for (j in 1:max(clusterindex)) {
tmp <- dat[clusterindex == j, ]
tmpmem <- memship[clusterindex == j, j]
if (((j - 1)%%(mfrow[1] * mfrow[2])) == 0) {
if (!is.na(filename)) {
pdf(filename, height=3*mfrow[1],width=3*mfrow[2])
}
par(mfrow = mfrow, cex=0.5)
if (sum(clusterindex == j) == 0) {
ymin <- -1
ymax <- +1
}
else {
ymin <- min(tmp)
ymax <- max(tmp)
}
plot.default(x = NA, xlim = c(1, dim(dat)[[2]]),
ylim = c(ymin, ymax), xlab = xlab, ylab = ylab,
main = paste("Cluster", j), axes = FALSE)
if (missing(time.labels)) {
axis(1, 1:dim(dat)[[2]], c(1:dim(dat)[[2]]))
axis(2)
}
else {
axis(1, 1:dim(dat)[[2]], time.labels)
axis(2)
}
}
else {
if (sum(clusterindex == j) == 0) {
ymin <- -1
ymax <- +1
}
else {
ymin <- min(tmp)
ymax <- max(tmp)
}
plot.default(x = NA, xlim = c(1, dim(dat)[[2]]),
ylim = c(ymin, ymax), xlab = xlab, ylab = ylab,
main = paste("Cluster", j), axes = FALSE)
if (missing(time.labels)) {
axis(1, 1:dim(dat)[[2]], c(1:dim(dat)[[2]]))
axis(2)
}
else {
axis(1, 1:dim(dat)[[2]], time.labels)
axis(2)
}
}
if (!(sum(clusterindex == j) == 0)) {
for (jj in 1:(length(colorseq) - 1)) {
tmpcol <- (tmpmem >= colorseq[jj] & tmpmem <=
colorseq[jj + 1])
if (sum(tmpcol, na.rm=T) > 0) {
tmpind <- which(tmpcol)
for (k in 1:length(tmpind)) {
lines(tmp[tmpind[k], ], col = colo[jj])
}
}
}
}
}
if (!is.na(filename))   dev.off()
}
## Plot results
mfuzz.plot(tData,cl=Bestcl,mfrow=c(round(sqrt(NClust)),ceiling(sqrt(NClust))),min.mem=0.5,colo="fancy")
library(vsclust)
knitr::opts_chunk$set(echo = TRUE)
library(vsclust)
require(yaml)
require(shiny)
require(clusterProfiler)
#### Input parameters, only read when now parameter file was provided #####
# All principal parameters for running VSClust can be defined as in the shiny app at computproteomics.bmb.sdu.dk/Apps/VSClust
Experiment <- "ProtExample" ## name of study
NumReps <- 3###886 ## Number of replicates per
NumCond <- 4###12 ## Number of different experimental conditions (e.g. time points)
isPaired <- F ## Paired or unpaired statistical tests
cores <- 4#4 # Number of cores to use ## 1 is for windows
PreSetNumClustVSClust <- 0 # If 0, then automatically take the one from Minimum Centroid Distance
PreSetNumClustStand <- 0 # If 0, then automatically take the one from Minimum Centroid Distance
maxClust <- 10 ## max. number of clusters when estimating the number of clusters
dat <- read.csv(system.file("extdata/ProtExample.csv",package="vsclust"),row.names=1,header=T)
dat <- dat[rownames(dat)!="",]
#### running statistical analysis and estimation of individual variances
statOut <- statWrapper(dat, NumReps, NumCond, isPaired, T)
dat <- statOut$dat
Sds <- dat[,ncol(dat)]
print(paste("Features:",nrow(dat),"<br/>Missing values:",
sum(is.na(dat)),"<br/>Median standard deviations:",
round(median(Sds,na.rm=T),digits=3)))
## Write output into file
write.csv(statOut$statFileOut,paste("",Experiment,"statFileOut.csv",sep=""))
