# HG changeset patch
# User vmarcon
# Date 1516280193 18000
# Node ID dc678d2c1976c299306d62bfd4c7fce45ccb1c57
planemo upload commit a2411926bebc2ca3bb31215899a9f18a67e59556
diff -r 000000000000 -r dc678d2c1976 LICENSE
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/LICENSE Thu Jan 18 07:56:33 2018 -0500
@@ -0,0 +1,674 @@
+ GNU GENERAL PUBLIC LICENSE
+ Version 3, 29 June 2007
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+EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE POSSIBILITY OF
+SUCH DAMAGES.
+
+ 17. Interpretation of Sections 15 and 16.
+
+ If the disclaimer of warranty and limitation of liability provided
+above cannot be given local legal effect according to their terms,
+reviewing courts shall apply local law that most closely approximates
+an absolute waiver of all civil liability in connection with the
+Program, unless a warranty or assumption of liability accompanies a
+copy of the Program in return for a fee.
+
+ END OF TERMS AND CONDITIONS
+
+ How to Apply These Terms to Your New Programs
+
+ If you develop a new program, and you want it to be of the greatest
+possible use to the public, the best way to achieve this is to make it
+free software which everyone can redistribute and change under these terms.
+
+ To do so, attach the following notices to the program. It is safest
+to attach them to the start of each source file to most effectively
+state the exclusion of warranty; and each file should have at least
+the "copyright" line and a pointer to where the full notice is found.
+
+ {one line to give the program's name and a brief idea of what it does.}
+ Copyright (C) {year} {name of author}
+
+ This program is free software: you can redistribute it and/or modify
+ it under the terms of the GNU General Public License as published by
+ the Free Software Foundation, either version 3 of the License, or
+ (at your option) any later version.
+
+ This program is distributed in the hope that it will be useful,
+ but WITHOUT ANY WARRANTY; without even the implied warranty of
+ MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+ GNU General Public License for more details.
+
+ You should have received a copy of the GNU General Public License
+ along with this program. If not, see .
+
+Also add information on how to contact you by electronic and paper mail.
+
+ If the program does terminal interaction, make it output a short
+notice like this when it starts in an interactive mode:
+
+ {project} Copyright (C) {year} {fullname}
+ This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'.
+ This is free software, and you are welcome to redistribute it
+ under certain conditions; type `show c' for details.
+
+The hypothetical commands `show w' and `show c' should show the appropriate
+parts of the General Public License. Of course, your program's commands
+might be different; for a GUI interface, you would use an "about box".
+
+ You should also get your employer (if you work as a programmer) or school,
+if any, to sign a "copyright disclaimer" for the program, if necessary.
+For more information on this, and how to apply and follow the GNU GPL, see
+.
+
+ The GNU General Public License does not permit incorporating your program
+into proprietary programs. If your program is a subroutine library, you
+may consider it more useful to permit linking proprietary applications with
+the library. If this is what you want to do, use the GNU Lesser General
+Public License instead of this License. But first, please read
+.
diff -r 000000000000 -r dc678d2c1976 h_clust.R
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/h_clust.R Thu Jan 18 07:56:33 2018 -0500
@@ -0,0 +1,391 @@
+# R Script producing a hierarchical clustering
+# Input : a file containing a table with numeric values
+# except for the first column containing sample names
+# and the first line containing variable names
+# separator expected is
+#
+# Clustering method :
+# euclidean, correlation, ...
+#
+# Ouptut : a file containing the image of the clustering
+#-----------------------------------------------------------------
+# Authors : sophie.lamarre(at)insa-toulouse.fr
+# ignacio.gonzalez(at)toulouse.inra.fr
+# luc.jouneau(at)inra.fr
+# valentin.marcon(at)inra.fr
+# Version : 0.9
+# Date : 13/3/2017
+
+# The function -------------------------------------
+#---------------------------------------------------
+h_clust <- function(input_file,
+ group_member_file = NULL,
+ output_file = "out/myplot",
+ log_file = "log/H_Clust.html",
+ format_image_out = "png",
+ distance_method = "euclidean",
+ agglomeration_method = "ward",
+ column_clustering = TRUE,
+ select = NULL,
+ plot_title = "",
+ xlab = "",
+ ylab = "Height",
+ width = 7,
+ height = 7,
+ ppi = 300,
+ na_encoding="NA"
+) {
+
+ # This function allows to generate hierarchical cluster analysis on a table according to differents parameters.
