MetaCell zoo

MetaCell zoo#

ov.single.MetaCell ships seven backend partitioners behind a single API. Each method has a different algorithmic philosophy and a different trade-off between speed, scalability, and downstream-task quality.

This zoo gives each backend a dedicated notebook so you can see how its particular hyperparameters and outputs behave on the same dataset (mouse pancreas, Bastidas-Ponce et al. 2019), then a final comparison notebook that runs them all side-by-side and ranks them on purity, mcRigor score, and runtime.

Backend

Paper

Capabilities

Tutorial

seacells

Persad 2023 (Nat Biotech)

soft, latent

t_metacell_seacells

metaq

Li 2025 (Nat Comms)

soft, latent, codebook, out_of_sample, multimodal, streaming

t_metacell_metaq

supercell

Bilous 2022 (BMC Bioinformatics)

hierarchical

t_metacell_supercell

kmeans

(baseline)

latent, codebook, out_of_sample

t_metacell_kmeans

random

(honest baseline)

—

t_metacell_random

geosketch

Hie 2019 (Cell Systems)

out_of_sample

t_metacell_geosketch

mc2

Ben-Kiki 2022 (Genome Biology)

streaming

pip install metacells + method='mc2'

Side-by-side comparison → t_metacell_compare.

Every per-backend notebook runs the same diagnostics (helpers live in ov.pl.* — the notebook cells stay short):

  • ov.pl.metacell_metrics(mc) — purity / separation / compactness with histograms.

  • mc.check_rigor() + ov.pl.rigor_scatter(rep) — mcRigor validator.

  • UMAP with metacell centroids overlaid — ov.pl.embedding + a 3-line scatter.

  • ov.pl.metacell_purity_box(mc) — per-celltype boxplot.

  • A metacell-level UMAP built via the standard ov.pp.preprocess → PCA → UMAP loop on the aggregated AnnData.

  • ov.single.find_markers + ov.pl.markers_dotplot — top markers per celltype.

Plus one backend-specific section per method (ov.pl.metacell_soft_heatmap for SEACells; ov.pl.metacell_codebook_umap + mc.assign_new_cells for MetaQ; mc.fit_multi_gamma for SuperCell; etc.).

Which one should I use?#

Task

Recommended

UMAP / Leiden / quick visualisation

kmeans or geosketch (cheap, ~as good)

Differential expression / cell–cell communication / GRN inference

seacells or metaq (proper aggregation)

Atlas-style workflow with new samples arriving over time

metaq (closed-form out-of-sample via encoder + codebook)

Multi-million cell dataset

metaq (linear-time) or mc2 (divide-and-conquer)

“Is my metacell pipeline even worth it?” sanity check

random (lower bound)

All seven backends write a unified AnnData schema (obs['metacell_id'], obs['metacell_conf'], optional obsm['X_metacell'] and obsm['metacell_soft'], uns['metacell']), so downstream tools (CellPhoneDB, SCENIC, pseudobulk DE) consume any of them without an if backend == ... branch.

Contents