#### Estimate number of clusters with maxClust as maximum number clusters to test for
clustNumOut <- estimClustNum(dat, maxClust, cores)
#### Use estimate cluster number or use own
if (PreSetNumClustVSClust == 0)
PreSetNumClustVSClust <- clustNumOut$numclust
if (PreSetNumClustStand == 0)
PreSetNumClustStand <- clustNumOut$numclust
## Create pdf-figure of validation indices "minimum centroid distance" and "Xie-Beni index"
#pdf(paste(Experiment,"EstimatedClustNumber.pdf", sep=""),height=6,width=15)
print(clustNumOut$p)
#dev.off()
#### Run clustering (VSClust and standard fcm clustering
ClustOut <- runClustWrapper(dat, PreSetNumClustVSClust, proteins, VSClust=T, cores)
#### Run clustering (VSClust and standard fcm clustering
ClustOut <- runClustWrapper(dat, PreSetNumClustVSClust, proteins, VSClust=T, cores)
proteins
#### Run clustering (VSClust and standard fcm clustering
ClustOut <- runClustWrapper(dat, PreSetNumClustVSClust, NULL, VSClust=T, cores)
roxygen2::roxygenize(clean=T)
library(vsclust)
runClustWrapper
knitr::opts_chunk$set(echo = TRUE)
library(vsclust)
require(yaml)
require(shiny)
require(clusterProfiler)
#### Input parameters, only read when now parameter file was provided #####
# All principal parameters for running VSClust can be defined as in the shiny app at computproteomics.bmb.sdu.dk/Apps/VSClust
Experiment <- "ProtExample" ## name of study
NumReps <- 3###886 ## Number of replicates per
NumCond <- 4###12 ## Number of different experimental conditions (e.g. time points)
isPaired <- F ## Paired or unpaired statistical tests
cores <- 4#4 # Number of cores to use ## 1 is for windows
PreSetNumClustVSClust <- 0 # If 0, then automatically take the one from Minimum Centroid Distance
PreSetNumClustStand <- 0 # If 0, then automatically take the one from Minimum Centroid Distance
maxClust <- 10 ## max. number of clusters when estimating the number of clusters
dat <- read.csv(system.file("extdata/ProtExample.csv",package="vsclust"),row.names=1,header=T)
dat <- dat[rownames(dat)!="",]
#### running statistical analysis and estimation of individual variances
statOut <- statWrapper(dat, NumReps, NumCond, isPaired, T)
dat <- statOut$dat
Sds <- dat[,ncol(dat)]
print(paste("Features:",nrow(dat),"<br/>Missing values:",
sum(is.na(dat)),"<br/>Median standard deviations:",
round(median(Sds,na.rm=T),digits=3)))
## Write output into file
write.csv(statOut$statFileOut,paste("",Experiment,"statFileOut.csv",sep=""))
#### Estimate number of clusters with maxClust as maximum number clusters to test for
clustNumOut <- estimClustNum(dat, maxClust, cores)
#### Use estimate cluster number or use own
if (PreSetNumClustVSClust == 0)
PreSetNumClustVSClust <- clustNumOut$numclust
if (PreSetNumClustStand == 0)
PreSetNumClustStand <- clustNumOut$numclust
## Create pdf-figure of validation indices "minimum centroid distance" and "Xie-Beni index"
#pdf(paste(Experiment,"EstimatedClustNumber.pdf", sep=""),height=6,width=15)
print(clustNumOut$p)
#dev.off()
