Interactive and Reproducible Workflows for Exploring and Modeling RNA-seq Data with pcaExplorer, Ideal, and GeneTonic

Area of Science:

• Bioinformatics
• Computational Biology
• Genomics

Background:

• RNA sequencing (RNA-seq) experiments generate large, complex datasets.
• Analyzing RNA-seq data involves intricate steps like quality control, alignment, quantification, exploration, statistical testing, visualization, and interpretation.
• Existing tools often require significant technical expertise, posing a barrier for life and clinical scientists.

Purpose of the Study:

• To present interactive and reproducible protocols for RNA-seq data analysis.
• To streamline the complex processes of data exploration, statistical testing, and interpretation.
• To make advanced transcriptome profiling analysis more accessible to a broader scientific audience.

Main Methods:

• Utilized a suite of Bioconductor packages: pcaExplorer, ideal, and GeneTonic.
• Developed Shiny web applications for interactive data exploration and analysis.
• Integrated RMarkdown for documenting analysis steps to ensure reproducibility.
• Focused on linking core analytical elements through interactive widgets for efficient drill-down analysis.

Main Results:

• Provided protocols for Exploratory Data Analysis (EDA), differential expression analysis, and result interpretation.
• Demonstrated interactive and reproducible workflows for complex RNA-seq data tasks.
• Reduced the time and technical expertise required for generating insights from transcriptome data.

Conclusions:

• The developed Bioconductor packages and protocols significantly enhance the accessibility and efficiency of RNA-seq data analysis.
• Interoperability with existing Bioconductor pipelines ensures compatibility and adherence to best practices.
• These tools empower life and clinical scientists to perform complex transcriptome analyses, accelerating scientific discovery.