# HG changeset patch
# User iuc
# Date 1496329822 14400
# Node ID cc96abdef027533377583544fd102f1e108f5260
# Parent  c8c290f3ea7d3c71eca9e637f59e5e98719de71d
planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/masigpro commit c4510bf402d10e8e3a3c4c90c2d96666c987a256

diff -r c8c290f3ea7d -r cc96abdef027 masigpro.R
--- a/masigpro.R	Mon May 15 07:29:03 2017 -0400
+++ b/masigpro.R	Thu Jun 01 11:10:22 2017 -0400
@@ -1,104 +1,153 @@
-#!/usr/bin/env Rscript
-
-# A command-line interface to maSigPro for use with Galaxy
-# written by Clemens Blank.
-# Thanks to Bjoern Gruening and Michael Love for their DESeq2
-# wrapper as a basis to build upon.
-
-# setup R error handling to go to stderr
-options( show.error.messages=F, error = function () { cat( geterrmessage(), file=stderr() ); q( "no", 1, F ) } )
-
-# we need that to not crash galaxy with an UTF8 error on German LC settings.
-loc <- Sys.setlocale("LC_MESSAGES", "en_US.UTF-8")
-
-suppressPackageStartupMessages({
-  library("maSigPro")
-  library("optparse")
-  library("mclust")
-})
-
-options(stringAsFactors = FALSE, useFancyQuotes = FALSE)
-args <- commandArgs(trailingOnly = TRUE)
-
-# specify our desired options in a list
-# by default OptionParser will add an help option equivalent to
-# make_option(c("-h", "--help"), action="store_true", default=FALSE,
-# help="Show this help message and exit")
-option_list <- list(
- make_option(c("-q", "--quiet"), action="store_false",
- dest="verbose", help="Print little output"),
- make_option(c("-e", "--edesign"), type="character"),
- make_option(c("-d", "--data"), type="character"),
- make_option(c("-o", "--outfile"), type="character"),
- make_option("--degree", type="integer", default=1),
- make_option("--time_col", type="integer", default=1),
- make_option("--repl_col", type="integer", default=2),
- make_option("--qvalue", type="double", default=0.05),
- make_option("--min_obs", type="integer", default=6),
- make_option("--step_method", type="character", default="backward"),
- make_option("--nvar_correction", type="logical", default=FALSE),
- make_option("--alfa", type="double", default=0.05),
- make_option("--rsq", type="double", default=0.7),
- make_option("--vars", type="character", default="groups"),
- make_option("--significant_intercept", type="character", default="dummy"),
- make_option("--cluster_data", type="integer", default=1),
- make_option(c("-k", "--k"), type="integer", default=9),
- make_option("--cluster_method", type="character", default="hclust"),
- make_option("--distance", type="character", default="cor"),
- make_option("--agglo_method", type="character", default="ward.D"),
- make_option("--iter_max", type="integer", default=500),
- make_option("--color_mode", type="character", default="rainbow"),
- make_option("--show_fit", type="logical", default=TRUE),
- make_option("--show_lines", type="logical", default=TRUE),
- make_option("--cexlab", type="double", default=0.8),
- make_option("--legend", type="logical", default=TRUE)
-)
-
-# get command line options, if help option encountered print help and exit,
-# otherwise if options not found on command line then set defaults
-opt <- parse_args(OptionParser(option_list=option_list))
-
-# enforce the following required arguments
-if (is.null(opt$edesign)) {
-  cat("'edesign' is required\n")
-  q(status=1)
-}
-if (is.null(opt$data)) {
-  cat("'data' is required\n")
-  q(status=1)
-}
-if (is.null(opt$outfile)) {
-  cat("'outfile' is required\n")
-  q(status=1)
-}
-
-verbose <- if (is.null(opt$quiet)) {
-  TRUE
-} else {
-  FALSE
-}
-
-edesign <- as.matrix(read.table(opt$edesign, header=TRUE, row.names = 1))
-
-data <- read.table(opt$data, header=TRUE)
-
-results <- maSigPro(data, edesign, degree = opt$degree, time.col = opt$time_col,
-         repl.col = opt$repl_col, Q = opt$qvalue, min.obs = opt$min_obs,
-         step.method = opt$step_method, nvar.correction = opt$nvar_correction,
-         alfa = opt$alfa, rsq = opt$rsq, vars = opt$vars,
-         significant.intercept = opt$significant_intercept,
-         cluster.data = opt$cluster_data, k = opt$k,
-         cluster.method = opt$cluster_method, distance = opt$distance,
-         agglo.method = opt$agglo_method, iter.max = opt$iter_max,
-         color.mode = opt$color_mode, show.fit = opt$show_fit,
-         show.lines = opt$show_lines, cexlab = opt$cexlab,
-         legend = opt$legend)
-
-filename <- opt$outfile
-
-write.table((results$summary), file=filename, sep="\t", quote=FALSE,
-            row.names=FALSE, col.names=TRUE)
-
-cat("Session information:\n\n")
-
-sessionInfo()
\ No newline at end of file
+#!/usr/bin/env Rscript
+
+# A command-line interface to maSigPro for use with Galaxy
+# written by Clemens Blank.
+# Thanks to Bjoern Gruening and Michael Love for their DESeq2
+# wrapper as a basis to build upon.
+
+# setup R error handling to go to stderr
+options( show.error.messages=F, error = function () { cat( geterrmessage(), file=stderr() ); q( "no", 1, F ) } )
+
+# we need that to not crash galaxy with an UTF8 error on German LC settings.
+loc <- Sys.setlocale("LC_MESSAGES", "en_US.UTF-8")
+
+suppressPackageStartupMessages({
+  library("maSigPro")
+  library("optparse")
+  library("mclust")
+})
+
+# The following code fixes an error in the stepback function
+# of the maSigPro package. This code is hopefully temporary 
+# and can be removed if the fix is included in a future 
+# version. The stepback function in the maSigPro namespace
+# will be overwritten by the following function.
