scRNASequest: an ecosystem of scRNA-seq analysis, visualization, and publishing.

Area of Science:

• Genomics
• Bioinformatics
• Computational Biology

Background:

• Single-cell RNA sequencing (scRNA-seq) provides high-resolution gene expression data from complex tissues.
• Increasing scRNA-seq data volume necessitates standardized and automated analysis pipelines for biological insights.

Purpose of the Study:

• To present scRNASequest, a semi-automated workflow for comprehensive scRNA-seq data analysis.
• To facilitate hypothesis generation and biological discovery through efficient data processing and interpretation.

Main Methods:

• Developed scRNASequest, an end-to-end pipeline for scRNA-seq data.
• Implemented modules for raw UMI count preprocessing, data harmonization, cell type label transfer, and differential gene expression analysis.
• Integrated visualization (cellxgene VIP) and data hosting (CellDepot) capabilities via h5ad file generation.

Main Results:

• scRNASequest enables preprocessing, harmonization, and cell type annotation of scRNA-seq data.
• The pipeline supports multi-sample, multi-condition differential gene expression analysis at the single-cell level.
• Generated h5ad files ensure seamless integration with visualization and data sharing platforms.

Conclusions:

• scRNASequest provides a robust, end-to-end solution for scRNA-seq data analysis, visualization, and publishing.
• The open-source code and detailed tutorial promote accessibility and adoption.
• The pipeline is adaptable for local or high-performance computing environments.