+ # It needs a dataset : the table of data and optionally a group_member data to set colored labels.
+ # It generates a clustering tree graphic from hierarchical clustering.
+ #
+ # Parameters :
+ # - input_file : input_file name
+ # - group_member_file : input sample/tag group_member_file name
+ # - output_file : output_file name
+ # - log_file : log file name
+ # - format_image_out : graphic format of the output_file. This must be one of "png", "jpeg", "tiff", "pdf"
+ # - distance_method : the distance measure to be used. This must be one of "euclidean", "correlation", "maximum", "manhattan", "canberra", "binary" or "minkowski"
+ # - agglomeration_method : the agglomeration_method to be used. This should be one of "ward", "single", "complete", "average", "mcquitty", "median" or "centroid"
+ # - column_clustering : if TRUE clustering is performed on the columns
+ # - select : number of top variables to use for clustering, selected by highest row variance. If NULL all the variables are selected
+ # - plot_title : an overall title for the plot
+ # - xlab : a title for the x axis
+ # - ylab : a title for the y axis
+ # - width : the width of the graphics region in inches
+ # - height : the height of the graphics region in inches
+ # - ppi : the nominal resolution in ppi
+ # - na_encoding : label used to indicate missing values
+
+ library(RColorBrewer)
+
+ #---------------------------------------------------
+ # Auxiliary function
+ #---------------------------------------------------
+ insert.blank = function(x) {paste(strsplit(x, "@$§", fixed = TRUE)[[1]], collapse = " ")}
+
+ #---------------------------------------------------
+ # Titles
+ #---------------------------------------------------
+ plot_title = insert.blank(plot_title)
+ xlab = insert.blank(xlab)
+ ylab = insert.blank(ylab)
+
+ #---------------------------------------------------
+ # Read and verify data
+ #---------------------------------------------------
+ #1°) Checks valid for all modules
+ if (column_clustering) {
+ variable_in_line=1
+ column_use="individual"
+ line_use="variable"
+ } else {
+ variable_in_line=0
+ line_use="individual"
+ column_use="variable"
+ }
+
+ log_error=function(message="") {
+ cat("Hierarchical clustering report\n",file=log_file,append=F,sep="")
+ cat("⚠ An error occurred while trying to read your table.\n ",file=log_file,append=T,sep="")
+ cat("Please check that:\n ",file=log_file,append=T,sep="")
+ cat("
\n",file=log_file,append=T,sep="")
+ cat("
the table you want to process contains the same number of columns for each line
\n",file=log_file,append=T,sep="")
+ cat("
the first line of your table is a header line (specifying the name of each ",column_use,")
\n",file=log_file,append=T,sep="")
+ cat("
the first column of your table specifies the name of each ",line_use,"
\n",file=log_file,append=T,sep="")
+ cat("
both individual and variable names should be unique
\n",file=log_file,append=T,sep="")
+ cat("
each value is separated from the other by a TAB character
\n",file=log_file,append=T,sep="")
+ cat("
except for first line and first column, table should contain a numeric value
\n",file=log_file,append=T,sep="")
+ cat("
this value may contain character '.' as decimal separator or '",na_encoding,"' for missing values
\n",file=log_file,append=T,sep="")
+ cat("
\n",file=log_file,append=T,sep="")
+ cat("------- \nError messages recieved : \n",conditionMessage(message),"\n",file=log_file,append=T,sep="")
+ cat("\n",file=log_file,append=T,sep="")
+ q(save="no",status=1)
+ }
+ tab_in=tryCatch(
+ {
+ tab_in=read.table(file=input_file,sep="\t",header=T,quote="\"",na.strings=na_encoding,check.names=FALSE)
+ },
+ error=function(cond) {
+ log_error(message=cond)
+ return(NA)
+ },
+ warning=function(cond) {
+ log_error(message=cond)
+ return(NA)
+ },
+ finally={
+ #Do nothing special
+ }
+ )
+
+ if (length(dim(tab_in)) != 2 | ncol(tab_in) < 2 | nrow(tab_in) < 2) {
+ log_error(simpleCondition("The table on which you want to do a clustering must be a data table with at least 2 rows and 2 columns."))
