MultiDCoX: Multi-factor analysis of differential co-expression.

Document Type

Article

Publication Date

12-28-2017

JAX Source

BMC Bioinformatics 2017 Dec 28; 18(Suppl 16):576

Volume

18

Issue

Suppl 16

First Page

576

Last Page

576

ISSN

1471-2105

PMID

29297310

DOI

https://doi.org/10.1186/s12859-017-1963-7

Abstract

BACKGROUND: Differential co-expression (DCX) 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. 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 co-expressed (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.

BMC Bioinformatics 2017 Dec 28; 18(Suppl 16):576.

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