#### Run clustering (VSClust and standard fcm clustering
ClustOut <- runClustWrapper(dat, PreSetNumClustVSClust, NULL, VSClust=T, cores)
Bestcl <- ClustOut$Bestcl
ClustOut$p
## Write clustering results (VSClust)
write.csv(data.frame(cluster=Bestcl$cluster,ClustOut$outFileClust,isClusterMember=rowMaxs(Bestcl$membership)>0.5,maxMembership=rowMaxs(Bestcl$membership),
Bestcl$membership), paste(Experiment, "FCMVarMResults", Sys.Date(), ".csv", sep=""))
## Write coordinates of cluster centroids
write.csv(Bestcl$centers, paste(Experiment,"FCMVarMResultsCentroids", Sys.Date(), ".csv", sep=""))
## Write pdf-figure of clusters
#pdf(paste(Experiment,"FCMVarMResults", Sys.Date(), ".pdf", sep=""),height=5*round(sqrt(PreSetNumClustVSClust)),width=5*ceiling(sqrt(PreSetNumClustVSClust)))
print(ClustOut$p)
#dev.off()
#  print(ClustOut$ClustInd)
ClustOut <- runClustWrapper(dat, PreSetNumClustStand, proteins, VSClust=F, cores)
proteins
ClustOut <- runClustWrapper(dat, PreSetNumClustStand, NULL, VSClust=F, cores)
ClustOut <- runClustWrapper(dat, PreSetNumClustStand, NULL, VSClust=F, cores)
Bestcl <- ClustOut$Bestcl
ClustOut$p
## Write clustering results (standard fcm)
write.csv(data.frame(cluster=Bestcl$cluster,ClustOut$outFileClust,isClusterMember=rowMaxs(Bestcl$membership)>0.5,maxMembership=rowMaxs(Bestcl$membership),
Bestcl$membership), paste(Experiment, "FCMResults", Sys.Date(), ".csv", sep=""))
## Write coordinates of cluster centroids
write.csv(Bestcl$centers, paste(Experiment,"FCMResultsCentroids", Sys.Date(), ".csv", sep=""))
## Write pdf-figure of clusters
#pdf(paste(Experiment,"FCMResults", Sys.Date(), ".pdf", sep=""),height=5*round(sqrt(PreSetNumClustStand)),width=5*ceiling(sqrt(PreSetNumClustStand)))
print(ClustOut$p)
ClustOut$p
## Write pdf-figure of clusters
#pdf(paste(Experiment,"FCMVarMResults", Sys.Date(), ".pdf", sep=""),height=5*round(sqrt(PreSetNumClustVSClust)),width=5*ceiling(sqrt(PreSetNumClustVSClust)))
ClustOut$p
ClustOut <- runClustWrapper(dat, PreSetNumClustStand, NULL, VSClust=F, cores)
ClustOut <- runClustWrapper(dat, PreSetNumClustStand, NULL, VSClust=F, cores)
Bestcl <- ClustOut$Bestcl
ClustOut$p
## Write clustering results (standard fcm)
write.csv(data.frame(cluster=Bestcl$cluster,ClustOut$outFileClust,isClusterMember=rowMaxs(Bestcl$membership)>0.5,maxMembership=rowMaxs(Bestcl$membership),
Bestcl$membership), paste(Experiment, "FCMResults", Sys.Date(), ".csv", sep=""))
## Write coordinates of cluster centroids
write.csv(Bestcl$centers, paste(Experiment,"FCMResultsCentroids", Sys.Date(), ".csv", sep=""))
## Write coordinates of cluster centroids
write.csv(Bestcl$centers, paste(Experiment,"FCMResultsCentroids", Sys.Date(), ".csv", sep=""))
## Write pdf-figure of clusters
#pdf(paste(Experiment,"FCMResults", Sys.Date(), ".pdf", sep=""),height=5*round(sqrt(PreSetNumClustStand)),width=5*ceiling(sqrt(PreSetNumClustStand)))
print(Clus
Out$p)
#dev.off()
print(ClustOut$ClustInd)
