single-multiomics_skill

Overview

This skill is a quick-reference cheat sheet for OmicVerse Jupyter tutorials covering MOFA, GLUE pairing, SIMBA batch integration, TOSICA reference transfer, and StaVIA trajectory cartography. It condenses setup steps, model training commands, result inspection tips, export workflows, and dependency/hardware notes across paired and unpaired multi-omic scenarios. Use it to find the right notebook and the minimal commands to reproduce core analyses.

How this skill works

Each notebook demonstrates a focused pipeline: prepare AnnData inputs, instantiate the relevant OmicVerse wrapper (pyMOFA, GLUE_pair, pySIMBA, pyTOSICA, or VIA), run preprocessing and training functions, then export factors, embeddings, or model checkpoints. The cheat sheet highlights which files to load/save, which helper functions to call for visualization, and which optional GPU-accelerated steps (MDE, transformer training, or embedding) can speed up workflows.

When to use it

• Integrating paired scRNA and scATAC to get joint latent factors (use MOFA).
• Aligning unpaired modalities with GLUE then learning joint factors (MOFA on GLUE embeddings).
• Correcting batch effects across multi-study single-cell datasets and deriving unified embeddings (SIMBA).
• Transferring cell-type labels or pathway-informed attention from a reference to a query dataset (TOSICA).
• Reconstructing cell-state trajectories, pseudotime, and atlas visualizations from RNA velocity or PCA space (StaVIA/VIA).

Best practices

• Always load preprocessed, log-normalized AnnData objects and confirm matching feature names between modalities before training.
• Persist model artifacts (MOFA HDF5, SIMBA workdir, TOSICA project folder) to reuse without retraining.
• Subset to highly variable features or apply GLUE pairing alignment before factor modelling to reduce noise and speed training.
• Use GPU for optional MDE or transformer-heavy steps but ensure CPU threads and memory are sufficient for graph training or large animations.
• Inspect variance explained, factor–cluster correlations, and ranked feature weights to validate biological signal before downstream annotation.

Example use cases

• Derive joint RNA–ATAC factors from paired assays to discover regulatory programs with pyMOFA.
• Use GLUE to align unpaired scRNA and scATAC and then run MOFA to capture shared latent factors.
• Integrate multiple pancreas studies with SIMBA to harmonize batches and produce a single embedding for cell-type mapping.
• Train TOSICA on a curated reference to transfer cell-type labels and interpret pathway attention on new samples.
• Run StaVIA on velocity-derived embeddings to map developmental trajectories and export animated stream plots for presentations.