@@ -1,172 +1,3 @@

-}

-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])

-print(colorseq)

-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")

-mfuzz.plot <- function (dat, cl, mfrow = c(1, 1), colo, min.mem = 0, time.labels,

-filename=NA,xlab="Time",ylab="Expression changes")

-{

-clusterindex <- cl[[3]]

-memship <- cl[[4]]

-memship[memship < min.mem] <- -1

-colorindex <- integer(dim(dat)[[1]])

-if (missing(colo)) {

-colo <- c("#FF8F00", "#FFA700", "#FFBF00", "#FFD700",

-"#FFEF00", "#F7FF00", "#DFFF00", "#C7FF00", "#AFFF00",

-"#97FF00", "#80FF00", "#68FF00", "#50FF00", "#38FF00",

-"#20FF00", "#08FF00", "#00FF10", "#00FF28", "#00FF40",

-"#00FF58", "#00FF70", "#00FF87", "#00FF9F", "#00FFB7",

-"#00FFCF", "#00FFE7", "#00FFFF", "#00E7FF", "#00CFFF",

-"#00B7FF", "#009FFF", "#0087FF", "#0070FF", "#0058FF",

-"#0040FF", "#0028FF", "#0010FF", "#0800FF", "#2000FF",

-"#3800FF", "#5000FF", "#6800FF", "#8000FF", "#9700FF",

-"#AF00FF", "#C700FF", "#DF00FF", "#F700FF", "#FF00EF",

-"#FF00D7", "#FF00BF", "#FF00A7", "#FF008F", "#FF0078",

-"#FF0060", "#FF0048", "#FF0030", "#FF0018")

-}

-colorseq <- seq(0, 1, length = length(colo))

-print(colorseq)

-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(Mfuzz)

-Mfuzz::mfuzz.plot2

-mfuzz.plot <- function (dat, cl, mfrow = c(1, 1), colo, min.mem = 0, time.labels,

-filename=NA,xlab="Time",ylab="Expression changes")

-{

-clusterindex <- cl[[3]]

-memship <- cl[[4]]

-memship[memship < min.mem] <- -1

-colorindex <- integer(dim(dat)[[1]])

-if (missing(colo)) {

-colo <- c("#FF8F00", "#FFA700", "#FFBF00", "#FFD700",

-"#FFEF00", "#F7FF00", "#DFFF00", "#C7FF00", "#AFFF00",

-"#97FF00", "#80FF00", "#68FF00", "#50FF00", "#38FF00",

-"#20FF00", "#08FF00", "#00FF10", "#00FF28", "#00FF40",

-"#00FF58", "#00FF70", "#00FF87", "#00FF9F", "#00FFB7",

-"#00FFCF", "#00FFE7", "#00FFFF", "#00E7FF", "#00CFFF",

-"#00B7FF", "#009FFF", "#0087FF", "#0070FF", "#0058FF",

-"#0040FF", "#0028FF", "#0010FF", "#0800FF", "#2000FF",

-"#3800FF", "#5000FF", "#6800FF", "#8000FF", "#9700FF",

-"#AF00FF", "#C700FF", "#DF00FF", "#F700FF", "#FF00EF",

-"#FF00D7", "#FF00BF", "#FF00A7", "#FF008F", "#FF0078",

-"#FF0060", "#FF0048", "#FF0030", "#FF0018")

-}    else {

-if (colo == "fancy") {

-fancy.blue <- c(c(255:0), rep(0, length(c(255:0))),

-rep(0, length(c(255:150))))

-fancy.green <- c(c(0:255), c(255:0), rep(0, length(c(255:150))))

-fancy.red <- c(c(0:255), rep(255, length(c(255:0))),

 c(255:150))

 colo <- rgb(b = fancy.blue/255, g = fancy.green/255,

 r = fancy.red/255)

@@ -510,3 +341,172 @@ 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()

@@ -3,8 +3,12 @@ Type: Package

 Title: Feature-based variance-sensitive quantitative clustering

 Version: 0.99.0

 Date: 2022-03-23

-Author: Veit Schwaemmle

-Maintainer: Veit Schwaemmle <[email protected]>

+Authors@R: 

+    person(

+        "Veit", "Schwämmle",

+        email = "[email protected]",

+        role = c("aut", "cre")

+    )

 Description: Feature-based variance-sensitive clustering of omics data. Optimizes cluster assignment by taking into account individual feature variance. Includes several modules for statistical testing, clustering and enrichment analysis.

 License: GPL-2

 Imports: 

@@ -18,9 +22,10 @@ Imports:

   stats,

   graphics

 Suggests:

-  knitr,

-  yaml,

-  RDAVIDWebService

+    knitr,

+    yaml,

+    RDAVIDWebService,

+    testthat (>= 3.0.0)

 LinkingTo: Rcpp

 biocViews: 

   Clustering,

@@ -29,5 +34,7 @@ biocViews:

   DifferentialExpression,

   Visualization

 VignetteBuilder: knitr

-RoxygenNote: 7.1.2

+RoxygenNote: 7.2.0

 Depends: R (>= 4.2.0)

+Config/testthat/edition: 3

+LazyData: false

@@ -1,17 +1,21 @@

 # Generated by roxygen2: do not edit by hand

 export(ClustComp)

+export(PrepareForVSClust)

 export(SignAnalysis)

 export(SignAnalysisPaired)

 export(averageCond)

 export(calcBHI)

 export(cvalidate.xiebeni)

+export(determine_fuzz)

+export(estimClust.plot)

 export(estimClustNum)

 export(mfuzz.plot)

+export(optimalClustNum)

 export(pcaWithVar)

 export(runClustWrapper)

 export(runFuncEnrich)

-export(statWrapper)

+export(runVSClustApp)

 export(vsclust_algorithm)

 import(grDevices)

 import(graphics)

new file mode 100644

@@ -0,0 +1,3 @@

+Changes in version 0.99.0 (2022-07-20)

++ Submitted to Bioconductor

+

new file mode 100644

@@ -0,0 +1,575 @@

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