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changeset 10:b147b17759a6 draft

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planemo upload for repository https://github.com/workflow4metabolomics/anova commit 28838bb8dafd6d286157db77f181ed8a1b586664
author lecorguille
date Wed, 28 Feb 2018 07:44:13 -0500
parents 4f06e1796334
children 102049093b7d
files README.rst abims_anova.r abims_anova.xml static/images/anova_filtered.png static/images/anova_pvalue.png static/images/anova_workflow.png test-data/dataMatrix.tsv_anova_filtered.tabular test-data/dataMatrix.tsv_anova_pvalue.tabular test-data/dataMatrix.tsv_anova_signif.pdf test-data/variableMetadata.tsv test-data/variableMetadata.tsv_anova_pvalue.tabular
diffstat 11 files changed, 183 insertions(+), 191 deletions(-) [+]
line wrap: on
line diff
--- a/README.rst	Thu Oct 26 09:30:56 2017 -0400
+++ b/README.rst	Wed Feb 28 07:44:13 2018 -0500
@@ -2,6 +2,14 @@
 Changelog/News
 --------------
 
+**Version 1.2.0 23/02/2018**
+
+- Fix for bug in multiple testing correction when more than one factor.
+- Adding results in variable metadata file instead of putting it in data matrix
+- Adding mean calculation
+- Converting filtered output into a pdf file with Venn diagram and boxplots + a "selected.var" column
+- New dependency : R package 'venn'
+
 
 **Version 1.1.4 04/04/2016**
 
--- a/abims_anova.r	Thu Oct 26 09:30:56 2017 -0400
+++ b/abims_anova.r	Wed Feb 28 07:44:13 2018 -0500
@@ -3,7 +3,7 @@
 
 # date: 06-06-2012
 # update: 18-02-2014
-# **Authors** Gildas Le Corguille  ABiMS - UPMC/CNRS - Station Biologique de Roscoff - gildas.lecorguille|at|sb-roscoff.fr 
+# **Authors** Gildas Le Corguille  ABiMS - UPMC/CNRS - Station Biologique de Roscoff - gildas.lecorguille|at|sb-roscoff.fr
 
 # abims_anova.r version 20140218
 
@@ -11,25 +11,35 @@
 
 
 # function avova
-anova = function (file, sampleinfo, mode="column", condition=1, interaction=F, method="BH", threshold=0.01, selection_method="intersection", sep=";", dec=".", outputdatapvalue="anova.data.output", outputdatafiltered="anova.datafiltered.output") {
-	
- 
+anova = function (file, sampleinfo, varinfo, mode="column", condition=1, interaction=F, method="BH", threshold=0.01, selection_method="intersection", sep=";", dec=".", outputdatapvalue="anova.data.output", outputdatasignif="anova.datasignif.output") {
+
+
 	if (sep=="tabulation") sep="\t"
     	if (sep=="semicolon") sep=";"
     	if (sep=="comma") sep=","
 
 	anova_formula_operator = "+"
 	if (interaction) anova_formula_operator = "*"
-  
+
   	# -- import --
-	data=read.table(file, header = TRUE, row.names=1, sep = sep, quote="\"", dec = dec, fill = TRUE, comment.char="",na.strings = "NA")
-	
+	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)
-	
+
 	sampleinfoTab=read.table(sampleinfo, header = TRUE, row.names=1, sep = sep, quote="\"")
 	rownames(sampleinfoTab) = make.names(rownames(sampleinfoTab))
 
-	
+	varinfoTab=read.table(varinfo, header = TRUE, sep = sep, quote="\"")
+	if(sum(colnames(data)!=varinfoTab[,1])!=0){ # if ID not exactly identical
+		if(sum(colnames(data)[order(colnames(data))]!=varinfoTab[order(varinfoTab[,1]),1])==0){
+			# reorder data matrix to match variable metadata order
+			data = data[,match(varinfoTab[,1],colnames(data))]
+		}else{
+			stop(paste0("\nVariables' ID do not match between your data matrix and your variable",
+			"metadata file. \nPlease check your data."))
+		}
+	}
+
 	# -- group --
 	match_data_sampleinfoTab = match(rownames(data),rownames(sampleinfoTab))
 	if (sum(is.na(match_data_sampleinfoTab)) > 0) {
@@ -41,10 +51,10 @@
 	  write(head(rownames(sampleinfoTab)), stderr())
 	  quit("no",status=10)
 	}
-  
-	
+
+
 	# -- anova --
-  
+
   	# formula
 	grps=list()
 	anova_formula_s = "data ~ "
@@ -60,16 +70,16 @@
 	anova_formula = as.formula(anova_formula_s)
 
