Interfacing Seurat with the R tidy universe

Area of Science:

• Bioinformatics
• Computational Biology
• Data Science

Background:

• Seurat is a widely used software suite for single-cell RNA sequencing (scRNA-seq) data analysis.
• The tidyverse ecosystem provides powerful tools for data manipulation and visualization, widely adopted by the data science community.

Purpose of the Study:

• To develop an interface that integrates Seurat with the tidyverse ecosystem.
• To enable users familiar with tidyverse grammar to analyze scRNA-seq data efficiently.

Main Methods:

• Developed tidyseurat, a lightweight adapter for Seurat.
• Represented Seurat objects as tibbles for seamless integration with dplyr, tidyr, ggplot2, and plotly.
• Utilized the nest-map framework for iterative analysis of data subsets.

Main Results:

• Tidyseurat provides an intuitive interface for Seurat objects within the tidyverse.
• Facilitates efficient data manipulation, integration, and visualization of scRNA-seq data.
• Enables streamlined iterative analyses on subsets of single-cell data.

Conclusions:

• Tidyseurat bridges the gap between Seurat and the tidyverse, enhancing usability for a broader data science audience.
• The package promotes efficient and familiar workflows for scRNA-seq data analysis.
• Tidyseurat is freely available, fostering accessibility and adoption in the research community.