+stepback <- function (y = y, d = d, alfa = 0.05, family = gaussian() , epsilon=0.00001) 
+{
+    lm1 <- glm(y ~ ., data = d, family=family, epsilon=epsilon)
+    result <- summary(lm1)
+    max <- max(result$coefficients[, 4][-1], na.rm = TRUE)
+    if (length(result$coefficients[, 4][-1]) == 1) {
+      if (max > alfa) {
+        max = 0 
+        lm1 <- glm(y ~ 1,  family=family, epsilon=epsilon)
+      }
+    }
+    while (max > alfa) {
+        varout <- names(result$coefficients[, 4][-1])[result$coefficients[, 
+            4][-1] == max][1]
+        pos <- position(matrix = d, vari = varout)
+        d <- d[, -pos]
+        if (length(result$coefficients[, 4][-1]) == 2) {
+            min <- min(result$coefficients[, 4][-1], na.rm = TRUE)
+            lastname <- names(result$coefficients[, 4][-1])[result$coefficients[,4][-1] == min]
+        }
+        if (is.null(dim(d))) {
+            d <- as.data.frame(d)
+            colnames(d) <- lastname
+        }
+        lm1 <- glm(y ~ ., data = d, family=family, epsilon=epsilon)
+        result <- summary(lm1)
+        max <- max(result$coefficients[, 4][-1], na.rm = TRUE)
+        if (length(result$coefficients[, 4][-1]) == 1) {
+            max <- result$coefficients[, 4][-1]
+            if (max > alfa) {
+                max = 0
+                lm1 <- glm(y ~ 1,  family=family, epsilon=epsilon)
+            }
+        }
+    }
+    return(lm1)
+}
+
+unlockBinding("stepback", as.environment("package:maSigPro"))
+assignInNamespace("stepback", stepback, ns="maSigPro", envir=as.environment("package:maSigPro"))
+assign("stepback", stepback, as.environment("package:maSigPro"))
+lockBinding("stepback", as.environment("package:maSigPro"))
+# End of temporary code to fix stepback.R
+
+options(stringAsFactors = FALSE, useFancyQuotes = FALSE)
+args <- commandArgs(trailingOnly = TRUE)
+
+# specify our desired options in a list
+# by default OptionParser will add an help option equivalent to
+# make_option(c("-h", "--help"), action="store_true", default=FALSE,
+# help="Show this help message and exit")
+option_list <- list(
+ make_option(c("-q", "--quiet"), action="store_false",
+ dest="verbose", help="Print little output"),
+ make_option(c("-e", "--edesign"), type="character"),
+ make_option(c("-d", "--data"), type="character"),
+ make_option(c("-o", "--outfile"), type="character"),
+ make_option("--degree", type="integer", default=1),
+ make_option("--time_col", type="integer", default=1),
+ make_option("--repl_col", type="integer", default=2),
+ make_option("--qvalue", type="double", default=0.05),
+ make_option("--min_obs", type="integer", default=6),
+ make_option("--step_method", type="character", default="backward"),
+ make_option("--nvar_correction", type="logical", default=FALSE),
+ make_option("--alfa", type="double", default=0.05),
+ make_option("--rsq", type="double", default=0.7),
+ make_option("--vars", type="character", default="groups"),
+ make_option("--significant_intercept", type="character", default="dummy"),
+ make_option("--cluster_data", type="integer", default=1),
+ make_option(c("-k", "--k"), type="integer", default=9),
+ make_option("--cluster_method", type="character", default="hclust"),
+ make_option("--distance", type="character", default="cor"),
+ make_option("--agglo_method", type="character", default="ward.D"),
+ make_option("--iter_max", type="integer", default=500),
+ make_option("--color_mode", type="character", default="rainbow"),
+ make_option("--show_fit", type="logical", default=TRUE),
+ make_option("--show_lines", type="logical", default=TRUE),
+ make_option("--cexlab", type="double", default=0.8),
+ make_option("--legend", type="logical", default=TRUE)
+)
+
+# get command line options, if help option encountered print help and exit,
+# otherwise if options not found on command line then set defaults
+opt <- parse_args(OptionParser(option_list=option_list))
+
+# enforce the following required arguments
+if (is.null(opt$edesign)) {
+  cat("'edesign' is required\n")
+  q(status=1)
+}
+if (is.null(opt$data)) {
+  cat("'data' is required\n")
+  q(status=1)
+}
+if (is.null(opt$outfile)) {
+  cat("'outfile' is required\n")
+  q(status=1)
+}
+
+verbose <- if (is.null(opt$quiet)) {
+  TRUE
+} else {
+  FALSE
+}
+
+edesign <- as.matrix(read.table(opt$edesign, header=TRUE, row.names = 1))
+
+data <- read.table(opt$data, header=TRUE, check.names=FALSE)
+
+results <- maSigPro(data, edesign, degree = opt$degree, time.col = opt$time_col,
+         repl.col = opt$repl_col, Q = opt$qvalue, min.obs = opt$min_obs,
+         step.method = opt$step_method, nvar.correction = opt$nvar_correction,
+         alfa = opt$alfa, rsq = opt$rsq, vars = opt$vars,
+         significant.intercept = opt$significant_intercept,
+         cluster.data = opt$cluster_data, k = opt$k,
+         cluster.method = opt$cluster_method, distance = opt$distance,
+         agglo.method = opt$agglo_method, iter.max = opt$iter_max,
+         color.mode = opt$color_mode, show.fit = opt$show_fit,
+         show.lines = opt$show_lines, cexlab = opt$cexlab,
+         legend = opt$legend)
+
+filename <- opt$outfile
+
+write.table((results$summary), file=filename, sep="\t", quote=FALSE,
+            row.names=FALSE, col.names=TRUE)
+
+cat("Session information:\n\n")
+
+sessionInfo()
diff -r c8c290f3ea7d -r cc96abdef027 masigpro.xml
--- a/masigpro.xml	Mon May 15 07:29:03 2017 -0400
+++ b/masigpro.xml	Thu Jun 01 11:10:22 2017 -0400
@@ -1,430 +1,430 @@
-<tool id="masigpro" name="maSigPro" version="1.49.0.0">
-    <description>Significant Gene Expression Profile Differences in Time Course Gene Expression Data</description>
-    <requirements>
-        <requirement type="package" version="1.49.0">bioconductor-masigpro</requirement>
-        <requirement type="package" version="1.3.2">r-optparse</requirement>
-        <requirement type="package" version="4.4">sed</requirement>
-    </requirements>