 
-	
+
 	# anova
 	manovaObjectList = manova(anova_formula)
 	manovaList = summary.aov(manovaObjectList)
-	
+
   	# condition renaming
 	manovaRownames = gsub(" ","",rownames(manovaList[[1]]))
 	manovaNbrPvalue = length(manovaRownames)-1
 	manovaRownames = manovaRownames[-(manovaNbrPvalue+1)]
-	
+
 	for (i in 1:length(condition)) {
 	  manovaRownames = sub(paste("grps\\[\\[",i,"\\]\\]",sep=""),condition[i],manovaRownames)
   	  anova_formula_s = sub(paste("grps\\[\\[",i,"\\]\\]",sep=""),condition[i],anova_formula_s)
@@ -77,45 +87,94 @@
 
   	# log
   	cat("\nanova_formula",anova_formula_s,"\n")
-	
+
 	# p-value
 	aovPValue = sapply(manovaList,function(x){x[-(manovaNbrPvalue+1),5]})
 	if(length(condition) == 1) aovPValue = t(aovPValue)
 	rownames(aovPValue) = paste("pvalue_",manovaRownames,sep="")
-  
+
 	# p-value adjusted
 	if(length(condition) == 1) {
 		aovAdjPValue = t(p.adjust(aovPValue,method=method))
 	} else {
-		aovAdjPValue = apply(aovPValue,2,p.adjust, method=method)
+		aovAdjPValue = t(apply(aovPValue,1,p.adjust, method=method))
 	}
-	rownames(aovAdjPValue) = paste("pvalueadjusted.",method,".",manovaRownames,sep="")
-	
+	rownames(aovAdjPValue) = paste("pval.",method,".",manovaRownames,sep="")
+
 	# selection
 	colSumThreshold = colSums(aovAdjPValue <= threshold)
 	if (selection_method == "intersection") {
-		datafiltered = data[,colSumThreshold == nrow(aovAdjPValue )]
+		datafiltered = data[,colSumThreshold == nrow(aovAdjPValue ), drop=FALSE]
 	} else {
-		datafiltered = data[,colSumThreshold != 0]
+		datafiltered = data[,colSumThreshold != 0, drop=FALSE]
 	}
-	
+	selected.var = rep("no",ncol(data))
+	selected.var[colnames(data)%in%colnames(datafiltered)] = "yes"
+
 	#data=rbind(data, aovPValue, aovAdjPValue)
-	data=rbind(data, aovAdjPValue)
+	varinfoTab=cbind(varinfoTab, round(t(aovAdjPValue),10), selected.var)
+
+	# group means
+	for (i in 1:length(condition)) {
+		for(j in levels(grps[[i]])){
+			subgp = rownames(sampleinfoTab[which(sampleinfoTab[,condition[i]]==j),])
+			modmean = colMeans(data[which(rownames(data)%in%subgp),],na.rm=TRUE)
+			varinfoTab=cbind(varinfoTab, modmean)
+			colnames(varinfoTab)[ncol(varinfoTab)] = paste0("Mean_",condition[i],".",j)
+		}
+	}
+	colnames(varinfoTab) = make.unique(colnames(varinfoTab))
 
