single-cell-multi-omics-integration

MOFA after GLUE pairing (t_mofa_glue.ipynb)

• Data preparation: Start from GLUE-derived embeddings (rna-emb.h5ad, atac.emb.h5ad), build a GLUE_pair object, and run correlation() to align unpaired cells before subsetting to highly variable features.
• Model training: Instantiate pyMOFA with the aligned AnnData objects, run mofa_preprocess(), and save the joint factors through mofa_run(outfile='models/chen_rna_atac.hdf5').
• Result inspection: Use pyMOFAART plus AnnData that now contains the GLUE embeddings to compute factors (get_factors) and visualise variance explained, factor–cluster correlations, and ranked feature weights.
• Export workflow: Reuse the saved MOFA HDF5 model for downstream inspection; GLUE embeddings can be embedded with scvi.model.utils.mde (GPU-accelerated MDE is optional, sc.tl.umap works on CPU).
• Dependencies & hardware: Requires both mofapy2 and the GLUE tooling (scglue, scvi-tools, pymde); GPU acceleration only affects optional MDE visualisation.

SIMBA batch integration (t_simba.ipynb)

• Data preparation: Fetch the concatenated AnnData (simba_adata_raw.h5ad) derived from multiple pancreas studies and pass it, alongside a results directory, to pySIMBA.
• Model training: Execute preprocess(...) to bin features and build a SIMBA-compatible graph, then call gen_graph() followed by train(num_workers=...) to launch PyTorch-BigGraph optimisation (can scale with CPU workers) and load(...) to resume trained checkpoints.
• Result inspection: Apply batch_correction() to obtain the harmonised AnnData with SIMBA embeddings (X_simba) and visualise using mde/sc.tl.umap coloured by cell type or batch.
• Export workflow: Training outputs reside in the workdir (e.g., result_human_pancreas/pbg/graph0); reuse them with simba_object.load(...) for later analyses.
• Dependencies & hardware: Requires installing simba and simba_pbg (PyTorch BigGraph backend). GPU is optional; make sure adequate CPU threads and memory are available for graph training.