DECODE: an integrated differential co-expression and differential expression analysis of gene expression data.

Area of Science:

• Genomics
• Bioinformatics
• Systems Biology

Background:

• Differential expression (DE) and differential co-expression (DC) analyses are key for understanding gene regulation in complex diseases.
• The combined performance of DE and DC analyses has not been previously explored.

Purpose of the Study:

• To introduce DECODE (Differential Co-expression and Differential Expression), a novel analytical approach integrating DE and DC analyses.
• To explore the combined features of DE and DC for each transcript between two conditions.

Main Methods:

• DECODE systematically defines optimal thresholds for gene expression and co-expression changes using chi-square maximization.
• Genes are categorized into four groups based on high or low DE and DC characteristics.
• The approach was applied to a large breast cancer microarray dataset of 2000 tumor samples.

Main Results:

• DECODE improved the detection of functional gene sets, including those related to immune response, metastasis, and metabolism.
• Identification of genes with high DE and high DC provides deeper insights into complex disease mechanisms.
• Further investigation of identified genes and pathways can enhance understanding of disease biology.

Conclusions:

• DECODE complements existing DC and traditional DE analyses, offering a valuable method for studying disease-associated genes.
• It reveals biological functions that may be missed by analyzing DE or DC alone.
• The DECODE R package is available on CRAN.