-    <stdio>
-        <regex match="Execution halted"
-            source="both"
-            level="fatal"
-            description="Execution halted." />
-        <regex match="Error in"
-            source="both"
-            level="fatal"
-            description="An undefined error occurred, please check your input carefully and contact your administrator." />
-        <regex match="Fatal error"
-            source="both"
-            level="fatal"
-            description="An undefined error occurred, please check your input carefully and contact your administrator." />
-    </stdio>
-    <version_command>
-    <![CDATA[
-        echo $(R --version | grep version | grep -v GNU)", maSigPro version" $(R --vanilla --slave -e "library(maSigPro); cat(sessionInfo()\$otherPkgs\$maSigPro\$Version)" 2> /dev/null | grep -v -i "WARNING: ")
-    ]]>
-    </version_command>
-    <command>
-    <![CDATA[
-    #if str($source.source_selector) == "advanced":
-        paste
-        #set $start = True
-        #set $header = ''
-        #for $time in $source.rep_time:
-            #for $file in $time.files:
-                #if $start:
-                    <(cut -f1 $file)
-                    #set $start = False
-                #end if
-                #set $header += ' "' + $file.name + '"'
-                <(cut -f2 $file)
-            #end for
-        #end for
-        > data && sed -i '1i$header' data &&
-        #if $source.enable_output:
-            ln -f data $data_out && ln -f $design_matrix $edesign_out &&
-        #end if
-        #set $data = 'data'
-        #set $edesign = $design_matrix
-    #else:
-        #set $data = $source.data
-        #set $edesign = $source.edesign
-    #end if
-    Rscript '${__tool_directory__}/masigpro.R'
-    -e '$edesign'
-    -d '$data'
-    -o '$masigpro_out'
-    #if str($source.source_selector) == "defaults":
-        --time_col $source.time_col
-        --repl_col $source.repl_col
-    #end if
-    --degree $makeDesignMatrix.degree
-    --qvalue $p_vector.qvalue
-    --min_obs $p_vector.min_obs
-    --step_method '$Tfit.step_method'
-    --nvar_correction $Tfit.nvar_correction
-    --alfa $Tfit.alfa
-    --rsq $getSiggenes.rsq
-    --vars '$getSiggenes.vars'
-    --significant_intercept '$getSiggenes.significant_intercept'
-    #if $pdf.pdf_selector:
-        --cluster_data $pdf.seeGenes.clusterData
-        -k $pdf.seeGenes.k
-        --cluster_method $pdf.seeGenes.clustering.clusterMethod
-        #if str($pdf.seeGenes.clustering.clusterMethod) == "hclust":
-            --distance $pdf.seeGenes.clustering.distance
-            --agglo_method $pdf.seeGenes.clustering.aggloMethod
-        #end if
-        #if str($pdf.seeGenes.clustering.clusterMethod) == "kmeans":
-            --iter_max $pdf.seeGenes.clustering.iterMax
-        #end if
-        --color_mode $pdf.seeGenes.colorMode
-        --show_fit $pdf.seeGenes.showFit
-        --show_lines $pdf.seeGenes.showLines
-        --cexlab $pdf.seeGenes.cexlab
-        --legend $pdf.seeGenes.legend
-    #end if
-    ]]>
-    </command>
-    <configfiles>
-<configfile name="design_matrix">#if str($source.source_selector) == "advanced":
-#set $header = "Name Time Replicate"
-#for $group in $source.rep_groups:
-    #set $header = $header + ' ' + str($group.name)
-#end for
-$header
-#set $c = len($source.rep_repl) + 1
-#for $time in $source.rep_time:
-    #for $file in $time.files:
-        #set $is_repl = False
-        #for $i, $repl in enumerate($source.rep_repl):
-            #if str($file) in str($repl.files):
-                #set $r = $i + 1
-                #set $is_repl = True
-            #end if
-        #end for
-        #if $is_repl == False:
-            #set $r = $c
-            #set $c += 1
-        #end if
-        #set $line = '"' + str($file.name) + '" ' + str($time.time) + ' ' + str($r)
-        #for $group in $source.rep_groups:
-            #if str($file) in str($group.files):
-                #set $line += " 1"
-            #else
-                #set $line += " 0"
-            #end if
-        #end for
-$line
-    #end for
-#end for
-#end if
-</configfile>
-    </configfiles>
-    <inputs>
-        <conditional name="source">
-            <param label="Choose data source" name="source_selector"
-                help="Choose if you want to provide seperate count files (e.g. from HTSeq-count or feature-seq) 
-                    and define your experiment design matrix here, or if you have maSigPro edesign and data input files already."
-                type="select">
-                <option value="defaults">Use maSigPro edesign and data files</option>
-                <option value="advanced">Seperate count data (e.g. from HTSeq-count or feature-count)</option>
-            </param>
-            <when value="defaults">
-                <param name="edesign" format="tabular,txt" type="data" label="Experiment matrix"
-                    help="Matrix describing experimental design. Rows must be arrays and columns experiment descriptors" />
-                <param name="data" format="tabular,txt" type="data" label="Gene expression matrix"
-                    help="Matrix containing normalized gene expression data. Genes must be in rows and arrays in columns" />
-                <param name="time_col" label="Column number containing time values" type="integer" value="1"
-                    help="Column number in edesign containing time values. Default is first column" />
-                <param name="repl_col" label="Column number containing replicate coding" type="integer" value="2"
-                    help="Column number in edesign containing coding for replicate arrays. Default is second column" />
-            </when>
-            <when value="advanced">
-                <param name="enable_output" type="boolean" truevalue="1" falsevalue="0" checked="false" label="Output generated maSigPro input files?"
