omicverse-single-cell-metatime-annotation

Name: omicverse-single-cell-metatime-annotation
Availability: InStock
Author: omicverse

Annotate tumor single-cell AnnData objects with MetaTiME cell-state labels.

13|2|Updated Apr 5, 2026

Search Tags:#single-cell #anndata #cell annotation #scvi #metatime #tumor microenvironment

Ready to run

npx skills add https://github.com/omicverse/omicverse-skills --skill omicverse-single-cell-metatime-annotation

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: omicverse-single-cell-metatime-annotation
Source: https://github.com/omicverse/omicverse-skills/tree/main/src/omicverse_skills/skills/single-cell-metatime-annotation
Command: npx skills add https://github.com/omicverse/omicverse-skills --skill omicverse-single-cell-metatime-annotation

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) and assets (resource) components.

What problem does it solve?

MetaTiME annotation depends on the right over-clustering, a batch-corrected embedding, and careful interpretation of cluster scores. This Skill turns that multi-step process into a dependable workflow for tumor single-cell data so you can produce consistent cell-state and major-category labels without assembling the pipeline yourself.

Core Features & Use Cases

Over-cluster a batch-corrected AnnData object at high Leiden resolution for MetaTiME scoring.
Score clusters against pretrained MetaTiME meta-components and write both fine-grained and major labels back into the dataset.
Visualize annotations on MDE or UMAP with collision-aware cluster labels.
Use it to profile tumor microenvironment cohorts, compare results against manual marker-based annotation, or diagnose why cells are ending up unassigned.

Quick Start

Ask the AI to annotate the attached scVI-corrected tumor AnnData with MetaTiME, save both MetaTiME and Major_MetaTiME labels, and plot the results on MDE.

Frequently Asked Questions about omicverse-single-cell-metatime-annotation

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I annotate tumor microenvironment cells in scRNA-seq using MetaTiME?▼

To annotate tumor microenvironment cells with MetaTiME, you over-cluster a batch-corrected AnnData object, score clusters against pretrained meta-components, and write both fine-grained and major labels back into the dataset for visualization.

What input data format is required for MetaTiME cell-state annotation?▼

MetaTiME cell-state annotation requires a human, log-normalized single-cell AnnData object containing a pre-computed batch-corrected embedding, such as X_scVI or X_harmony, to accurately execute the over-clustering and scoring workflow.

Why does MetaTiME require over-clustering before scoring single-cell clusters?▼

MetaTiME requires over-clustering at a high Leiden resolution to ensure homogeneous cell populations, which allows precise scoring against pretrained meta-components and accurate assignment of both fine-grained and major cell-state labels.

Can I visualize MetaTiME annotations on UMAP coordinates?▼

Yes, you can visualize MetaTiME annotations on UMAP or MDE coordinates using collision-aware cluster labels, allowing you to plot both fine-grained cell states and major category labels directly on the embedded single-cell data.

How do I diagnose unassigned cells after MetaTiME annotation?▼

To diagnose unassigned cells after MetaTiME annotation, compare the generated meta-component scores against manual marker-based annotation to identify why specific clusters lack defined cell-state or major category labels.