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<body>
<h1>Ensemble Network Aggregation</h1>
<h2>Define Simulation Settings</h2>
<pre><code class="r">SIZE <- c("tiny", "small", "moderate", "middle", "large")
NP <- as.character(c(20, 50, 100, 200, 500, 1000))
NOISE <- as.character(c(0.25, 0.5, 1, 1.5))
BOOTSTRAP_COUNT <- 140
set.seed(1234)
library(knitr)
</code></pre>
<h2>Simulate Networks</h2>
<p>We'll first define the function used to simulate the networks.</p>
<pre><code class="r">#' Read the edge definition and simulate the regulation ###
#'
#' @param size should be one of
#' c('tiny','small','moderate','middle','large', 'huge') @param sample can
#' be any positive number. @param noise can be any positive number (for
#' our study, we should try 0.25, 0.5, 0.75, 1, 1.5 and 2) @author
#' Guanghua Xiao \email{Guanghua.Xiao@@UTSouthwestern.edu}
network.simulation <- function(size, sample, noise, outputDir) {
dir.create(outputDir)
dat <- read.csv(paste("../truth/", size, ".csv", sep = ""))
dat <- dat[dat$Source != dat$Target, ]
dat[dat$Source > dat$Target, ] <- dat[dat$Source > dat$Target, 2:1]
dat[dat$Source > dat$Target, ]
Gene <- union(dat$Source, dat$Target)
nGene <- length(Gene)
N <- dim(dat)[1]
dat$regulation <- round(sign(rbinom(N, 1, 0.5) - 0.5) * rnorm(N, 0.5, 0.2),
2)
dat$type <- sign(dat$regulation)
write.csv(dat, paste("../truth/truth ", size, ".csv", sep = ""), row.names = F,
quote = F)
#### initiate the matrix expr to store the simulated expression level ####
nSamp <- sample
expr <- matrix(0, nGene, nSamp)
rownames(expr) <- Gene
colnames(expr) <- paste("S", 1:nSamp, sep = "")
sigma <- noise
unknown <- unique(dat$Target)
known <- setdiff(Gene, unknown)
for (i in known) {
expr[as.character(i), ] <- rbinom(nSamp, 1, 0.5) * 2 + rnorm(nSamp,
0, sigma) - 1
}
while (length(unknown) > 0) {
for (i in unknown) {
source <- as.character(dat$Source[dat$Target == i])
if (all(source %in% known)) {
val <- ifelse(rep(length(source) == 1, nSamp), expr[as.character(source),
] * dat[dat$Target == i, 3], dat[dat$Target == i, 3] %*% expr[as.character(source),
])
expr[as.character(i), ] <- as.numeric(val > 0) * 2 - 1 + rnorm(nSamp,
0, sigma)
known <- append(known, i)
unknown <- setdiff(unknown, i)
}
}
}
write.csv(round(expr, 2), paste(outputDir, size, "nSamp", nSamp, "Sigma",
noise, ".csv", sep = ""), row.names = T)
}
</code></pre>
<p>Now we'll generate the networks described in the existing variables <code>SIZE</code>, <code>NP</code>, and <code>NOISE</code> defined elsewhere.</p>
<pre><code class="r">
for (size in SIZE) {
for (sample in NP) {
for (noise in NOISE) {
network.simulation(size = size, sample = as.integer(sample), noise = as.numeric(noise),
outputDir = "../data/simulations/")
}
}
}
</code></pre>
<h2>Rebuild Networks</h2>
<p>This section will document the construction of the networks for all of the considered methods on all of the simulated datasets.</p>
<h3>Loading Packages</h3>
<p>We'll load in all the relevant packages. <code>parmigene</code> is a package created to allow R users to work with Aracne. <code>bnlearn</code> is a package that implements a variety of Bayesian methods. <code>WGCNA</code>, <code>space</code>, and <code>GeneNet</code> all implement partial-correlation or correlation-based learning methods and will be identified by their package name for the duration of the study. All of these packages are supported by our <code>ENA</code> package which offers helper functions for all of these packages.</p>
<pre><code class="r">library(WGCNA)
library(GeneNet)
library(space)
library(ENA)
</code></pre>
<h3>Process Simulated Networks</h3>
<p>We'll first need a function to extract the true network for inclusion in our aggregated tables.</p>
<pre><code class="r">#' Convert an adjacency-like list (which may or may not contain all the
#' gene IDs in the network) into an adjacency matrix.
#'
#' @param IDs A vector of Gene IDs in this network @param truthList An
#' adjacency list containing the truth to be converted to a matrix. Should
#' have vectors for 'Source', 'Target', and 'Regulation.' @author Jeffrey
#' D. Allen \email{Jeffrey.Allen@@UTSouthwestern.edu}
truthToMat <- function(IDs, truthList) {
truth <- matrix(0, ncol = length(IDs), nrow = length(IDs))
rownames(truth) <- IDs
colnames(truth) <- IDs
diag(truth) <- 1
for (i in 1:dim(truthList)[1]) {
s <- truthList[i, ]$Source
t <- truthList[i, ]$Target
r <- truthList[i, ]$regulation
truth[which(IDs == s), which(IDs == t)] <- r
}
truth <- symmetricize(truth, "ud")
return(truth)
}
</code></pre>
<p>We'll then need a helper function which, given all of the information about a simulation, can extract the simulated dataset, aggregate the results into a table, and write the output.</p>
<pre><code class="r">#' Read and process an expression matrix using all three methods and ENA
#' and return the generated results. @param size The size of the network
#' to use ('tiny', 'small', etc.) @param ns The number of samples in the
#' desired dataset. @param dataDir Where to find the expression data.
#' @param bootstrapCount The number of iterations to perform while
#' bootstrapping. @param bootstrapPercentage The percentage of available
#' samples to include in each bootstrapped iteration. @param Cluster an
#' MPI cluster to which we can distribute the work. @param truth The
#' truth for the network -- can be combined into the resultant output for
#' easier analysis of performance later. @author Jeffrey D. Allen
#' \email{Jeffrey.Allen@@UTSouthwestern.edu}
processMat <- function(size, ns, sigma, dataDir, bootstrapCount,
bootstrapPercentage = 0.7, cluster, truth) {
simul <- read.csv(paste(dataDir, size, "nSamp", ns, "Sigma", sigma, ".csv",
sep = ""), row.names = 1)
addressing <- getTableAddressing(rownames(simul), truth)
sp <- bootstrap(simul, "buildSpace", cluster = cluster, iterations = bootstrapCount,
sample.percentage = bootstrapPercentage)
wg <- symmetricize(abs(buildWgcna(simul)))
wg <- wg[upper.tri(wg)]
gn <- bootstrap(simul, "buildGenenet", cluster = cluster, iterations = bootstrapCount,
sample.percentage = bootstrapPercentage)
joint1 <- ena(cbind(gn[, 3], wg, sp[, 3]))
joint1 <- data.frame(addressing, sp[, 3], wg, gn[, 3], joint1, row.names = NULL)
colnames(joint1) <- c("Source", "Dest", "Truth", "BootstrappedSPACE", "WGCNA",
"BootstrappedGenenet", "ENA")
# Since this graph is undirected, for consistency, make Source always
# smaller than Dest
allGenes <- union(joint1$Source, joint1$Dest)
levels(joint1$Source) <- allGenes
levels(joint1$Dest) <- allGenes
joint1[as.character(joint1[, 1]) > as.character(joint1[, 2]), 1:2] <- joint1[as.character(joint1[,
1]) > as.character(joint1[, 2]), 2:1]
return(joint1)
}
</code></pre>
<p>Finally, we'll loop through all of the simulated datasets to construct all of the networks associated. We'll simulate on 6 network sizes, 6 numbers of samples, and 6 noise values, for \(6^3=216\) total datasets. If we're able to load the <code>Rmpi</code> and <code>snow</code> packages, we'll assume we are to distribute the load across a cluster. Otherwise, we'll just run it in this R process in a traditional way.</p>
<pre><code class="r">cluster <- NULL
if (require(Rmpi) && require(snow)) {
invisible(capture.output(cluster <- makeCluster(mpi.universe.size() - 1,
type = "MPI")))
warning(paste("Distributing bootstrapped networks across a cluster of",
mpi.universe.size() - 1, "CPUs."))
}
</code></pre>
<pre><code>## Warning: Distributing bootstrapped networks across a cluster of 47 CPUs.
</code></pre>
<pre><code class="r">
dataDir <- "../data/simulations/"
dir.create("../data/results-ena/")
</code></pre>
<pre><code>## Warning: '../data/results-ena' already exists
</code></pre>
<pre><code class="r">
for (size in SIZE) {
truthList <- read.csv(paste("../truth/truth ", size, ".csv", sep = ""))
simul <- read.csv(paste(dataDir, size, "nSamp", NP[1], "Sigma", NOISE[1],
".csv", sep = ""), row.names = 1)
truth <- truthToMat(rownames(simul), truthList)
for (ns in NP) {
for (sigma in NOISE) {
results <- processMat(size, ns, sigma, dataDir, BOOTSTRAP_COUNT,
cluster = cluster, truth = truth)
saveRDS(results, file = paste("../data/results-ena/", size, "nSamp",
ns, "Sigma", sigma, ".Rds", sep = ""))
}
}
}
</code></pre>
<pre><code>## Error: 'recvOneData.MPIclusted' is not an exported object from
## 'namespace:snow'
</code></pre>
<h2>Calculate Area Under the ROC Curve</h2>
<p>Here we'll document the computation of the AUCs and partial-AUCs of the computed networks for the Joint GRN study.</p>
<p>The following two functions will be used to calculate the AUC or pAUC of some prediction, respectively.</p>
<pre><code class="r">library(ROCR)
#' Compute the AUC of the provided predictions. @param truth The binary
#' truth vector @param predicted The continuous predicted connection
#' strengths. @author Jeffrey D. Allen
#' \email{Jeffrey.Allen@@UTSouthwestern.edu}
getAUC <- function(truth, predicted) {
pred <- prediction(predicted, truth)
auc <- performance(pred, "auc")@y.values[[1]]
auc
}
#' Compute the pAUC of the provided predictions. @param truth The binary
#' truth vector @param predicted The continuous predicted connection
#' strengths. @param threshold The value at which to control the partial
#' AUC. @author Jeffrey D. Allen
#' \email{Jeffrey.Allen@@UTSouthwestern.edu}
getpAUC <- function(truth, predicted, threshold = 0.005) {
pred <- prediction(predicted, truth)
auc <- performance(pred, "auc", fpr.stop = threshold)@y.values[[1]]
auc
}
</code></pre>
<p>We now want to produce 2 4D arrays to store the AUC and pAUC. The dimensions of the arrays are: network size, sample count, noise, and network construction method. We'll just loop through all of the arrangements of the variables until we've calculated the AUC and pAUC for every possible combination.</p>
<pre><code class="r">
METHODS <- c("Joint", "Space", "WGCNA", "GeneNet")
resultsDir <- "../data/results-ena/"
# 4D arrays
AUCs <- array(NA, dim = c(length(METHODS), length(SIZE), length(NP),
length(NOISE)), dimnames = list(Method = METHODS, Size = SIZE, Samples = NP,
Noise = NOISE))
pAUCs <- array(NA, dim = c(length(METHODS), length(SIZE), length(NP),
length(NOISE)), dimnames = list(Method = METHODS, Size = SIZE, Samples = NP,
Noise = NOISE))
for (size in SIZE) {
for (ns in NP) {
for (sigma in NOISE) {
results <- readRDS(paste(resultsDir, size, "nSamp", ns, "Sigma",
sigma, ".Rds", sep = ""))
truthCol <- results$Truth != 0
AUCs["Joint", size, ns, sigma] <- getAUC(truthCol, results$ENA)
pAUCs["Joint", size, ns, sigma] <- getpAUC(truthCol, results$ENA)
AUCs["WGCNA", size, ns, sigma] <- getAUC(truthCol, results$WGCNA)
pAUCs["WGCNA", size, ns, sigma] <- getpAUC(truthCol, results$WGCNA)
AUCs["GeneNet", size, ns, sigma] <- getAUC(truthCol, results$BootstrappedGenenet)
pAUCs["GeneNet", size, ns, sigma] <- getpAUC(truthCol, results$BootstrappedGenenet)
AUCs["Space", size, ns, sigma] <- getAUC(truthCol, abs(results$BootstrappedSPACE))
pAUCs["Space", size, ns, sigma] <- getpAUC(truthCol, abs(results$BootstrappedSPACE))
}
}
saveRDS(AUCs, file = "AUCs.Rds")
saveRDS(pAUCs, file = "pAUCs.Rds")
}
</code></pre>
<p>It's probably easiest to keep a 4D array within R for now, rather than writing a series of CSVs. We can output selected 2D tables of interest at a later point.</p>
<pre><code class="r">saveRDS(AUCs, file = "AUCs.Rds")
saveRDS(pAUCs, file = "pAUCs.Rds")
</code></pre>
<p>Alternatively, we can convert the 4D matrix to a data.frame.</p>
<pre><code class="r">aucTab <- as.data.frame.table(AUCs, responseName = "AUC")
paucTab <- as.data.frame.table(pAUCs, responseName = "pAUC")
</code></pre>
<h3>Visualization</h3>
<p>We can now plot the performance on particular networks for closer inspection.</p>
<p>We can visualize the performance for a particular network and noise value in 2 dimensions by plotting the AUC of each method for the possible numbers of samples.</p>
<pre><code class="r">library(lattice)
size <- SIZE[min(length(SIZE), 3)]
noise <- "0.25"
print(xyplot(Freq ~ Samples, group = Method, data = as.data.frame.table(AUCs[,
size, , noise]), type = "b", auto.key = list(space = "right")))
</code></pre>
<p><img 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" alt="plot of chunk unnamed-chunk-9"/> </p>
<p>And we can select on particular network from that graph and plot more specific information, such as the total number of identified connections vs. the number of false positives after reading in that network's results again.</p>
<pre><code class="r">par(mfrow = c(1, 2), mar = c(5.1, 4.1, 4.1, 0.6))
library(RColorBrewer)
colors <- brewer.pal(4, "Dark2")
# First plot
samples <- "200"
results <- readRDS(paste(resultsDir, size, "nSamp", samples, "Sigma",
noise, ".Rds", sep = ""))
results$Truth <- as.integer(results$Truth != 0)
plot(0, 0, type = "n", xlim = c(0, 50), ylim = c(0, 35), xlab = "Predicted Connection Strength",
ylab = "False Positive Strength", main = "200 Samples")
legend(0, 30, c("SPACE", "WGCNA", "GeneNet", "ENA"), col = colors,
lwd = 3, lty = 1:4, y.intersp = 1.5)
#' Plots a single line of the total identifid on the x-axis vs the false
#' positives on the y-axis. @param df the data frame to use in the format
#' of having the first column be the truth and the second column be the
#' predictions for this method @param color dictates the color of the line
#' plotted
plotPerformance <- function(df, color = 1, lty) {
# normalize to 0:1
df[, 2] <- df[, 2]/max(df[, 2])
df <- df[order(df[, 2], decreasing = TRUE), ]
df$FalsePos <- df[, 2]