-	
-	if (mode == "row") {
-	  data=t(data)
-	  datafiltered=t(datafiltered)
+	# pdf for significant variables
+	pdf(outputdatasignif)
+	### Venn diagram
+	if(nrow(aovAdjPValue)>5){
+		pie(100,labels=NA,main=paste0("Venn diagram only available for maximum 5 terms\n",
+		    "(your analysis concerns ",nrow(aovAdjPValue)," terms)\n\n",
+			"Number of significant variables relatively to\nyour chosen threshold and ",
+			"selection method: ",ncol(datafiltered)),cex.main=0.8)
+	}else{
+		vennlist = list(NULL)
+		names(vennlist) = rownames(aovAdjPValue)[1]
+		if(length(colnames(aovAdjPValue))==0){colnames(aovAdjPValue)=varinfoTab[,1]}
+		for(i in 1:nrow(aovAdjPValue)){
+			vennlist[[rownames(aovAdjPValue)[i]]]=colnames(aovAdjPValue[i,which(aovAdjPValue[i,]<=threshold),drop=FALSE])
+		}
+		if(length(vennlist)==0){ pie(100,labels=NA,main="No significant ions was found.")
+		}else{ library(venn) ; venn(vennlist, zcolor="style", cexil=2, cexsn=1.5) }
 	}
-	
+	### Boxplot
+	par(mfrow=c(2,2),mai=rep(0.5,4))
+	data <- as.data.frame(data)
+	for(i in 1:nrow(aovAdjPValue)){
+		factmain = gsub(paste0("pval.",method,"."),"",rownames(aovAdjPValue)[i])
+		factsignif = aovAdjPValue[i,which(aovAdjPValue[i,]<=threshold),drop=FALSE]
+		if((ncol(factsignif)!=0)&(factmain%in%colnames(sampleinfoTab))){
+			for(j in 1:ncol(factsignif)){
+				varsignif = gsub(" Response ","",colnames(factsignif)[j])
+				boxplot(as.formula(paste0("data$",varsignif," ~ sampleinfoTab$",factmain)),
+				        main=paste0(factmain,"\n",varsignif), col="grey", mai=7)
+			}
+		}
+	}
+	dev.off()
+
+	# summary for significant variables
+	cat("\n\n- - - - - - - number of significant variables - - - - - - -\n\n")
+	for(i in 1:nrow(aovAdjPValue)){
+		cat(rownames(aovAdjPValue)[i],"-",
+		    sum(aovAdjPValue[i,]<=threshold),"significant variable(s)\n")
+	}
+	cat("\nIf some of your factors are missing here, this may be due to\neffects",
+	    "not estimable; your design may not be balanced enough.\n")
+
 	# -- output / return --
-	write.table(data, outputdatapvalue, sep=sep, quote=F, col.names = NA)
-	write.table(datafiltered, outputdatafiltered, sep=sep, quote=F, col.names = NA)
-	
-	# log 
+	write.table(varinfoTab, outputdatapvalue, sep=sep, quote=F, row.names=FALSE)
+
+	# log
 	cat("\nthreshold:",threshold,"\n")
-	cat("result:",nrow(datafiltered),"/",nrow(data),"\n")
-  
+	cat("result:",ncol(datafiltered),"/",ncol(data),"\n\n")
+
 	quit("no",status=0)
 }
 
@@ -127,6 +186,3 @@
 
 listArguments = parseCommandArgs(evaluate=FALSE)
 do.call(anova, listArguments)
-
-
-
--- a/abims_anova.xml	Thu Oct 26 09:30:56 2017 -0400
+++ b/abims_anova.xml	Wed Feb 28 07:44:13 2018 -0500
@@ -1,9 +1,10 @@
-<tool id="abims_anova" name="Anova" version="1.1.4">
-    
+<tool id="abims_anova" name="Anova" version="1.2.0">
+
     <description>N-way anova. With ou Without interactions</description>
-    
+
     <requirements>
 	<requirement type="package" version="1.1_4">r-batch</requirement>
+	<requirement type="package" version="1.2">r-venn</requirement>
     </requirements>
 
     <stdio>
@@ -11,39 +12,40 @@
     </stdio>
 
     <command>
-Rscript $__tool_directory__/abims_anova.r file '$input' sampleinfo '$sampleinfo' mode '$mode' 
+Rscript $__tool_directory__/abims_anova.r file '$input' sampleinfo '$sampleinfo' varinfo '$varinfo' mode '$mode'
 condition "c('$condition_1'
 #for $i, $s in enumerate( $conditions )
 ,'${s.condition}'
 #end for
 )"
-interaction $interaction 
-method $method 
-threshold $threshold 
-selection_method $selection_method 
-sep '$sep' 
-dec '$dec' 
-outputdatapvalue '$dataMatrixPValue' 
-outputdatafiltered '$dataMatrixFiltered'
-    </command> 
+interaction $interaction
+method $method
+threshold $threshold
+selection_method $selection_method
+sep '$sep'
+dec '$dec'
+outputdatapvalue '$varMetaPValue'
+outputdatasignif '$dataSignif'
+    </command>
 
