Granatum: a graphical single-cell RNA-Seq analysis pipeline for genomics scientists

Area of Science:

• Genomics
• Bioinformatics
• Computational Biology

Background:

• Single-cell RNA sequencing (scRNA-Seq) is a powerful technique for analyzing cellular heterogeneity.
• Existing computational methods for scRNA-Seq analysis often demand significant bioinformatics expertise, limiting accessibility for bench scientists.

Purpose of the Study:

• To develop an accessible, web-based computational pipeline for single-cell RNA sequencing (scRNA-Seq) data analysis.
• To empower researchers without extensive bioinformatics training to perform complex scRNA-Seq analyses.

Main Methods:

• Development of Granatum, a web-based graphical user interface (GUI) for scRNA-Seq data analysis.
• Integration of multiple analysis modules including data normalization, imputation, clustering, differential gene expression, and pathway analysis.

Main Results:

• Granatum provides an intuitive, click-through interface for scRNA-Seq data processing and visualization.
• The pipeline supports a comprehensive suite of analyses, from basic data preprocessing to advanced network and pseudo-time analyses.
• Successful implementation of Granatum enables users to perform sophisticated analyses without writing any code.

Conclusions:

• Granatum democratizes scRNA-Seq analysis by offering an accessible platform for bench scientists.
• The tool facilitates broader adoption and utilization of scRNA-Seq technology in biological research.
• Granatum is freely available for research purposes, promoting collaborative scientific advancement.