-                    help="Choose if you want to output the generated edesign and data files for direct use in maSigPro as history elements." />
-                <repeat name="rep_time" title="Time values" min="1" default="1">
-                    <param name="time" type="integer" value="0" label="Specify a numerical time value" help="Only numbers will be allowed">
-                    <sanitizer>
-                        <valid initial="string.digits"></valid>
-                    </sanitizer>
-                    </param>
-                    <param name="files" type="data" format="tabular" multiple="true" label="Counts file(s) at this time value" />
-                </repeat>
-                <repeat name="rep_groups" title="Experimental groups" min="1" default="1">
-                    <param name="name" type="text" value="Group title" label="Specify the name of this experimental group"
-                        help="Use a single name without spaces or special characters">
-                    </param>
-                    <param name="files" type="data" format="tabular" multiple="true"
-                        label="Counts file(s) belonging to this experimental group" />
-                </repeat>
-                <repeat name="rep_repl" title="Replicates" min="0" default="0">
-                    <param name="files" type="data" format="tabular" multiple="true" label="Counts files that are replicates" />
-                </repeat>
-            </when>
-        </conditional>
-        <section name="makeDesignMatrix"
-            title="Step 1: make.Design.Matrix - Defining the regression model"
-            help="‘make.design.matrix’ creates the design matrix of dummies for
-                fitting time series micorarray gene expression experiments.">
-            <param name="degree" type="integer" value="1"
-                label="Degree of regression fit polynome"
-                help="The degree of the regression fit polynome. ‘degree’ = 1 returns 
-                    linear regression, ‘degree’ = 2 returns quadratic regression, etc" />
-        </section>
-        <section name="p_vector"
-            title="Step 2: p.vector - Finding significant genes"
-            help="‘p.vector’ performs a regression fit for each gene taking all
-                variables present in the model given by a regression matrix and
-                returns a list of FDR corrected significant genes">
-            <param name="qvalue" type="float" value="0.05" label="Q" help="Significance level" />
-            <param name="min_obs" label="Minimum values" type="integer" value="6"
-                help="Genes with less than this number of true numerical values
-                    will be excluded from the analysis. Minimum value to estimate
-                    the model is (degree+1)xGroups+1. Default is 6." />
-        </section>
-        <section name="Tfit" title="Step 3: T.fit - Finding significant differences"
-            help="‘T.fit’ selects the best regression model for each gene using
-                stepwise regression. In the maSigPro approach ‘p.vector’ and ‘T.fit’ are subsequent
-                steps, meaning that significant genes are first selected on the
-                basis of a general model and then the significant variables for
-                each gene are found by step-wise regression.">
-            <param name="step_method" type="select" label="Step regression"
-                help="The step regression can be ‘backward’ or ‘forward’ indicating
-                    whether the step procedure starts from the model with all or none
-                    variables. With the ‘two.ways.backward’ or ‘two.ways.forward’
-                    options the variables are both allowed to get in and out. At each
-                    step the p-value of each variable is computed and variables get
-                    in/out the model when this p-value is lower or higher than given
-                    threshold alfa.">
-                <option selected="True" value="backward">backward</option>
-                <option value="forward">forward</option>
-                <option value="two.ways.backward">two.ways.backward</option>
-                <option value="two.ways.forward">two.ways.forward</option>
-            </param>
-            <param type="boolean" name="nvar_correction" label="nvar correction" truevalue="TRUE" falsevalue="FALSE" checked="false"
-                help="When nvar.correction is TRUE the given significance
-                    level is corrected by the number of variables in the model.">
-                <option selected="True" value="FALSE">False</option>
-                <option value="TRUE">True</option>
-            </param>
-            <param name="alfa" type="float" value="0.05" label="alfa" help="Significance level used for variable selection in the stepwise regression" />
-        </section>
-        <section name="getSiggenes"
-            title="Step 4: get.siggenes - Obtaining lists of significant genes"
-                help="This function creates lists of significant genes for a set of
-                    variables whose significance value has been computed with the
-                    ‘T.fit’ function.">
-            <param name="rsq" type="float" value="0.7" label="rsq"
-                help="cut-off level at the R-squared value for the stepwise
-                    regression fit. Only genes with R-squared more than rsq are
-                    selected" />
-            <param name="vars" type="select" label="Variables"
-                help="Variables for which to extract significant genes.
-                    ‘all’: generates one single matrix or gene list with all
-                    significant genes.
-
-                    ‘each’: generates as many significant genes extractions as
-                    variables in the general regression model. Each extraction
-                    contains the significant genes for that variable.
-
-                    ‘groups’: generates a significant genes extraction for each
-                    experimental group.
-
-                    The difference between ‘each’ and ‘groups’ is that in the
-                    first case the variables of the same group (e.g.  ‘TreatmentA’
-                    and ‘time*TreatmentA’) will be extracted separately and in t
-                    he
-                    second case jointly.">
-                <option selected="True" value="groups">Groups</option>
-                <option value="each">Each</option>
-                <option value="all">All</option>
-            </param>
-            <param name="significant_intercept" type="select" label="Significant intercept"
-                help="The argument ‘significant.intercept’ modulates the treatment for
-                    intercept coefficients to apply for selecting significant genes
-                    when ‘vars’ equals ‘groups’. There are three possible values:
-                    ‘none’, no significant intercept (differences) are considered
-                    for significant gene selection, ‘dummy’, includes genes with
-                    significant intercept differences between control and experimental
-                    groups, and ‘all’ when both significant intercept coefficient
-                    for the control group and significant intercept differences are
-                    considered for selecting significant genes.">
-                <option selected="True" value="dummy">Dummy</option>
-                <option value="none">None</option>
-                <option value="all">All</option>
-            </param>
-        </section>
-        <conditional name="pdf">
-            <param label="Generate visualization PDF" name="pdf_selector" type="boolean"
-                truevalue="1" falsevalue="0" checked="true"
-                help="Choose if you want to generate a PDF file containing the visualizations" />
-            <when value="1">
-                <section name="seeGenes" title="Step 5: see.genes - Visualization"
-                    help="This function provides visualisation tools for gene expression
-                        values in a time course experiment. The function first calls the
-                        heatmap function for a general overview of experiment results.
-                        Next a partioning of the data is generated using a clustering
-                        method.  The results of the clustering are visualized both as gene
-                        expression profiles extended along all arrays in the experiment,
-                        as provided by the plot.profiles function, and as summary
-                        expression profiles for comparison among experimental groups.">
-                    <param name="clusterData" label="Cluster Data" type="integer" value="1"
-                        help="Data clustering can be done on the basis of either the original
-                            expression values, the regression coefficients, or the t.scores.