     <inputs>
         <param name="input" type="data" label="Data Matrix file" format="tabular" 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="mode" type="select" help="Perform the anova tests on column/row" format="text" optional="true">
+        <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." />
+
+        <param name="mode" type="select" help="Perform the anova tests on column/row; for W4M 3-tables format, use 'row'." format="text" optional="true">
             <label>Mode</label>
-            <option value="row">row</option>
+            <option value="row" selected="True">row</option>
             <option value="column">column</option>
         </param>
-    
+
         <param name="condition_1" type="text" label="Condition" value="" help="The column name of the condition. ex: hour or treatment" optional="false" />
         <repeat name="conditions" title="Conditions for N-ways anova">
             <param name="condition" type="text" label="Condition" value="" help="The column name of the condition. ex: hour or treatment" />
         </repeat>
 
         <param name="interaction" type="boolean" label="Enable interaction response p-values" truevalue="T" falsevalue="F" help="Used if more than 1 conditon. The anova will produse p-value according to the interaction between your condition (ex: condition1:conditions2, condition1:conditions3, condition2:conditions3 and condition1:condition2:conditions3)" />
-            
+
         <param name="method" type="select" help="Method used to apply a correction on the pvalue because of the number of test" >
             <label>PValue adjusted method</label>
             <option value="BH">BH</option>
@@ -53,9 +55,9 @@
             <option value="hommel">hommel</option>
             <option value="BY">BY</option>
             <option value="fdr">fdr</option>
-            <option value="none">none</option>		
-        </param>        
-        
+            <option value="none" selected="True">none</option>
+        </param>
+
         <param name="threshold" type="float" label="Threshold" value="0.01" help="max adjusted p.value accepted" />
 
         <param name="selection_method" type="select" help="Intersection: all condition p-value must be under the threshold. Union: at least condition p-value must be under the threshold. ">
@@ -70,20 +72,21 @@
             <option value="semicolon">;</option>
             <option value="comma">,</option>
         </param>
-        
+
         <param name="dec" type="text" label="Decimal separator" value="." help="" />
 
     </inputs>
 
     <outputs>
-        <data name="dataMatrixPValue" format_source="input" label="${input.name}_anova_pvalue.${input.ext}"/>
-        <data name="dataMatrixFiltered" format_source="input" label="${input.name}_anova_filtered.${input.ext}"/>
+        <data name="varMetaPValue" format_source="varinfo" label="${varinfo.name}_anova_pvalue.${varinfo.ext}"/>
+        <data name="dataSignif" format="pdf" label="${input.name}_anova_signif"/>
     </outputs>
-    
+
     <tests>
         <test>
             <param name="input" value="dataMatrix.tsv"/>
             <param name="sampleinfo" value="sampleMetadata.tsv"/>
+            <param name="varinfo" value="variableMetadata.tsv"/>
             <param name="mode" value="row"/>
             <param name="condition_1" value="age"/>
             <param name="conditions_0|condition" value="gender"/>
@@ -93,8 +96,8 @@
             <param name="selection_method" value="union"/>
             <param name="sep" value="tabulation"/>
             <param name="dev" value="."/>
-            <output name="dataMatrixPValue" file="dataMatrix.tsv_anova_pvalue.tabular" />
-            <output name="dataMatrixFiltered" file="dataMatrix.tsv_anova_filtered.tabular" />
+            <output name="varMetaPValue" file="variableMetadata.tsv_anova_pvalue.tabular" />
+            <output name="dataSignif" file="dataMatrix.tsv_anova_signif.pdf" compare="sim_size" delta="600" />
         </test>
     </tests>
 
@@ -102,7 +105,8 @@
 
 .. class:: infomark
 
-**Authors** Gildas Le Corguille  ABiMS - UPMC/CNRS - Station Biologique de Roscoff - gildas.lecorguille|at|sb-roscoff.fr 
+**Authors** Gildas Le Corguille  ABiMS - UPMC/CNRS - Station Biologique de Roscoff - gildas.lecorguille|at|sb-roscoff.fr
+Melanie Petera - PFEM ; INRA ; MetaboHUB
 
