Using RNentropy to Detect Significant Variation in Gene Expression Across Multiple RNA-Seq or Single-Cell RNA-Seq...

Area of Science:

• Bioinformatics
• Computational Biology
• Genomics

Background:

• RNA sequencing (RNA-Seq) is standard for transcriptome characterization and quantification.
• Current differential gene expression methods primarily focus on pairwise comparisons.
• This limits the ability to detect expression variations across multiple conditions simultaneously.

Purpose of the Study:

• Introduce RNentropy, an information theory-based methodology.
• Overcome limitations of pairwise comparisons in differential gene expression analysis.
• Enable detection of gene expression variations across any number of samples and conditions.

Main Methods:

• Utilize information theory principles for expression analysis.
• Apply RNentropy to gene or transcript expression values from RNA-Seq data.
• The method is compatible with various quantification pipelines and expression measures.

Main Results:

• RNentropy successfully detects significant gene expression variations across multiple samples and conditions.
• Identifies specific genes/transcripts with significant expression changes.
• Pinpoints samples with over- or underexpression for these genes/transcripts.

Conclusions:

• RNentropy provides a robust solution for multi-sample differential gene expression analysis in RNA-Seq.
• The methodology is versatile and applicable to standard and single-cell RNA sequencing data.
• RNentropy is available as a free R package on CRAN.