Date: Thu, 14 May 2026 13:25:00 %+0000
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Below you should see a heatmap produced if everything (Tiki, R, PluginR, and the required R packages) are successfuly instaled and configured.
You can [tiki-editpage.php?page=HeatMaps Test|edit this page] to tweak some parameters by hand if you wish or needed.

 {RR(loadandsave="1", echo="1", svg="1", pdf="1")}
##################################################################
### Chunk 1: Installation of easyHeatMap package and dependencies
##################################################################

##### NOTE:
# * You need a few packages: install the ones you are missing in the central server location for R packages.
# * In this example we install them with this params: lib="/usr/lib/R/site-library", repos="http://ftp.heanet.ie/mirrors/cran.r-project.org/"
#   but adapt the server paths or urls to your specific case
# * Remember you can install packages:
#  * from the terminal with some command like:
#      R CMD INSTALL /home/foo/myPackage.tar.gz --library="/usr/lib/R/site-library"
#  * or from an R session with something like:
#      install.packages(pkgs="/home/foo/myPackage.tar.gz", lib="/usr/lib/R/site-library", repos="http://ftp.heanet.ie/mirrors/cran.r-project.org/")

cat("Cheacking dependencies:\n")
if (is.na(match("gplots", installed.packages()))) {
    install.packages("gplots", repos="http://ftp.heanet.ie/mirrors/cran.r-project.org/")
} else {
    cat("  Package gplots is already installed. OK.\n")
}
if (is.na(match("gtools", installed.packages()))) {
    install.packages("gtools", repos="http://ftp.heanet.ie/mirrors/cran.r-project.org/")
} else {
    cat("  Package gtools is already installed. OK.\n")
}
if (is.na(match("gdata", installed.packages()))) {
    install.packages("gdata", repos="http://ftp.heanet.ie/mirrors/cran.r-project.org/")
} else {
    cat("  Package gdata is already installed. OK.\n")
}
if (is.na(match("caTools", installed.packages()))) {
    install.packages("caTools", repos="http://ftp.heanet.ie/mirrors/cran.r-project.org/")
} else {
    cat("  Package caTools is already installed. OK.\n")
}
if (is.na(match("easyHeatMap", installed.packages()))) {
  ##### Install the package "easyHeatMap"
  ### for the time being you can fetch it from:
  ### https://ueb.ir.vhebron.net/files/easyHeatMap_1.18.tar.gz
  ### or
  ### http://dl.dropbox.com/u/24830595/easyHeatMap_1.18.tar.gz
    cat("Attempting to download easyHeatMaps from http://ueb.ir.vhebron.net/files/easyHeatMap_1.18.tar.gz \n")
    download.file("http://ueb.ir.vhebron.net/files/easyHeatMap_1.18.tar.gz", "./easyHeatMap_1.18.tar.gz")
    cat("Attempting to install package easyHeatMaps \n")
    install.packages(pkgs="easyHeatMap_1.18.tar.gz")
    if (is.na(match("easyHeatMap", installed.packages()))) {
        cat("  Something must have gone wrong, since the package didn't get successfully installed for some reason")
    } else {
        cat("  Package easyHeatMaps successfully installed. \n  You can now proceed to create some <a href=\"tiki-index.php?page=HeatMaps&cookietab=2\">HeatMaps</a>")
    }
} else {
    cat("  Package easyHeatMap is already installed. OK.\n")
    cat("\n  You can now proceed to create some <a href=\"tiki-index.php?page=HeatMaps&cookietab=2\">HeatMaps</a>\n")
}


#######################################################################################
### Chunk 2: R commands to generate the required files server-side from BioConductor
###            "expres.filtered.ALL.txt", "expres.selected.ALL.txt" and "colorsForColumns.txt"
#############################################################################################

##### A few packages are required from Bioconductor to produce HeatMaps with real data
# source("http://www.bioconductor.org/biocLite.R")
# biocLite("ALL", lib="/usr/lib/R/site-library")
# biocLite("genefilter", lib="/usr/lib/R/site-library")
# biocLite("hgu95av2.db", lib="/usr/lib/R/site-library")

# require(ALL)
# require(genefilter)
# require(hgu95av2.db)

# The ALL package contains a sample "ExpressionSet" with expression values form
# 128 samples and 12625 genes
# ("features =probesets, remembering there may be more than one probeset per gene")
# The dataset contains five different genotypes
#  ALL1/AF4  BCR/ABL E2A/PBX1      NEG   NUP-98  p15/p16
#       10            37           5                  74        1             1
# The two example datasets keep only samples from the first and second groups
# To use a small number of genes two standard 'filtering approaches' are tested
# 1) Non specific filtering
# Only most variables are kept. Their expressions are stored in file "expres.filtered.ALL.txt"
# 2) Specific filtering
# We retain genes according to a test comparison between two groups.
# Their expressions are stored in file "expres.selected.ALL.txt"
#
#
# 1- Selecting genes by non-specific filtering
#
# require(ALL)
# data(ALL)
# require(genefilter)
# filtered095 <- nsFilter (ALL, var.cutoff=0.95)
# filtered099 <- nsFilter (ALL, var.cutoff=0.99)
# esetSel095<-filtered095$eset
# esetSel099<-filtered099$eset
# esetSel <- esetSel099
# esetSel<- esetSel[,pData(esetSel)$mol.bio %in% c("ALL1/AF4", "BCR/ABL")]
# colnames(exprs(esetSel))<- paste("X", colnames(exprs(esetSel)), sep="")
# write.exprs(esetSel, file="expres.filtered.ALL.txt")
#
# 2- Selecting genes by specific filtering
# require ("ALL")
# data("ALL")
# eset <- ALL[, ALL$mol.biol %in% c("BCR/ABL", "ALL1/AF4")]
# library("limma")
# f <- factor(as.character(eset$mol.biol))
# design <- model.matrix(~f)
# fit <- eBayes(lmFit(eset,design))
# selected05  <- p.adjust(fit$p.value[, 2]) <0.05
# selected01  <- p.adjust(fit$p.value[, 2]) <0.01
# esetSel <- eset [selected05, ]
# colnames(exprs(esetSel))<- paste("X", colnames(exprs(esetSel)), sep="")
# write.exprs(esetSel, file="expres.selected.ALL.txt")