df$FalsePos[df$Truth == 1] <- 0
# Done organizing the data.frame, now can format for plotting.
df$FalsePos <- cumsum(df$FalsePos)
df[, 2] <- cumsum(df[, 2])
lines(x = c(0, df[, 2]), y = c(0, df[, 3]), col = color, lwd = 2, lty = lty)
}
sp <- results[, c(3, 4)]
plotPerformance(sp, colors[1], 2)
wg <- results[, c(3, 5)]
plotPerformance(wg, colors[2], 3)
gn <- results[, c(3, 6)]
plotPerformance(gn, colors[3], 4)
ena <- results[, c(3, 7)]
plotPerformance(ena, colors[4], 5)
## second plot
par(mar = c(5.1, 2.1, 4.1, 1.1))
samples <- "1000"
results <- readRDS(paste(resultsDir, size, "nSamp", samples, "Sigma",
noise, ".Rds", sep = ""))
results$Truth <- as.integer(results$Truth != 0)
plot(0, 0, type = "n", xlim = c(0, 50), ylim = c(0, 30), xlab = "Predicted Connection Strength",
ylab = "", main = "1,000 Samples")
sp <- results[, c(3, 4)]
plotPerformance(sp, colors[1], 2)
wg <- results[, c(3, 5)]
plotPerformance(wg, colors[2], 3)
gn <- results[, c(3, 6)]
plotPerformance(gn, colors[3], 4)
ena <- results[, c(3, 7)]
plotPerformance(ena, colors[4], 5)
</code></pre>
<p><img 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" alt="plot of chunk unnamed-chunk-10"/> </p>
<h3>Rank Summary</h3>
<p>The simplest way to analyze the relative pAUC or AUCs would just be to rank them, rather than trying to determine how “substantial” an improvement of .01 in the AUC, for instance, really is. So we can use the rank function, which will give us an order to all of the methods in which the highest ranked methods performed the best.</p>
<p>We could summarize these for a network size/sample size combination across all noise values, for instance. The code below does that, then further summarizes those summaries across all sample size values – producing a 2D table showing the average rank for a method at a network size across all noise and sample size values.</p>
<p>AUCs are shown here.</p>
<pre><code class="r">rankAvg <- array(dim = c(length(METHODS), length(SIZE)), dimnames = list(Method = METHODS,
Size = SIZE))
for (size in SIZE) {
thisSize <- array(dim = c(length(NOISE), length(METHODS)), dimnames = list(Noise = NOISE,
Method = METHODS))
for (sigma in NOISE) {
thisSize[as.character(sigma), ] <- apply(apply(AUCs[, size, , as.character(sigma)],
"Samples", function(x) {
rank(x, na.last = "keep")
}), 1, mean)
}
rankAvg[, size] <- apply(thisSize, 2, mean)
}
rankAvg
</code></pre>
<pre><code>## Size
## Method tiny small moderate middle large
## Joint 2.875 3.333 3.375 3.500 3.458
## Space 2.479 2.250 1.625 1.292 1.250
## WGCNA 1.500 1.458 1.958 2.292 2.458
## GeneNet 3.146 2.958 3.042 2.917 2.833
</code></pre>
<pre><code class="r">apply(rankAvg, 2, rank)
</code></pre>
<pre><code>## Size
## tiny small moderate middle large
## Joint 3 4 4 4 4
## Space 2 2 1 1 1
## WGCNA 1 1 2 2 2
## GeneNet 4 3 3 3 3
</code></pre>
<p>And pAUCs here.</p>
<pre><code class="r">rankpAvg <- array(dim = c(length(METHODS), length(SIZE)), dimnames = list(Method = METHODS,
Size = SIZE))
for (size in SIZE) {
thisSize <- array(dim = c(length(NOISE), length(METHODS)), dimnames = list(Noise = NOISE,
Method = METHODS))
for (sigma in NOISE) {
thisSize[as.character(sigma), ] <- apply(apply(pAUCs[, size, , as.character(sigma)],
"Samples", function(x) {
rank(x, na.last = "keep")
}), 1, mean)
}
rankpAvg[, size] <- apply(thisSize, 2, mean)
}
rankpAvg
</code></pre>
<pre><code>## Size
## Method tiny small moderate middle large
## Joint 2.125 2.188 2.542 2.792 3.167
## Space 3.188 3.125 3.125 3.125 2.750
## WGCNA 1.417 1.396 1.417 1.375 1.708
## GeneNet 3.271 3.292 2.917 2.708 2.375
</code></pre>
<pre><code class="r">apply(rankpAvg, 2, rank)
</code></pre>
<pre><code>## Size
## tiny small moderate middle large
## Joint 2 2 2 3 4
## Space 3 3 4 4 3
## WGCNA 1 1 1 1 1
## GeneNet 4 4 3 2 2
</code></pre>
<h2>Merge Datasets</h2>
<p>This section will test the reconstruction of three different datasets using a naive merging approach and a meta-analysis approach based on ENA.</p>
<p>First, we'll read in three simulated datasets.</p>
<pre><code class="r">#' Create a simulated randomized 'true' regulatory network based on the
#' truth file provided. @param size should be one of
#' c('tiny','small','moderate','middle','large', 'huge') @author Jeffrey
#' D. Allen \email{Jeffrey.Allen@@UTSouthwestern.edu}
randomizeRegulation <- function(size) {
dat <- read.csv(paste("../truth/", size, ".csv", sep = ""))
dat <- dat[dat$Source != dat$Target, ]
dat[dat$Source > dat$Target, ] <- dat[dat$Source > dat$Target, 2:1]
dat[dat$Source > dat$Target, ]
N <- dim(dat)[1]
dat$regulation <- round(sign(rbinom(N, 1, 0.5) - 0.5) * rnorm(N, 0.5, 0.2),
2)
dat$type <- sign(dat$regulation)
dat
}
#' Read the edge definition and simulate the regulation ###
#'
#' @param dat the regulatory truth obtained from randomizeRegulation()
#' @param sample can be any positive number. @param noise can be any
#' positive number (for our study, we should try 0.25, 0.5, 0.75, 1, 1.5
#' and 2) @author Jeffrey D. Allen
#' \email{Jeffrey.Allen@@UTSouthwestern.edu}
network.simulation <- function(dat, sample, noise) {
Gene <- union(dat$Source, dat$Target)
nGene <- length(Gene)
#### initiate the matrix expr to store the simulated expression level ####
nSamp <- sample
expr <- matrix(0, nGene, nSamp)
rownames(expr) <- Gene
colnames(expr) <- paste("S", 1:nSamp, sep = "")
sigma <- noise
unknown <- unique(dat$Target)
known <- setdiff(Gene, unknown)
for (i in known) {
expr[as.character(i), ] <- rbinom(nSamp, 1, 0.5) * 2 + rnorm(nSamp,
0, sigma) - 1
}
while (length(unknown) > 0) {
for (i in unknown) {
source <- as.character(dat$Source[dat$Target == i])
if (all(source %in% known)) {
val <- ifelse(rep(length(source) == 1, nSamp), expr[as.character(source),
] * dat[dat$Target == i, 3], dat[dat$Target == i, 3] %*% expr[as.character(source),
])
expr[as.character(i), ] <- as.numeric(val > 0) * 2 - 1 + rnorm(nSamp,
0, sigma)
known <- append(known, i)
unknown <- setdiff(unknown, i)
}
}
}
round(expr, 2)
}
reg <- randomizeRegulation("middle")
sim25 <- network.simulation(reg, 200, 0.25)
sim1 <- network.simulation(reg, 200, 1)
sim2 <- network.simulation(reg, 200, 2)
</code></pre>
<pre><code class="r">library(WGCNA)
library(GeneNet)
library(space)
library(ENA)
library(ROCR)
</code></pre>
<p>We'll first need a function to extract the true network for inclusion in our aggregated tables.</p>
<pre><code class="r">#' Convert an adjacency-like list (which may or may not contain all the
#' gene IDs in the network) into an adjacency matrix.
#'
#' @param IDs A vector of Gene IDs in this network @param truthList An
#' adjacency list containing the truth to be converted to a matrix. Should
#' have vectors for 'Source', 'Target', and 'Regulation.' @author Jeffrey
#' D. Allen \email{Jeffrey.Allen@@UTSouthwestern.edu}
truthToMat <- function(IDs, truthList) {
truth <- matrix(0, ncol = length(IDs), nrow = length(IDs))
rownames(truth) <- IDs
colnames(truth) <- IDs
diag(truth) <- 1
for (i in 1:dim(truthList)[1]) {
s <- truthList[i, ]$Source
t <- truthList[i, ]$Target
r <- truthList[i, ]$regulation
truth[which(IDs == s), which(IDs == t)] <- r
}
truth <- symmetricize(truth, "ud")
return(truth)
}
</code></pre>
<p>We'll then need a helper function which, given all of the information about a simulation, can extract the simulated dataset, aggregate the results into a table, and write the output.</p>
<pre><code class="r">#' Read and process an expression matrix using all three methods and ENA
#' and return the generated results. @param simul The simulated
#' expression data @param bootstrapCount The number of iterations to
#' perform while bootstrapping. @param bootstrapPercentage The percentage
#' of available samples to include in each bootstrapped iteration. @param
#' Cluster an MPI cluster to which we can distribute the work. @param
#' truth The truth for the network -- can be combined into the resultant
#' output for easier analysis of performance later. @author Jeffrey D.
#' Allen \email{Jeffrey.Allen@@UTSouthwestern.edu}
processMat <- function(simul, bootstrapCount = 140, bootstrapPercentage = 0.7,
cluster, truth) {
