T-Cell Network Example for GeneNet (August 2015) or later
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1 T-Cell Network Example for GeneNet (August 2015) or later This note reproduces the T-Cell network example from R. Opgen-Rhein and K. Strimmer. 2006a. Using regularized dynamic correlation to infer gene dependency networks from time-series microarray data. Proceedings of WCSB 2006 (June 12-13, 2006, Tampere, Finland) and R. Opgen-Rhein and K. Strimmer. 2006b. Inferring gene dependency networks from genomic longitudinal data: a functional data approach. REVSTAT 4: ( Load GeneNet library library("genenet") Loading required package: corpcor Loading required package: longitudinal Loading required package: fdrtool get T cell data data(tcell) tc44 = combine.longitudinal(tcell.10, tcell.34) Estimate partial correlations pc1 = ggm.estimate.pcor(tc44, lambda=0) # static, no shrinkage Specified shrinkage intensity lambda (correlation matrix): 0 pc2 = ggm.estimate.pcor(tc44, method="dynamic", lambda=0) # dynamic, no shrinkage Specified shrinkage intensity lambda (correlation matrix): 0 pc3 = ggm.estimate.pcor(tc44) # static, with shrinkage Estimating optimal shrinkage intensity lambda (correlation matrix): pc4 = ggm.estimate.pcor(tc44, method="dynamic") # dynamic, with shrinkage Estimating optimal shrinkage intensity lambda (correlation matrix):
2 Find significant edges We use as selection criterion local fdr <= 0.2 static, no shrinkage t1.edges = network.test.edges(pc1) Type of Statistic: Correlation (kappa = 104, eta0 = ) (first row) and Distribution Function (second row) Fdr (tail area based) t1.net = extract.network(t1.edges) # prob > 0.8 Significant edges: 6 Corresponding to 0.36 % of possible edges t1.net pcor node1 node2 pval qval prob e e
3 e e e e dynamic, no shrinkage t2.edges = network.test.edges(pc2) Type of Statistic: Correlation (kappa = 110.7, eta0 = 0.964) (first row) and Distribution Function (second row) 0.5 fdr (density based) 0.5 t2.net = extract.network(t2.edges) # prob > 0.8 Significant edges: 9 Corresponding to 0.54 % of possible edges t2.net pcor node1 node2 pval qval prob 3
4 e e e e e e e e e e e e e e e e e e static, with shrinkage t3.edges = network.test.edges(pc3) Type of Statistic: Correlation (kappa = 151, eta0 = ) 0 6 (first row) and Distribution Function (second row) fdr (density based) t3.net = extract.network(t3.edges) # prob > 0.8 Significant edges: 10 Corresponding to 0.6 % of possible edges 4
5 t3.net pcor node1 node2 pval qval prob e e e e e e e e e e dynamic, with shrinkage t4.edges = network.test.edges(pc4) Type of Statistic: Correlation (kappa = 189.7, eta0 = ) 0 8 (first row) and Distribution Function (second row) fdr (density based) 5
6 t4.net = extract.network(t4.edges) # prob > 0.8 Significant edges: 21 Corresponding to 1.27 % of possible edges t4.net pcor node1 node2 pval qval prob e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e Produce plots using graph and Rgraphviz library("rgraphviz") Loading required package: graph Loading required package: grid node.labels = colnames(tc44) gr1 = network.make.graph( t1.net, node.labels) gr2 = network.make.graph( t2.net, node.labels) gr3 = network.make.graph( t3.net, node.labels) gr4 = network.make.graph( t4.net, node.labels) globalattrs = list() globalattrs$edge <- list(color = "blue", lty = "solid", lwd = 1, arrowsize=1) globalattrs$node <- list(fillcolor = "lightyellow", shape = "ellipse", fixedsize = FALSE) edgeattrs1 <- list() 6
7 edgeattrs1$dir = edge.info(gr1)$dir edgeattrs2 <- list() edgeattrs2$dir = edge.info(gr2)$dir edgeattrs3 <- list() edgeattrs3$dir = edge.info(gr3)$dir edgeattrs4 <- list() edgeattrs4$dir = edge.info(gr4)$dir par(mfrow=c(2,2)) plot(gr1, main="static, no shrinkage", attrs = globalattrs, edgeattrs = edgeattrs1, "fdp") plot(gr2, main="dynamic, no shrinkage", attrs = globalattrs, edgeattrs = edgeattrs2,"fdp") plot(gr3, main="static, with shrinkage", attrs = globalattrs, edgeattrs = edgeattrs3, "fdp") plot(gr4, main="dynamic, with shrinkage", attrs = globalattrs, edgeattrs = edgeattrs4, "fdp") Static, no shrinkage CD69 C3X1 TCF8 SOD1 MCL1 CDK4 IL4R IRAK1 TCF12 IFNAR1 GATA3 RB1 SCYA2 MAPK9 LAT TRAF5 CDC2 ID3 FYB IL2RG API1 SLA CASP8 ZNFN1A1 API2 NFKBIA SKIIP CYP19 JUND IL16 CCNG1 PIG3 JUNB CSF2RA EGR1 CCNA2 MPO RBL2 IL3RA CTNNB1 MAP2K4 RPS6KA1 AKT1 SIVA ITGAM MAP3K8 MYD88 CCNC CASP4 CASP7 PDE4B SMN1 PCNA CLU LCK APC E2F4 CIR Dynamic, no shrinkage ID3 MPO CD69 SIVA AKT1 JUNB RBL2 SMN1 PDE4B TRAF5 ITGAM SCYA2 RB1 TCF12 RPS6KA1 CLU MCL1 MAPK9 SLA IRAK1 ZNFN1A1 API2 GATA3 IL4R MAP3K8 SOD1 NFKBIA MYD88 CASP4 IL16 CDC2 JUND CCNA2 CIR CYP19 EGR1 CASP8 IFNAR1 CDK4 IL2RG API1 MAP2K4 E2F4 CSF2RA CTNNB1 LAT CCNG1 PCNA CCNC CASP7 IL3RA SKIIP LCK APC FYB PIG3 TCF8 C3X1 Static, with shrinkage JUNB AKT1 RBL2 SOD1 CDK4 CCNC PDE4B CD69 GATA3 MYD88 ITGAM SLA CASP4 CTNNB1 RPS6KA1 IL2RG API1 RB1 TRAF5 ZNFN1A1 JUND IL16 IL4R CLU CASP8 FYB EGR1 SIVA IFNAR1 E2F4 CDC2 MAPK9 MCL1 SCYA2 C3X1 MPO CCNA2 API2 CYP19 NFKBIA MAP2K4 TCF12 SKIIP PIG3 CSF2RA CCNG1 IRAK1 MAP3K8 SMN1 CASP7 PCNA IL3RA CIR LCK APC LAT ID3 TCF8 Dynamic, with shrinkage JUNB MPO TCF8 C3X1 CDK4 PDE4B SMN1 RPS6KA1 ID3 RBL2 TRAF5 MAP2K4 PCNA CTNNB1 SIVA MYD88 CIR IRAK1 CDC2 CCNA2 FYB GATA3 SOD1 IL4R CLU IL3RA IL16 API2 JUND ITGAM EGR1 MCL1 CCNG1 NFKBIA TCF12 CYP19 MAPK9 API1 CASP4 AKT1 CASP7 SLA IL2RG SCYA2 CASP8 IFNAR1 RB1 ZNFN1A1 LAT CCNC CSF2RA MAP3K8 E2F4 PIG3 CD69 LCK SKIIP APC par(mfrow=c(1,1)) 7
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