 ---------------------------------------------------
 
@@ -123,14 +127,15 @@
 Input files
 -----------
 
-+---------------------------+------------+
-| Parameter : num + label   |   Format   |
-+===========================+============+
-| 1 : Data Matrix file      | Tabular    |
-+---------------------------+------------+
-| 2 : Sample Metadata file  | Tabular    |
-+---------------------------+------------+
-
++----------------------------+------------+
+| Parameter : num + label    |   Format   |
++============================+============+
+| 1 : Data Matrix file       | Tabular    |
++----------------------------+------------+
+| 2 : Sample Metadata file   | Tabular    |
++----------------------------+------------+
+| 3 : Variable Metadata file | Tabular    |
++----------------------------+------------+
 
 
 ------------
@@ -141,118 +146,28 @@
 
 ***.anova_pvalue.tabular**
 
-    | A tabular file which represents for each metabolite (row), the value of the intensity in each sample (column) + two columns (aovPValue and aovAdjPValue).
-
-***.anova_filtered.tabular**
-
-    | The tabular file xset.anova_pvalue.tabular containing only the metabolites that have been filtered by aovAdjPValue.
-    
-    
-------
-
-.. class:: infomark 
+    | Your variable metadata file completed with columns of p-values, result of selection method and means of subgroups.
 
-The outputs ***.anova_filtered.tabular** or ***.anova_pvalue.tabular** are tabular files. You can continue your analysis using it in the following tools:
-
-    | PCA
-    | Hierarchical Clustering
+***.anova_signif.pdf**
 
-    
-    
----------------------------------------------------
-
----------------
-Working example
----------------
+    | A pdf file containing a Venn diagram and boxplots of significant variables.
 
 
-Input files
------------
-
-**>A part of an example of Data Matrix file input**
-    
+------
 
-+--------+------------------+----------------+
-| Name   | Bur-eH_FSP_12    | Bur-eH_FSP_24  |	                                       
-+========+==================+================+
-|M202T601| 91206595.7559783 |106808979.08546 |
-+--------+------------------+----------------+
-|M234T851| 27249137.275504  |28824971.3177926|  
-+--------+------------------+----------------+       
+.. class:: infomark
 
-**>A part of an example of Sample Metadata file input**
-    
-    
-+---------------------------+------------+------------+------------+
-| Sample name               |   class    |   time     |   batch    |
-+===========================+============+============+============+
-| Bur-eH_FSP_12             | Bur-eH     |    12      |     1      |
-+---------------------------+------------+------------+------------+
-| Bur-eH_FSP_24             | Bur-eH     |    24      |     1      |
-+---------------------------+------------+------------+------------+
-| Bur-NI_FSP_12             | Bur-NI     |    12      |     2      |
-+---------------------------+------------+------------+------------+
-| Bur-NI_FSP_24             | Bur-NI     |    24      |     2      |
-+---------------------------+------------+------------+------------+
-
-Parameters
-----------
+The outputs ***.anova_filtered.tabular** is a tabular file. You can continue your analysis using it in the following tools:
 
-    | Mode -> **row**
-    | column name of condition -> **class**
-    | Separator of columns: -> **tabulation**
-    | Decimal separator -> **.**
-    | PValue adjusted method -> **BH**
-    | Threshold -> **0.001**
-    
-
+    | Generic_filter
+    | Hierarchical Clustering
 
-Output files
-------------
-
-**Part of an example of xset.anova_filtered.tabular:**
-
-.. image:: anova_pvalue.png
-
-**Part of an example of xset.anova_pvalue.tabular:**
-
-.. image:: anova_filtered.png
 
 
 ---------------------------------------------------
 
-    
----------------------------
-Position in workflow4metabo
----------------------------
 
 
-**Upstream tools**
-
-+---------------------------+----------------------------------------+--------+------------------------+
-| Name                      | Output file                            | Format | parameter              |   
-+===========================+========================================+========+========================+
-|CAMERA.annotateDiffreport  |xset.annotatediffreport.data_matrix.tsv | Tabular| Data table file        |   
-+---------------------------+----------------------------------------+--------+------------------------+
-|xcms.xcmsSet               |sample_info.tab                         | Tabular| sample info table file |
-+---------------------------+----------------------------------------+--------+------------------------+
-
-
-**Downstream tools**
-
-+---------------------------+---------------------------------------------------------+--------+
-| Name                      | Output file                                             | Format | 
-+===========================+=========================================================+========+
-|PCA                        |xset.anova_pvalue.tabular OR xset.anova_filtered.tabular | Tabular|
-+---------------------------+---------------------------------------------------------+--------+
-|Hierarchical Clustering    |xset.anova_pvalue.tabular OR xset.anova_filtered.tabular | Tabular|
-+---------------------------+---------------------------------------------------------+--------+
-
-
-**General schema of the metabolomic workflow**
-
-.. image:: anova_workflow.png
-
     </help>
 
