Í NDICE DE CONTENIDOS
L A RUPTURA DEL MODELO Y LA LLEGADA DE NUEVAS IDEAS MUSICALES
A co-expression module may reveal a true biological pathway, or it may reflect noise (e.g. experimental errors, technical artifacts, or false positives) [6]. Module eigengene detection is highly robust even though noise genes (i.e. genes that truly do not belong to a given module) may be included in modules. Peripheral genes hardly affect module delineation. This is true because eigengenes are principally defined by the most highly connected intramodular hub genes [12].
Hierarchical clustering is a useful method in exploratory data analysis because it does not need a priori information such as specifying the number of clusters. However, cluster analysis always creates a set of clusters regardless if biologically relevant groups of genes or eigengenes actually exist. The cornerstone of gene co-expression networks is the module eigengene. Since an eigengene is defined as the first principal component, if a module eigengene is found to execute a biologically important action, then most genes in that module probably perform similarly.
Weighted gene co-expression network analysis (WGCNA) is a biologically driven data reduction technique. Similar to principal component analysis (PCA), it reduces high-dimensional data into a few meaningful groups. However, unlike PCA which requires component orthogonality between components, WGCNA allows for component interdependency. Modules may characterize biological pathways, so independence between modules cannot be assumed.
Creating an eigengene correlation network with nodes represented as eigengenes, enables the analysis of intermodule relationships. Figure 6 illustrates an eigengene network
32
through average linkage hierarchical clustering. The eigengene dendrogram (Figure 6) and eigengene adjacency heatmap (Figure 7) both give evidence that the some modules can be grouped into meta-modules (Module 1: Blue & Green, Module 2: Pink, Module 3: Yellow, Brown & Red, Module 4: Black, Module 5: Magenta & Turquoise). The brown and red eigengenes are highly related which is demonstrated by their low merging height in Figure 6, and also evidenced by reddish squares in Figure 7. The yellow eigengene also joins that group for similar reasons.
When constructing a network via VisANT, a TOM-based threshold for displaying connections between genes can be chosen. Depending on the degree of topological overlap, increasing the threshold can decrease the number of connections as well as the number of genes that have connections. Small threshold increases may vastly decrease the number of connections. It is important to note that the threshold can be different for every module depending on the topological overlap matrix, and the number of displayed connections needed. The initial thresholds for each module were chosen arbitrarily, and then adjusted to increase readability as well as present a similar number of top hub genes per module and a similar number of minimum connections to be considered a top hub.
Two methods (intramodular connectivity and TOM-based connectivity followed by network mapping) for identifying candidate hub genes were performed. Most modules provided similar results between the two methods (e.g. the black, pink and magenta modules). In some modules there were discrepancies between the two methods (e.g. the turquoise, and blue modules). The larger modules seemed to have the largest discrepancy between the methods, and the smaller modules had the smallest discrepancy. For these reasons, similar rankings
33
between the two methods can be considered equivalent and both can be used to detect candidate hub genes.
There are limitations in the analysis of this dataset. External trait information for S.
sanguinis is not available to perform additional analyses (e.g. calculating the gene significance
based on the correlation of the eigengene with an important sample trait). The resulting modules can be investigated with gene ontology information to evaluate biological significance. It is challenging to determine if modules should be individual modules or should be combined into a single meta-module. For example, the brown and the red modules are highly correlated, but it may make more sense to merge them into one module. Gene ontology information may unveil evidence that modules should be merged. The analysis of hub genes or module eigengenes may result in biologically significant pathways. Intramodular hub genes are highly correlated, therefore multiple statistically equivalent potential biomarkers are produced. These candidate biomarkers can be preferentially selected using gene ontology information. Module significance can be compared with gene significance information to determine a module of interest. Once an important module is chosen, the most highly connected genes (genes found at the tip of dendrogram branches) can be visualized (e.g. Figure 10 – Figure 18) and selected for a future study. This network analysis would help researchers create new research hypotheses and design experiments for validation of candidate hub genes in biologically significant modules.
34
References
[1] P. Xu, X. Ge, L. Chen, X. Wang, Y. Dou, J. Z. Xu, J. R. Patel, V. Stone, M. Trinh, K. Evans, T. Kitten, D. Bonchev and G. A. Buck, "Genome-wide essential gene identification in Streptococcus sanguinis,"
Scientific Reports, 20 October 2011.
