# HG changeset patch
# User lecorguille
# Date 1520948841 14400
# Node ID 102049093b7d1c1e56f685fc24532e7b6c71bee7
# Parent  b147b17759a6ea287c701ce7b4e9e3a478108a12
planemo upload for repository https://github.com/workflow4metabolomics/anova commit 4922313a0e9569326b7723c41babb89f998dbfd9

diff -r b147b17759a6 -r 102049093b7d abims_anova.r
--- a/abims_anova.r	Wed Feb 28 07:44:13 2018 -0500
+++ b/abims_anova.r	Tue Mar 13 09:47:21 2018 -0400
@@ -24,7 +24,7 @@
   	# -- import --
 	data=read.table(file, header = TRUE, row.names=1, sep = sep, quote="\"", dec = dec, fill = TRUE, comment.char="",na.strings = "NA", check.names=FALSE)
 
-  	if (mode == "row") data=t(data)
+  	if (mode == "row") {data=t(data)} else {data=as.matrix(data)}
 
 	sampleinfoTab=read.table(sampleinfo, header = TRUE, row.names=1, sep = sep, quote="\"")
 	rownames(sampleinfoTab) = make.names(rownames(sampleinfoTab))
diff -r b147b17759a6 -r 102049093b7d abims_anova.xml
--- a/abims_anova.xml	Wed Feb 28 07:44:13 2018 -0500
+++ b/abims_anova.xml	Tue Mar 13 09:47:21 2018 -0400
@@ -1,4 +1,4 @@
-<tool id="abims_anova" name="Anova" version="1.2.0">
+<tool id="abims_anova" name="Anova" version="1.2.1">
 
     <description>N-way anova. With ou Without interactions</description>
 
@@ -29,7 +29,7 @@
     </command>
 
     <inputs>
-        <param name="input" type="data" label="Data Matrix file" format="tabular" help="Matrix of numeric data with headers." />
+        <param name="input" type="data" label="Data Matrix file" format="tabular,csv" help="Matrix of numeric data with headers." />
         <param name="sampleinfo" type="data" label="Sample Metadata file" format="tabular" help="Tabular file with the data metadata : one sample per line and at least two columns : ids and one condition" />
         <param name="varinfo" type="data" label="Variable Metadata file" format="tabular" help="Tabular file with information about your tested variables. Only used to aggregate generated information." />
 
@@ -121,6 +121,11 @@
 Analysis of variance (ANOVA) is used to analyze the differences between group means and their associated procedures,
 in which the observed variance in a particular variable is partitioned into components attributable to different sources of variation.
 
+**Note about sum of squares (SS) calculation of N-way ANOVA in this module.**
+This module use R function *manova()* (and thus R function *aov()*) to establish N-way ANOVA. 
+Therefore calculated sum of squares are sequential ones (sometimes called "Type I SS"). 
+If your design is unbalanced, this may not correspond to the type of hypothesis being of interest. 
+Note that you can obtain adjusted sums of squares ("Type II SS") by running several times this module with different orders in factors. 
 
 
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