SeuratIntegrate: an R package to facilitate the use of integration methods with Seurat

ABSTRACT Motivation Integrating multiple datasets has become an increasingly common task in scRNA-seq analysis. The advent of single-cell atlases adds further complexity to this task, as they often involve combining data with complex, nested batch effects - such as those arising from multiple studies, organs or disease states. Accurate data integration is essential to distinguish cell types with sufficient granularity, thereby reflecting true biological patterns, and to create reliable reference datasets for the community. In this context, the latest version of Seurat (v5) introduced a multi-layered object structure to facilitate the integration of scRNA-seq datasets in a unified manner. However, the panel of available batch-correction methods remains limited to five algorithms within Seurat, restricting users from accessing a broader diversity of available tools, particularly Python-based methods. Furthermore, no existing R tool assists the user in making an informed decision in selecting the most appropriate integration approach. Results To overcome these challenges, we developed SeuratIntegrate, an open source R package that extends Seurat’s functionality. SeuratIntegrate supports eight integration methods, incorporating both R- and Python-based tools, and enables performance evaluation of integration through several scoring methods. This functionality allows for a more versatile and informed integration process. Availability SeuratIntegrate is available at https://github.com/cbib/Seurat-Integrate/ . The package is released under the MIT License.