     <citations>
Binary file static/images/anova_filtered.png has changed
Binary file static/images/anova_pvalue.png has changed
Binary file static/images/anova_workflow.png has changed
--- a/test-data/dataMatrix.tsv_anova_filtered.tabular	Thu Oct 26 09:30:56 2017 -0400
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,3 +0,0 @@
-	HU_017	HU_028	HU_034	HU_051	HU_060	HU_078	HU_091	HU_093	HU_099	HU_110	HU_130	HU_134	HU_138	HU_149	HU_152	HU_175	HU_178	HU_185	HU_204	HU_208
-HMDB00208	747080	13420742	595872	1172376	7172632	3143654	4059767	1433702	5593888	5402629	2477288	3346077	4230072	7621236	8960828	10335722	7037373	1574738	3359238	2540044
-HMDB00512	53304	319783	280560	85009	1333877	556003	590779	209285	342532	198512	569970	525240	246282	1140422	542345	1171008	827723	222953	438839	85554
--- a/test-data/dataMatrix.tsv_anova_pvalue.tabular	Thu Oct 26 09:30:56 2017 -0400
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,16 +0,0 @@
-	HU_017	HU_028	HU_034	HU_051	HU_060	HU_078	HU_091	HU_093	HU_099	HU_110	HU_130	HU_134	HU_138	HU_149	HU_152	HU_175	HU_178	HU_185	HU_204	HU_208	pvalueadjusted.BH.age	pvalueadjusted.BH.gender
-HMDB03193	76043	412165	44943	27242	436566	173175	242549	57066	559869	3732	339188	471368	262271	127285	451270	212500	79673	NA	891129	43907	0.740856975457218	0.740856975457218
-HMDB01101	30689	6877586	52217	3158	10789748	229568	4763576	3878773	976436	831937	608298	1605075	72021	442510	1107705	1464339	31250	2724553	72900	32742	0.347623051099051	0.234554902330543
-HMDB10348	47259	544877	60885	34582	529874	168264	176500	76457	610110	16262	279156	524468	451573	591487	433529	161069	214392	13781	1580343	39315	0.720303175717481	0.720303175717481
-HMDB59717	357351	1030464	301983	67604	306862	1028110	1530493	270027	1378535	289677	808334	1132813	871209	895435	715190	1563158	784738	146195	994336	239030	0.252979062487671	0.517822514350462
-HMDB00822	483755	579287	1132413	157113	1577570	1469735	1085454	477909	814755	245417	610681	763706	2406336	827531	992508	569605	355321	150259	1334200	271010	0.808532959173048	0.808532959173048
-HMDB13189	2644620	727587	1661412	619181	136278	2755434	593863	837865	3526136	2003278	1608814	3446611	1941527	113937	3132404	2893445	2092753	1034666	1517319	841661	0.329174238111018	0.329174238111018
-HMDB00299	250551	1046138	456162	159386	1013302	808657	614370	250403	768004	242085	504108	1014041	1362408	1057660	1110050	566050	411886	142233	1992420	284775	0.995859884733937	0.995859884733937
-HMDB00191	560002	771533	575790	392284	888498	785428	645785	591569	960658	910201	639437	1092885	1409045	2292023	1246459	1945577	710519	773384	1061418	622898	0.15341384456659	0.15341384456659
-HMDB00518	34236	58249	85944	NA	342102	129886	175800	13154	230242	NA	440223	315368	10657	419508	48673	28361	514579	23108	867108	73831	0.439012867631325	0.596200901535843
-HMDB00715	1252089	2547452	905408	371059	4983588	5140022	2658555	814523	2558923	859466	4184204	3865723	3236644	2615560	3820724	3577833	2295288	625924	7517724	1341900	0.814406453193777	0.814406453193777
-HMDB01032	2569205	26023086	1604999	430453	8103558	26222916	257139	675754	59906109	263055	31151730	18648127	14989438	1554658	20249262	5588731	871010	15920	9120781	44276	0.288354536353544	0.288354536353544