addressing <- getTableAddressing(rownames(simul), truth)
sp <- bootstrap(simul, "buildSpace", cluster = cluster, iterations = bootstrapCount,
sample.percentage = bootstrapPercentage)
wg <- symmetricize(abs(buildWgcna(simul)))
wg <- wg[upper.tri(wg)]
gn <- bootstrap(simul, "buildGenenet", cluster = cluster, iterations = bootstrapCount,
sample.percentage = bootstrapPercentage)
joint1 <- ena(cbind(gn[, 3], wg, sp[, 3]))
joint1 <- data.frame(addressing, sp[, 3], wg, gn[, 3], joint1, row.names = NULL)
colnames(joint1) <- c("Source", "Dest", "Truth", "BootstrappedSPACE", "WGCNA",
"BootstrappedGenenet", "ENA")
# Since this graph is undirected, for consistency, make Source always
# smaller than Dest
allGenes <- union(joint1$Source, joint1$Dest)
levels(joint1$Source) <- allGenes
levels(joint1$Dest) <- allGenes
joint1[as.character(joint1[, 1]) > as.character(joint1[, 2]), 1:2] <- joint1[as.character(joint1[,
1]) > as.character(joint1[, 2]), 2:1]
return(joint1)
}
</code></pre>
<p>We'll want to grab the truth for this network:</p>
<pre><code class="r">truthList <- read.csv(paste("../truth/truth middle.csv", sep = ""))
truth <- truthToMat(rownames(sim2), truthList)
</code></pre>
<h3>Individual Network Reconstruction</h3>
<p>We'll now reconstruct each of these networks individually.</p>
<pre><code class="r">cluster <- NULL
if (require(Rmpi) && require(snow)) {
invisible(capture.output(cluster <- makeCluster(mpi.universe.size() - 1,
type = "MPI")))
warning(paste("Distributing bootstrapped networks across a cluster of",
mpi.universe.size() - 1, "CPUs."))
}
</code></pre>
<pre><code>## Warning: Distributing bootstrapped networks across a cluster of 47 CPUs.
</code></pre>
<pre><code class="r">net25 <- processMat(sim25, cluster = cluster, truth = truth)
net1 <- processMat(sim1, cluster = cluster, truth = truth)
net2 <- processMat(sim2, cluster = cluster, truth = truth)
</code></pre>
<h3>Merged Network Reconstruction</h3>
<p>We'll now want to compute the merged networks using either the naive method or the meta-analysis ENA method. We'll perform the naive first by just stacking the three simulations on top of one another.</p>
<pre><code class="r">stackedSim <- cbind(sim25, sim1, sim2)
stackedNet <- processMat(stackedSim, cluster = cluster, truth = truth)
</code></pre>
<p>And the alternative ENA method can be performed by joining the three previous results.</p>
<pre><code class="r">enaNet <- ena(cbind(net25$ENA, net1$ENA, net2$ENA))
save(enaNet, stackedNet, net25, net1, net2, file = "nets.Rda")
</code></pre>
<h3>Performance Review</h3>
<p>We can now calculate the the ROCs/AUCs of these networks.</p>
<pre><code class="r">truVec <- net25$Truth != 0
pred25 <- prediction(net25$ENA, truVec)
perf25 <- performance(pred25, "tpr", "fpr")
performance(pred25, "auc")@y.values[[1]]
</code></pre>
<pre><code>## [1] 0.9496
</code></pre>
<pre><code class="r">
pred1 <- prediction(net1$ENA, truVec)
perf1 <- performance(pred1, "tpr", "fpr")
performance(pred1, "auc")@y.values[[1]]
</code></pre>
<pre><code>## [1] 0.9444
</code></pre>
<pre><code class="r">
pred2 <- prediction(net2$ENA, truVec)
perf2 <- performance(pred2, "tpr", "fpr")
performance(pred2, "auc")@y.values[[1]]
</code></pre>
<pre><code>## [1] 0.9036
</code></pre>
<pre><code class="r">
predStac <- prediction(stackedNet$ENA, truVec)
perfStac <- performance(predStac, "tpr", "fpr")
performance(predStac, "auc")@y.values[[1]]
</code></pre>
<pre><code>## [1] 0.9631
</code></pre>
<pre><code class="r">
predENA <- prediction(enaNet, truVec)
perfENA <- performance(predENA, "tpr", "fpr")
performance(predENA, "auc")@y.values[[1]]
</code></pre>
<pre><code>## [1] 0.9667
</code></pre>
<p>And plot them</p>
<pre><code class="r">plot(perfENA, col = 1, lwd = 2)
plot(perfStac, col = 2, lwd = 2, add = TRUE)
plot(perf25, col = 3, add = TRUE)
plot(perf1, col = 4, add = TRUE)
plot(perf2, col = 5, add = TRUE)
legend(0.1, 0.4, c("ENA", "Simple Merge", expression(paste("Individual Network, ",
sigma, "=0.25", sep = "")), expression(paste("Individual Network, ", sigma,
"=1", sep = "")), expression(paste("Individual Network, ", sigma, "=2",
sep = ""))), col = 1:5, lwd = c(2, 2, 1, 1, 1))
</code></pre>
<p><img 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" alt="plot of chunk unnamed-chunk-8"/> </p>
<h2>Test Bootstrapping</h2>
<p><img 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KBrJPqOEPXXN8yYDcre0xLd758SvYXZHnLBEH3V+MiTGVIpgiCdxmKxLFmy5KuvvmpqakpISMjPz58xY4aSMWJIR3TtRA9Mrtc9vRTaj0YJbG30rp8adee+ohaz2QxYogdBQkNZWdno0aNnzZqVkJBgtVo/+eST0tLSV155JdJ+dWG6QKJPXrygFSC1fWc9x3F04mNiN7c3guSjV2NgGmIRTRLN8zyxh8gHacUInxrykQyGonbip9e27EgWohftoMiiUCPaloxG6qdI6XWvg6Khp0+Jhj319JgHy88ghnFgIRrZMAYAjUZz6623kuWkpKTbb799/fr1gHSCaE/02g1r6HLy4gX6oV/Rjyev3iASq1QqUaoio+1DoZEmeqLheZ4VS9uSsPZXI/oYsIb46bWtaAfJX2PnNSS5KNSwiZ44KT04Us+9HsAANKJ9kdGIxnYSjXxb6bGS0QQrjAMLUSWa0IUxALhcro8//njgwIFGo5Hc0Us9RPwi2hP9rubvBp5bZrM8AFy0Z/SxK9ewFpfLJRpQzhZFCaJGEASXy6VWtzt6TqeT2Fmx17Zut9tfDcdxoiIhSjQej4do2Hsop9NJ7dSoVqvJCtm2REwt5ArBVqRQoiHrVKhhs5Lb7Xa73YIgiPaR4zjR2kQakBxSJRryg0ahhs075KSL9lHaluyaEk0QwzjgEI1gGAPA3LlzFy9evGjRIqvVSvroy8vLAekE0Z7o/36tbuBhF0iyPKH7/rGiXI8gSFfHbDY//fTTkfYipugC79GzlBzeSRfIP6+yOR/5LgaLIEh0cs0110TahVgj2u/oX/ntEw0zWmd/aC45DFU9rqvqcZ0oubfP6Wav9vkjjlPLgtu8NmynFPH4ulSZb7sc3fePpcv4ewiJQnbt2hVpF2KNaE/0LB3dv/uEzemzP5R7G/ec0rum/bUh4/UJgbkTRbBJ39J3XQQ9QRBKRUXFgAEDbr75Zo7jvvvuu8OHDw8ZMkSj0UTary5Ml+m6qepxXaRdiHHMewsj7QKCAAB8+umn27dvr6ysBIC4uLjvvvsOH8Z2ki6T6AHOdt3Q5cg6E5N03z+WvcdHkIhgNBrnzp27Z8+eb7/99pJLLnnkkUcsnR4LeYETjq6bxsbGtWvXkld6i4qKTCaTvF2EqLOlqsd1x65cU7W/Xa4PuFcn4pDuoHD2/h+7co18Npf/lu3WT9g5TF5GVtU8cAt5P8rrmqPnOcGDb2rmjwAAmPNR5vwRx70+xfnH/4nfYkRCAc/z06dPLy8vX7JkSUtLS1NTU6Q96tqEI9FXV1fn5ub269fvm2++2bRp05133ilvDwA26dPE0VHCKjm8kyZWUtSMfnXugW37EgjtNQBgNpvr6uoA/Kh20z5riGdM9pZTfM+qDKAF8DE5OIB+/gh7R9+VHN7p728jhbf8rMy4Y6hMQ7rv80d0+MdM2jq25E/K7qg0dCYAVP44lRVIPlaIvn1csjbqTEcvbj34pgYgU/R4ZvaH5sofp6YCTMpeKnPNvv+1s376QklRl3aaZwpPSR2m7x1QiKaqxygF6xcT2PX4sbUpogOS+W2RSHO6/7+9tu3du/edd9551113abXaOXPmBLB1hMIJkoklg87TTz89ZcqUuLi4lpaWF198cebMmfJ2Ftsby6e4HpDmcZ1Ol/bNyI62yN5OKoe08lLrhkn05M6UJHp25mWbzdbU1JSent7U1ESnSI6StzznjziemXnWE6fT+eCbGnI8RT+DvP5CokZySaD/k6/oghI36EWF3a7oNSo2gVosFq1W+9jasxc8UdaOLKyfBbumdd+3pPLHqV6HekBEf26yfq77qm3DXm1gfZ5B3AWvYTZ/xPHGxka7/ewdCfmtX1JS8vXXX7e1tV188cXdunWjD2Pz8vKC5cyFQzju6B0Oh8FgAAC9Xs9O996RfdmyZTSxbj32n+0rFoL6GP32BsMNZOG3AD/bz9tZusNvA/CTtPqtoXtHqxWtPPeinD179pDln3/+ubCwUDh9CgDqNakBbD10fLIA3n333f79+wNAa2vr6r/kJAAAwGq4lJUlMMv0qwTGksD8T76iC0ogbdmVpzlPZQGs1px34xrn4xMnTiTLY8eO3VdjOaM+luDqDgCrAQA+JK3qNanULgNRylsA4My56GJXeEbdLgZE2+J3Dl6+fDlZrllWvfODS6vUx+CfXpQgOc7h5JsXE2iI/rL9w9XPPAYd76yMpaNdUHIWRHgNs08WwOrVq6m9rKyM1Lo5ePDg6tWrr7rqqu3bt2/fvt2vDSEs4Uj0JI8bDAaa2eXthYWF9IZ6xBr35WU/A2TTb/+R/TwAaDQaMmx62I+PBNfbnwHYzcmwYuAKutytW7efH43f8WV+Rc/HvYo3mEvIwmhLFV3uCKpRIpZZCVmYcmNDTk4OWdZoNJtGnHVy1qFniLGi5+NkWWRnvxIts9BdZlcixWtbVj/lxoarrrrqvL2i4tnNcQCwwVzCHoczEA8AANlnvO2v/HE+11ZE9rlvvRjZr+gmZg0/X8q0qNfrz5U9ONryude9m3XomYqej9OzyXro1Ul61kQykZE09LpCdm1PFZ0v1XB38aD/nFnl1UnqZ0ffysukRuqbwk1MubFBo9HQ2zubzTZ8+HAAKCoqevnll1evXt2Zfl0EwtN1s2rVqpycnL59++7du/fAgQPjxo2Tt7O8VN02hbuJfqQdhaJ+84i8KyIMO/971mazTXknXtS9IPqZ7NiSL/p179iSD+eqO7DLMhAZUdIVUiMLXdWxK9ewXTeeOY9Oyl5a+eNUJfsYTkiNaPrxoTeFym4DSw7vlOkViQjs8QSAqSuFFzMG0l4Inwe28/siCiRpXBF+7bOWreHe3NycsHOY104Y4rPoAQaxkJCW7lRHIUTCEpj49BqcdCVkW6emzWK7bu67775HHnkkJyfn/ffff/fdd9euXXvnnXeuWnX2KoVdNwEQjkTf1NS0bt06lUrldrsLCwtNJlN5eXlpaanULm0rSvTA9NFLZ4/iNnvpf2QfIpH+9277xlBLfb8PWltbA7hOWPquE/XRT3knHiQ91/K5mCL/99BJ2AQa9ETv/OsCzTz/5nqkXtFlo9GoUqmamprYBFrymrCix0Dp9a/udxvpUxAA0Ov1RqOxvr6eXbkoPJRotFqtyWSqaz/NQEca1s9nPnR+bznbfezzqIpmvBGtn+d5s9ks84iIIP8YidX4TPTzRxyXthU9avK6/rS0tObmZrbHVYlGq9WSwmqsxmaz2e12NtHX1NTMmzfvwIEDPXr0mDNnTm5u7quvvvqnP/2JfIuJPgDC0XVjMpmKi4tZS2lpqVe7lD/m26Z8rXRDv/ZZyyZx6OBVAamxo4e3UjuxnHvrph0kxTu25OuHnr3ekCSlJIMTjWjCrNDhNR81PvKk2+0+PwH6XxcAgNVqpQJp1cnExERon7yohk7WSJK4uvwvVEY0wKzHK29dPIweOseWfNJcVMo8Gphw3RnyxHj+iOOnRpw/FF6TOCibETN00F+Z0fNWq5RevXq9+eabrIVmeSQwulIJBFAQnfRl7WNXrpHeYihs+2ufteztErnr99mcpPjKH8/nJuWbpkmQLPh1KySjSUlJsdvtYG/3eqX0cnJq2iy1Wg2CwHqSqNx72T3y+lEJLr4lpD90gsiC2yxmsznSOdwPojnLI6GgCyT6dj82r/T9x9SZICbdr4ENw5uUvRSgXXeNCJJhuXnPSn/zgj8XpM7jKl3odrubm