Abstract
Background Differential co-expression signifies change in degree of co-expression of a set of genes among different biological conditions. It has been used to identify differential co-expression networks or interactomes. Many algorithms have been developed for single-factor differential co-expression analysis and applied in a variety of studies. However, in many studies, the samples are characterized by multiple factors such as genetic markers, clinical variables and treatments. No algorithm or methodology is available for multi-factor analysis of differential co-expression.
Results We developed a novel formulation and a computationally efficient greedy search algorithm called MultiDCoX to perform multi-factor differential co-expression analysis of transcriptomic data. Simulated data analysis demonstrates that the algorithm can effectively elicit differentially co-expressed (DCX) gene sets and quantify the influence of each factor on co-expression. MultiDCoX analysis of a breast cancer dataset identified interesting biologically meaningful differentially coexpressed (DCX) gene sets along with genetic and clinical factors that influenced the respective differential co-expression.
Conclusions MultiDCoX is a space and time efficient procedure to identify differentially co-expressed gene sets and successfully identify influence of individual factors on differential co-expression.
Software R function will be available upon request.
Footnotes
Email addresses: HL: e0146315{at}u.nus.edu JCR: as.jagath{at}ntu.edu.sg RKMK: krish.karuturi{at}jax.org
Abbreviations
- DCX
- Differential Co-expression/Differentially Co-expressed
- DE
- Differential Expression
- GO
- Gene Ontology
- KEGG
- Kyoto Encyclopaedia of Genes and Genomes
- FPs
- False Positives
- FNs
- False Negatives
- FNR
- False Negative Rate
- FDR
- False Discovery Rate
- FPR
- False Positive Rate
- OR
- Odds Ratio
- HPC
- Hi Performance Computing
- ER
- Oestrogen Receptor