[2] P. Xu, J. M. Alves, T. Kitten, A. Brown, Z. Chen, L. S. Ozaki, P. Manque, X. Ge, M. S. Serrano, D. Puiu, S. Hendricks, Y. Wang, M. D. Chaplin, D. Akan, S. Paik, D. L. Peterson, F. L. Macrina and G. A. Buck, "Genome of the Opportunistic Pathogen Streptococcus sanguinis," Journal of Bateriology, vol. 189, no. 8, pp. 3166-3175, 2007.
[3] S. Paik, S. Das, J. C. Noe, C. L. Munro and T. Kitten, "Identification of Virulence Determinants for Endocarditis in Streptococcus sanguinis by Signature-Tagged Mutagenesis," Infection and
Immunity, vol. 73, no. 9, pp. 6064-6074, 2005.
[4] M. Trihn, X. Ge, A. Dobson, T. Kitten and C. L. Munro, "Two-Component System Response Regulators Involved in Virulence of Streptococcus".
[5] B. Zhang and S. Horvath, "A General Framework for Weighted Gene Co-Expression Network Analysis," Statistical Applications in Genetics and Molecular Biology, 2005.
[6] P. Langfelder and S. Horvath, "WGCNA: an R package for weighted correlation network analysis,"
BMC Bioinformatics, vol. 9, no. 559, 2008.
[7] Z. Hu, E. S. Snitkin and C. DeLisi, "VisANT: an integrative framwork for networks in systems biology," Briefings in Bioinformatics, vol. 9, no. 4, p. 317–325, 2009.
[8] S. L. Carter, C. M. Brechbhler, M. Griffin and A. T. Bond, "Gene co-expression network topology provides a framework for molecular characterization of cellular state," Bioinformatics, vol. 20, no. 14, pp. 2242-50, 2004.
[9] R. Albert and A. L. Barbasi, "Topology of evolving networks: local events and universality.," Physical
Review Letters, vol. 85, no. 24, pp. 5234-7, 2000.
[10] A. L. Barabasi and R. Albert, "Emergence of scaling in random networks science," Science, vol. 286, no. 5439, pp. 509-512, 1999.
[11] P. Langfelder, B. Zhang and S. Horvath, "Defining clusters from a hierarchical cluster tree: the Dynamic Tree Cut package for R," Bioinformatics, vol. 24, no. 5, pp. 719-720, 2008.
[12] P. Langfelder and S. Horvath, "Eigengene networks for studying the relationships between co- expression modules," BMC Systems Biology, pp. 1-54, 2007.
35
Appendix: R Code
library(xtable) options(xtable.floating = FALSE) options(xtable.timestamp = "") setwd("C:/Users/edver/Desktop/Microarray Analysis") #==================================================================================== # Display the current working directorygetwd();
# If necessary, change the path below to the directory where the data files are stored. # "." means current directory. On Windows use a forward slash / instead of the usual \. workingDir = ".";
setwd(workingDir);
# Load the WGCNA package library(WGCNA);
# The following setting is important, do not omit. options(stringsAsFactors = FALSE);
#Read in the microarray data set
LeiData0 = read.csv("Lei_Micarray_Summary.csv"); # Take a quick look at what is in the data set: dim(LeiData0);
names(LeiData0);
LeiData = LeiData0[-125,] #Remove duplicate gene, row 125 # Concatinating gene ID and gene names
LeiData$Locus <- paste(LeiData$Locus, LeiData$GeneN, sep='->') LeiData$Locus <- gsub(" ", "_",LeiData$Locus, fixed = TRUE)
#==================================================================================== datExpr0 = as.data.frame(t(LeiData[, -c(1:3)])) names(datExpr0) = LeiData$Locus; rownames(datExpr0) = names(LeiData)[-c(1:3)]; dim(datExpr0) #==================================================================================== # Check samples and genes for excess missing values
gsg = goodSamplesGenes(datExpr0, verbose = 3); gsg$allOK
#Excluding 538 genes from the calculation due to too many missing samples or zero variance.