5vDuVF/admSbxi6C0Lcr3Xh8Pzxd+gy1ve+0OhKJRCiH3pNkiYmcksbwI3tBYuD19O+LzxuwQWz/IUGJvogIxrU0/zE3FPTZmGeQiLOI5l3RdoFJGJ0ga6brgJTcI15DTRy/nR1Zl9RsXj/NMCfQcHj2JVrpG/UIBcCXSnRx8a8H4hCSoefsI0sMxqN0P61SARB/KULJHrySsOvv/4K0O7VOq9v2omMIdKQKaoFQejobT/WHhQfyLJXN2Q03DmkrTqy++uDT410Wwr9TPr781yrQwBIBTg9/S/K/QzMouSYUA176qULnfRKagxYIwpRmVMv+uhTE5ifSsIYCQVdINGfK1PcbdHt5+/sVCqVdMYZjUbDvlwYUo1arXY6nVqtlnzk//cl+wD2zO80HbXlOE6tVrOvbyrR8Dwv1YgaymjYP3iNRsPzvCAIorb0f+oVEXdSw/O8yDEZjdvtJusUIbObarWa4zjRjouOjEKNaENet0U0brebLLPuya+fHH+FmmCFMRuidOtqtVpJeMtrQhfGSCiI9kT/1lfni5M8sT6N9t5wHCftZ3Q6nWxUhU4jCILoT8iTN2Byze/J8vwRx+Gc3mtbl8vFGpVopO/IK9F4PB6iYe+Y2tra3G632+1m25I/bLfbTS16vZ6IqUU6YEqJhhwlhRqvWZ5tTnaEXRu5aIkOoOiQKtEAgHINm+VJMIi88uqDQk0Qw5gNUbJ14q3P8FOiCVEYI6GgK711E8199J8dPH9LEiXVibs6jQ+dLVeHD2MRpJNE+x39H/Nt3xzR+tZFB+TFG3xJOVjYZuLDWAQJAtGe6OH8+PLovZ0HgOsvc95y8mzxNRx2iCBIVNGVum4QBEGQAMBEH3zwdh5BkKiiC3TddBVw2CGCINFJOBI9merX4/HwPF9UVMROMOLxeCorKydPnhwGNxAEQS5MwtF1U11dnZubO2HChNzc3E2bNlH7gQMHli1bJp3BA0EQBAki4Uj0tbW1ZHLqnJyc2tpaas/Ozp44cWIYHEAQBLmQCUfXjcPhMBgMAKDX69kJNzqqcfHOO+/Qbu7+/ftfeumlAJCamspqeJ5nRwYSkpOTw6PheV6k4TiOzjjouHG7TFu1Wp2QkBAfH995jdFojIuLk9FoNBqVSsVqACA1NZWMg9XpztfWJCeCHbIoLQkQmIaMg2WHkirRAEBSUpJKpVKpVPKnnuf5YGnUanUAGp1OR86UkhCNbBiTU28ymUQ7JQ0tJZrghnFjYyMgISOEif7jjz/+4osvevfuTfK7wWCgGV+eq6++mo7FT0xMtNvtOp2upaWF1ajValf7SUd1Op3dbheloRBptFqt3W5nx5e/atlAl/Wf3ny6/787apuQkNDa2koGzXdS09bWJipjINIYjUaiYXN6S0tLXFycx+NhL7pk8D37R0jOFDtVYWAavV7P8zx7+mQ0rJ92u12j0ej1evlTHyyNWq2Oi4tTqGH9dLlcTqfTZ4hyHKdQE8QwZv0UBKGlpUWtVjscDtFOidoq0YQujJFQEMJEf8stt9xyyy0AsGrVqpqamr59+x48eDArK8tnQ9LPQ2hoaCD5yN5+4lPpfLDJyckOh4NNH6HTmEwmh8PBPlVmOXblGuqttG18fHxbW5toKkGfGmmREKKRnzPWYDAQDXtzRy5RbrebbSutdUPu01mNtEaNEg25K1eoYf1sbW0VBIFcU6mR1CZj84JUA5JDqkSj1Wr1er1CDesnrRok35Zc3pRoghjGrEUQBLvdbjQaW1tb2QMobatEE7owRkJBOProCwoKdu/evXLlyt27dw8fPhwAysvLw7DdcPJAxqhIu4AgCOKdcPTRm0ym4uJi1lJaWkqXy8rKwuBDGMD36BEEiU5wZCyCIEiMg4keQRAkxsFEjyAIEuNgokcQBIlxMNEjCILEOJjoEQRBYhxM9AiCIDFOtNej5ziOVBpgB3MDgEqlElkAQKvViiqohEhDvGptbWXtOp2O53mNRsMW8JG2JRr59XvViOoCEQ07PFJGQ2pEUz+Jkt0oz/OCILDVZlQqFbQ/7KQ2EbEr16jVap7nFWrcbje1azQajUYjaivdzY40rEWJRuqnjMbpdNIKPzzPKwlR4rMSTRDDmA1RUt1IeYhGKoyRUBDtiV4QBDL+SDS6WjreGgDa2trkx4UHS0O8EhWNam1t9Xg8TqdTfuw40bBGJRrp2HElmvj4eKKhWZ74qdfr3W4321ZaAoH80bIaaekCJRpSWE2hhs3+TqeTXI3YttISCFINSA6pEo0gCB6PR5VmzyIAACAASURBVKGGrePm8XiUhCg5BUo0QQzjlJQU9iD4FaKRCmMkFGDXDYIgSIyDiR5BECTGwUSPIAgS42CiRxAEiXEw0SMIgsQ4mOgRBEFiHEz0CIIgMQ4megRBkBgHEz2CIEiMw7Gj1LoQ7Fj5aNCEev1sDYPOaGTaksoEMhpBEARBkN9EsDTyrvpsG0unPpb87EyIIp2hqyZ6BEEQRCF4dUUQBIlxMNEjCILEONFevbKhoaG1tdVsNh8/fpy1S0vlZWZmWiwW+ZJ+wdKYzea6urqMjAxqsdlsTU1N6enpTU1N8mX/0tLSmpubHQ6HXxpp2b+0tDSbzWa322U0KSkpdrvdbrdnZmYSi9PprKurM5lMbre7ubmZKqXVKxMTEwHAarVSi7TqpBKN0WhUqVRNTU1KNNRPALBYLFqt1mg01tfXU6O0eqVerxdpQHJIlWi0Wq3JZKqrq1OiYf08ffp0W1ubzxDleV6hJohhbDabqaW5udlqtSoJ0YiEcWNjIxvMMuTl5SmRISx4R48gCBLjYKJHEASJcTDRIwiCxDiY6BEEQWIcTPQIgiAxDiZ6BEGQGCccr1c2NjauXbuWjH4uKioymUzE7na7161bZ7Va7Xb70KFDs7Ozw+AMgiDIhUY47uirq6tzc3MnTJiQm5u7adMmat+/f7/RaCwpKRk3btzGjRvD4AmCIMgFSDju6Gtra0eOHAkAOTk5mzdvpva0tDQy8ESr1bLDZxobG+mQCpfLRaogieolqVQqaQUlnufZslyh03AcJ21FxgHxPM9+5bVtYBqPx8OONAlMQ2Q8zwuCIGorqnpEjoBIw3GcaP0+NTzPS/30qaF+ig41+chapBqQHNKQashBBl8hKj1WHWmCGMYiS6hDNChhjISCcCR6h8NhMBgAQK/Xs8PkSJY/evToxo0bhw4dSu0rVqygQ+yGDx+em5sLAOnp6ew6RZmCkJaWFh4Nx3EpKSmshef59PR0nueTkpLk2/I8bzKZRLEemCYxMTEhIUHeT61Wy2oAID09neSIuLg4+bYAQE5cRzLlGp1O568mOTmZJCb5U0+KbnZeA+dOor8anU5HeiOVhGgEw9ivEA1/GJ86dQqQkBGORE/yu8FgoBmfIAjCtm3bjhw5Mnr06O7du1P79OnT6XJDQ8PJkyfJuHB2nV7HfJ88edLnuPCgaKQlEDwej8Viif4SCABgsVi6RAmE+vr6LlECweFwWK1WnyFKyxv41AQxjNkSCMpDNLJhjISCcPTRZ2Vl1dTUAMDBgwezsrKo/YcffrBarcXFxWyWRxAEQYJLOO7oCwoK1q1bt3fvXrfbXVhYCADl5eWlpaU//fTTL7/8UllZSWQPPfRQGJxBEAS50AhHojeZTMXFxayltLQUAMgTWgRBECSk4IApBEGQGAcTPYIgSIyDiR5BECTGwUSPIAgS42CiRxAEiXEw0SMIgsQ40T45OACo1WoA0Gg0rJHneZEFzg3vDIOG4zi1Wu1yuYhvBI1GQ+w+26pUKtaoROO1Fo1PDakiotFoBEGgpVE0Gg2pgiLyAdpXR+lIw5ZYUaIhJWsUakiJU2okY2hFB0e611KNaItKNGq1WrnG7XbT8izkpIOvECX7q0QTxDAOOEQjGMZIKOgCiZ6My2dH5wOASqUSWYiGDaOQatiUxPrp8XhYsbStIAgBaHieD4qG7p3UDu0PMjkCndd4PB6O4xRq2OxPnBQVZpDupleN6JAq0ZDErVDD+gnKQpQ0UagJVhgHFqJKNKELYyQUdIFETwKarewB5+JDqmSNodMQi+iviGQTj8cTIh8IfmnIR5JGRX6K2pJ6lqK1QfvDTkqDBaARrVlGI/VTun6ffno9Mj419PQp0bCnnh5z+bakiRJNEMM4sBCNbBgjoQD76BEEQWIcTPQIgiAxDiZ6BEGQGAcTPYIgSIyDiR5BECTGwUSPIAgS44Tj9crGxsa1a9eSl9KKiorIBJsEj8dTWVk5efLkMLiBIAhyYRKOO/rq6urc3NwJEybk5uZu2rSJ2g8cOLBs2TLRLJ0IgiBIcAlHoq+trc3JyQGAnJyc2tpaas/Ozp44cWIYHEAQBLmQCUfXjcPhMBgMAKDX69kp4ckISam+oqKCykaNGpWXlwcAmZmZPjfEznkfak1GRgb7ked54mFaWprPtikpKUHRaLXa5ORkeY1erxdp6JFMTEz0uQmj0RgUTXx8vL+a9PR0UnJAyamPoMZgMJCTFaz1hyiMVSqV8hANfxg3Njb6VCIBE45ET/K7wWCgGV+e6dOn00HSNpvtxIkTGRkZFouF1Wi12ra2NtZiNptPnDjBjq4OnSYjI6O+vj49PZ1aPB6PxWJJS0uzWq2sWNo2JSXFZrO1trb6pdFoNC6Xi/UqJSWlpaWFvXBKNcnJyXa73eFwsH/zFoslMTHR7XbbbDZqlFZ6SUhIAIAzZ850UhMfH69SqaxWqxIN62d9fb1Wq42Pjz99+jQ1knpbTqeTWnQ6nUgDkkOqRKPVahMTE+vr65VoWD8dDofVavUZohzHKdQEMYzZ2xG3260wRCMbxkgoCEeiz8rKqqmp6du378GDB7Oysnzq9Xo9XXY4HC6XC6Ky1o2oVahr3dDaL8o10jozEN21bkR7F7W1bnz6KW0bkVo3Iks01LrxGcZIKAgk0T/33HNz5sypqqoSBGH8+PE+9QUFBevWrdu7d6/b7S4sLASA8vLy0tLSADYdEYyLnhL1TTgB4JEnI+IMgiCIvwSS6L/99luHwzFixIg//vGPShK9yWQqLi5mLWyWLysrk2+evHhBK0AqwKlpswLwViGpSyrIQiuAj25vAABIev5v8PTS0PmDIAgSLAJJ9FqtFgBcLpeoSy7UpC6pCDjXkzxOkjhZCc3svp8kIgiCdGUCSfTXX3/96tWrL7vssueeey7oDonQbljTyTXQhC5jQRAEiWECSfT33nvvvffeG3RXvNI2eqz6u71k2d/beWnferDQLHyhrq4uw7cQQRAk8viX6KWvvUuf7IcIv7J8wPfsp6bNkrZtfmIu+4oYAPh+hxlBECRq8C/Rhy2tszTMmG02m+H4cSXiAFK86BKie3qpxWJh3/fS+btGBEGQaCKQEggcQ1pa2ssvvxx0tzoidUmFTCpPXrxApu2pabN0Ty9tmDGbtTQ/MTeY/iEIgkQfgfTRz5o169FHHwWAF154YcSIEbfeemtIy0/S1yspqUsqbDPL2IzfMGN268ypMitpmDGbXNM4jqO5ngfgOE40gTJIjFINsYgmieZ5ntjl2ypZv1eNtGKETw35SAZDUTvx02tb9hcb0Yt2UGRRqBFtS0Yj9VOk9LrXQdHQ06dEw556esyD5Wdg4eFVE1iIRjaMkVAQSKL/8ssvyanasWPH7bffTt62DDPxT5exH+Xv5a2P/VV1blmlUonSGRmRz6JEI030RMPzPCuWtiVh7a9G9DFgDfHTa1vRDpJT3HkNSS4KNWyiJ05KD47Uc68HMACNaF9kNKI+TKKRbys9VjKaoISoSqUKLESVaEIXxgBgsViWLFny1VdfNTU1JSQk5Ofnz5gxQ0l5H6QjAkn048aN69Wrl9vtXrBgwfr16994442guxUszva/M6VRXC6XaNA5WzhFoUYQBJfLpVa3O3pOp5PYWbHXtm63218Nx3GiIiFKNB6Ph2jYeyin00nt1KhWq8kK2bZETC3kCkEqUijXkHUq1LBZye12u91uQRBE+8hxnGhtIg1IDqkSDflBo1DDJiZy0kX7KG1Ldk2JJighSjQBh2gEwxgAysrKRo8ePWvWrISEBKvV+sknn5SWlr7yyiuABEogiX7SpEmTJk0KuiteCeA9evpwVafTQfu3ZRAEiX40Gs2tt95KlpOSkm6//fb169dH1qWuTiBdYyNHjvTa14YgQeTy7+5J/d9tuk9u6r5/bKR9QcKKy+X6+OOPrVarx+NpbGxcv369tHMJ8YtA7ugNBoPL5QrboZ+UvbTyR7kHrSwdvW4/56OzdcAX3KZoPUQ/f8RxkYWwvMRLsb1zgjRRQyRgSg7vBICqHtdF2hEkrMydO3fx4sWLFi2yWq2kj768vDzSTnVtAkn01157bVNTk5IpBTpP2+ix8L4XOzuIib5+0zBjNnird8rm6NkfmkGSiFkBANARURL7WR6o4gEyXp/QodtzPsqM5lw/5R0yxUdiNDs5+tDZX+slh3fCldHrJxJ0zGbz008/HWkvYopAEv1jjz322GOPkeWIDKE6NW2Wrv1HIHP3tJ/VATrO1B3ZgwjZxDOFp4KytifWp1X+OJUM3O3oV8sT69P+duuvCh0TLVeMOin6aukfmjvlcadJdKn+fsnZe/n50NmSRyGl274xsA8A4NiVUe1nV+Gaa67ZtWtXpL2IKQJJ9OFP7vqhX9Fln39LYUjineSxted/DNF7avIjgHE+pfLHqZOy21VCTl1SMSl7KXxEDVpRTeUn/91NtK3niuxKXJq18SKRZeq7pFCQUeaun2q8fsvuzvwRTSA5NQp/T5Qc3jnncPT2hn3/yizIO7vcff9YzPWdB7N80PG71o3dbmenA1SS9BsbG9euXUte6S0qKjKZTPJ2GcjfvIiOetJDysKRJwB8lzV7fF1q+/Qtxuv9NUGU5UUfFfLo+8nzR7TL9dLrhzwBH1KZXfNqJBcDSpPaDQAlh3cSh4lYyU8WpKtDMkNJScnXX3/d1tZ28cUXd+vWTaPRRNqvLkwgtW7uu+++qVOn3nXXXUOHDlXSqrq6Ojc3t1+/ft98882mTZvuvPNOeXtHkEdzLFU9ris5vJPcRt3/GshUGyNKr3YluyBlIaxTqAw4UZIE529e9smpabOY3wRRxJyPMtnHHvSNLnb3pT9ZzqHkIHvRKPyhIPPQ5Yo/VzjODd/TD/2KviNU3+8DeoPfff/YX/us5TZfR+/3ldz7d/i60X4FHu+HX/us9brCksM7yZ+D1+AnV9aEc7vDfuXVYa99iWcfm817VmRP3z1Kfm2EsrIy8nrlwYMHV69efdVVV23fvn379u0d6RGfBNJ109LS0tbWNn369G3btinR19bWjhw5EgBycnI2b97s087y558XJcDCjtZMc7fXZCqK4+C+vGHeWygMa3fl6OhaIoK8QeQ1d4teLqKvG0lzPV1JR+8jyVwb6LGiB8Tr37zPfaHJQtqctmWPCatUcqBerX1K4/Et6ySSyPH922v+iHb2h7f8WiXJiQCQ9s1IYJJ1t31joH3u9p7H9ynzWwHd9o0RzOcPoGrHJ+TiKT1l7Hmp6nEdQLuP5//KmB/T589j9lL4qF0sAUAlwKTspVWfdBhg7X+aa0U/Om022/DhwwGgqKjo5ZdfXr16tc+7QESeQBI9x3H//e9/CwoKPv/8cyV6h8NBenv0ej073XtH9mXLltE3alxH46ve0ie4ugNAlVoPAGSZckZ9LMHVfTVcSpYD2J2Aufqiq/fs2UOWV/3nMPx593Z7EgDUa1JFyjTnKWJMc5664qztTekKrxAb3qQWLuVDAKg7A+kJUHcGtjtPiQQS3qRbvGFB6rvvvtu/f38AaG1tXf2XHDh3rLLsegDYDklgcIjaV4FevEoJRCNVshavy6yRntAz6mPXOF+YOHEi+Thk6+f7ai4l51e6aXpIO8KnIGBW/wXuHD14+fLl5OO3H7yt3/IK/BMAIMuur5UcyQjChuiD/+9QwjMn6d9LAiOjH8nCargUGBn54xKRAJDmPLVacyn9SKhS6xNc3VcDAHwoMrLNRZZPFsDq1avpR6fTuXfv3pycnPfffz8tLS3QvUfOE0iiT0xMfOONN3Jzc9nOehlIHjcYDDSzy9sLCwvp8O6WTx0ew/k7qA3mkjMAoy1VG8wloy1V1EgsANkB7E7ArBi4gi6nXZS5Y1jhwAHfAcCOL3sPHPDdji97V/R8fNahZ9gmFT0f32AuIcs7vuztdbVkJR1x5uz/8V6/ZddJ1rPpy3zbvQ/k5OQQo0ajOVMWDwA7viyU27cwQvd3x5eFWYXnvaqoqHh2cxw545TRlir2kLJHmBxzukwOETkXcC5m6Boqej5OlLS5jIUY2338v/PPk24f9uC+azeS5VpmixTiFWuUakR0FBsdwR4H1s+nis4Pdnm2+I6mMwPO70L7PQo18pubcmODRqOht3dPPvnkvHnzDhw40KNHDzKn9LBhw8LjZ6zCKXyFxuFw6PV6l8v17bff5ubm+jUmdtWqVTk5OX379t27d++BAwfGjRsnb2dpaGh49P3zL5Z01MMgsh+7co3y4ZSivsLMzExus3grrKb7/rHuIZ/X1dVlZJx/GGuz2Yw7hjq25JOP9Ic8tYjs4eTYlWsyM892OzidTu32G0VeRQ86Zr51i8WSvHjBpOylUTJgiu2DYnvtnvnQ+b3Fy3NC5aP8RATxkQz7zKO5udlqtZIXt+jjH2jfASiz6Y5650Dx432ZrcwfcbyxsdFuV/SGWF5enm8R0h7fif7XX3+9++67BwwYUFFRYbPZzGbz5Zdfvn79+u7dvfyg9kpTU9O6detUKpXb7S4sLDSZTOXl5aWlpVK7tK0o0UPHAUftNCnrdLq0b0ayOTozM1M8qYhOJ5o9SonGbDaLEr39021xrU86tuRHJJXL4zPRs/Nq0QdriYmJAKCZN5vaVSpV0vN/C5GTrtKFTU1N1E8AsFgsWq3WaDTW19dHVRUE9ngCwOnTp9n3ZaMKaaJPT09vampqa2ujdml4K9GkpaU1NzezPa5KNFqtlhRWYzU2m81ut2OiDym+u24mT56cm5s7d+5cAIiPj6+rq5sxY8b06dPfe+89hdswmUzFxcWspbS01KtdCSWHd84fcZz+5dN3IV7P3ClK0BDGASyevAGO8rNZPjrTfUeQzN706BxR9Ur2W0rTo3No1cnUJRXOvy4AAKvVSgXSypRGo1GlUjU1NfnQyDrp1080UUMA0Ov15ILBfkUTk8I1H7tyjVarNZlMdXV1AXgSfhbcZsFZLxGC70S/devWo0eP0j50vV6/aNGinj17htixs7z1lbgzmmT2+n4ftEZZZcpT02bBuZTR+Vx/Ku9Dh8PRUQ46duWatLQ03Sc3ya/EfsN/7Ha76EaJ+EZu6v2db72dh9NmJQbc2H9IyuY4TqPRsDeb8klc+Zq9JnGF61lwm4WU1gguJNSpD+wLQgtuU/LDNOgeIV0V34me53mjsd3QR6PRKC1+HQaidmwkgSZlkkZJJiU1eUT5miRx+rGj37zA/CKR/uYFgNP9/80mca8aKeQKRP4P61tKMc3rE+B4+2mNRaeV53mz2axEI/1hSqDxj0kc8Rffib5Xr167du0aMOD88/pPP/300ku9vHEVCv6Yb/vmSARmsPKX5Sc2sh/P18QHgHP5moyRoUkcQRAkPPiuR19WVjZ+/PgPPvjAZrPZbLb169ffc8895J2n8LDgNsvrE6L9dv6BDN9D/rAKCoIgEcF3or/11ltfffXVZ555JjU1NSUlZdGiRcuXLy8sjJa3sKONut9t9C2KKOR60zRgE154EOQCQdGAqSFDhgwZMiTUrnR1jl25hryXFmlHfEAeGJi+LAD8kYEgFwaBjIyNCNIhWl4HbYmMIdKQaRQFQWDtHEPQfSDLXt2Q0fic8VEq9tcHnxqpD/76Kb/XXjUBWDo6d1417KmXLnTSK6kxYE1gIRrZMEZCQRdI9GRWB1GXiEqlkpYt1Wg07DsnIdWo1Wqn06nVatlWxC7fluM4tVrNvlahRMPzvFQjaiijEf3Bi3wmben/1CtWELCG53mRYzIat9tNp6jUaDRqtZrjONFOidbmVSM6Mgo1on3xui2icbvdZJm6Km0rWj85/go1wQrjwEJUiSZ0YYyEAt+JXpQgeJ4fNGjQypUre/ToETKvzvPnnxeRhfTdo9h+Bo7j2JepCU6nk42q0GkEQRD9CQFAW1sbsbNir21dLpe/Gumrk0o0Ho+HaNiTSAYfNQ3Y1NzcTJuTP2x2wJReryc7RS3SgU5KNOQoKdSwExE7nU5y7yw6VqK18Twv0oDkkCrRAIByDZvlyUkXeeXVB4WaIIZxwCEawTBGQoHvh7FCe1paWgYPHvzwww+HwTkkRDQP3BJpFxAECR9+d93odLrZs2ezZV4QBEGQaMb3Hb2XNjwftocnr/z2CTJRDr4fgiAIEhh+J3qn0/nss88OHDgwFN50hGguJwRBECX88MMPHMeRgpdOp9NgMHi9SbVare+88w7V+7XyIHobOnwneq49er3+gw8+ePnll8PgHIIgSOf5/vvvbTbb3r172VcAWI4fPz5+/PgwexVO/H4Y63a7P//880suuST0viEIggSBPn36fPXVV7t27frd735HLCdOnBg8eLBWq+3fv/+BAwfuuusuABg6dCj5dsyYMTqd7vHHHweA3bt35+XlaTSa7Ozsjz76CAD27duXm5ur0+keffTRCO2Q3wTSR3/o0KH58+cr1zc2NlZVVb322mtVVVWigaMejwd/HCAIElIGDhy4c+fOL7/88rrrzk5PNHfuXLVabbVar7zyysmTJ5N+my1bzr6NNmHChFdfffWFF14AgPvuu++66647fvz4pEmTxo8f39raev/99w8aNKimpqYL3e/6kehtNtvKlSuHDh2alZX18ccfK29YXV2dm5s7YcKE3NzcTZs2UfuBAweWLVvWVaZxQBCkizJw4MAdO3Z8+eWX1157LbHs2bNny5YtBoPhzTffpPOnU2677bZrrrmG9PPs37//T3/6U3p6+qRJk5qamo4ePbpnz54HH3ywR48ef/rTn8K9J4GiqOvms88+e+CBB7p16/bEE09s3bp1165dn332mfJt1NbWksmpc3JyamtrqT07O3vixIkBOI0gCKKc66677tNPPz169GifPn2IpWfPnmPGjGlra1u3bt3ixYuJkU6zwT5i7d279/Lly+vr6ysrK00mU/fu3fv06bN8+fKjR48uX748zDsSMIrq0Tc1Nd1xxx0bNmy44YYb1Gp1//79/dqGw+EgE1Tp9Xp2wo2Oaly88847dKRc//79Se371NRUVsPzvHRyhuTk5PBoeJ4XaTiOS01NVavVJpNJNOZb1FatVickJMTHx3deYzQa4+LiZDQajUalUrEaACB+CoKg0+lY/wGAHbIoLQkQmIaMg2WHkirRAEBSUpJKpVKpVPKnnuf5YGnUanUAGp1OR86UkhCNbBgrD9Hwh3FjYyOEjIsvvjgpKSkrK4vG2IIFC8aPHx8XF3f55Ze/8sormZmZPXr0uP32259//nlR2xUrVkyYMMFsNmdlZb3zzjt6vf4f//jH//3f/y1btizMLx92Bt+Jvqmp6aKLLsrKyurVqxc7Qt0nH3/88RdffNG7d2+S3w0GA8348lx99dV0LH5iYqLdbtfpdC0tLe38VqtFD9B1Op3dbheloRBptFqt3W4XjS9vaWlRq9UOh4MVS9smJCS0trayU3QFrGlra2PHjks1RqORaNic3tLSEhcX5/F42IsuGXzP/hGSM8XOYBWYRq/X8zzPnj4ZDeun3W7XaDR6vV7+1AdLo1ar4+LiFGpYP10ul9Pp9BmiHMcp1AQxjFk/ybB2JSEa2TAOLjk5OeRAHTp0iFjIx0svvXTXrl2sUiSgDfv167d7925WmZ+f//3334fI4RDhO9EfO3Zs48aNr7/++pw5c2666SYAqK+vT0tL89nwlltuueWWWwBg1apVNTU1ffv2PXjwYFZWls+GpJ+H0NDQQPKRaOJT6fR7ycnJDodDfiLNYGlMJpPD4TCZTNQiCILdbjcaja2trWzUStvGx8e3tbXJTyUo1UiLhBCN/FSCBoOBaNibO3KJcrvdbFtprRtyn85qpDVqlGjIXblCDetna2urIAjkmkqNnGTOWKkGJIdUiUar1er1eoUa1k+32038kW9LLm9KNEEMY9aiPEQjG8ZIKPDdR6/VaseOHfvhhx/+8ssvQ4YMyc7O7tGjx0MPPaR8GwUFBbt37165cuXu3buHDx8OAOXl5YG7jCAIgviDH7VuMjMzZ82aNXPmzB07dlRVVSlvaDKZiouLWUtpaSldDueshAiCIBcgfhc14zhu0KBBgwYNCoU3CIIgwcLtdvM8zz4k8IlGo+kqVQ38ogtMPIIgCBIAgiCIngb7xOPxkFkWYoxARsYiCIIgXQj/Ev1LL70UIj8QBEGQEOFfop8yZUqI/EAQBEFCBHbdIAiCeGHDhg39+/e/8cYb8/LySJEujuOuvfbaAQMGXH755f/85z+JbNq0aZmZmWQMisvlevTRR/v06ZOXlzds2LAjR44QDWlIGDNmTPj3BR/GIgiCeOH+++//+uuvL7nkkn379hUUFBw/fhwAvvjiCwA4fPjw7373u3vvvdflcm3dunXgwIH//e9/b7755gULFpw8eXL37t1qtXrdunVTp05du3YtWRtpGCmiPdFzHEcqDbCDuQFApVKJLACg1WpFFVRCpCFetba2snadTsfzvOj1LGlbopFfv1eN6K0vomGHR8poPB4PGXVJ/CRKdqM8zwuCwFabIeUuRPVwOI5jy2Ao0ajVap7nFWrcbje1azQajUYjaivdzY40rEWJRuqnjMbpdNIKPzzPKwlR4rMSTRDDmA1RUt1IeYhGKoxDjXb+k3S5bc7fZJQ6na66uvqPf/zjVVdd9c0331C7IAg//vgjuYX/z3/+c/PNNxcUFLzzzjs333zza6+9tm3bNvJ3NHr06F69eoVsP/wj2hO9IAhkKLZodLV0vDUAtLW1yY8LD5aGeCUqGtXa2urxeJxOp/zYcaJhjUo00rHjSjTx8fFEQ7M88VOv17vdbrattAQC+aNlNdLSBUo0pLCaQg2b/Z1OJ7kasW2lJRCkGpAcUiUaQRA8Ho9CDVvHzePxKAlRcgqUaIIYxikpKexB8CtEIxXG0cPmzZuXLVt27bXXZmRklJWVKGQwHQAAIABJREFUmc1mOHcxTkxMfPHFFwHg7bffnjhx4tVXX/3oo486nc4jR4707NmTNOd5/sorr6Rro1e4IUOG0ML3YcO/RE/3AUEQJLbZu3fvkiVLOI6rrq4eO3bsiRMnoH3VVYfD8f77769YsYJ83Lp1a2Zm5qFDh0jBXUEQJk6cWFlZSa7fbMPw49/D2F9++SU0biAIgoQD2l0j328DALNmzfr8888B4Le//S37m5jy73//+/bbbydzrK5YseKdd94pKSmZM2cOGaL1wQcf/PLLL14bhp9o77pBEAQJLj5TPGHFihUPP/wwKfX62muvSQVvv/32n//8Z7I8ZsyYmTNn1tTUPPnkk3369ElMTExOTn711VepmE5uBQDbtm0TzRIRajDRIwiCeOHmm28WzTIo6n5ZvXo1XU5MTLRYLACwdOlS6aoi228D/iZ6abmfiO8AgiAIIo9//UekN+rhhx8+efLkyZMnn3jiiRC5hSAIggSLQB4U7N27lyxEdggAgiAIooRA+ujHjh172WWXcRz31FNPBd0hBEGQoEBGXfhVX140Q33MEMheTZ06derUqUF3BUEQJLiwQ/BCoe8qcAE8TX3uuefmzJlTVVUlCML48eND4ZZP2DH90aCJBh864ycJA/l7n3BqZAjn4WJLMvhLVzn1XcXPAHC5XC6Xy98ZpnDikbN8++23DodjxIgRb731VtAdUoiScAmnJhp86IyfpP5M9GhkCOfh6sz9XVc59V3FT6QzBHLcSQknl8vFVsNAEARBopNAEv3111+fkpJy2WWX3XXXXUF3CEEQBAkugfTRh5OGhobW1laz2UyKQVOkpfIyMzMtFot8Sb9gacxmc11dXUZGBrXYbLampqb09PSmpib5sn9paWnNzc0Oh8MvjbTsX1pams1ms9vtMpqUlBS73W632zMzM4nF6XTW1dWZTCa3293c3EyV0uqViYmJAGC1WqlFWnVSicZoNKpUqqamJiUa6icAWCwWrVZrNBrr6+upUVq9Uq/XizQgOaRKNFqt1mQy1dXVKdGwfp4+fbqtrc1niPI8r1ATxDAmBRcJzc3NVqtVSYhGJIwbGxvZYJYhLy9PiQw610d/xx13FBcXjxo16qeffrrssstOnjyZnp7+z3/+85NPPnn11VeXLVv2j3/8Q6vVtrS05OfnP/fcc4mJiTNnzqyurtZqtSkpKa+//vpvfvMbAOA47u233ybPMjnufL6dNm3ae++9d+TIkfA8/g3kjn7JkiXJyclJSUk4hSyCIDHJzTffvHPnTgDYvHlzdnY2KSz8xRdf3HTTTf/4xz/ee++97du379q16//9v/+XlJT05z//mU458r///W/y5Mnse4mvv/46e1sDAOx0JeHZnUBer1yzZs2BAwcAYNy4cQ8//HCwXUIQBAkViV8Mp8vWa6s7kg0ePHjy5MkAsHnz5r/97W8ffvjh+PHjv/jii5kzZ/7+97/fsGFDUlISAKhUqsWLFwNAz549O5pypKio6F//+te9995LLaLpSoK/kxIC7KPneZ7n+dzc3KA7hCAIEnF69+596NAhh8Nx9OjRMWPG/O9//7PZbM3NzT169Pjll1+ys7MBYMuWLdw5ZKYcuf/++9944w32pv7tt9++9957hw8fvm3bNr96lgImkES/cOHC9PT09PT0F198keO4+fPnB90tBEGQCMJxXH5+/uuvv56Xl6dSqXr37v3GG28MGjQIAHr06PH9998DwNChQwVBIM+fyJQjpK0gCA8++CB9iKLT6f7whz/QycTJdCXXXnutXq+vqanZunVrGHYnkEQvtGfOnDlBdwtBECQU0O4amX4bws0337xw4cKhQ4cCwPDhwxcuXHjTTTcBwLRp06ZPn97Y2AgALpfrr3/9KwDITzly3333rVy5kixLpysJ/k5KwIexCIJcWFivrfaZ5QFg8ODBx44dGzx4MAAMGzbs6NGjJNFPnTp15MiR119//YABA4YNG3bjjTdmZ2eXlpamp6f36dPnmmuueemll9gpRwBAo9Hcc889ZPntt9+my2PGjPn444/ZF5NCRDgexjY2Nq5du5aMfi4qKjKZTMTudrvXrVtntVrtdvvQoUNJtxeCIEg0cMUVV9Dul9/85jf0zUiO42bMmDFjxgyqHDt2LPiacqSkpKSkpAQ6mK4k1ITjYWx1dXVubu6ECRNyc3M3bdpE7fv37zcajSUlJePGjdu4cWMAniAIgiA+CeSOfuHChQsXLiTLL7744rx58+S76Wtra0eOHAkAOTk5mzdvpva0tDQy8ESr1bKPpBsbG+ll0OVyka4u0bAClUolHWjA8zxbRCV0Go7jpK3IOCCe59mvvLYNTOPxeNiRJoFpiIzneUEQRG1FQ+fIERBp2BEfCjXknsBfDfVTdKjJR9Yi1YDkkIZUQw4y+ApR6bHqSBPEMBZZQh2iQQljJBQEkujpedq0aVNBQYFPvcPhMBgMAKDX69neKJLljx49unHjRvLEg7BixQo6xG748OHkd0N6ejq7TlGmIKSlpYVHw3FcSkoKa+F5Pj09ned58natTFue500mkyjWA9MkJiYmJCTI+6nValkNAKSnp5McwU5P7LUtAJAT15FMuUan0/mrSU5OJolJ/tSTl9s6r4FzJ9FfjU6nI72RSkI0gmHsV4iGP4xPnToFSMgIJNHfcccda9asAYCLL774yJEjPvUkvxsMBprxCYIgbNu27ciRI6NHj+7evTu1T58+nS43NDScPHmSjAtn1+l1zPfJkyd9jgsPikZaAsHj8VgslugvgQAAFoulS5RAqK+v7xIlEBwOh9Vq9RmitLyBT00Qw5gtgaA8RCMbxkgoCCTRq1SqN998c8CAAe+9954SfVZWVk1NTd++fQ8ePJiVlUXtP/zwg9VqLS4u7kzRWgRBEK+oVCq1Wh2T9eX9JZBE39DQkJmZuWvXLjpAQJ6CgoJ169bt3bvX7XYXFhYCQHl5eWlp6U8//fTLL