Simba

SIMBA is a versatile single-cell data analysis tool designed to co-embed cells and various features such as genes, chromatin-accessible regions, and DNA sequences into a shared latent space. This joint embedding framework allows for comprehensive data connectivity and analysis capabilities, supporting both single-modal and multi-modal analyses. SIMBA's graph-based architecture models relationships between cells and biological entities, enabling tasks like cellular heterogeneity study, marker discovery, gene regulation inference, batch effect removal, and omics data integration. The tool operates without requiring prior cell clustering, offering a flexible and unbiased approach to feature discovery.

SIMBA is ideal for researchers and bioinformaticians working with single-cell datasets, providing a unified analysis framework that can handle both single and multi-modal analyses. However, it requires computational resources for large datasets and may need expertise in bioinformatics for optimal usage.