@@ -1,3 +1,5 @@

 ^.*\.Rproj$

 ^\.Rproj\.user$

 ^data-raw$

+^doc$

+^Meta$

@@ -1,11 +1,3 @@

-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)

@@ -510,3 +502,11 @@ shiny::runApp('inst/shiny')

 library(vsclust)

 library(vsclust)

 shiny::runApp('inst/shiny')

+devtools::load_all()

+data("protein_expressions")

+BiocStyle::markdown()

+BiocStyle::markdown()

+if (!require("BiocManager", quietly = TRUE))

+install.packages("BiocManager")

+BiocManager::install("vsclust")

+library(vsclust)

@@ -4,3 +4,5 @@

 .Ruserdata

 *.o

 *.so

+/doc/

+/Meta/

@@ -1,11 +1,12 @@

 Package: vsclust

+Encoding: UTF-8

 Type: Package

 Title: Feature-based variance-sensitive quantitative clustering

-Version: 1.9.2

+Version: 1.9.3

 Date: 2022-03-23

 Authors@R: 

     person(

-        "Veit", "Schwaemmle",

+        "Veit", "Schwammle",

         email = "[email protected]",

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

     )

@@ -41,4 +42,4 @@ VignetteBuilder: knitr

 Depends: R (>= 4.2.0)

 Config/testthat/edition: 3

 LazyData: false

-RoxygenNote: 7.2.1

+RoxygenNote: 7.3.2

@@ -1,5 +1,6 @@

 # Generated by roxygen2: do not edit by hand

+S3method(print,fclust)

 export(ClustComp)

 export(PrepareForVSClust)

 export(PrepareSEForVSClust)

@@ -36,5 +37,4 @@ importFrom(shiny,getDefaultReactiveDomain)

 importFrom(shiny,incProgress)

 importFrom(shiny,need)

 importFrom(shiny,validate)

-useDynLib("")

-useDynLib(vsclust)

+useDynLib(vsclust, .registration = TRUE)

@@ -102,7 +102,7 @@ determine_fuzz <- function(dims, NClust, Sds = 1) {

 #' clres <- vsclust_algorithm(data, centers=5, m=1.5)

 #' head(clres$membership)

 #' @export

-#' @useDynLib

+#' @useDynLib vsclust, .registration = TRUE

 #' @references

 #' Schwaemmle V, Jensen ON. VSClust: feature-based variance-sensitive clustering 

 #' of omics data. Bioinformatics. 2018 Sep 1;34(17):2965-2972. doi: 

@@ -16,6 +16,7 @@ erf.inv <- function(x)

 # Function to return content of fclust object

+#' @export

 print.fclust <-

   function(x, ...)

   {

@@ -1,11 +1,5 @@

-#' @section VSClust functions:

 #' Functions for running VSClust analysis

 #'

-#' @docType package

-#' @name vsclust

-#' @useDynLib vsclust

-NULL

-#> NULL

 #' Wrapper for statistical analysis

 #'

 #' Prepare data for running vsclust clustering.

@@ -569,7 +563,7 @@ runFuncEnrich <-

 #'

 #' @return The shiny app should open in a browser or in RStudio.

 #' @examples

-#' \donttest{

+#' \dontrun{

 #' runVSClustApp()}

 #' @export

 #' @references

@@ -2,7 +2,7 @@

 % Please edit documentation in R/WrapperFuncs.R

 \name{PrepareForVSClust}

 \alias{PrepareForVSClust}

-\title{Wrapper for statistical analysis}

+\title{Functions for running VSClust analysis}

 \usage{

 PrepareForVSClust(dat, NumReps, NumCond, isPaired = FALSE, isStat)

 }

@@ -30,6 +30,9 @@ the statistical tests (each conditions versus the first), `StatFileOut` all

 of before for saving in file

 }

 \description{

+Wrapper for statistical analysis

+}

+\details{

 Prepare data for running vsclust clustering.

 This includes visualization running the functions for the principal component

 analysis and its visualization, statistical testing with LIMMA, as well as

@@ -4,7 +4,7 @@

 \alias{estimClustNum}

 \title{Wrapper for estimation of cluster number}

 \usage{

-estimClustNum(dat, maxClust = 25, cores = 1)

+estimClustNum(dat, maxClust = 25, scaling = "standardize", cores = 1)

 }

 \arguments{

 \item{dat}{matrix of features averaged over replicates. The last column

@@ -12,6 +12,9 @@ contains their standard deviation}

 \item{maxClust}{Maximal number of cluster. The minimum is 3}

+\item{scaling}{Either `standardize` (default), `center` or `none`. Standardized 

+features get mean 0 and standard deviation 1. Centered samples get mean 0.}

+

 \item{cores}{The number of threads to be used for parallelisation}

 }

 \value{

@@ -9,6 +9,7 @@ runClustWrapper(

   NClust,

   proteins = NULL,

   VSClust = TRUE,

+  scaling = "standardize",

   cores,

   verbose = FALSE

 )

@@ -24,6 +25,9 @@ to be added to the results}

 \item{VSClust}{boolean. TRUE for running the variance-sensitive clustering.

 Otherwise, the function will call standard fuzzy c-means clustering}

+\item{scaling}{Either `standardize` (default), `center` or `none`. Standardized 

+features get mean 0 and standard deviation 1. Centered samples get mean 0.}

+

 \item{cores}{Number of threads for the parallelization}

 \item{verbose}{Show more information during execution}

@@ -14,7 +14,7 @@ You will get the full functionality of the VSClust workflow with multiple

 visualizations and downloads

 }

 \examples{

-\donttest{

+\dontrun{

 runVSClustApp()}

 }

 \references{

@@ -39,9 +39,10 @@ from Bioconductor, such as `QFeatures`, `SummarizedExperiment` and

 Use the common Bioconductor commands for installation:

 ```{r eval=FALSE}

-if (!require("BiocManager", quietly = TRUE))

-    install.packages("BiocManager")

-BiocManager::install("vsclust")

+# uncomment in case you have not installed vsclust yet

+#if (!require("BiocManager", quietly = TRUE))

+#    install.packages("BiocManager")

+#BiocManager::install("vsclust")

 ```

 The full functionality can be obtained by additionally installing and loading the

 packages `yaml`, `shiny`, `clusterProfiler`, and `matrixStats`.

@@ -51,9 +51,10 @@ corresponding command line script. For the source code, see

 Use the common Bioconductor commands for installation:

 ```{r eval=FALSE}

-if (!require("BiocManager", quietly = TRUE))

-    install.packages("BiocManager")

-BiocManager::install("vsclust")

+# Uncomment in case you have not installed vsclust yet

+#if (!require("BiocManager", quietly = TRUE))

+#    install.packages("BiocManager")

+#BiocManager::install("vsclust")

 library(vsclust)

 ```