36 if (!gsg$allOK)
{
# Optionally, print the gene and sample names that were removed: if (sum(!gsg$goodGenes)>0)
printFlush(paste("Removing genes:", paste(names(datExpr0)[!gsg$goodGenes], collapse = ", "))); if (sum(!gsg$goodSamples)>0)
printFlush(paste("Removing samples:", paste(rownames(datExpr0)[!gsg$goodSamples], collapse = ", ")));
# Remove the offending genes and samples from the data: datExpr0 = datExpr0[gsg$goodSamples, gsg$goodGenes] }
dim(datExpr0) # 42 x 1734
#==================================================================================== sampleTree = hclust(dist(datExpr0), method = "average");
# Sample outlier detection
# sample network based on squared Euclidean distance. note that we transpose the data A = adjacency(t(datExpr0), type = "distance")
# this calculates the whole network connectivity k = as.numeric(apply(A, 2, sum)) - 1
# standardized connectivity Z.k = scale(k)
# Designate samples as outlying if their Z.k value is below the threshold thresholdZ.k = -5
outlierColor = ifelse(Z.k < thresholdZ.k, "red", "black") datColors = data.frame(outlierC = outlierColor)
plotDendroAndColors(sampleTree, groupLabels = names(datColors), colors = datColors,main = "Sample Dendrogram and Outlier Detection")
#==================================================================================== # Choose a set of soft-thresholding powers
powers = c(1:20)
# Call the network topology analysis function
sft = pickSoftThreshold(datExpr0, powerVector = powers, verbose = 5, RsquaredCut=0.96) # Plot the results:
sizeGrWindow(9, 5) par(mfrow = c(1,2)); cex1 = 0.9;
# Scale-free topology fit index as a function of the soft-thresholding power plot(sft$fitIndices[,1], -sign(sft$fitIndices[,3])*sft$fitIndices[,2],
xlab="Soft Threshold (power)",ylab="Scale-Free Topology Model Fit,signed R^2",type="n", main = paste("Scale Independence"));
37
text(sft$fitIndices[,1], -sign(sft$fitIndices[,3])*sft$fitIndices[,2], labels=powers,cex=cex1,col="red");
# this line corresponds to using an R^2 cut-off of h abline(h=0.96,col="red")
# Mean connectivity as a function of the soft-thresholding power plot(sft$fitIndices[,1], sft$fitIndices[,5],
xlab="Soft Threshold (power)",ylab="Mean Connectivity", type="n", main = paste("Mean Connectivity"))
text(sft$fitIndices[,1], sft$fitIndices[,5], labels=powers, cex=cex1,col="red") #create Rsq table
xtable(sft$fitIndices) sft$powerEstimate
################## Check Scale-free topology
# here we define the adjacency matrix using soft thresholding with beta=6 ADJ1=abs(cor(datExpr0,use="p"))^6
k=softConnectivity(datE=datExpr0,power=6)
# Plot a histogram of k and a scale free topology plot sizeGrWindow(5,5)
par(mfrow=c(1,2)) hist(k)
scaleFreePlot(k, main="Check Scale-free Topology\n")
#==================================================================================== # One-step network construction and module detection
#==================================================================================== # power = 6, based on soft threshold calculation above
net = blockwiseModules(datExpr0, power = 6, #checkMissingData = FALSE,
TOMType = "unsigned", minModuleSize = 30, reassignThreshold = 0, mergeCutHeight = 0.25, numericLabels = TRUE, pamRespectsDendro = FALSE, saveTOMs = TRUE,
saveTOMFileBase = "Lei_TOM", verbose = 3)
table(net$colors) # 9 Modules detected
#==================================================================================== # open a graphics window
38 # Convert labels to colors for plotting
mergedColors = labels2colors(net$colors)
# Plot the dendrogram and the module colors underneath
plotDendroAndColors(net$dendrograms[[1]], mergedColors[net$blockGenes[[1]]], "Module Colors",
dendroLabels = FALSE, hang = 0.03, addGuide = TRUE, guideHang = 0.05)
moduleAssignments<- data.frame(sort(table(mergedColors), decreasing = TRUE)) # Module Assignments Table
