scglue-complete

To integrate unpaired single-cell multi-omics data using GLUE, this skill guides you through data preprocessing, guidance graph construction, model training, and embedding extraction. It provides complete documentation and example pipelines for immediate adoption.

Building cross-modal embeddings for scRNA-seq and scATAC-seq requires the scglue library and AnnData formatted datasets. This skill provides the documented framework to configure datasets, train models, and extract cell and feature embeddings.

Yes, you can perform regulatory inference after extracting single-cell embeddings. This skill provides guidance on downstream regulatory inference and graph-based analyses using the trained GLUE embeddings.

Yes, this toolkit supports the complete GLUE workflow from preprocessing to evaluation. It covers Stage 1 preprocessing, Stage 2 model training, and Stage 3 regulatory inference, including evaluating integration quality.

Yes, comprehensive API references and tutorials are available for scglue data integration. This skill includes full documentation, data processing workflows, and example pipelines to enable immediate adoption of the framework.

GLUE is specifically designed for unpaired multi-omics integration, meaning it requires distinct scRNA-seq and scATAC-seq datasets rather than paired measurements. This skill provides the complete documented framework to navigate these specific data requirements.