Single-cell RNA sequencing (scRNA-Seq) is an increasingly popular platform to study heterogeneity at the single-cell level. Computational methods to process scRNA-Seq have limited accessibility to bench scientists as they require significant amounts of bioinformatics skills.
Researchers at the University of Hawaii at Manoa, have developed Granatum, a web-based scRNA-Seq analysis pipeline to make analysis more broadly accessible to researchers. Without a single line of programming code, users can click through the pipeline, setting parameters and visualizing results via the interactive graphical interface. Granatum conveniently walks users through various steps of scRNA-Seq analysis. It has a comprehensive list of modules, including plate merging and batch-effect removal, outlier-sample removal, gene filtering, gene-expression normalization, cell clustering, differential gene expression analysis, pathway/ontology enrichment analysis, protein-network interaction visualization, and pseudo-time cell series construction. Granatum enables broad adoption of scRNA-Seq technology by empowering the bench scientists with an easy-to-use graphical interface for scRNA-Seq data analysis.
Granatum is built with the Shiny framework, which integrates the front-end with the back-end. A public server has been provided for easy access, and local deployment is also possible. The user uploads one or more expression matrices with corresponding metadata for samples. The back-end stores data separately for each individual user, and invokes third-party libraries on demand.
Availability – The package is freely available for research use at http://garmiregroup.org/granatum/app