79UVlYS2UMPPRSAMwiCIF5xu91ut9vfomaiLsTYIJBE37NnT6fTWV5enpOTo0RvMpmKi4tZS2lpKQCQJ7QIgiAhgoxL8ksfOmciSCCJ/u677x48ePCPP/4YdG8QBEGQoBPIe/QbNmwQPbNCEARBopZAEv0LL7xw0UUXdXLmTwRBECQ8+JfoLRYLm9wvvvjiYPuDIAgSeQRBqKysvPrqq6+55pqrrrqqpKTk9OnT/q6E47h//etfdNmrZsWKFZ3xUyH+JXqz2SwIwoYNG8gjDiUv0SMIgkQV01YlTFuVIK/xOo1UANuSTi8lghTACTWBdN28//77u3fvvvzyy8kMLAiCIF0FmuLlc/2zzz770ksvsdNIrV69+ttvvx0yZMjvf//7G264Yc+ePQDAcdy8efPy8vKKi4ul38K56aXoakWaJUuWAMDs2bNDtLOUQN66aWlpaWtrmz59+rZt24LukBQyO5dGo2GNPM+LLHBueGcYNBzHqdVql8tFfCNoNBpi99lWpVKxRiUar7VofGpIFRGNRiMIAv3lqNFoSBUUkQ/QvjpKRxr2F6gSDSlZo1BDSpxSIxlDKzo40r2WakRbVKJRq9XKNW63m5ZnIScdfIUo2V8lmiCGccAhGsEwjhLYaaSGDRtGjHl5eStXrszJydm/f/8999yze/duACgqKpoxY0ZCQsL+/ful395///233XYbmRwczhWmZzXTp09fsGBBqHcnkETPcdx///vfgoKCzz//POgOSSHj8tnR+QCgUqlEFqJhwyikGjYlsX56PB5WLG0rCEIAGp7ng6Kheye1Q/uDTI5A5zUej4fjOIUaNvsTJ0WFGaS76VUjOqRKNCRxK9SI+luVhChpolATrDAOLESVaEIXxlECmUaqT58+ZBopq9VqMpn27NlzxRVXEAG90vfu3ZucOK/fiqaX8qoJA4Ek+hMnTrzxxhu5ubkKR8Z2EhLQbGUPOBcfUiVrDJ2GWER/RSSbeDyeEPkgHfrhU0M+kjQq8lPUltSzFK0N2h928p5VABrRmmU0Uj+l6/fpp9cj41NDT58SDXvq6TGXb0uaKNEEMYwDC9HIhnGoWXLnGSUyMo3UmjVrkpKS6DRSV1xxxfvvv9+rV6+jR4/SKQBp0Hr9FgDuu+++W265RV4TavxL9BaLpVu3bmR5+PDh+NYNgiAxydSpUz0ez/XXXx8fHx8XF/fwww9//PHHVVVVJSUlOp2upaWlvLxc1KSjb8n0UqSjW6rp1avX5MmTX3755ZDujn+Jnrx1s3HjxlGjRoXIIQRBkIjDdTCN1GeffcbK6K8TstDRt3R6qf79+4s04SkxEMhbN5jlEQRBuhCBJPrly5enpKSkpKS8/vrrQXcIQRAECS6BJPrVq1f/+OOPBw4cWLVqVdAdQhAEQYJLIG/dwLkXJ4LrCoIgSBDhOE6v1/v1kn6szl4b4FSCl112GQA8++yzSvSNjY1r164lL6UVFRWRCTYJHo+nsrISR9giCBJ0SNaO1dztF4Ek+gceeOCBBx5Qrq+urs7Nze3Xr98333yzadOmO++8k9gPHDiwdetWrHiMIAgSUsLxMLa2tpbMRZWTk1NbW0vt2dnZEydODMABBEEQRDmB3NGTh7GCINx7773333+/T73D4TAYDACg1+vZKeE76uivqKigslGjRuXl5QFAZmamzw2xc96HWpORkcF+5HmeeJiWluazbUpKSlA0Wq02OTlZXqPX60UaeiQTExN9bsJoNAZFEx8f768mPT2d/OhWcuojqDEYDORkBWv9IQpjlUqlPETDH8aNjY0+lUjAhONhLMnvBoOBZnx5pk+fTgca2Gy2EydOZGRkWCwWVqPVatva2liL2Ww+ceIEO7o6dJqMjIz6+vr09HRq8Xg8FoslLS3NarWyYmnblJQUm83W2trql0aj0bhcLtarlJSUlpYW9sIp1SQnJ9vtdofDwf7NWyyWxMREt9tts9moUVrpJSEhAQDOnDnTSU18fLxKpbJarUo0rJ/19fVarTY+Pp6tA07qbbHTPet0OpEGJIdUiUar1SYmJtbX1yvRsH46HA6r1eozRDmOU6gJYhiztyNut1thiEY2jJFQEI6HsVlZWTU1NX379j148GBWVpZPvV6vp8sOh4NUc47CWjeiVqGudUNrvyjXSOvMQHTXuhHtXdTWuvHpp7RtRGrdiCzRUOvGZxgjoSCQPvoHHnigoaGhoaFhwoQJSvQFBQW7d+9euXLl7t27hw8fDgDSMhEIgiBIiAgk0S9evNhkMqWkpFRWVirRm0ym4uLie+65p7i4mLxbWVpaSr8tKysLwAcEQRBEIYEk+rVr19bU1Hz//ffszCkIgiBIdBJIH32/fv1IZ2J+fn6w/UEQBEGCjH+Jnr5pQ6Y6BIDnnnsuyB4hCIIgQUVp1w15/8npdO7evZu+tiF9rI8gCIJEG74T/a+//jp48GDyyLS1tfWGG27Iz88/duxYyF1DEARBgoHvRD958uTc3Ny5c+cCQHx8fF1dXX5+/vTp00PvG4IgCBIEfPfRb9269ejRo3REq16vX7RoUc+ePUPs2HnIgwHRNMccx4ksUmPoNMQimiSa53liD5EP0tHIPjXkIxkMRe3ET69t2b446WEny6JWSjSibclopH6Cr1MfLA09fUo07KmnxzxYfgYxjAML0ciGMRIKfCd6nudFxUyMRiM7Bj3UeK01ynGctPqoSqUSparQaaSJnmh4nmfF0rYkrP3ViD4GrCF+em0r2kHy19h5DUkuCjVsoidOSg+O1HOvBzAADfgKM6oRPZ1SEqLSYyWjCVYYBxaiSjShC2MkFPhO9L169dq1a9eAAQOo5dNPP7300ktD6VU7SAkE0aWF53npxcblcokGlIdIIwiCy+VSq9sdPafTSeys2Gtbt9vtr4bjOFGRECUaj8dDNOw9lNPppHZqVKvVZIVsW2h/2MkVgpwO5RqyToUaNiu53W632y0IgmgfOY4TrU2kAckhVaIhP2gUatjERE46+ApRsmtKNEEM44BDNIJhjIQC37+YysrKxo8f/8EHH9hsNpvNtn79+nvuuQeHs8qTuqQi0i4gCIKcxfcd/a233vrqq6+Wl5ffcccdgiD0799/+fLlBQUFYXCui0KyPPm/+Ym5kXYHQZALHUUDpoYMGTJkyJBQuxIbJDyD9doQJGh4PJ7PPvvsxhtvjLQjXRt82O0HqUsq5PtkHnxTPA2xcdFTofQIiQqSFy9onTk10l7EJoIgvPTSS1jHuJMEOPFIOElevKAVIBUAAE5NmxWirZzP4E8vFVsAjO2VTgB45EnRGuZ8lFn5o5e/9tQlFR25TTchmqJJAIgH8DEn07xnpX6KEJ5aJL8OpPPoV789KXspAMBHMH/E8Ui7Ewv0799fZLnrrrtWrVoVEWdiA9+JXvTSK8/zgwYNWrlyZY8ePULmVYB02zeGLBy7co1XQevMqcnerhZsriQanyQ9/zd6SSB4zfJ0/aemzSJbUZTEFSD89bE4gDhZDTf3CZiFPylCy2LnbaCCyh+nTspeOuejTJLrybk2MsFG7leAib3UJRX4CMcrX3/9daRdiDV8J3rRK8Otra0LFix4+OGHN2zYoHAbjY2Na9euJa/0FhUVkZL0Mnav6Id+BQCOJRXQQabmhn5FP3bfP1a6BseW/LPraf8tsYeaSL2HE1cx197+cLEHp6MrIuIvk7KXnr3S/9jOLjrvoo/GRU+Jfsy1AkjvM6QamDE7YFeRCxC/u250Ot3s2bNFU2PLU11dnZub269fv2+++WbTpk133nmnvF1Ew4zZ5Fa9o0wNADDUtxt65kpAcGzJlxpZPRVIFwBA8Kb37UdQOdtpwEBuLcn/xDIfOuxP6L5/bJfI9eTi5NXV1P/dVnJ4J0Cmz24TmZV0hk/zX+i+bwkwuZ49KR1ZpEjjh6yQ2tnTWrn47GqrelxXcngnAFT1uI42P6v5cYHoRydywcIJ/legdDqdF110UUNDg0L9008/PWXKlLi4uJaWlhdffHHmzJnydpaGhobW1tZu+8bIJ2Von4J9ijsD/bsShu2kRpvNZtwxtOTwTppnfa5HmqNFsMla+VdS5o84npmZSZadTqd2u9IXGM5ct1k0qbdooFNiYiIAWK1Wrz+hfHL+yg3QPHBLU1MT9RMALBaLVqs1Go26T25iW9X9buMT69NIgmPTHFmgkENU1eM6dnNsZqSwGqrUD/1KKiMW9ngCwEX/GTHy54/Az5MiT7BW9Toz12dzc7Nmnvh3QLuLx7lrknzggSR0ZS5mXtcjXf/8EccbGxvtdruSncrLy1MiQ1j8TvROp/PZZ5/97LPPPvzwQ4VNnnrqqdLSUlJ5Y968eaQ+mox92bJldOZ4lUq1ePHin+3ei2Vm2fW1BkeWXe/1WwrR1BoctIlPnxNc3cnCGfUxuizit5kJe/bsIcv79u0bNKSQfpXmPFWvSU1znvLasF6T6tOBIJKeAO+++y55wNXc3JyQnRaU1f7W0F3+vMg3J2eNlf3W0P3xxx+fOHEi+Tho0CCLxSLdREenQyEdnVCZEy3lztGDly9fTpZ7Pzb4+7d3krZn1F68JWsmX7EL7KZFTfx1viPYEN25c2fB7feQZRKfMg19CoLbMD0BVq9erfDVGkz0ARDIw9hrr732rbfeUr4NvV7vcDgMBgP536e9sLCQnvLGxsYp/CsA2fTb0ZaqDeYS8n8tAAC89WVvAKjo+fisQ8+INl3R83EAeOvQMxU9H+9zzkg+Ug1Zm/LdoTxVdH4cfLdu3QZPjMa3Ah4d+v/bO/f4qKpz76+955pkkiE3CYMCOWpMRUkFrRdsvRS5qNiU2+tbewp8OO2LHz5Vi1ppxcgleqicqlBLbD89YpXTozlgOFCrQeW1xyp9j2gUTyoINgI6hE+uk2Qyt+zZ7x9PWKysNbNnz33P8Hz/yGfvld9a8+y9n3n2mrXXerY3GAzW1tbCrsViIWtrtKtoA2eeEPJ3QtjrwtKhox2qoSd/T9Xy+vqzN8tNmzZ965NV3EdQ8Zrjm6+7+m+0EC59Si6rHtb84OzzpJYfPlv7D4sHR/f4EzJICCE1N505abD7Rs2Tt3y2GszbU7Ucat3BaCJaDsqbhH/RI4XqbF3WRf/U/cpNK5s1TovOMybK9JRot//jb/VZLBbavUNSTiJDN/HS3NxcW1s7bdq0Q4cOHTlyZPHixdrlLN7f/87h+lfYhp/e7H+1fyGmG/Z38Za/v3Tf51u4H/vpoHGee+1rLrGQEGK1WiGZCS0vKyvz+Xw+ny/a0A2MfsT8UCqj4yR0m0T5OZ8Y7Cl17p81oHgjfjpscKNkMNhChwVgzJpaJT664AQi8EHi0XFDN729veUf3CZW757+x0AgQB8MyLJcVVXldo95kGCz2djoBprOzk62b8tpCCEulws08Ihl7WuuzfU9oKG+8dwK0tnZWVVVRWs9sLO11zOHCMNcnANw/6LjY/QScBscrIfQEm6XbRw2vpq6C4du0koigf748eMvvvji2rVrdeo9Hs/u3btNJpOiKPX19U6nc8OGDQ0NDWK5WFc70GeGzfU9D+4e/TUKIbWqqqqrq4t9Iu31es0bfsaaJ44F6+fZfwyufNFKdxvnucUgXlFR4fV62e+GzkD/f16w0C9eBu5MccEG+n/c7rWEi7g4Cw8nyRnL4b/Sxn/p7u6mE1p67l0D0xwnti+ExwDeh9Y5HI7u7m72s7gAarVanU5nV1cXex+lAZTT6A/0dDcdgT6mhg30Q0ND97w8On8HHACeS4t1KysrPR5PMBjUaL+iomJoaAhePKdfo+HGGOjTShyzbrxeb0tLy/PPP79///7rrrtOf6B3Op1Lly5lSxoaGiKWiyy77PPlHx1gHxORWM+LUgUzi8OmcyFMzCeEET+C+4ZMbF+4/c+EMKsUtrdHqBu44c9UT+KfTKI/xGfgVAON89yE8D9W6EWnPfczc0tGd70PrYPoxU663VzfEwiQr6bu6plKCCExnuFEMINii6fqWYw5lwl+Dj7xne7GoBHNQ9KHrnn077777vPPP9/c3OxwOE6dOvX++++LS9fSjfhMX38Aol9dja4QXeoianTSc+8a0r4Q+kq0y6wxMPLV1F2JTVYBuLkoGk2JQSfiqEvE+UJ312yNeZLZyCjOzHE4HCaTyePx6NCMaXbP5O8s7HiTMPd11jYa1uMK4mni1OUtYm/dmPzmByHuPCPnArry0Xs8nkWLFu3Zs+eb3/ym2WzOZJQvOP6/dE72gkFJGqDhB7j+JekpWbwevPG/9E9eTCbKJ4N4PmkAFWeFNn12D13T27/64SjTK9Ni5/zj/8lZQmN9+jJhIEheEjupmcfjOe+886qrqy+++OLMvwjmG45L2Rnx0ablimG6cZ57c33k2Y3nOOx4d0wxhNSee9dkPraWjJx1Nvh0MHjwwYYMW4IguU7sHv1XX321d+/e5557bu3atTfccAMhpLu7u6IiNXOxE0AcXjBU/67rir2VbfPpbvf0P1Z8eHsW7RFpnOfWyMdATyb01tPVXY8fMEySJD5BKIIgsYgd6K1W68KFCxcuXOh2u1944YUTJ05MmjRp2bJl27Zty4B9d13lXRRo+fmJMYXG/Al/ZrZGxVfzdnH5ZPSP0sB4elzTFUik9DVQ4vvm/4VZN+xHTGxf6Cd8qgbP/Wudv2w01Pl86thDd9dsITB8NM9AhiFIzhHHrBuXy7VmzZqHHnrovffe2749XUtROKx7dhX+7RARFkwP1T9qqOUVfzk2pqPJPf9kdysqKtjnqF9N3SXOS4sXaJ+9GSQw66N/9cOEeWds1un7yc8go4uhbj8IkovEndRMkqSZM2fOnDkzHdZEg5sc0nvrz4iwZJeap7GbKg28olpV1Yh1177meuzWUxrt98x4lYvsMW2A7YhmaGikM3At0wRtsNt7389MQms6bYipEW3QbyfMf5d6xjxriWlnYiURbRAtFy+9uJGkVWJhwhrORSUG7aZiahKzU48bI+kgB148QuM7HZq3WCwmk8li4UdrLRYLO7iRVo3ZbA6FQlbr6LKm6y8KvXxwdPuJ73QTYolWV5Iks9nMTt/Uo5FlWdRwFTU07Be+64q9DodDUZTBK0aHdCyEyLJMzvylVsGpYNtPQCPLMmeYhkZRFPrA32KxmM1mSZK4g+Jai6jhzoxODXcsET8LNIqiwDY1VazLtQ/nX6cmVW7Muij9dLPZrMe9tTXpc2MkHRg90AfvWEheIYQblw8GJUliV+4BoVCI9ar0aVRV5b5ChJDGeW5xSWHEuiMjI/FqxDF6PZpwOAwatscUDAYVRVEUha0LX2yFGbqx2+0gpiXi/Hc9GjhLOjXstK5QKAR9Z+5cca3JssxpiHBK9WgIIfo1bJQHZ+CsimiDTk0K3Zhz0WAwCNbGdD89mjS5MZIOjB7oWe6u2YqvakMQBIkXo78c/Ed/x7eeIgiCJIXRAz3Rl04AQRAEiYbRh25u6FlffHJ0nHH5iQNkKg7dIAiCxEcO9OgpOECPIAiSAEYP9Hdd5c22CQiCILlNJoZu+vv7W1pawuGwLMsLFixgXzASDoebmppWrVqVATMQBEHOTTLRo9+3b19dXd2KFSvq6upaW1tp+ZEjR5599tmuri6d7Yiv0EMQBEFikolA39HRAS+nrq2t7eg4++LompqalStXZsAABEGQc5lMDN34/f6CggJCiN1uZzMyRstx8dJLL9GVcldeeSUhpbDd9P0AIaPvbpVlWXwJVGlpKbubPo0sy5xGkqTy8nKz2ex0Ork131xds9lcXFxcVFSUvMbhcBQWFmpoIFcEqyGEgJ2qqtpsZ9+TBxeCXbIopgRITAPrYNmlpHo0hJBx48aZTCaTyVReXs6Wc4cpy3KqNGazOQGNzWaDK6VdF9CjSZ8b63fRzLtxf38/QdJGGgP966+//te//vXSSy+F+F5QUEAlaMWvAAAgAElEQVQjvjZf//rX6Vr8kpKSJxf2jxs3rq+vb3j4rMZsNrNr6AkhNpvN5/NxYShNGqvV6vP5uPXlw8PDZrPZ7/ezYrFucXFxIBCARfNJaoLBIJfGgNM4HA7QsDF9eHi4sLAwHA6zN11YfM9+CeFKsSmOE9PY7XZZloeZi6ehYe30+XwWi8Vut7N1xcNMlcZsNhcWFurUsHaOjIyEQiGbzaZdV5IknZoUujFrp6qqOl00u26MpIM0Bvq5c+fOnTuXENLc3Hz06NFp06YdO3asuro6ZkUY5wH6+vogHnFJ1cXUvqWlpX6/P+L7YFOucTqdfr+ffaqsqqrP53M4HIFAgPVasW5RUVEwGGSDrB6NmCQENOxpETUFBQWgYTt3cItSFIWtK+a6gX46qxFz1OjRQK9cp4a1MxAIqKoK91RaCLnJ2DMsaohwSvVorFar3W7XqWHtpFmDtOvC7U2PJoVuzJbod9HsujGSDjIxRj9nzpy2trYdO3a0tbXNnj2bELJhw4YMfC6CIAhCMjNG73Q6ly5dypY0NJx97ee6desyYAOCIMg5i9EXTCEIgiBJgoEeQRAkz8FAjyAIkudgoEcQBMlzjJ6mOIco37IpTEgxvOwQQRDEMGCPPjXIH/y/bJuAIAgSGQz0CIIgeY7Rh24kSYJMA+xibkKIyWTiSgghVquVy6CSJg1YFQgEaHl4xtXyq7thmxWLdWVZtlgs2u1H1HB5gUDDLo/U0ECOaGoeKNkPlWVZVVU224zJZOKOBXITQbl+jdlslmVZp0ZRFFpusVgsFgtXVzzMaBq2RI9GtFNDEwqFaIYfWZb1uCjYrEeTQjdmXRSyG4FLsCcwmvvp0aTDjZF0YPRAr6oqLMXmVleL660JIcFgUHtdeKo0YBWXNKrn3jWVlZUejyfIiMW64XA4FAoF4tSIa8f1aIqKikBDozwhJBAI2O12RVHYumIKBPjSshoxdYEeDSRW06lho38oFIK7EVtXTIEgaohwSvVoVFUNh8M6NWwet3A4rMdF4RLo0aTQjcvKytiTEAgEwG200xvo16TDjZF0gEM3CIIgeQ4GegRBkDwHAz2CIEieg4EeQRAkz8FAjyAIkudgoEcQBMlzMNAjCILkORjoEQRB8hwM9AiCIHmOxK5SyyHYNf1G0BjBhmTsBDfglqdnUaNBJk8Xm5IhXnLl0ueKnUgy5GqgRxAEQXSCd1cEQZA8BwM9giBInmP07JV9fX2BQKCqqsrtdrPlYqo8l8vV2dmpndIvVZqqqqqurq7x48fTEq/X6/F4RrNXaqb9q6ioGBoa8vv9cWnEtH8VFRVer9fn82loysrKfD6fz+dzuVxQEgqFurq6nE6noihDQ0NUKWavLCkpIYQMDAzQEjHrpB6Nw+EwmUwej0ePhtpJCOns7LRarQ6Ho7u7mxaK2SvtdjunIcIp1aOxWq1Op7Orq0uPhrWzt7c3GAzGdFFZlnVqUujGVVVVtGRoaGhgYECPi2bFjfv7+1ln1mDGjBl6ZAgL9ugRBEHyHAz0CIIgeY7Rh26AwEP3lBNC8L3bCIIg8ZNjPfryLZuybQKCIEiOkWOBHkEQBImXHAv0OHSDIAgSL5kYo+/v729paYHVzwsWLHA6nVCuKMru3bsHBgZ8Pt+sWbNqamoiVi996vEMGIkgCJKvZKJHv2/fvrq6uhUrVtTV1bW2ttLy9vZ2h8OxfPnyxYsX7927V09TOEaPIAgSL5no0Xd0dNx+++2EkNra2jfeeIOWV1RUwMITq9XKLp9hF4PIslw4tjWLxUIIMZvN7IIRAFb9sLtp0kiSZDbzp85isUB5zLomkwmOIi4NOZMOTL9GlmVOA3ZCYim2HFJ3sQmnomnYZGR6NCaTSZbleDVwOLC0ii0Xz7CoIcIp1aMxm82JaWRZhjOvXReOV48mhW7M7oLlOl00i26MpINMBHq/319QUEAIsdvt7DI5iPJffvnl3r17Z82aRct3795Nl9jdeOONpWNbKysri/ZBpaWl0f6VWo0kSePGjWNLZFkuKyuTZbmkpEQ7T5wsy8XFxSnROByOoqIibY3ZbOY0YKeqqna7XbsuIcRmsyWpkSRJkiSr1RqvpqSkBAKrxhWndbOosVqtxcXFRNMzKXo0aXJjsFyni2bejbk1yUhqyUSgh/heUFBAIz6gqur+/ftPnjx5xx13TJw4kZavXLmSbvf19XGtnT59mkRZ893V1RVzXXhKNFVVVd3d3WwKhHA4fPr0aeOnQCCEnD59OidSIPT29uZECgS/3z8wMFBVVQWeGa0upDfQo0mhG7MpEPS7aHbdGEkHmRijr66uPnr0KCHk2LFj1dXVtPzw4cMDAwNLly5lo7xI309+nnYTEQRB8pdMBPo5c+a0tbXt2LGjra1t9uzZhJANGzYQQj7//PMvvviiqalp27Zt27Zt02jB9outGbATQRAkL8nE0I3T6Vy6dClb0tDQQAiBJ7R6CDx0D90u37IJZ9MjCILoJwcWTOE8egRBkGTIgUCPIAiCJIPRA711z65o/yrfsgnXTyEIgsTE6GmKg3csNP/tkFjueGI9bMCQffmWTQFCYNow7I7KzpRkwlYEQRBDYvQePYk0vZLryGvvRixBEAQ5d8iBQE9SMb0SYz2CIOcsRh+6IWfyYww88EjJv2xMph0Y3jE/2MBlgxHzbHCJPkQNJAMZGRlh04lETCQSsS6X3EOPxmQycUvJ9WhorhtVVWlumYi5bqBEO9cNlGjnuhE1Yh4bDQ2kOKWFEXPUiEcdMf9MvJpoeWwiahRFgYw9hEl8pF03Yq6biJqYLqRfk7CLZtGNkXSQA4Ee1uUnGeUpxZs3EGY4yGQysev+6SdyabNEDRuSWDvD4TArFuuqqpqARpbllGjo0Ynl9BBoXa4kMU04HJYkSaeGjf5gJJeYQTzMiBrulOrRQODWqWHtpLW060IVnZqY7qdTk5iL6tGkz42RdJADgR4cmn3EmjwwN7/n3jXgZ+InsoWiBkq4bxFEk3A4rF1XT/sRNUBcGtiFMMrZydWFNGdcayCmJZDSKwEN17KGRrRTbD+mnRHPTEwNvXx6NOylp+dcuy5U0aNJzD0iahJz0ey6MZIOcmOMHohr8kzPvWuGfvqodpXyLZscT6wX7x/sEi0c3EcQJNfJgR59ArDxXc9PAVYQEEocT6wP4ARNBEFyltwI9IGH7iFRQi2N6S6Xi31jiahJpm+OCXYQBMldciDQw0BKxDAd72COy+Vi86PFBWtAiBCy+uHE2kEQBMkwORDoo5FYF7vvJz+nvf5k+vjjnnyMYPJkBEFygVx6GEtSncwAHtimsEEEQRADkokefX9/f0tLC0xKW7BggdPppP8Kh8NNTU2rVq3SqN73k5+XPvU4DfGwYbPZyNj3liUMd/Ngh3eSH9xHEATJOpkI9Pv27aurq5s+ffqHH37Y2tq6ZMkSKD9y5Mhbb72l56XAtl9sJW53ms08Czu8QwgZ+umjgUAAwj2Efnix5/ioDSAIghiITAT6jo4OeJlUbW3tG2+8QctramouvvhieK0gi9/vp0sq6CpEbumHJElciViYWg0sppWj15JlGcrTZ4O4fFxbA0uTuMbBTrEurEViS0ikpAhcLT0a7rP0aGhhTKUe99CjoZcvAU2qbABNCt2YK9Hpotl1YyQdZCLQ+/3+goICQojdbmdfCU+/IRxPP/00lc2fP3/GjBmEEPZ99tEYPz52JztVmsrKSnZXlmWwsKysLGZdq9WaMY3NZuNK6JksLi6OWb2oqCglmsLCwng1FRUVkHJAz6XPosZut5eWlqaw/TS5sclk0u+imXfj3t7emEokYTIR6CG+FxQU0IivzZo1ZwfN+/r6Ojs7q6qq3GOHbmw2W2DsGL04jz59mtGhG+aLFA6H3W53ZWWlx+MJBoMadSsqKoaGhtgbnh6N1WoNhUJsV6iiosLr9fp8Pg1NWVmZz+fz+Xwul4sWut1up9OpKMrQ0BAthCRWbMqRkpISQsjAwAAtgdRgIyMjcWkcDofJZPJ4PHo0rJ1dXV1Wq9XhcHR3d9NCyCnGnmG73c5piHBK9WisVqvT6eQGEqNpWDt9Pp/H44npotAV0KNJoRuz9xVFUXS6aHbdGEkHmfjFVF1dffToUULIsWPHqqurM/CJCIIgCCUTgX7OnDltbW07duxoa2ubPXs2IUQcl0cQBEHSRCaGbpxO59KlS9mShoYGur1u3boM2IAgCHLOgg+7EQRB8hwM9AiCIHkOBnoEQZA8BwM9giBInoOBHkEQJM/BQJ9iijdvwCRoCIIYihzIR2+QXDeigHtJtCzL4bX3w3b5lk2QGyeFNogZI2JqaGYYVVVpuZFz3Yh2ktTlkElhrhv20hs5143oolnPdRPTjZF0kAOBHhKewF+KJElcCWi4UJU+jRjoOQ28GGt4zXrRclmW2UKxfVHD7SasIWcyEIh1uQOEb2PyGgguOjVsoAcjxZMjWh7xBCagIbHcjGq4tFx6XFQ8VxqaVLlxRBeN6X56NOlzY45wOPyXv/zlW9/6VjQBooccCPSQFCUUCrGFsixzJaBkE4CkT6Oq6sjIiNk85uyJ7RBCCjc9yuW7h5QyrDhi+5wGksOw3209mnA4DBq2DxUKhWg5LRRz3cAZYDVijho9GmhTp4aNSoqiKIqiqip3jJIkca1xGiKcUj0a+EGjU8MGJnAGEstF4dD0aFLoxqKLgrUx3U+PJk1uzKGq6jPPPHP99ddjrz8ZciDQ5wo4NI8gKeHKK6/kSu68887m5uasGJMfYKBPO+wbS4zG2tdchJDGeZl7qQuCxOTgwYPZNiHfwF9D5y4Q5dkNJDEmfPJd6Y1rJ7YvzLYhCBIZDPQpwz7rffus97NthV7u+48Sdnf8x9+pOlSPoSpJ8AQixgQDfYqJFuuNNoK/fdK1EbcxVCXM8hMHlp84kG0r8oFvfOMb2TYh38Ax+lSy/MSB7ZOuvbtmKyGk6bN7CCF312xt+uweKCGvjRkNX/uaq3GeO1uj5DQksVEeEGP9V1N3ZcKmXAYuPSFkezuermT57//+72ybkG9kItD39/e3tLTAlN4FCxY4nU7t8rwB4vtolCeEnB0NLxu7O+ahaPmWTSohRYQ8ULMV7hYRUQkha9YnbBsX3yHui0EfgNDfOW13wh+XdWx/viFm/A2vvZ/E/8z8fx9bT4RfRRjrkwEiw/Llyw8ePBgMBs8///wJEyZYLJZs25XDZCLQ79u3r66ubvr06R9++GFra+uSJUu0y3MaJmLG/St+7WsuGtnpTYIrgd8Ho78SXiFNnz2q/WCg98o/aZuqn6pD9cSovXu4FYFtsO1/8ypy5qRBFJ7YvpAWwll1CO2II2ycJkyIeDMQb5Psr6Kvpu6a2L7w1OUtRM/I2Cdj9iKebemNCPdj/delePMG9kcnOXOMtEfS9Nk9lWecCpqt+PB2nY3HS+CGP4uF69atu/XWWwkhx44d27lz52WXXfb222+//fbbabLhXCATgb6jo+P2228nhNTW1r7xxhsxy+OCdoqfW5G0ockhDobAhv/Nq9hOPQX+K0ZbKt4+6drlJw7YZ70fLSJvn3Tt9kkxrCo7eKsYAhzvzfK/eRUh93CN0/GHiIdGwyUUQrP3vOwgxNE4z017/Wv2ngcCbpyKazP50SoxaELJ8hMHzg6XMfYTQu6uIYQZVYODgugPJXDOCRMEyZnnLmevb/u1quvsSfv3ix594JPRT4l49sCqCZ98lwviiR2jXmU7gVsLS/mWTeGxPzHpPY8tpMe7fdK19JRG+7WngXj/oyXQ4PITB1a+SAixNs4b83Jwr9cL7xxdsGDBr3/96507d+ZHLzCLSNxi7nSwfv36hoYGyLyxcePGRx99VLv86aefpq+Tt1qtGzdujNayN8CXFNlSb380SkpKvvjiC9j+6KOPrp55M2wH5UFOWTpiIoT0mRVruDgoD5aOmPrMCpRDIdSCDQq0A1Xov0SZ+IlclSIb+dOf/nTNNdcQQoaGhiZNmtQXGrSGi4vCw165UP/xip+iYVICFNlIQ0PDfffdB7uXXXaZ283fDLwBUhQehrMHhwDbEQ3TY5se40XNd+fPfumll2C7sbHxycca+8xK6YjJKxeKVz+LTC6fSF30nXfemTX3OzovekRv1HOV9VQUS4ps5K233qJrfZctW7Z69era2tpXXnnl5ZdfbmlpWbJkCV0wNWPGjJhmIByZ6NHb7Xa/319QUAB/Y5YvW7aM3n76+vquv/76J94qh90Hb+6GDavVGgwGN++vED+Oarj/Pnhzd0VFRU9PD3tvi9gCx8NzB4LBIFtl02K1v7+/vLycFk6ePPnj789/pHr0XrXTtYhtoe/MRnDs7nvvXv61mZ98/O7lX5v5yafvXg7Vd7oWLXLvpH/vcO/c6VoUZFqD7UXunTEtBx68uXvatGmwbbVaX3/99asPr/z43cupAD53Y8d6uq0BGKbzo+Nl2bIyuv3iiy9KklRYWNjf3w8l9GJt7Fgf006q/NrMT2IazB1UzGN8etnZWHnXXXd9bP6eaBLnA4vcO+EME0LgcsNfMvacg8Exjko3b3/rFbr9ta997Y5HDrK2cYdM7UzVp+vnwZu7rVar3++H3Ycffnjjxo1HjhyZNGkSvFP6lltuybxV+UQmevTNzc21tbXTpk07dOjQkSNHFi9erF3O0tfXFwgEfv5qFS2Bn/w2m+3B3eWiPpOwg0Ver9e84WeEkLtrtoKF+n9006EDgB1WpqMuVqs1FAq5/mcB+yOaDjJwQ0NcSeM8t8s1OnISCoW6urrYhwEi7MMAwgxiiANQdKADZPQnP9Rl/0uYn/BcIbvLntLOzk6r1epwOLq7u8u3bNIYwooGPYcT2xcGbvhzd/doD4BeGtE8dvwt2sex55MQ0tvb+0BLWUSleGmI5vnUqBuxlvj8hhOw5/M/dx6udy7X+EQg8zNEf7mgz+fz9ff3+3y+2Grs0SdEJgK9x+PZvXu3yWRSFKW+vt7pdG7YsKGhoUEsF+tqB3qNb2MGYL9Fvv/av+ro6NBN02f3QMID8Ykc1wLEcZvNRoeqgIqKiqGhIdrBIWcCPXuxKioqvF4v/W6wg+DsFx6MEQM9GTsMrZOIUYYFYj29ddlnve9/8yrxL2EWHLCxVb3l7NWkgV595AH6/Jkd3tU2lX0GYLfb4YbBCrjTbrVanU5nV1eXHg0b6H/5uy/apSlsLTHgZgudgZ57ilNZWenxeNhfsXpcNEk3xkCfVjIxdON0OpcuXcqWNDQ0RCzXD+3OR3ukmUXurtnaSNyECe7wRRqdwCB4f0ponOem36KeeWsIIY3EXVZWNuxbTyJ9fxKI8npqcb9OYJeN71TA3AxGe/riQ+OiX6yjIQGiPGEe5UF5z4xXowTxeA4sCcITJpLO0e3Ru8tnhETpzmvz+G2dbJ8mtXx77vnsRDB6tsUAjeQfObBgSnR9cQpHqrr27G0jYpu08J9vP03IeFoennE1ORqhwe7pfzTgtyixKB+TnnvXiCmIYcLiV1N39UwdLTGZTOOefIwKIOKPNPyzx+PhGqRzQsRRIOPM8nyz8oGJnVsIIdsnXdtIdpEzCexsNltj4OyvClmWq6qq6ONl6sNjZx/Jz60gnZ2dbApiMRC7XC5OQ9dhsF+Nx2/rJOTsd+dJ90t02zhnD8kMRg/01j27CPmniP/i5nuxsSDaVLCIgVujZY3/bj9E2HEGlqbP7oE+tTFZ+5qLaC7FihftxJyBhxtNJhMZG8Q9969lbwYOhyPaiycg1sO4P51LarQ4lcDUQzoCmZJuwOb6HrgZ0NuGy+Xq7NSsg5xLGD3QE6E3B30W7QCt0VRqbaP87vTeps/eS1PjqYXr92kQeuTxgYEBusv1xKmGMJoUsnHvuO1Trl1+4sCZQZsDy0+Q7ZOu7bpiLzt8jOhktevOR4//a7atQLKD0QN98I6F5CB6ZyqZ2L7Q/9lVMWWhRx4XC7meeElJiahJLdq/rozAv1/0aDAYZIdlDMtXU3eJD1qRcwGjB3og2kS9dMMuqWdRvv0uNz3jn8bPt8/aAsPNxnzHiAY9964RXyWY9hAeC59sFxdkui97JZoeQZBo5ECgZ7/tEUM8hGOXyxUxB4hO6AwZ9ZYD3JMunQ9Ul584ACvsYcqNYVl+4gAhaXkYm1rsYV/TlJuzbQWC5AM5EOhZtLt4py5vmfDJd2k5rJ6l/3X9zwJOw7XANsW+TTviLrwnmpb/5djZ1HprX3M9duupaHX1tC8WwnZEMzQ00hm4lu8e+zCWVhSVMW2IqRFt0G8nB1ygmHYmVhLRBtFy8dKLG0laJRYmrGHtZO3X496psiFeN0bSQY4FeiLEejZ5qcVi6bpiL901mUzsf7uu2AspEFiN2WxmB51pO+yaDq4dWjEUClmtVtHCJ77TTYglWl1JksxmM/uLQY9GlmVRw1XU0HBfeMLE+sEHGyyEyLJMzvylVpGxpzcxjSzLnGEaGkVRoE1CyKD57OETQuhV41ozm82SJHEHzp0ZnRruWCJ+FmgURYFtADTa7cP516mJ6X46NZyLggaG6bTrxtSkz42RdJAbgT7ivEkYsWH77KFQiPUqSZLEh04p0aiqyn2Frr8o9PLB0V1WHLHuyMhIvBpxSaEeTTgcBg0b5ekDj9FsDcEgOfPFZsfo7XY7dyziHHk9GjhLOjU0yhNCSkbGTLmEFuBA2NZkWVZVlTuB3CnVoyGE6NewUR6cgbMqog06NSl0Y64jEgwGwdqY7qdHkyY3RtJBbgT6nKBxnvvMlIZsmxILer+EBT7GZN0XD2ybkm0j9FH61OMBEiFPPYIYhFx6Z6xhJ9jlFkZbbRSNi759mKZJMLLN9p1/uLtm6901W432WmAEoeRMjx6jfKoo37LJT0YDKE1LYEy0X55lEJrk0bcv0RxHCGI0jB7o/+39oogh3pg5ZJAUcuTSHTA/ysjdeQTJCYwe6O+6yvvhK1rvrUbymIiZKY0Gm70SQYyJ0QP9j/7+xM5Jb3PJyLCLlww9966B0WTjr+At/+A22DDyFV9x7WCupEBAzlkyEej7+/tbWlrC4bAsywsWLGBfMBIOh5uamlatWqXdAve6JeRc4JK/fT/bJiBInpCJWTf79u2rq6tbsWJFXV1da2srLT9y5Mizzz7LJY3h+O0//DT9Bp5b0MkhOEsEQc4RMhHoOzo6amtrCSG1tbUdHR20vKamZuXKlTGrn7q8Rb3lAHbnzzWOXLoj2yYgSJ6QiaEbv99fUFBACLHb7ewr9KLluHj++efpSrnrrrvukksuIYRUVlayGsg3wlUsLy/PjEaWZU4jy3JlZaXZbB43bhwrFuuazeaSkpLi4uKUaBwOh7bGYrGwGg56VsW6sEjVZrOxhZxMj0aWZUmSuCWaejSlpaWhm94xmUxcjgquLqyh13aPtGpsNhtcKT0umkU3jstFM+/G/f39BEkbaQz0r7/++l//+tdLL70U4ntBQQGN+NrceOONdHm33W4fHBwsKysbGPt2C8jjwZaUl5cPDg6ybpQ+TVlZ2eDgYGlpKS1RVXVgYGDcuHFer5cNTGJdp9Pp8/nYNd96NGazWVEU1iqn0+n3+9k5pqKmuLg4GAwGAgH2Oz/000eLiorC4bDP56PvDDGZTKqqsqvqCwsLCSHDw8O0BMIxmyZBj6agoECWZa/Xq0fD2un1ei0WS0FBAXvpIdRy6RM4DRFOqR6N2Wx2OBw6NaydoVAoEAjEdFFJknRqUujGZWVltES/i2bXjZF0kMZAP3fu3Llz5xJCmpubjx49Om3atGPHjlVXV8esOGXKFLrd19cHTiC6glgSDAbZUJU+jZgURVXVQCAQDoe5JCFiXdBwhTE1kH6Ey2MTU1NUVBTxs+x2u6Io3E2Cy3UD/XRWI+ao0aOxWCwmkyleDTmTFoZ7YyrkJuNSrER8vTVXK6YG7nMJaMLhMNijXRfy2OjRpNCN2d14XTRbboykg0yM0c+ZM6etrW3Hjh1tbW2zZ88mhGzYsCEDn4sgCIKQzIzRO53OpUuXsiUNDQ10e926dRmwAUEQ5Jwll5KaIQiCIAmAgR5BECTPwUCPIAiS52CgRxAEyXMw0CMIguQ5GOgRBEHyHAz0CIIgeY7R89HTFChcQhWTycSVEEKsViuXiSVNGrAqEAiw5TabTZZli8XCJvAR64JGu/2IGi4vEGjY5ZEaGsgRTe0EJfuhsiyrqmo2m9nWyNjTDrmJoFy/xmw2y7KsU6MoCi23WCwWi4WrKx5mNA1bokcj2qmhCYVCFouFnjo9Lgo269Gk0I1ZF1VVNS4XzZYbI+nA6IFeVVV4+wSXvTLiinZuXXj6NJACgc11QwiJuL5crCuu+dajsVqtMdeOixqaAoFGeWLsFAhs9A+FQmLqAp0pEMRaMTUR0xtE09AoT6KnQODqRkyBEFGTQjdmc91IkhSXi2bLjZF0YPShmx/9/QnYmNi+MLuWIAiC5ChGD/QIgiBIkhg90OMbphAEQZLE6GP0//Z+0fKTZ94MPhVfvowgCBI3Ru/RIwiCIEmCgR5BECTPMXqgv+uq0VfQNc7DcRsEQZBEMPoYPSHk8ds6q6qq3BjnEQRBEsLoPXoEQRAkWVTDEwwGm5ubuUJ4lzxLc3NzMBjMjKalpcXr9Yqmtra2dnV1adfdv3+/2+1Oieb48ePamnfeeYfTAB988MGnn37KlkCaBLbk0KFDhw4dSl7z6aeffvDBB/FqALfbvX//fq6QO8xUabq6ulpbWznNyMhITI2qz0X1a9LkxoAeF82uGyPpIAd69IqitLe3c4Xsmn6gvb2dXcGfVs3hw4dDoZBo6ueffz48PKxdt6OjY3BwMCUaj8ejrTlx4gSnAU6dOtXd3c2WQP4ZtuT06dOnT59OXtPd3X3q1Kl4NcDg4GBHRwdXyB1mqjTDw8Off/45p2FTMkTTEBO2wl4AAAXxSURBVH0uql+TJjcG9Lhodt0YSQc5EOgRBEGQZMBAjyAIkueY1q1bl20bYqCq6vDw8CWXXKItGxwcrKmp4X5rp0kzNDR04YUXsikMgeHh4QsuuKCgoECj7vDwsMvlcjgcyWsmTJhQUlKiofH5fOPHjxc1gUCgtLSUTW0oEggESkpKKisrk9QEg8GioqLx48cnoBkZGZFledKkSRp1U6WB4eMpU6YkoNHjovo1aXVjnS5qKDdGkkdSmZShCIIgSP6BQzcIgiB5jtEXTPX397e0tMALkhYsWOB0Otn/KoqyefNm+N03derUuro6VlxcXNzU1LRq1SqxHVVVYVeSJK/X++Mf/zhaa4qi9PX1jRs3LhQKzZo167zzzovYDrwFQpblPLbT6XSGw2EjmIp25rGLRjQPSRKjB/p9+/bV1dVNnz79ww8/bG1tXbJkCfvf/v7+2tra+vp62G1ubqbinTt3BgKBrq6uiO0QQurq6oqKil599dWBgQGN1sxm88cff2yz2W666aYXXnjhggsuENuZPn36b3/7W1VVf/jDH+arna2trXV1dW+99ZYRTEU789hFI5qHJInRh246Ojpqa2sJIbW1teJU6N7e3i+//PKxxx7bunXryZMnWXF3d/fKlSujtQO7NTU1P/rRj7Rbq6iouOmmmzo6OqxW68jISMR2oG5fX18e29nR0VFTU2MQU9HOPHbRiOYhSWL0Hr3f74en/3a73e/3c/81m81XXXXVjBkzDh48uGfPHk7Mrs4Q2ykoKJAkqbCwULu1wsLCcDjs8/n+8Ic/zJo1649//KPYDmHeBZrHdnILnbJoKtqZxy4a0TwkSYzeo6dXnboXS3V19dVXX202m6+88sqenh4NMfcvdle7NVVV9+3bZzKZ7rjjjhkzZkRrx2az2e32c8fO7JqKduaxi0Y0D0kSowf66urqo0ePEkKOHTtWXV3N/fedd96BZeWnTp2aMGGChpj7F7ur3drhw4fdbndtbe3EiRM12ikvL4dp6eeIndk1Fe3MYxeNaB6SJEafR+/xeHbv3m0ymRRFqa+v557FDw8P7969OxAIKIoyf/58u93OidetWwcrwrh2CCF0t6OjAzQRW+vt7fV6vU6nE9ae3HXXXRHbgd/FNpstv+0khBjBVLQzj100onlIkhg90CMIgiBJYvShGwRBECRJMNAjCILkORjoEQRB8hwM9EjqodPD77zzzmTaodW5F5UgCBIX+DAWST2SNOpXdCPJdvx+P8wBRxAkAbBHj6SL733ve4SQa665pre3d9myZRdddNEFF1wwf/78EydOgECSpKampnHjxhFCduzYceGFF15wwQVTpkx56qmn2OojIyOwgqa7u3vJkiXnn3/++eefv2TJEvo2REmSfvOb33z9618/77zzNm3alJWDRRBDk95X0iLnJNSvYOMHP/jB66+/DgkU9+zZM3PmTPrfhoaGwcFBVVUvv/zyF198UVXVkydPWiyWiO0sWrRo9erVoVBoZGRk9erVixcvpv995plnVFVtb2+ndREEoeDQDZJ6uKGb4uLioaEh+l+LxRIMBuG/g4OD8AYiRVHee++9w4cPf/TRR9u2bVPHjvzAhsPh+PLLL+EXQF9f3+TJkyFdoiRJw8PD0OtPcrAIQfISHLpB0k44HIZuu6qqIyMjx48fp/+i75mrr6///e9/X1lZed9990Vrh4vgiqLQbcyOgiAaYKBH0ks4HL711lsbGhpCoVA4HG5sbLz//vtF2Ztvvrlp06b6+vqTJ08SJqbD6zKAefPmPfbYY4qiKIrS2Nh42223ZeYQECTXwUCPpJErrrjikksueeaZZ9xu95QpUyZPnvzJJ5/86le/EpXr16+/+uqrr7vuOngjByRAh+ojIyOgefbZZzs6OiZPnjx58uSTJ082NTVl9GAQJGfBAU0EQZA8B3v0CIIgeQ4GegRBkDwHAz2CIEieg4EeQRAkz8FAjyAIkudgoEcQBMlzMNAjCILkORjoEQRB8hwM9AiCIHkOBnoEQZA85/8DiA6fHcpc3oQAAAAASUVORK5CYII=" alt="plot of chunk bootstrap"/> <img src="data:image/png;base64,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" alt="plot of chunk bootstrap"/> <img src="data:image/png;base64,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" alt="plot of chunk bootstrap"/> </p>
<p>This section will compare the single execution of a networkreconstruction technique to a bootstrapped execution. The single execution will use all 100% of the samples in its construction, whereas the bootstrapped version will only use 70% each iteration.</p>
<p>We'll first define the function that will give us the resultant network from a single iteration of 100% and a series of smaller iterations at 70%.</p>
<pre><code class="r">library(WGCNA)
library(GeneNet)
library(space)
library(ENA)
</code></pre>
<p>For this analysis, we really aren't concerned about the larger network, so we'll cut the size down. </p>
<pre><code class="r">SIZE <- c("tiny", "small", "moderate", "middle")
</code></pre>
<pre><code class="r"># ' Create a data.frame storing one single iteration of the selecte method
# along with multiple bootstrapped iterations of the function. This can
# later be used to analyze the performance of the ENA bootstrapping
# approach. @param data the matrix of gene expression to reconstruct
# @param the character representation of the function to use to
# reconstruct @param iterations the number of iterations to bootstrap
# through @param cluster the MPI cluster to use when bootstrapping @param
# sample.percentage the percentage of total samples to use in each
# bootstrapped iteration. @author Jeffrey D. Allen
# \email{Jeffrey.Allen@@UTSouthwestern.edu}
single_vs_bootstrapped <- function(data, fun, iterations = 500, cluster = NULL,
sample.percentage = 0.7) {
funName <- fun
fun <- get(fun)
funWrapper <- function(rand.seed, fun, data, sample.percentage, ...) {
set.seed(rand.seed)
sampledData <- data[, sample(1:ncol(data), round(sample.percentage *
ncol(data)))]
net <- symmetricize(abs(fun(sampledData)))
return(net[upper.tri(net)])
}
toReturn <- getTableAddressing(rownames(data), truth)
if (!missing(cluster) && "MPIcluster" %in% class(cluster)) {
clusterExport(cluster, c("symmetricize"))
result <- clusterApplyLB(cluster, 1:iterations, funWrapper, fun, data,
sample.percentage)
} else {
result <- lapply(1:iterations, funWrapper, fun, data, sample.percentage)
}
result <- as.data.frame(result)
colnames(result) <- 1:iterations
single <- symmetricize(abs(fun(data)))
single <- single[upper.tri(single)]
toReturn <- cbind(single, result)
colnames(toReturn)[1] <- "Single"
return(toReturn)
}
</code></pre>
<p>We can now define a handful of functions used in analyzing the AUC of the above function.</p>
<pre><code class="r">library(ROCR)
#' Compute the AUC of the given data @param truth a vector of binary truth
#' values @param predicted a vector of numeric prediction values. @author
#' Jeffrey D. Allen \email{Jeffrey.Allen@@UTSouthwestern.edu}
getAUC <- function(truth, predicted) {
pred <- prediction(predicted, truth)
auc <- performance(pred, "auc")@y.values[[1]]
auc
}
#' Compue the AUCs of all sequential ENA datasets in the provided
#' data.frame and return the AUCs. @param mat the data.frame/matrix with
#' each column representing a single iteration's resultant network and
#' each row representing an edge in the network. @param truth a vector of