xtable(moduleAssignments) #==================================================================================== moduleLabels = net$colors moduleColors = labels2colors(net$colors) # Module Eigengenes MEs = net$MEs; geneTree = net$dendrograms[[1]];
save(MEs, moduleLabels, moduleColors, geneTree, file = "Lei-networkConstruction-auto.RData")
#==================================================================================== # Calculate topological overlap
dissTOM = 1-TOMsimilarityFromExpr(datExpr0, power = 6)
# Transform dissTOM with a power to make moderately strong connections more visible in the heatmap plotTOM = dissTOM^6
# Set diagonal to NA for a nicer plot diag(plotTOM) = NA
# TOM plot
TOMplot(plotTOM, geneTree, moduleColors, main = "Network Heatmap Plot, All Genes") dim(dissTOM)
length(moduleColors)
#==================================================================================== # Recalculate module eigengenes
MEs = moduleEigengenes(datExpr0, moduleColors)$eigengenes MEs=orderMEs(MEs, greyLast = TRUE,
greyName = paste(moduleColor.getMEprefix(), "grey", sep=""), orderBy = 1, order = NULL,
39 # Plot the relationships among the eigengenes sizeGrWindow(5,7.5);
par(cex = 0.9)
plotEigengeneNetworks(MEs, "", marDendro = c(0,4,1,2), marHeatmap = c(3,4,1,2), cex.lab = 0.8, xLabelsAngle
= 90)
#==================================================================================== # Plot the dendrogram
sizeGrWindow(6,6); par(mfrow=c(1, 2))
plotEigengeneNetworks(MEs, "Eigengene Dendrogram", marDendro = c(0,4,2,0), plotHeatmaps = FALSE)
# Plot the heatmap matrix (note: this plot will overwrite the dendrogram plot)
plotEigengeneNetworks(MEs, "Eigengene Adjacency Heatmap", marHeatmap = c(3,4,2,2), plotDendrograms = FALSE, xLabelsAngle = 90)
#==================================================================================== # Truncate gene names to fit on VisANT plots
colnames(datExpr0)<-substring(colnames(datExpr0),1,25); # Recalculate topological overlap
TOM = TOMsimilarityFromExpr(datExpr0, power = 6);
#==================================================================================== # Export data to VisANT
#==================================================================================== # Select module
module = "turquoise"; # Select module probes probes = names(datExpr0)
inModule = (moduleColors==module); modProbes = probes[inModule];
modProbes = substring(modProbes,1,25); # Select the corresponding Topological Overlap modTOM = TOM[inModule, inModule];
dimnames(modTOM) = list(modProbes, modProbes) # Export the network into an edge list file VisANT can read nTop = 30;
IMConn = softConnectivity(datExpr0[, modProbes]); top = (rank(-IMConn) <= nTop)
vis = exportNetworkToVisANT(modTOM[top, top],
file = paste("VisANTInput-", module, "-top30.txt", sep=""), weighted = TRUE,
40 threshold = 0
#probeToGene = data.frame(annot$substanceBXH, annot$gene_symbol) )
# Select top 30 interconnected genes
topGenesTurquoise<-data.frame(IMConn,modProbes)[order(-IMConn),] topGenesTurquoise<-topGenesTurquoise[1:30,]
names(topGenesTurquoise)<-c("IMConnectivity","Genes") # Select module
module = "blue"; # Select module probes probes = names(datExpr0)
inModule = (moduleColors==module); modProbes = probes[inModule];
# Select the corresponding Topological Overlap modTOM = TOM[inModule, inModule];
dimnames(modTOM) = list(modProbes, modProbes) # Export the network into an edge list file VisANT can read nTop = 30;
IMConn = softConnectivity(datExpr0[, modProbes]); top = (rank(-IMConn) <= nTop)
vis = exportNetworkToVisANT(modTOM[top, top],
file = paste("VisANTInput-", module, "-top30.txt", sep=""), weighted = TRUE,
threshold = 0
#probeToGene = data.frame(annot$substanceBXH, annot$gene_symbol) )
# Select top 30 interconnected genes
topGenesBlue<-data.frame(IMConn,modProbes)[order(-IMConn),] topGenesBlue<-topGenesBlue[1:30,]
names(topGenesBlue)<-c("IMConnectivity","Genes") # Select module
module = "green"; # Select module probes probes = names(datExpr0)