-                            In case ‘data’ is a ‘get.siggenes’ object, this is given by
-                            providing the element names of the list
-                            ‘c(sig.profiles,coefficients,t.score)’ of their list
-                            position (1,2 or 3)." />
-                    <param name="k" type="integer" label="Number of clusters for data partioning" value="9" />
-                    <conditional name="clustering">
-                        <param name="clusterMethod" label="Cluster Method" type="select"
-                            help="clustering method for data partioning. Currently
-                                ‘hclust’, ‘kmeans’ and ‘Mclust’ are supported">
-                            <option selected="True" value="hclust">hclust</option>
-                            <option value="kmeans">kmeans</option>
-                            <option value="Mclust">Mclust</option>
-                        </param>
-                        <when value="hclust">
-                            <param name="distance" type="select" label="Distance measure"
-                                help="Distance measurement function when ‘cluster.method’ is
-                                    ‘hclust’. Default uses correlation.">
-                                <option selected="True" value="cor">Correlation</option>
-                                <option value="euclidean">Euclidean</option>
-                                <option value="maximum">Maximum</option>
-                                <option value="manhattan">Manhattan</option>
-                                <option value="Canberra">Canberra</option>
-                                <option value="binary">Binary</option>
-                                <option value="minkowski">Minkowski</option>
-                            </param>
-                            <param name="aggloMethod" type="select" label="Agglomeration method"
-                                help="The agglomeration method to be used when ‘cluster.method’ is ‘hclust’.">
-                                <option selected="True" value="ward.D">ward.D</option>
-                                <option value="ward.D2">ward.D2</option>
-                                <option value="single">single</option>
-                                <option value="complete">complete</option>
-                                <option value="average">average (= UPGMA)</option>
-                                <option value="mcquitty">mcquitty (= WPGMA)</option>
-                                <option value="median">median (= WPGMC)</option>
-                                <option value="centroid">centroid (= UPGMC)</option>
-                            </param>
-                        </when>
-                        <when value="kmeans">
-                            <param name="iterMax" type="integer" label="Maximum number of iterations" value="500"
-                                help="Maximum number of iterations when ‘cluster.method’ is ‘kmeans’" />
-                        </when>
-                    </conditional>
-                    <param name="colorMode" label="Color Mode" type="select" help="Color scale for plotting profiles. Can be either ‘rainbow’ or ‘gray’">
-                        <option selected="True" value="rainbow">Rainbow</option>
-                        <option value="gray">Gray</option>
-                    </param>
-                    <param name="showFit" type="boolean" truevalue="TRUE" falsevalue="FALSE" checked="true" label="Show regression fit curves?"
-                        help="Indicating whether regression fit curves must be plotted" />
-                    <param name="showLines" type="boolean" truevalue="TRUE" falsevalue="FALSE" checked="true" label="Draw lines?"
-                        help="Indicating whether a line must be drawn joining plotted data points for each group" />
-                    <param name="cexlab" type="float" value="0.8" label="Magnification for x labels"
-                        help="Graphical parameter maginfication to be used for x labels in plotting functions" />
-                    <param name="legend" type="boolean" truevalue="TRUE" falsevalue="FALSE" checked="true" label="Add legend to plotting profiles?"
-                        help="Indicating whether legend must be added when plotting profiles" />
-                </section>
-            </when>
-        </conditional>
-    </inputs>
-    <outputs>
-        <data format="tabular" name="masigpro_out" label="maSigPro result file on ${on_string}">
-        </data>
-        <data format="txt" name="edesign_out" label="maSigPro edesign file on ${on_string}">
-            <filter>
-                (( 
-                    source['source_selector'] == 'advanced' and
-                    source['enable_output'] == True 
-                ))
-            </filter>
-        </data>
-        <data format="txt" name="data_out" label="maSigPro data file on ${on_string}">
-            <filter>
-                ((
-                    source['source_selector'] == 'advanced' and
-                    source['enable_output'] == True
-                ))
-            </filter>
-        </data>
-        <data format="pdf" name="pdf_out" from_work_dir="Results.pdf" label="maSigPro Plot file on ${on_string}">
-            <filter>
-                ((
-                    pdf['pdf_selector'] == True
-                ))
-            </filter>
-        </data>
-    </outputs>
-    <tests>
-        <test>
-            <param name="source_selector" value="advanced" />
-            <param name="enable_output" value="1" />
-            <repeat name="rep_time">
-                <param name="time" value="1" />
-                <param name="files" value="control_1H.counts,treat_1H.counts" />
-            </repeat>
-            <repeat name="rep_time">
-                <param name="time" value="2" />
-                <param name="files" value="control_2H.counts,treat_2H.counts" />
-            </repeat>
-            <repeat name="rep_time">
-                <param name="time" value="3" />
-                <param name="files" value="control_3H.counts,treat_3H_1.counts,treat_3H_2.counts" />
-            </repeat>
-            <param name="replicates_selector" value="advanced" />
-            <repeat name="rep_repl">
-                <param name="files" value="treat_3H_1.counts,treat_3H_2.counts" />
-            </repeat>
-            <repeat name="rep_groups">
-                <param name="name" value="Control" />
-                <param name="files" value="control_1H.counts,control_2H.counts,control_3H.counts" />
-            </repeat>
-            <repeat name="rep_groups">
-                <param name="name" value="Treatment" />
-                <param name="files" value="treat_1H.counts,treat_2H.counts,treat_3H_1.counts,treat_3H_2.counts" />
-            </repeat>
-            <output name="masigpro_out" file="masigpro_out.tab" />
-            <output name="data_out" file="data_out.txt" />
-            <output name="edesign_out" file="edesign_out.txt" />
-            <output name="pdf_out" file="Results.pdf" />
-        </test>
-        <test>
-            <param name="source_selector" value="defaults" />
-            <param name="edesign" value="edesign_out.txt" />
-            <param name="data" value="data_out.txt" />
-            <output name="masigpro_out" file="masigpro_out.tab" />
-            <output name="pdf_out" file="Results.pdf" />
-        </test>
-    </tests>
-    <help>
-<![CDATA[
-.. class:: infomark
-
-**What it does**
-
-maSigPro_ is a regression based approach to find genes for which there are significant gene expression profile differences between experimental groups in time course microarray and RNA-Seq experiments.
-
-**Inputs**
-
-The maSigPro wrapper has two options for input data:
-
-    - directly through two seperate text files containing the experiment design (edesign) and the data or
-    - count tables generated from HTSeq-count. Count tables must be generated for each sample individually.
-      
-To set up an experimental design from seperate count files you first have to select which files belong to a certain time point.
-Likewise you can specify which files are replicates. In a third step you have to create the experimental groups and select the related files.
-For a more comfortable setup in future analysis you have the option to output the generated edesign and data files.
-
-**Output**
-
-maSigPro_ generates a summary file containing the list of significant genes. Additionally you can obtain a PDF file containing plots of profiles and groups that visualize the clustering analysis.
