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MultiDCoX: Multi-factor Analysis of Differential Coexpression

Herty Liany, Jagath C. Rajapakse, View ORCID ProfileR. Krishna Murthy Karuturi
doi: https://doi.org/10.1101/114397
Herty Liany
1School of Computing, National University of Singapore, Singapore (current for HL)
2Computational and System Biology, Genome Institute of Singapore, A-STAR, 60 Biopolis Street, S138672, Republic of Singapore (previous for HL and RKMK)
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Jagath C. Rajapakse
4School of Computer Science and Engineering, Nanyang Technological University, Singapore
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R. Krishna Murthy Karuturi
2Computational and System Biology, Genome Institute of Singapore, A-STAR, 60 Biopolis Street, S138672, Republic of Singapore (previous for HL and RKMK)
3The Jackson Laboratory, 10 Discovery Dr, Farmington, CT 06032, USA (current affiliation of RKMK)
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  • ORCID record for R. Krishna Murthy Karuturi
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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
  • Copyright 
    The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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    Posted March 06, 2017.
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    MultiDCoX: Multi-factor Analysis of Differential Coexpression
    Herty Liany, Jagath C. Rajapakse, R. Krishna Murthy Karuturi
    bioRxiv 114397; doi: https://doi.org/10.1101/114397
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    MultiDCoX: Multi-factor Analysis of Differential Coexpression
    Herty Liany, Jagath C. Rajapakse, R. Krishna Murthy Karuturi
    bioRxiv 114397; doi: https://doi.org/10.1101/114397

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