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omicverse-single-cell-lda-topic-clustering

Fit MIRA-backed LDA topic models on single-cell AnnData objects and generate cluster labels.

13|2|Updated Apr 5, 2026
Search Tags:#single-cell#rfc#clustering#mira#anndata#topic-modeling#lda
One-click install
npx skills add https://github.com/omicverse/omicverse-skills --skill omicverse-single-cell-lda-topic-clustering-omicverse
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: omicverse-single-cell-lda-topic-clustering
Source: https://github.com/omicverse/omicverse-skills/tree/main/src/omicverse_skills/skills/single-cell-lda-topic-clustering
Command: npx skills add https://github.com/omicverse/omicverse-skills --skill omicverse-single-cell-lda-topic-clustering-omicverse

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires anndata, numpy, pandas, omicverse, and includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

This Skill turns OmicVerse LDA topic modeling into a reusable workflow for single-cell data, removing the need to manually reconstruct notebook steps for fitting topics, predicting cluster labels, and choosing the right execution mode.

Core Features & Use Cases

  • Topic Modeling for Single-Cell Data: Fit MIRA-backed LDA topics on count-like AnnData inputs for expression or accessibility workflows.
  • Flexible Execution Paths: Choose in-memory or on-disk training depending on dataset size and runtime constraints.
  • Hard Label Generation: Convert topic usage into LDA-based cluster labels and optional RFC-derived labels for downstream analysis.
  • Use Case: A researcher with a count matrix and precomputed highly variable features can run topic clustering, inspect predicted topic assignments, and derive classifier-based labels without rebuilding the analysis logic.

Quick Start

Use this skill to fit OmicVerse single-cell LDA topics on your AnnData object and return topic-derived cluster labels with optional RFC-based hard labeling.

Frequently Asked Questions about omicverse-single-cell-lda-topic-clustering

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

FAQPage Schema
How do I run LDA topic modeling on single-cell AnnData objects?▼

To fit LDA topic models on single-cell AnnData objects, use MIRA-backed training on a usable counts layer with highly variable features flagged. This generates topic-derived cluster labels like LDA_cluster for downstream analysis.

What is MIRA topic clustering for single-cell expression data?▼

MIRA topic clustering applies LDA topic models to single-cell expression or accessibility count matrices. It converts topic usage into hard cluster labels, revealing cellular states without manual notebook reconstruction.

Can I train single-cell LDA topics on-disk for large datasets?▼

Yes, you can train single-cell LDA topics on-disk for large datasets. The workflow supports flexible execution paths, allowing you to choose between in-memory or on-disk training depending on your dataset size and runtime constraints.

How does RFC-based relabeling work for LDA cluster annotations?▼

RFC-based relabeling for LDA clusters uses a random forest classifier trained on valid embeddings and topic assignments. It outputs refined hard labels, writing LDA_cluster_rfc and LDA_cluster_clf fields to the AnnData object.

What inputs are required for MIRA LDA topic prediction in AnnData?▼

MIRA LDA topic prediction requires an AnnData object with a usable counts layer, a highly variable feature flag, and a valid embedding for classifier-based outputs. These inputs enable topic fitting and optional RFC-based relabeling.

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