-
-.. _maSigPro: https://bioconductor.org/packages/release/bioc/html/maSigPro.html
-]]>
-    </help>
-    <citations>
-        <citation type="doi">10.1093/bioinformatics/btl056</citation>
-    </citations>
-</tool>
\ No newline at end of file
+<tool id="masigpro" name="maSigPro" version="1.49.3.0">
+    <description>Significant Gene Expression Profile Differences in Time Course Gene Expression Data</description>
+    <requirements>
+        <requirement type="package" version="1.49.3">bioconductor-masigpro</requirement>
+        <requirement type="package" version="1.3.2">r-optparse</requirement>
+        <requirement type="package" version="4.4">sed</requirement>
+    </requirements>
+    <stdio>
+        <regex match="Execution halted"
+            source="both"
+            level="fatal"
+            description="Execution halted." />
+        <regex match="Error in"
+            source="both"
+            level="fatal"
+            description="An undefined error occurred, please check your input carefully and contact your administrator." />
+        <regex match="Fatal error"
+            source="both"
+            level="fatal"
+            description="An undefined error occurred, please check your input carefully and contact your administrator." />
+    </stdio>
+    <version_command>
+    <![CDATA[
+        echo $(R --version | grep version | grep -v GNU)", maSigPro version" $(R --vanilla --slave -e "library(maSigPro); cat(sessionInfo()\$otherPkgs\$maSigPro\$Version)" 2> /dev/null | grep -v -i "WARNING: ")
+    ]]>
+    </version_command>
+    <command>
+    <![CDATA[
+    #if str($source.source_selector) == "advanced":
+        paste
+        #set $start = True
+        #set $header = ''
+        #for $time in $source.rep_time:
+            #for $file in $time.files:
+                #if $start:
+                    <(cut -f1 $file)
+                    #set $start = False
+                #end if
+                #set $header += ' "' + $file.name + '"'
+                <(cut -f2 $file)
+            #end for
+        #end for
+        > data && sed -i '1i$header' data &&
+        #if $source.enable_output:
+            ln -f data $data_out && ln -f $design_matrix $edesign_out &&
+        #end if
+        #set $data = 'data'
+        #set $edesign = $design_matrix
+    #else:
+        #set $data = $source.data
+        #set $edesign = $source.edesign
+    #end if
+    Rscript '${__tool_directory__}/masigpro.R'
+    -e '$edesign'
+    -d '$data'
+    -o '$masigpro_out'
+    #if str($source.source_selector) == "defaults":
+        --time_col $source.time_col
+        --repl_col $source.repl_col
+    #end if
+    --degree $makeDesignMatrix.degree
+    --qvalue $p_vector.qvalue
+    --min_obs $p_vector.min_obs
+    --step_method '$Tfit.step_method'
+    --nvar_correction $Tfit.nvar_correction
+    --alfa $Tfit.alfa
+    --rsq $getSiggenes.rsq
+    --vars '$getSiggenes.vars'
+    --significant_intercept '$getSiggenes.significant_intercept'
+    #if $pdf.pdf_selector:
+        --cluster_data $pdf.seeGenes.clusterData
+        -k $pdf.seeGenes.k
+        --cluster_method $pdf.seeGenes.clustering.clusterMethod
+        #if str($pdf.seeGenes.clustering.clusterMethod) == "hclust":
+            --distance $pdf.seeGenes.clustering.distance
+            --agglo_method $pdf.seeGenes.clustering.aggloMethod
+        #end if
+        #if str($pdf.seeGenes.clustering.clusterMethod) == "kmeans":
+            --iter_max $pdf.seeGenes.clustering.iterMax
+        #end if
+        --color_mode $pdf.seeGenes.colorMode
+        --show_fit $pdf.seeGenes.showFit
+        --show_lines $pdf.seeGenes.showLines
+        --cexlab $pdf.seeGenes.cexlab
+        --legend $pdf.seeGenes.legend
+    #end if
+    ]]>
+    </command>
+    <configfiles>
+<configfile name="design_matrix">#if str($source.source_selector) == "advanced":
+#set $header = "Name Time Replicate"
+#for $group in $source.rep_groups:
+    #set $header = $header + ' ' + str($group.name)
+#end for
+$header
+#set $c = len($source.rep_repl) + 1
+#for $time in $source.rep_time:
+    #for $file in $time.files:
+        #set $is_repl = False
+        #for $i, $repl in enumerate($source.rep_repl):
+            #if str($file) in str($repl.files):
+                #set $r = $i + 1
+                #set $is_repl = True
+            #end if
+        #end for
+        #if $is_repl == False:
+            #set $r = $c
+            #set $c += 1
+        #end if
+        #set $line = '"' + str($file.name) + '" ' + str($time.time) + ' ' + str($r)
+        #for $group in $source.rep_groups:
+            #if str($file) in str($group.files):
+                #set $line += " 1"
+            #else
+                #set $line += " 0"
+            #end if
+        #end for
+$line
+    #end for
+#end for
+#end if
+</configfile>
+    </configfiles>
+    <inputs>
+        <conditional name="source">
+            <param label="Choose data source" name="source_selector"
+                help="Choose if you want to provide seperate count files (e.g. from HTSeq-count or feature-seq) 
+                    and define your experiment design matrix here, or if you have maSigPro edesign and data input files already."
+                type="select">
+                <option value="defaults">Use maSigPro edesign and data files</option>
+                <option value="advanced">Seperate count data (e.g. from HTSeq-count or feature-count)</option>
+            </param>
+            <when value="defaults">
+                <param name="edesign" format="tabular,txt" type="data" label="Experiment matrix"
+                    help="Matrix describing experimental design. Rows must be arrays and columns experiment descriptors" />
+                <param name="data" format="tabular,txt" type="data" label="Gene expression matrix"
+                    help="Matrix containing normalized gene expression data. Genes must be in rows and arrays in columns" />
+                <param name="time_col" label="Column number containing time values" type="integer" value="1"
+                    help="Column number in edesign containing time values. Default is first column" />
+                <param name="repl_col" label="Column number containing replicate coding" type="integer" value="2"
+                    help="Column number in edesign containing coding for replicate arrays. Default is second column" />
+            </when>
+            <when value="advanced">
+                <param name="enable_output" type="boolean" truevalue="1" falsevalue="0" checked="false" label="Output generated maSigPro input files?"
