DiCoExpress

DiCoExpress performs comprehensive analysis of multifactorial RNA sequencing (RNAseq) experiments in R by integrating quality control, generalized linear model-based differential expression, co-expression clustering, and annotation enrichment to interpret transcriptomic changes.

Key Features:

• Implementation: Implemented in R and integrates the FactoMineR, edgeR and coseq packages.
• Quality Control: Performs an initial quality control step on mapped read expression data.
• Differential Expression Analysis: Conducts differential expression using generalized linear models (GLMs) with automated contrast writing to model multifactorial experimental designs.
• Co-expression Analysis: Identifies gene clusters with correlated expression patterns using the coseq package.
• Annotation Enrichment Analysis: Tests lists of differentially expressed genes and co-expression clusters for enrichment of user-provided annotations.

Scientific Applications:

• Multifactorial plant RNAseq (Brassica napus L.): Demonstrated on a public dataset analyzing the transcriptional response of Brassica napus L. to silicon treatment in roots and mature leaves, with two biological factors and three replicates per condition.

Methodology:

Implemented in R; integrates FactoMineR, edgeR and coseq; performs quality control, GLM-based differential expression with automated contrast writing, co-expression clustering via coseq, and annotation enrichment; methods were selected based on performance in neutral comparison studies and focus on statistical modeling of gene expression according to the experimental design.