorama) before MetaTiME, see the existing single-cell-batch-integration skill.
• For non-MetaTiME annotators (CellTypist / gpt4celltype / scsa), see the existing single-cell-annotation skill.
• For Cell Ontology mapping of MetaTiME labels, see omicverse-single-cell-cellmatch-ontology.

Examples

• "Annotate a scVI-corrected tumor scRNA-seq AnnData with MetaTiME at over-cluster resolution 8 and plot fine and major categories on MDE."
• "Compare MetaTiME's Major_MetaTiME rollup with manual marker-based annotation; flag clusters where they disagree."
• "Run MetaTiME on a 100k-cell pan-cancer cohort with resolution=10 and report the cell-state distribution."
• "Diagnose why MetaTiME assigned all cells to a single major category — likely the input wasn't log-normalised."

References

• Tutorial notebook: t_metatime.ipynb — TIL annotation walkthrough on a scVI-corrected cohort.
• MetaTiME paper: Yi et al. 2023 — "MetaTiME integrates single-cell gene expression to characterize the meta-components of the tumor immune microenvironment".
• Live API verified — see references/source-grounding.md.