+                    help="Choose if you want to output the generated edesign and data files for direct use in maSigPro as history elements." />
+                <repeat name="rep_time" title="Time values" min="1" default="1">
+                    <param name="time" type="integer" value="0" label="Specify a numerical time value" help="Only numbers will be allowed">
+                    <sanitizer>
+                        <valid initial="string.digits"></valid>
+                    </sanitizer>
+                    </param>
+                    <param name="files" type="data" format="tabular" multiple="true" label="Counts file(s) at this time value" />
+                </repeat>
+                <repeat name="rep_groups" title="Experimental groups" min="1" default="1">
+                    <param name="name" type="text" value="Group title" label="Specify the name of this experimental group"
+                        help="Use a single name without spaces or special characters">
+                    </param>
+                    <param name="files" type="data" format="tabular" multiple="true"
+                        label="Counts file(s) belonging to this experimental group" />
+                </repeat>
+                <repeat name="rep_repl" title="Replicates" min="0" default="0">
+                    <param name="files" type="data" format="tabular" multiple="true" label="Counts files that are replicates" />
+                </repeat>
+            </when>
+        </conditional>
+        <section name="makeDesignMatrix"
+            title="Step 1: make.Design.Matrix - Defining the regression model"
+            help="‘make.design.matrix’ creates the design matrix of dummies for
+                fitting time series micorarray gene expression experiments.">
+            <param name="degree" type="integer" value="1"
+                label="Degree of regression fit polynome"
+                help="The degree of the regression fit polynome. ‘degree’ = 1 returns 
+                    linear regression, ‘degree’ = 2 returns quadratic regression, etc" />
+        </section>
+        <section name="p_vector"
+            title="Step 2: p.vector - Finding significant genes"
+            help="‘p.vector’ performs a regression fit for each gene taking all
+                variables present in the model given by a regression matrix and
+                returns a list of FDR corrected significant genes">
+            <param name="qvalue" type="float" value="0.05" label="Q" help="Significance level" />
+            <param name="min_obs" label="Minimum values" type="integer" value="6"
+                help="Genes with less than this number of true numerical values
+                    will be excluded from the analysis. Minimum value to estimate
+                    the model is (degree+1)xGroups+1. Default is 6." />
+        </section>
+        <section name="Tfit" title="Step 3: T.fit - Finding significant differences"
+            help="‘T.fit’ selects the best regression model for each gene using
+                stepwise regression. In the maSigPro approach ‘p.vector’ and ‘T.fit’ are subsequent
+                steps, meaning that significant genes are first selected on the
+                basis of a general model and then the significant variables for
+                each gene are found by step-wise regression.">
+            <param name="step_method" type="select" label="Step regression"
+                help="The step regression can be ‘backward’ or ‘forward’ indicating
+                    whether the step procedure starts from the model with all or none
+                    variables. With the ‘two.ways.backward’ or ‘two.ways.forward’
+                    options the variables are both allowed to get in and out. At each
+                    step the p-value of each variable is computed and variables get
+                    in/out the model when this p-value is lower or higher than given
+                    threshold alfa.">
+                <option selected="True" value="backward">backward</option>
+                <option value="forward">forward</option>
+                <option value="two.ways.backward">two.ways.backward</option>
+                <option value="two.ways.forward">two.ways.forward</option>
+            </param>
+            <param type="boolean" name="nvar_correction" label="nvar correction" truevalue="TRUE" falsevalue="FALSE" checked="false"
+                help="When nvar.correction is TRUE the given significance
+                    level is corrected by the number of variables in the model.">
+                <option selected="True" value="FALSE">False</option>
+                <option value="TRUE">True</option>
+            </param>
+            <param name="alfa" type="float" value="0.05" label="alfa" help="Significance level used for variable selection in the stepwise regression" />
+        </section>
+        <section name="getSiggenes"
+            title="Step 4: get.siggenes - Obtaining lists of significant genes"
+                help="This function creates lists of significant genes for a set of
+                    variables whose significance value has been computed with the
+                    ‘T.fit’ function.">
+            <param name="rsq" type="float" value="0.7" label="rsq"
+                help="cut-off level at the R-squared value for the stepwise
+                    regression fit. Only genes with R-squared more than rsq are
+                    selected" />
+            <param name="vars" type="select" label="Variables"
+                help="Variables for which to extract significant genes.
+                    ‘all’: generates one single matrix or gene list with all
+                    significant genes.
+
+                    ‘each’: generates as many significant genes extractions as
+                    variables in the general regression model. Each extraction
+                    contains the significant genes for that variable.
+
+                    ‘groups’: generates a significant genes extraction for each
+                    experimental group.
+
+                    The difference between ‘each’ and ‘groups’ is that in the
+                    first case the variables of the same group (e.g.  ‘TreatmentA’
+                    and ‘time*TreatmentA’) will be extracted separately and in t
+                    he
+                    second case jointly.">
+                <option selected="True" value="groups">Groups</option>
+                <option value="each">Each</option>
+                <option value="all">All</option>
+            </param>
+            <param name="significant_intercept" type="select" label="Significant intercept"
+                help="The argument ‘significant.intercept’ modulates the treatment for
+                    intercept coefficients to apply for selecting significant genes
+                    when ‘vars’ equals ‘groups’. There are three possible values:
+                    ‘none’, no significant intercept (differences) are considered
+                    for significant gene selection, ‘dummy’, includes genes with
+                    significant intercept differences between control and experimental
+                    groups, and ‘all’ when both significant intercept coefficient
+                    for the control group and significant intercept differences are
+                    considered for selecting significant genes.">
+                <option selected="True" value="dummy">Dummy</option>
+                <option value="none">None</option>
+                <option value="all">All</option>
+            </param>
+        </section>
+        <conditional name="pdf">
+            <param label="Generate visualization PDF" name="pdf_selector" type="boolean"
+                truevalue="1" falsevalue="0" checked="true"
+                help="Choose if you want to generate a PDF file containing the visualizations" />
+            <when value="1">
+                <section name="seeGenes" title="Step 5: see.genes - Visualization"
+                    help="This function provides visualisation tools for gene expression
+                        values in a time course experiment. The function first calls the
+                        heatmap function for a general overview of experiment results.
+                        Next a partioning of the data is generated using a clustering
+                        method.  The results of the clustering are visualized both as gene
+                        expression profiles extended along all arrays in the experiment,
+                        as provided by the plot.profiles function, and as summary
+                        expression profiles for comparison among experimental groups.">
+                    <param name="clusterData" label="Cluster Data" type="integer" value="1"
+                        help="Data clustering can be done on the basis of either the original
+                            expression values, the regression coefficients, or the t.scores.