knitr::opts_chunk$set(echo = TRUE)
library(vsclust)
require(yaml)
require(shiny)
require(clusterProfiler)
#### Input parameters, only read when now parameter file was provided #####
# All principal parameters for running VSClust can be defined as in the shiny app at computproteomics.bmb.sdu.dk/Apps/VSClust
Experiment <- "ProtExample" ## name of study
NumReps <- 3###886 ## Number of replicates per
NumCond <- 4###12 ## Number of different experimental conditions (e.g. time points)
isPaired <- F ## Paired or unpaired statistical tests
cores <- 4#4 # Number of cores to use ## 1 is for windows
PreSetNumClustVSClust <- 0 # If 0, then automatically take the one from Minimum Centroid Distance
PreSetNumClustStand <- 0 # If 0, then automatically take the one from Minimum Centroid Distance
maxClust <- 10 ## max. number of clusters when estimating the number of clusters
dat <- read.csv(system.file("extdata/ProtExample.csv",package="vsclust"),row.names=1,header=T)
dat <- dat[rownames(dat)!="",]
#### running statistical analysis and estimation of individual variances
statOut <- statWrapper(dat, NumReps, NumCond, isPaired, T)
dat <- statOut$dat
Sds <- dat[,ncol(dat)]
print(paste("Features:",nrow(dat),"<br/>Missing values:",
sum(is.na(dat)),"<br/>Median standard deviations:",
round(median(Sds,na.rm=T),digits=3)))
## Write output into file
write.csv(statOut$statFileOut,paste("",Experiment,"statFileOut.csv",sep=""))
#### Estimate number of clusters with maxClust as maximum number clusters to test for
clustNumOut <- estimClustNum(dat, maxClust, cores)
#### Use estimate cluster number or use own
if (PreSetNumClustVSClust == 0)
PreSetNumClustVSClust <- clustNumOut$numclust
if (PreSetNumClustStand == 0)
PreSetNumClustStand <- clustNumOut$numclust
## Create pdf-figure of validation indices "minimum centroid distance" and "Xie-Beni index"
#pdf(paste(Experiment,"EstimatedClustNumber.pdf", sep=""),height=6,width=15)
print(clustNumOut$p)
#dev.off()
#### Run clustering (VSClust and standard fcm clustering
ClustOut <- runClustWrapper(dat, PreSetNumClustVSClust, NULL, VSClust=T, cores)
Bestcl <- ClustOut$Bestcl
ClustOut$p
## Write clustering results (VSClust)
write.csv(data.frame(cluster=Bestcl$cluster,ClustOut$outFileClust,isClusterMember=rowMaxs(Bestcl$membership)>0.5,maxMembership=rowMaxs(Bestcl$membership),
Bestcl$membership), paste(Experiment, "FCMVarMResults", Sys.Date(), ".csv", sep=""))
## Write coordinates of cluster centroids
write.csv(Bestcl$centers, paste(Experiment,"FCMVarMResultsCentroids", Sys.Date(), ".csv", sep=""))
ClustOut$p
ClustOut <- runClustWrapper(dat, PreSetNumClustStand, NULL, VSClust=F, cores)
Bestcl <- ClustOut$Bestcl
ClustOut$p
## Write clustering results (standard fcm)
write.csv(data.frame(cluster=Bestcl$cluster,ClustOut$outFileClust,isClusterMember=rowMaxs(Bestcl$membership)>0.5,maxMembership=rowMaxs(Bestcl$membership),
Bestcl$membership), paste(Experiment, "FCMResults", Sys.Date(), ".csv", sep=""))