inModule = (moduleColors==module); modProbes = probes[inModule];
# Select the corresponding Topological Overlap modTOM = TOM[inModule, inModule];
dimnames(modTOM) = list(modProbes, modProbes) # Export the network into an edge list file VisANT can read nTop = 30;
IMConn = softConnectivity(datExpr0[, modProbes]); top = (rank(-IMConn) <= nTop)
41
file = paste("VisANTInput-", module, "-top30.txt", sep=""), weighted = TRUE,
threshold = 0
#probeToGene = data.frame(annot$substanceBXH, annot$gene_symbol) )
# Select top 30 interconnected genes
topGenesGreen<-data.frame(IMConn,modProbes)[order(-IMConn),] topGenesGreen<-topGenesGreen[1:30,]
names(topGenesGreen)<-c("IMConnectivity","Genes") # Select module
module = "red"; # Select module probes probes = names(datExpr0)
inModule = (moduleColors==module); modProbes = probes[inModule];
# Select the corresponding Topological Overlap modTOM = TOM[inModule, inModule];
dimnames(modTOM) = list(modProbes, modProbes) # Export the network into an edge list file VisANT can read nTop = 30;
IMConn = softConnectivity(datExpr0[, modProbes]); top = (rank(-IMConn) <= nTop)
vis = exportNetworkToVisANT(modTOM[top, top],
file = paste("VisANTInput-", module, "-top30.txt", sep=""), weighted = TRUE,
threshold = 0
#probeToGene = data.frame(annot$substanceBXH, annot$gene_symbol) )
# Select top 30 interconnected genes
topGenesRed<-data.frame(IMConn,modProbes)[order(-IMConn),] topGenesRed<-topGenesRed[1:30,]
names(topGenesRed)<-c("IMConnectivity","Genes") # Select module
module = "black"; # Select module probes probes = names(datExpr0)
inModule = (moduleColors==module); modProbes = probes[inModule];
# Select the corresponding Topological Overlap modTOM = TOM[inModule, inModule];
dimnames(modTOM) = list(modProbes, modProbes) # Export the network into an edge list file VisANT can read nTop = 30;
42 top = (rank(-IMConn) <= nTop)
vis = exportNetworkToVisANT(modTOM[top, top],
file = paste("VisANTInput-", module, "-top30.txt", sep=""), weighted = TRUE,
threshold = 0
#probeToGene = data.frame(annot$substanceBXH, annot$gene_symbol) )
# Select top 30 interconnected genes
topGenesBlack<-data.frame(IMConn,modProbes)[order(-IMConn),] topGenesBlack<-topGenesBlack[1:30,]
names(topGenesBlack)<-c("IMConnectivity","Genes") # Select module
module = "pink"; # Select module probes probes = names(datExpr0)
inModule = (moduleColors==module); modProbes = probes[inModule];
# Select the corresponding Topological Overlap modTOM = TOM[inModule, inModule];
dimnames(modTOM) = list(modProbes, modProbes) # Export the network into an edge list file VisANT can read nTop = 30;
IMConn = softConnectivity(datExpr0[, modProbes]); top = (rank(-IMConn) <= nTop)
vis = exportNetworkToVisANT(modTOM[top, top],
file = paste("VisANTInput-", module, "-top30.txt", sep=""), weighted = TRUE,
threshold = 0
#probeToGene = data.frame(annot$substanceBXH, annot$gene_symbol) )
# Select top 30 interconnected genes
topGenesPink<-data.frame(IMConn,modProbes)[order(-IMConn),] topGenesPink<-topGenesPink[1:30,]
names(topGenesPink)<-c("IMConnectivity","Genes") # Select module
module = "magenta"; # Select module probes probes = names(datExpr0)
inModule = (moduleColors==module); modProbes = probes[inModule];
# Select the corresponding Topological Overlap modTOM = TOM[inModule, inModule];
dimnames(modTOM) = list(modProbes, modProbes) # Export the network into an edge list file VisANT can read
43 nTop = 30;
IMConn = softConnectivity(datExpr0[, modProbes]); top = (rank(-IMConn) <= nTop)
vis = exportNetworkToVisANT(modTOM[top, top],
file = paste("VisANTInput-", module, "-top30.txt", sep=""), weighted = TRUE,
threshold = 0
#probeToGene = data.frame(annot$substanceBXH, annot$gene_symbol) )
# Select top 30 interconnected genes
topGenesMagenta<-data.frame(IMConn,modProbes)[order(-IMConn),] topGenesMagenta<-topGenesMagenta[1:30,]
names(topGenesMagenta)<-c("IMConnectivity","Genes") # Select module
module = "brown"; # Select module probes probes = names(datExpr0)
