Application to breast cancer data from the Cancer Genome Atlas Sample Clauses

Application to breast cancer data from the Cancer Genome Atlas. In the real data application, we use the High-quality INTeractomes (HINT) database for the biological network (Das and Yu, 2012). We apply our method to the TCGA breast cancer (BRCA) RNA-seq gene expression dataset with 762 subjects and 10, 792 genes in the network. The response variable we consider here is ER status - whether Method PMSE TP FP AUC CE TP FP AUC Linear regression Logistic regression true signal nodes are connected Lasso 21.7(0.6) 9.5(0.1) 54.4(3.8) 0.982(0.004) 43.3(1.6) 8.4(0.2) 29.6(3.4) 0.954(0.007) Elastic-net 23.2(0.7) 9.6(0.1) 69.0(3.9) 0.975(0.005) 57.9(2.4) 7.7(0.2) 22.4(3.2) 0.961(0.006) TGLG (ε = 10−5) 21.8(0.9) 9.3(0.1) 14.6(1.5) 0.968(0.006) 35.2(1.3) 8.0(0.2) 7.8(0.9) 0.902(0.011) TGLG (logε ∼ N (−5, 9)) 20.7(0.7) 9.1(0.1) 10.1(1.5) 0.957(0.006) 35.4(1.4) 7.9(0.3) 8.3(1.0) 0.893(0.011) TGLG (logε ∼ N (−5, 9)) misspecified 21.2(0.8) 9.1(0.1) 11.3(1.5) 0.952(0.007) 37.1(1.3) 7.8(0.2) 9.3(1.1) 0.892(0.012) T rue signal nodes are disconnected Lasso 20.8(0.6) 9.8(0.1) 55.0(3.7) 0.989(0.003) 43.4(1.2) 8.9(0.2) 26.8(3.0) 0.979(0.004) Elastic-net 22.2(0.7) 9.8(0.1) 68.6(3.9) 0.988(0.003) 55.7(1.9) 8.4(0.2) 27.3(4.0) 0.981(0.003) TGLG (ε = 10−5) 21.7(0.8) 9.4(0.1) 16.7(1.9) 0.971(0.006) 35.5(1.4) 8.4(0.2) 7.8(0.9) 0.922(0.010) TGLG (logε ∼ N (−5, 9)) 20.6(0.8) 9.6(0.1) 11.6(2.1) 0.980(0.004) 36.9(1.5) 8.5(0.2) 9.4(1.1) 0.925(0.009) TGLG (logε ∼ N (−5, 9)) misspecified 21.3(0.9) 9.4(0.1) 11.4(1.7) 0.969(0.005) 35.3(1.2) 8.5(0.2) 8.4(0.9) 0.928(0.008) Table 2.4: Simulation results for scale free network. True TP is 10. Sample size is 200 and dimension is 1,000. γ γ the cancer cells grow in response to the estrogen. The ER status is a molecular char- acteristic of the cancer which has important implications in prognosis. The purpose here is not focused on prediction. Rather we intend to find genes and functional modules that are associated with ER status, through which biological mechanisms differentiating the two subgroups of cancer can be further elucidated. α ω We code ER-positive as 1 and ER-negative as 0. We remove subjects with un- known ER status. In total, there are 707 subjects with 544 ER-positive and 163 ER-negative. We remove 348 gene nodes with low count number, which leaves us with 10,444 nodes. To apply our methods, we first standardize the gene nodes and then apply a logistic regression model for network marker selection. For prior settings, γ we use σ2 ∼ IG(0.01, 0.01), σ2 ∼ IG(0.01, 0.01) and σ2 = 50. We fix λ at differen...
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