+ }
+
+ rn=as.character(tab_in[,1])
+ if (length(rn)!=length(unique(rn))) {
+ duplicated_rownames=table(rn)
+ duplicated_rownames=duplicated_rownames[duplicated_rownames>1]
+ duplicated_rownames=names(duplicated_rownames)
+ if (length(duplicated_rownames)>3) {
+ duplicated_rownames=c(duplicated_rownames[1:3],"...")
+ }
+ duplicated_rownames=paste(duplicated_rownames,collapse=", ")
+ log_error(simpleCondition(
+ paste("The table on which you want to do a clustering have duplicated values in the first column (",
+ line_use," names) - duplicated ",line_use," names : ",duplicated_rownames,sep="")
+ ))
+ }
+ tab=tab_in[,-1]
+ rownames(tab)=rn
+
+ #Transpose if clustering on columns
+ if (column_clustering) {
+ tab=t(tab)
+ }
+
+ #Check all columns are numeric
+ tab=as.matrix(tab)
+ cell.with.na=c()
+ for (i in 1:ncol(tab)) {
+ na.v1=is.na(tab[,i])
+ na.v2=is.na(as.numeric(tab[,i]))
+ if (sum(na.v1)!=sum(na.v2)) {
+ sel=which(na.v1!=na.v2)
+ sel=sel[1]
+ value=tab[sel,i]
+ log_error(simpleCondition(
+ paste("Column '",colnames(tab)[i],"' of your table contains non numeric values. Please check its content (on line #",sel," : value='",value,"').",sep="")
+ ))
+ }
+ if (length(cell.with.na)==0 & sum(na.v1)!=0) {
+ cell.with.na=c(i,which(na.v1)[1])
+ }
+ }
+
+ #2°) Check specific to clustering procedure
+ if (!is.null(group_member_file)) {
+ group_member <- read.table(group_member_file, header = FALSE,sep="\t")
+ #pour l'instant, on prend la première colonne du fichier de groupe
+ if (length(dim(group_member))==2) {
+ ncol_group_file=ncol(group_member)
+ nrow_group_file=nrow(group_member)
+ n_tab = nrow(tab)
+ if (nrow_group_file == n_tab + 1) {
+ #We suppose first line is a header
+ header=drop(t(group_member[1,]))
+ colnames(group_member)=header
+ group_member=group_member[-1,]
+ nrow_group_file=nrow(group_member)
+ }
+
+ if (ncol_group_file==1) {
+ if (length(dim(group_member))==2) {
+ group_member=as.factor(group_member[,1])
+ } else {
+ group_member=as.factor(as.character(group_member))
+ }
+ nrow_group_file=length(group_member)
+ } else {#first column specifies the names
+ rn=as.character(group_member[,1])
+ group_member=as.factor(group_member[,2])
+ names(group_member)=rn
+ cluster_names = rownames(tab)
+ n1=sum(names(group_member) %in% cluster_names)
+ n2=sum(cluster_names %in% names(group_member))
+ if (n1 != n2 | n1!=n_tab | n2 !=n_tab) {
+ names_in_error="
"
+ for( name in names(group_member)) {
+ if (!(name %in% cluster_names)) {
+ names_in_error=paste(names_in_error,"