## Write coordinates of cluster centroids
write.csv(Bestcl$centers, paste(Experiment,"FCMResultsCentroids", Sys.Date(), ".csv", sep=""))
ClustOut$p
ClustOut <- runClustWrapper(dat, PreSetNumClustStand, NULL, VSClust=F, cores)
ClustOut <- runClustWrapper(dat, PreSetNumClustStand, NULL, VSClust=F, cores)
Bestcl <- ClustOut$Bestcl
## Write clustering results (standard fcm)
write.csv(data.frame(cluster=Bestcl$cluster,ClustOut$outFileClust,isClusterMember=rowMaxs(Bestcl$membership)>0.5,maxMembership=rowMaxs(Bestcl$membership),
Bestcl$membership), paste(Experiment, "FCMResults", Sys.Date(), ".csv", sep=""))
## Write coordinates of cluster centroids
write.csv(Bestcl$centers, paste(Experiment,"FCMResultsCentroids", Sys.Date(), ".csv", sep=""))
#### Run clustering (VSClust and standard fcm clustering
ClustOut <- runClustWrapper(dat, PreSetNumClustVSClust, NULL, VSClust=T, cores)
Bestcl <- ClustOut$Bestcl
#ClustOut$p
## Write clustering results (VSClust)
write.csv(data.frame(cluster=Bestcl$cluster,ClustOut$outFileClust,isClusterMember=rowMaxs(Bestcl$membership)>0.5,maxMembership=rowMaxs(Bestcl$membership),
Bestcl$membership), paste(Experiment, "FCMVarMResults", Sys.Date(), ".csv", sep=""))
## Write coordinates of cluster centroids
write.csv(Bestcl$centers, paste(Experiment,"FCMVarMResultsCentroids", Sys.Date(), ".csv", sep=""))
shiny::runApp('inst/shiny')
data(Pima.tr2)
# the data.frame is in the Pima.tr2 object
dim(Pima.tr2)
# add your code here:
plot(Pima.tr2)
library(MASS)
data(DNase)
a <- data(DNase)
a
a
force(DNase)
AirPassengers
gene.values <- c(-0.156779906618839, -1.19258492462641, -0.584097898869027,
-1.05973269590046, 0.241034604602436, 0.622092984491641, 0.429889283129897,
1.21134263252275)
plot(gene.values)
mean1 <- mean(gene.values[1:4])
mean2 <- mean(gene.values[5:8])
mean1
mean2
abline(h=mean1)
abline(h=mean2)
sd1 <- sd(gene.values[1:4])
sd1 <- sd(gene.values[5:8])
sd1 <- sd(gene.values[1:4])
sd2 <- sd(gene.values[5:8])
sd1
sd2
meansds <- mean(c(sd1, sd2))
t.val <- (mean1 - means2) * sqrt(16/8) / meansds
t.val <- (mean1 - mean2) * sqrt(16/8) / meansds
t.val
x <- seq(-10,10,0.01)
plot(x, dt(x, df=6))
plot(x, dt(x, df=6), type="L")
plot(x, dt(x, df=6), type="l")
plot(x, dt(x, df=6), type="l")
abline(v=t.val)
plot(x ,pt(x, df=6), type="l")
pt(t.val, df=6)
plot(gene.values)
plot(x, dt(x, df=6), type="l")
abline(v=t.val)
abline(v=-t.val)
2 * pt(t.val, df=6)
t.test(gene.values[1:4], gene.values[5:8], var.equal = T)
data(ToothGrowth)
?ToothGrowth
ToothGrowth
sapply(ToothGrowth, class)
ToothGrowth$dose <- as.factor(ToothGrowth$dose)
interaction.plot(ToothGrowth$supp,ToothGrowth$dose,ToothGrowth$len)
interaction.plot(ToothGrowth$dose,ToothGrowth$supp,ToothGrowth$len)
plot(len ~ supp + dose, data=ToothGrowth)
lm.out <- lm(len ~ supp + dose, data=ToothGrowth)
summary(lm.out)
lm.out <- lm(len ~ supp * dose, data=ToothGrowth)
lm.out <- lm(len ~ supp + dose, data=ToothGrowth)
summary(lm.out)