inModule = (moduleColors==module); modProbes = probes[inModule];
# Select the corresponding Topological Overlap modTOM = TOM[inModule, inModule];
dimnames(modTOM) = list(modProbes, modProbes) # Export the network into an edge list file VisANT can read nTop = 30;
IMConn = softConnectivity(datExpr0[, modProbes]); top = (rank(-IMConn) <= nTop)
vis = exportNetworkToVisANT(modTOM[top, top],
file = paste("VisANTInput-", module, "-top30.txt", sep=""), weighted = TRUE,
threshold = 0
#probeToGene = data.frame(annot$substanceBXH, annot$gene_symbol) )
# Select top 30 interconnected genes
topGenesBrown<-data.frame(IMConn,modProbes)[order(-IMConn),] topGenesBrown<-topGenesBrown[1:30,]
names(topGenesMagenta)<-c("IMConnectivity","Genes")
# Select module module = "yellow"; # Select module probes probes = names(datExpr0)
inModule = (moduleColors==module); modProbes = probes[inModule];
44 modTOM = TOM[inModule, inModule];
dimnames(modTOM) = list(modProbes, modProbes) # Export the network into an edge list file VisANT can read nTop = 30;
IMConn = softConnectivity(datExpr0[, modProbes]); top = (rank(-IMConn) <= nTop)
vis = exportNetworkToVisANT(modTOM[top, top],
file = paste("VisANTInput-", module, "-top30.txt", sep=""), weighted = TRUE,
threshold = 0
#probeToGene = data.frame(annot$substanceBXH, annot$gene_symbol) )
# Select top 30 interconnected genes
topGenesYellow<-data.frame(IMConn,modProbes)[order(-IMConn),] topGenesYellow<-topGenesYellow[1:30,] names(topGenesYellow)<-c("IMConnectivity","Genes") rownames(topGenesRed)<-NULL rownames(topGenesYellow)<-NULL rownames(topGenesBrown)<-NULL rownames(topGenesMagenta)<-NULL rownames(topGenesPink)<-NULL rownames(topGenesGreen)<-NULL rownames(topGenesBlue)<-NULL rownames(topGenesBlack)<-NULL rownames(topGenesTurquoise)<-NULL # Latex tables for top 30 genes
xtable(topGenesTurquoise) xtable(topGenesBlue) xtable(topGenesBrown) xtable(topGenesYellow) xtable(topGenesGreen) xtable(topGenesRed) xtable(topGenesBlack) xtable(topGenesPink) xtable(topGenesMagenta) #==================================================================================== # Intramodular Connectivity #==================================================================================== colorh1=moduleColors ADJ1=abs(cor(datExpr0,use="p"))^6 Alldegrees1=intramodularConnectivity(ADJ1, colorh1) head(Alldegrees1) datME=moduleEigengenes(datExpr0,colorh1)$eigengenes
45 signif(cor(datME, use="p"), 2)
signif(datME,2)
# Calculate Module membership
datKME=signedKME(datExpr0, datME, outputColumnName="MM.") # Display the first few rows of the data frame
head(datKME)
#==================================================================================== # Relationship between the module membership measures (e.g. MM.turquoise)
# and intramodular connectivity
#==================================================================================== # Plot module membership vs. intramodular connectivity by module
sizeGrWindow(8,6) par(mfrow=c(3,3))
# For simplicity, the code is written explicitly for each module. which.color="turquoise";
restrictGenes=colorh1==which.color
verboseScatterplot(Alldegrees1$kWithin[ restrictGenes],
abs(datKME[restrictGenes, paste("MM.", which.color, sep="")])^5, col=which.color, xlab="Intramodular Connectivity", ylab="abs(Module Membership)^5") which.color="blue"; restrictGenes=colorh1==which.color verboseScatterplot(Alldegrees1$kWithin[ restrictGenes],
abs(datKME[restrictGenes, paste("MM.", which.color, sep="")])^5, col=which.color, xlab="Intramodular Connectivity", ylab="abs(Module Membership)^5") which.color="brown"; restrictGenes=colorh1==which.color verboseScatterplot(Alldegrees1$kWithin[ restrictGenes],
abs(datKME[restrictGenes, paste("MM.", which.color, sep="")])^5, col=which.color, xlab="Intramodular Connectivity", ylab="abs(Module Membership)^5") which.color="yellow"; restrictGenes=colorh1==which.color verboseScatterplot(Alldegrees1$kWithin[ restrictGenes],