-HMDB00208	747080	13420742	595872	1172376	7172632	3143654	4059767	1433702	5593888	5402629	2477288	3346077	4230072	7621236	8960828	10335722	7037373	1574738	3359238	2540044	0.659785284053633	0.00376251274734483
-HMDB04824	374028	1144386	539206	178517	1046190	959381	605191	310260	1253319	477259	477995	825691	1157093	1089284	1411802	1020206	782673	346761	1824553	387811	0.646714644805001	0.646714644805001
-HMDB00512	53304	319783	280560	85009	1333877	556003	590779	209285	342532	198512	569970	525240	246282	1140422	542345	1171008	827723	222953	438839	85554	0.13976111393526	0.0491480895853803
-HMDB00251	368600	616555	94936	622468	180988	293988	352855	767894	268331	167246	310918	1248919	577184	10985	335711	403815	80614	63393	454489	616061	0.565910223149305	0.565910223149305
Binary file test-data/dataMatrix.tsv_anova_signif.pdf has changed
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/variableMetadata.tsv	Wed Feb 28 07:44:13 2018 -0500
@@ -0,0 +1,16 @@
+variables	numHMDB
+HMDB03193	03193
+HMDB01101	01101
+HMDB10348	10348
+HMDB59717	59717
+HMDB00822	00822
+HMDB13189	13189
+HMDB00299	00299
+HMDB00191	00191
+HMDB00518	00518
+HMDB00715	00715
+HMDB01032	01032
+HMDB00208	00208
+HMDB04824	04824
+HMDB00512	00512
+HMDB00251	00251
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/variableMetadata.tsv_anova_pvalue.tabular	Wed Feb 28 07:44:13 2018 -0500
@@ -0,0 +1,16 @@
+variables	numHMDB	pval.BH.age	pval.BH.gender	selected.var	Mean_age.20-30	Mean_age.30-40	Mean_age.40-50	Mean_age.50-60	Mean_age.60-70	Mean_gender.F	Mean_gender.M
+HMDB03193	3193	0.7937753308	0.7855401401	no	270572	392525.5	279004.4	135576.75	161111	199371.111111111	311760.1
+HMDB01101	1101	0.7449065381	0.4721949521	no	530010.25	647011.75	1923248.66666667	3888168.75	2397413	2430552.8	1228955.3
+HMDB10348	10348	0.780857123	0.8311190489	no	279384	653013.75	291703.666666667	170869.5	195446	257916.8	397501.5
+HMDB59717	59717	0.7449065381	0.7855401401	no	600089.75	1065315.75	761023.666666667	292137.25	1157615.5	603835.3	867319.1
+HMDB00822	822	0.8085329592	0.7855401401	no	558846.5	835504.25	975513	858327.25	720387.5	572562.1	1057894.4
+HMDB13189	13189	0.7449065381	0.5606315068	no	2029845.5	1533378.75	2091740.83333333	1159708.25	1343308	1359519	2053360.1
+HMDB00299	299	0.780857123	0.9958598847	no	580553.75	1030059.5	770671.333333333	490488	513128	603356.5	802112.4
+HMDB00191	191	0.7449065381	0.4721949521	no	805574.75	1484613.75	898712.833333333	741514.5	678152	1055337.6	832201.7
+HMDB00518	518	0.7449065381	0.7855401401	no	117582	438800	95250.6666666667	147066.666666667	345189.5	188551.375	230261.8
+HMDB00715	715	0.780857123	0.8725783427	no	2023151.5	4473830.25	2777975.66666667	1890746.25	2476921.5	2303879.4	3213381.5
+HMDB01032	1032	0.7449065381	0.5606315068	no	20157525	11853975	14744782	2661841.5	564074.5	6314400.9	16514619.8
+HMDB00208	208	0.780857123	0.0282188456	yes	4566784	5948371	4410393.83333333	3651208.75	5548570	6523832	2898663.8
+HMDB04824	4824	0.780857123	0.7855401401	no	807862.25	1103009.5	801223.333333333	593228.75	693932	788488.9	832671.7
+HMDB00512	512	0.7449065381	0.1843053359	no	263860	830059.75	320594.166666667	505558.5	709251	592718.6	381279.4
+HMDB00251	251	0.780857123	0.7855401401	no	460642.75	295051.75	528106.5	302766	216734.5	309783.6	473811.4