lm.out_i <- lm(len ~ supp * dose, data=ToothGrowth)
summary(lm.out_i)
anova(lm.out, lm.out_i)
library(vsclust)
set.seed(0)
data("artificial_clusters")
dat <- averageCond(artificial_clusters, 5, 10)
dat <- scale(dat)
dat <- cbind(dat, 1)
ClustInd <- estimClustNum(dat, 10)
optimalClustNum(ClustInd, index="MinCentroidDist", method="VSClust")
expect_equal(optimalClustNum(ClustInd, index="MinCentroidDist", method="VSClust"), 6)
estimClust.plot(ClustInd)
library(vsclust)
```{r echo=F}
library(vsclust)
require(yaml)
require(shiny)
require(clusterProfiler)
require(matrixStats)
```
## Initialization
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
knitr::opts_chunk$set(echo = TRUE)
data(protein_expressions)
dat <- protein_expressions
#### running statistical analysis and estimation of individual variances
statOut <- PreparedForVSClust(dat, NumReps, NumCond, isPaired, TRUE)
#### Estimate number of clusters with maxClust as maximum number clusters to run
#### the estimation with
ClustInd <- estimClustNum(dat, maxClust, cores)
#### Use estimate cluster number or use own
if (PreSetNumClustVSClust == 0)
PreSetNumClustVSClust <- optimalClustNum(ClustInd)
if (PreSetNumClustStand == 0)
PreSetNumClustStand <- optimalClustNum(ClustInd, method="FCM")
#### Visualize
estimClust.plot(ClustInd)
ClustInd
par(mfrow=c(1,3))
maxClust <- ncol(ClustInd)-2
plot(3:maxClust,ClustInd[1:nrow(ClustInd),"MinCentroidDist_VSClust"],col=2 , type="b",
main="Min. centroid distance\n(Highest jump is best)",
xlab="Number of clusters", ylab="Index",
ylim=c(min(ClustInd[,grep("MinCentroidDist", colnames(ClustInd))],na.rm=T),
max(ClustInd[,grep("MinCentroidDist", colnames(ClustInd))],na.rm=T)))
ClustInd[1:nrow(ClustInd),"MinCentroidDist_VSClust"]
3:maxClust
library(vsclust)
par(mfrow=c(1,3))
maxClust <- nrow(ClustInd)-2
plot(3:maxClust,ClustInd[1:nrow(ClustInd),"MinCentroidDist_VSClust"],col=2 , type="b",
main="Min. centroid distance\n(Highest jump is best)",
xlab="Number of clusters", ylab="Index",
ylim=c(min(ClustInd[,grep("MinCentroidDist", colnames(ClustInd))],na.rm=T),
max(ClustInd[,grep("MinCentroidDist", colnames(ClustInd))],na.rm=T)))
plot(3:maxClust,ClustInd[1:nrow(ClustInd),"MinCentroidDist_VSClust"])
3:maxClust
maxClust <- nrow(ClustInd)+2
plot(3:maxClust,ClustInd[1:nrow(ClustInd),"MinCentroidDist_VSClust"],col=2 , type="b",
main="Min. centroid distance\n(Highest jump is best)",
xlab="Number of clusters", ylab="Index",
ylim=c(min(ClustInd[,grep("MinCentroidDist", colnames(ClustInd))],na.rm=T),
max(ClustInd[,grep("MinCentroidDist", colnames(ClustInd))],na.rm=T)))
library(vsclust)
#### Estimate number of clusters with maxClust as maximum number clusters to run
#### the estimation with
ClustInd <- estimClustNum(dat, maxClust, cores)
library(vsclust)
require(yaml)
require(shiny)
require(clusterProfiler)
require(matrixStats)
## at computproteomics.bmb.sdu.dk/Apps/VSClust
# name of study
Experiment <- "ProtExample"