+                            In case ‘data’ is a ‘get.siggenes’ object, this is given by
+                            providing the element names of the list
+                            ‘c(sig.profiles,coefficients,t.score)’ of their list
+                            position (1,2 or 3)." />
+                    <param name="k" type="integer" label="Number of clusters for data partioning" value="9" />
+                    <conditional name="clustering">
+                        <param name="clusterMethod" label="Cluster Method" type="select"
+                            help="clustering method for data partioning. Currently
+                                ‘hclust’, ‘kmeans’ and ‘Mclust’ are supported">
+                            <option selected="True" value="hclust">hclust</option>
+                            <option value="kmeans">kmeans</option>
+                            <option value="Mclust">Mclust</option>
+                        </param>
+                        <when value="hclust">
+                            <param name="distance" type="select" label="Distance measure"
+                                help="Distance measurement function when ‘cluster.method’ is
+                                    ‘hclust’. Default uses correlation.">
+                                <option selected="True" value="cor">Correlation</option>
+                                <option value="euclidean">Euclidean</option>
+                                <option value="maximum">Maximum</option>
+                                <option value="manhattan">Manhattan</option>
+                                <option value="Canberra">Canberra</option>
+                                <option value="binary">Binary</option>
+                                <option value="minkowski">Minkowski</option>
+                            </param>
+                            <param name="aggloMethod" type="select" label="Agglomeration method"
+                                help="The agglomeration method to be used when ‘cluster.method’ is ‘hclust’.">
+                                <option selected="True" value="ward.D">ward.D</option>
+                                <option value="ward.D2">ward.D2</option>
+                                <option value="single">single</option>
+                                <option value="complete">complete</option>
+                                <option value="average">average (= UPGMA)</option>
+                                <option value="mcquitty">mcquitty (= WPGMA)</option>
+                                <option value="median">median (= WPGMC)</option>
+                                <option value="centroid">centroid (= UPGMC)</option>
+                            </param>
+                        </when>
+                        <when value="kmeans">
+                            <param name="iterMax" type="integer" label="Maximum number of iterations" value="500"
+                                help="Maximum number of iterations when ‘cluster.method’ is ‘kmeans’" />
+                        </when>
+                    </conditional>
+                    <param name="colorMode" label="Color Mode" type="select" help="Color scale for plotting profiles. Can be either ‘rainbow’ or ‘gray’">
+                        <option selected="True" value="rainbow">Rainbow</option>
+                        <option value="gray">Gray</option>
+                    </param>
+                    <param name="showFit" type="boolean" truevalue="TRUE" falsevalue="FALSE" checked="true" label="Show regression fit curves?"
+                        help="Indicating whether regression fit curves must be plotted" />
+                    <param name="showLines" type="boolean" truevalue="TRUE" falsevalue="FALSE" checked="true" label="Draw lines?"
+                        help="Indicating whether a line must be drawn joining plotted data points for each group" />
+                    <param name="cexlab" type="float" value="0.8" label="Magnification for x labels"
+                        help="Graphical parameter maginfication to be used for x labels in plotting functions" />
+                    <param name="legend" type="boolean" truevalue="TRUE" falsevalue="FALSE" checked="true" label="Add legend to plotting profiles?"
+                        help="Indicating whether legend must be added when plotting profiles" />
+                </section>
+            </when>
+        </conditional>
+    </inputs>
+    <outputs>
+        <data format="tabular" name="masigpro_out" label="maSigPro result file on ${on_string}">
+        </data>
+        <data format="txt" name="edesign_out" label="maSigPro edesign file on ${on_string}">
+            <filter>
+                (( 
+                    source['source_selector'] == 'advanced' and
+                    source['enable_output'] == True 
+                ))
+            </filter>
+        </data>
+        <data format="txt" name="data_out" label="maSigPro data file on ${on_string}">
+            <filter>
+                ((
+                    source['source_selector'] == 'advanced' and
+                    source['enable_output'] == True
+                ))
+            </filter>
+        </data>
+        <data format="pdf" name="pdf_out" from_work_dir="Results.pdf" label="maSigPro Plot file on ${on_string}">
+            <filter>
+                ((
+                    pdf['pdf_selector'] == True
+                ))
+            </filter>
+        </data>
+    </outputs>
+    <tests>
+        <test>
+            <param name="source_selector" value="advanced" />
+            <param name="enable_output" value="1" />
+            <repeat name="rep_time">
+                <param name="time" value="1" />
+                <param name="files" value="control_1H.counts,treat_1H.counts" />
+            </repeat>
+            <repeat name="rep_time">
+                <param name="time" value="2" />
+                <param name="files" value="control_2H.counts,treat_2H.counts" />
+            </repeat>
+            <repeat name="rep_time">
+                <param name="time" value="3" />
+                <param name="files" value="control_3H.counts,treat_3H_1.counts,treat_3H_2.counts" />
+            </repeat>
+            <param name="replicates_selector" value="advanced" />
+            <repeat name="rep_repl">
+                <param name="files" value="treat_3H_1.counts,treat_3H_2.counts" />
+            </repeat>
+            <repeat name="rep_groups">
+                <param name="name" value="Control" />
+                <param name="files" value="control_1H.counts,control_2H.counts,control_3H.counts" />
+            </repeat>
+            <repeat name="rep_groups">
+                <param name="name" value="Treatment" />
+                <param name="files" value="treat_1H.counts,treat_2H.counts,treat_3H_1.counts,treat_3H_2.counts" />
+            </repeat>
+            <output name="masigpro_out" file="masigpro_out.tab" />
+            <output name="data_out" file="data_out.txt" />
+            <output name="edesign_out" file="edesign_out.txt" />
+            <output name="pdf_out" file="Results.pdf" />
+        </test>
+        <test>
+            <param name="source_selector" value="defaults" />
+            <param name="edesign" value="edesign_out.txt" />
+            <param name="data" value="data_out.txt" />
+            <output name="masigpro_out" file="masigpro_out.tab" />
+            <output name="pdf_out" file="Results.pdf" />
+        </test>
+    </tests>
+    <help>
+<![CDATA[
+.. class:: infomark
+
+**What it does**
+
+maSigPro_ is a regression based approach to find genes for which there are significant gene expression profile differences between experimental groups in time course microarray and RNA-Seq experiments.
+
+**Inputs**
+
+The maSigPro wrapper has two options for input data:
+
+    - directly through two seperate text files containing the experiment design (edesign) and the data or
+    - count tables generated from HTSeq-count. Count tables must be generated for each sample individually.
+      
+To set up an experimental design from seperate count files you first have to select which files belong to a certain time point.
+Likewise you can specify which files are replicates. In a third step you have to create the experimental groups and select the related files.
+For a more comfortable setup in future analysis you have the option to output the generated edesign and data files.
+
+**Output**
+
+maSigPro_ generates a summary file containing the list of significant genes. Additionally you can obtain a PDF file containing plots of profiles and groups that visualize the clustering analysis.
+
+.. _maSigPro: https://bioconductor.org/packages/release/bioc/html/maSigPro.html
+]]>
+    </help>
+    <citations>
+        <citation type="doi">10.1093/bioinformatics/btl056</citation>
+    </citations>
+</tool>
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