# #
# # I per crear l'arxiu amb un color diferent per cada grup (basat en la columna "mol.biol")
# #
#  write.table(cbind(paste("X",rownames(pData(esetSel)),sep=""),
#                   as.integer(pData(esetSel)$mol.biol)+1), sep="\t",
#              row.names=FALSE, col.names=c("Column", "Group"), quote=FALSE,
#              file="colorsForColumns.txt")


                                        #
# #
# # Això és un exemple del que voldríem haver capturat a través del formulari
# # COMPTE perque, en aquesta versió, no considerem la possibilitat de treure
# # - Ni, com paràmetre, la possibilitat d'enviar els gràfics a un arxiu PDF
# #   --> toPDF es deixa a FALSE
# # - Ni un arxiu amb els gens de cada cluster
# #   --> numClusters es deixa a 0
# # - Ni un arxiu/gràfic amb els perfils de cada cluster
# #   --> plotProfiles es deixa a FALSE
# #

#######################################################################################
### Chunk 3: Call to easyHeatMap functions
#############################################################################################

require(easyHeatMap)
    download.file("http://ueb.ir.vhebron.net/dl220", "./expres.filtered.ALL.txt")
    download.file("http://ueb.ir.vhebron.net/dl221", "./colorsForColumns.txt")

clustPar<-newClustPar(expres = NULL , # We'll give it a file attached to the tracker item
                       expresFileName ="expres.filtered.ALL",
                                        # For a file "foo.txt", you have to indicate here "foo" (without extension)
                       fileType = "txt",
                       comparisonName = "ALL",
                       Title = "My Plot: BCR/ABL vs ALL1/AF4",
                       numClusters = 0,      # Confirm that with zero nothing is done
                       plotProfiles=FALSE,
                       rowDistance = 'cor',
                       colDistance = 'euclidean',
                       RowVals = TRUE,
                       ColVals = TRUE,
                       escala = "row",
                       colorsSet =  redblue(64), #colors,
                       colsForGroups = NULL,
                       colsForGroupsFileName="colorsForColumns",
                       densityInfo = "density",
                       cexForColumns = 0.8,
                       cexForRows = 0.8,
                       outputDir=".",
                       toPDF = FALSE,
)

# HeatMap generation
hm.ALL <- doHMAnalysis(clustPar)

# Now it is easy to change paramters and re-draw the heatmap

## We can use a different colorset
#clustPar$colorsSet <-  heat.colors(64)
#hm.ALL <- doHMAnalysis(clustPar)

## We can change the distance function for rows and/or columns
#clustPar$rowDistance = 'cor'
#clustPar$colDistance = 'minkowski'
#hm.ALL <- doHMAnalysis(clustPar)

## We can use a different dataset
#clustPar$expresFileName="expres.selected.ALL"
#hm.ALL <- doHMAnalysis(clustPar)



#############################################################
### Optional change 1: PDF generation server side
#############################################################
# # Imprimim a pdf
# # COMPTE: els arxius s'anomenen per defecte
# # el del heatmap:  paste(paste("HM", comparisonName, sep=""),"pdf", sep=".")
# # el dels perfils:  paste(paste("plotProfiles", comparisonName, sep=""),"pdf", sep=".")
# # el dels gens als clusters:  paste(paste("genesInClusters", comparisonName, sep=""),"txt", sep=".")
#
# # Això es important de cara a descarregar-los des de l'aplicatiu web
#
# clustPar$toPDF<-TRUE
# clustPar$plotProfiles <-TRUE
#
# hm.ALL <- doHMAnalysis(clustPar)

#############################################################
### Optional change 2: Changingthecolour palette
#############################################################
# clustPar$toPDF<-FALSE
# clustPar$plotProfiles <-FALSE
#
# # We can try with other colour palettes
# colores1 <- heat.colors(64) # Veure a RColorBrewer altres formes
# colores2 <- redblue(64) # Veure a RColorBrewer altres formes
# colores3 <- greenred(16) # Veure a RColorBrewer altres formes
# colores4 <- topo.colors(64) # Veure a RColorBrewer altres formes
# colores5 <- terrain.colors (64)
# colores6 <- colorpanel(36, "green", "white", "red")
# colores7 <- rainbow(36)
#
# clustPar$toPDF<-FALSE
# clustPar$plotProfiles <-FALSE
# clustPar$colorsSet <-colores2
# hm.ALL <- doHMAnalysis(clustPar)

{RR}