MethodsOutcome-guided mutual information network construction

1) Integrative network construction

2) Network-based survival analysis

Outcome-guided mutual information networks for investigating gene-gene in-teraction effects on clinical outcomesHyun-hwan Jeong, So Yeon Kim, Kyubum Wee, Kyung-Ah SohnDepartment of Information and Computer Engineering, Ajou University, Suwon 443-749, S. Koreae-mail : {libe,jebi1771,kbwee,kasohn}@ajou.ac.kr

IntroductionNetwork-based analysis frameworks have gained huge popularity recently as network in-formation can provide a more systematic and global view of the underlying biological sys-tem. However, most network-based studies rely on feature-wise networks which can reveal the relation between a pair of features, but do not consider the effect of pair-wise feature in-teractions on the outcome. To detect significant feature pairs associated with the outcome, we employ the Mutual In-formation measure, which is a non-parametric, information-theoretic measure and has been successfully used to detect linear or non-linear association between the features. Based on the extension of Mutual Information measure, we propose a simple but powerful scheme to construct an outcome-guided network with appropriate edge significance filtering.

We demonstrate the utility of the proposed network construction approach in two main ap-plications: the integrative network analysis of multiple genomic profiles, and the network-based survival analysis. In both applications, datasets from ovarian serous cystadenocarci-noma patients in The Cancer Genome Atlas (TCGA) are used. The results highlight the use-fulness of the outcome-guided mutual information networks in both applications for inves-tigating gene-gene interaction effects associated with clinical outcomes.

References[1] Cerami, E., et al., The cBio Cancer Genomics Portal: An Open Platform for Exploring Multidimensional Cancer Genomics Data. Cancer Discovery, 2012. 2(5): p. 401-404.[2] TCGA, Comprehensive genomic characterization defines human glioblastoma genes and core pathways. Nature, 2008. 455(7216): p. 1061-1068.[3] Steuer, R., et al., The mutual information: detecting and evaluating dependencies between variables. Bioinformatics, 2002. 18(suppl 2): p. S231-S240.[4] Butte AJ, Kohane IS, Mutual information relevance networks: functional genomic clustering using pairwise entropy measurements. Pacific Symposium on Biocomputing Pacific Symposium on Biocomputing, 2000:418-429.[5] Li C, Li H, Network-constrained regularization and variable selection for analysis of genomic data. Bioinformatics, 2008. 24(9):1175-1182.

Results

Empirical distribution of mutual information values

Heatmap for the regression coefficients of 15 selected genes

(1) Significance of outcome-guided mutual information values

penalty termNetwork constrained regularized Cox regression

: identity matrix : normalized Laplacian matrix : parameter which controls the contribution of network informa-tion

Prediction accuracy of the mutual information network-based Net-Cox model

(2) Integrative network analysis

(3) Network-based survival analysis

Significant GO terms

Intersection-network of whole genomic profiles

Category Description p-value FDR

BP hemopoiesis 1.82E-05 6.81E-03

BP immune system development 4.12E-05 6.81E-03

BP aging 3.03E-04 1.36E-02

BP T cell differentiation 4.69E-04 1.99E-02

BP positive regulation of apoptotic process 7.47E-04 2.02E-02

BP apoptotic process 5.92E-04 2.02E-02

BP placenta development 1.07E-03 2.44E-02

BP positive regulation of T cell activation 1.08E-03 2.44E-02

BP signal transduction by phosphorylation 1.49E-03 2.90E-02

BP cellular response to abiotic stimulus 1.68E-03 2.93E-02

Networks for single profile

G1 G2 … Survival month

0.5 -0.7 ... 15.01.0 0.4 ... 46.0... ... ... ...

Integrative networks

a binary clinical outcome

discrete genomic profiles

Mutual information(M.I.)

Statistically significantgene pair

gene

ExtractionGene pairs using

Mutual Information

Single profile networks

Integrationscheme

Outcome-guidedmutual information network