scCloudMine: A cloud-based app for visualization, comparison, and exploration of single-cell transcriptomic data.

Area of Science:

• Bioinformatics
• Computational Biology
• Genomics

Background:

• Single-cell RNA sequencing (scRNA-seq) generates vast datasets requiring specialized tools for analysis.
• Existing computational infrastructure for scRNA-seq analysis can be costly and complex.
• There is a need for accessible platforms for comparing and exploring scRNA-seq data.

Purpose of the Study:

• To introduce scCloudMine, a cloud-based application for visualizing, comparing, and exploring scRNA-seq data.
• To enable researchers to conduct comparative analyses of scRNA-seq datasets without high-performance computing resources.
• To facilitate the discovery of novel biological insights through data re-use and hypothesis generation.

Main Methods:

• Development of a web-based application, scCloudMine, for scRNA-seq data analysis.
• Implementation of visualization modes (Cluster and Values) and keyword/gene ID querying.
• Comparative analysis of multiple scRNA-seq datasets to identify gene expression patterns and cell specificities.

Main Results:

• Identified study-specific gene markers, challenging common assumptions.
• Observed differences in PHO1;H3 gene expression between GFP-tagging and scRNA-seq studies.
• Found that some phosphate transporter genes, while induced by protoplasting, retained cell specificity.
• Discovered 132 hormone-responsive genes with restricted expression and identified cell-type-specific functions for jasmonate response genes like TIFY8.

Conclusions:

• scCloudMine provides an accessible platform for comparative scRNA-seq data analysis.
• Comparative analysis reveals nuances in gene expression, including study-specific markers and stress responses.
• The platform supports hypothesis formation and promotes the re-use of valuable scRNA-seq data.