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

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.

journal_name

BMC Bioinformatics

journal_title

BMC bioinformatics

authors

Liany H,Rajapakse JC,Karuturi RKM

doi

10.1186/s12859-017-1963-7

subject

Has Abstract

pub_date

2017-12-28 00:00:00

pages

576

issue

Suppl 16

issn

1471-2105

pii

10.1186/s12859-017-1963-7

journal_volume

18

pub_type

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