abs(datKME[restrictGenes, paste("MM.", which.color, sep="")])^5, col=which.color,
xlab="Intramodular Connectivity", ylab="abs(Module Membership)^5") which.color="green";
46 restrictGenes=colorh1==which.color
verboseScatterplot(Alldegrees1$kWithin[ restrictGenes],
abs(datKME[restrictGenes, paste("MM.", which.color, sep="")])^5, col=which.color, xlab="Intramodular Connectivity", ylab="abs(Module Membership)^5") which.color="red"; restrictGenes=colorh1==which.color verboseScatterplot(Alldegrees1$kWithin[ restrictGenes],
abs(datKME[restrictGenes, paste("MM.", which.color, sep="")])^5, col=which.color, xlab="Intramodular Connectivity", ylab="abs(Module Membership)^5") which.color="black"; restrictGenes=colorh1==which.color verboseScatterplot(Alldegrees1$kWithin[ restrictGenes],
abs(datKME[restrictGenes, paste("MM.", which.color, sep="")])^5, col=which.color, xlab="Intramodular Connectivity", ylab="abs(Module Membership)^5") which.color="pink"; restrictGenes=colorh1==which.color verboseScatterplot(Alldegrees1$kWithin[ restrictGenes],
abs(datKME[restrictGenes, paste("MM.", which.color, sep="")])^5, col=which.color, xlab="Intramodular Connectivity", ylab="abs(Module Membership)^5") which.color="magenta"; restrictGenes=colorh1==which.color verboseScatterplot(Alldegrees1$kWithin[ restrictGenes],
abs(datKME[restrictGenes, paste("MM.", which.color, sep="")])^5, col=which.color,
xlab="Intramodular Connectivity", ylab="abs(Module Membership)^5")
sizeGrWindow(8,6) par(mfrow=c(3,3))
# For simplicity, the code is written explicitly for each module. which.color="turquoise";
restrictGenes=colorh1==which.color
verboseScatterplot(Alldegrees1$kWithin[ restrictGenes],
abs(datKME[restrictGenes, paste("MM.", which.color, sep="")])^1, col=which.color,
xlab="Intramodular Connectivity", ylab="abs(Module Membership)") which.color="blue";
47 restrictGenes=colorh1==which.color
verboseScatterplot(Alldegrees1$kWithin[ restrictGenes],
abs(datKME[restrictGenes, paste("MM.", which.color, sep="")])^1, col=which.color, xlab="Intramodular Connectivity", ylab="abs(Module Membership)") which.color="brown"; restrictGenes=colorh1==which.color verboseScatterplot(Alldegrees1$kWithin[ restrictGenes],
abs(datKME[restrictGenes, paste("MM.", which.color, sep="")])^1, col=which.color, xlab="Intramodular Connectivity", ylab="abs(Module Membership)") which.color="yellow"; restrictGenes=colorh1==which.color verboseScatterplot(Alldegrees1$kWithin[ restrictGenes],
abs(datKME[restrictGenes, paste("MM.", which.color, sep="")])^1, col=which.color, xlab="Intramodular Connectivity", ylab="abs(Module Membership)") which.color="green"; restrictGenes=colorh1==which.color verboseScatterplot(Alldegrees1$kWithin[ restrictGenes],
abs(datKME[restrictGenes, paste("MM.", which.color, sep="")])^1, col=which.color, xlab="Intramodular Connectivity", ylab="abs(Module Membership)") which.color="red"; restrictGenes=colorh1==which.color verboseScatterplot(Alldegrees1$kWithin[ restrictGenes],
abs(datKME[restrictGenes, paste("MM.", which.color, sep="")])^1, col=which.color, xlab="Intramodular Connectivity", ylab="abs(Module Membership)") which.color="black"; restrictGenes=colorh1==which.color verboseScatterplot(Alldegrees1$kWithin[ restrictGenes],
abs(datKME[restrictGenes, paste("MM.", which.color, sep="")])^1, col=which.color, xlab="Intramodular Connectivity", ylab="abs(Module Membership)") which.color="pink"; restrictGenes=colorh1==which.color verboseScatterplot(Alldegrees1$kWithin[ restrictGenes],
abs(datKME[restrictGenes, paste("MM.", which.color, sep="")])^1, col=which.color,
xlab="Intramodular Connectivity", ylab="abs(Module Membership)")
48 which.color="magenta";
restrictGenes=colorh1==which.color
verboseScatterplot(Alldegrees1$kWithin[ restrictGenes],
abs(datKME[restrictGenes, paste("MM.", which.color, sep="")])^1, col=which.color,
xlab="Intramodular Connectivity", ylab="abs(Module Membership)")