muon-multiomics-singlecell

To integrate RNA and ATAC-seq data for joint single-cell clustering, use the muon framework to load modalities into a MuData container and apply Weighted Nearest Neighbor (WNN) graphs for unified dimensionality reduction and clustering.

Preprocessing 10x Multiome ATAC-seq and protein data requires modality-specific transformations, utilizing TF-IDF for chromatin accessibility and CLR normalization for protein expression before constructing WNN graphs.

Yes, you can use scanpy and anndata for multiomics single-cell analysis, as the muon framework integrates directly with scanpy and scvi-tools to process MuData objects for comprehensive cross-modal workflows.

You need WNN graphs for multi-modal single-cell data when you want to perform joint dimensionality reduction that integrates signals across RNA, ATAC, and protein modalities to reveal cell states missed by individual modalities.

Yes, muon supports CITE-seq data integration by managing protein and RNA modalities within a single MuData object, applying CLR preprocessing to proteins, and performing joint clustering across both modalities.

MuData containers manage multiple single-cell modalities but rely on dependencies like scanpy, mofapy2, and leidenalg, requiring an advanced understanding of Python workflows to execute joint WNN analysis effectively.