# Number of replicates/sample per different experimental condition (sample type)
NumReps <- 3
# Number of different experimental conditions (e.g. time points or sample types)
NumCond <- 4
# Paired or unpaired statistical tests when carrying out LIMMA for statistical testing
isPaired <- FALSE
# Number of threads to accelerate the calculation (use 1 in doubt)
cores <- 2
# If 0 (default), then automatically estimate the cluster number for the vsclust run
# from the Minimum Centroid Distance
PreSetNumClustVSClust <- 0
# If 0 (default), then automatically estimate the cluster number for the original
# fuzzy c-means from the Minimum Centroid Distance
PreSetNumClustStand <- 0
# max. number of clusters when estimating the number of clusters. Higher numbers
# can drastically extend the computation time.
maxClust <- 10
data(protein_expressions)
dat <- protein_expressions
#### running statistical analysis and estimation of individual variances
statOut <- PrepareForVSClust(dat, NumReps, NumCond, isPaired, TRUE)
dat <- statOut$dat
Sds <- dat[,ncol(dat)]
print(paste("Features:",nrow(dat),"<br/>Missing values:",
sum(is.na(dat)),"<br/>Median standard deviations:",
round(median(Sds,na.rm=T),digits=3)))
## Write output into file
write.csv(statOut$statFileOut,paste("",Experiment,"statFileOut.csv",sep=""))
#### Estimate number of clusters with maxClust as maximum number clusters to run
#### the estimation with
ClustInd <- estimClustNum(dat, maxClust, cores)
#### Use estimate cluster number or use own
if (PreSetNumClustVSClust == 0)
PreSetNumClustVSClust <- optimalClustNum(ClustInd)
if (PreSetNumClustStand == 0)
PreSetNumClustStand <- optimalClustNum(ClustInd, method="FCM")
#### Visualize
#### Use estimate cluster number or use own
if (PreSetNumClustVSClust == 0)
PreSetNumClustVSClust <- optimalClustNum(ClustInd)
if (PreSetNumClustStand == 0)
PreSetNumClustStand <- optimalClustNum(ClustInd, method="FCM")
#### Visualize
estimClust.plot(ClustInd)
if (PreSetNumClustVSClust == 0)
PreSetNumClustVSClust <- optimalClustNum(ClustInd)
if (PreSetNumClustStand == 0)
PreSetNumClustStand <- optimalClustNum(ClustInd, method="FCM")
estimClust.plot(ClustInd)
ClustInd
colnames(ClustInd)
library(vsclust)
library(vsclust)
test_that("estimate_clust_num", {
set.seed(0)
data("artificial_clusters")
dat <- averageCond(artificial_clusters, 5, 10)
dat <- scale(dat)
dat <- cbind(dat, 1)
ClustInd <- estimClustNum(dat, 10)
expect_equal(optimalClustNum(ClustInd, index="MinCentroidDist", method="VSClust"), 6)
})
set.seed(0)
data("artificial_clusters")
dat <- averageCond(artificial_clusters, 5, 10)
dat <- scale(dat)
dat <- cbind(dat, 1)
ClustInd <- estimClustNum(dat, 10)
expect_equal(optimalClustNum(ClustInd, index="MinCentroidDist", method="VSClust"), 6)
optimalClustNum(ClustInd, index="MinCentroidDist", method="VSClust")
library(testthat)
test_that("estimate_clust_num", {
set.seed(0)
data("artificial_clusters")
dat <- averageCond(artificial_clusters, 5, 10)
dat <- scale(dat)
dat <- cbind(dat, 1)
ClustInd <- estimClustNum(dat, 10)
expect_equal(optimalClustNum(ClustInd, index="MinCentroidDist", method="VSClust"), 6)
})
optimalClustNum(ClustInd, index="MinCentroidDist", method="VSClust")
unlist(optimalClustNum(ClustInd, index="MinCentroidDist", method="VSClust"))
optimalClustNum(ClustInd, index="MinCentroidDist", method="VSClust")
?expect_equal
expect_equal(as.numeric(optimalClustNum(ClustInd, index="MinCentroidDist", method="VSClust")), 6)
roxygen2::roxygenise()
roxygen2::roxygenise()
library(vsclust)
roxygen2::roxygenise()
library(vsclust)
roxygen2::roxygenise()
library(vsclust)
roxygen2::roxygenise()
BiocCheck::BiocCheck()
?seq_len
seq_len(1,10)
seq_len(10)
library(shiny)
??inputPanel
install.packages("DT")
q()