multiome-scatac

To integrate paired scRNA-seq and scATAC-seq multiome data, this skill applies quality control, feature harmonization, and modality integration to produce unified multimodal embeddings. It enables joint interpretation of gene activity and chromatin accessibility across various cell types.

Multimodal integration for single-cell RNA and ATAC data combines gene expression and chromatin accessibility into a unified representation. This approach reveals joint cellular states and supports regulatory analysis across different cell types and conditions.

Yes, you can use scanpy and anndata for single-cell multiome regulatory analysis. This skill requires these Python-based tools to perform quality control, modality integration, and export joint embeddings alongside regulatory annotations for multiome datasets.

The best way to harmonize features across RNA and ATAC modalities is through coordinated quality control and feature harmonization steps. This skill processes paired multiome data to align modalities, generating reliable joint embeddings for downstream regulatory interpretation.

Yes, single-cell multiome integration supports export of regulatory annotations. Following modality integration, this skill exports joint embeddings and regulatory annotations, enabling downstream interpretation of gene activity, motif accessibility, and regulatory linkages.

You need joint embeddings for multiome datasets across different cell conditions when investigating joint cellular states and regulatory linkages. This skill provides integrated multimodal representations to interpret gene activity and chromatin accessibility variations across conditions.