Application of Transcriptional Gene Modules to Analysis of Caenorhabditis elegans' Gene Expression Data

Identification of co-expressed sets of genes (gene modules) is used widely for grouping functionally related genes during transcriptomic data analysis. An organism-wide atlas of high-quality gene modules would provide a powerful tool for unbiased detection of biological signals from gene expression data. Here, using a method based on independent component analysis we call DEXICA, we have defined and optimized 209 modules that broadly represent transcriptional wiring of the key experimental organismC. elegans. These modules represent responses to changes in the environment (e.g., starvation, exposure to xenobiotics), genes regulated by transcriptions factors (e.g.,,), genes specific to tissues (e.g., neurons, muscle), genes that change during development, and other complex transcriptional responses to genetic, environmental and temporal perturbations. Interrogation of these modules reveals processes that are activated in long-lived mutants in cases where traditional analyses of differentially expressed genes fail to do so. Additionally, we show that modules can inform the strength of the association between a gene and an annotation (e.g., GO term). Analysis of module-weighted annotations improves on several aspects of traditional annotation-enrichment tests and can aid in functional interpretation of poorly annotated genes. We provide an online interactive resource with tutorials at, in which users can find detailed information on each module, check genes for module-weighted annotations, and use both of these to analyze their own gene expression data (generated using any platform) or gene sets of interest.