# HG changeset patch
# User ethevenot
# Date 1477851429 14400
# Node ID 140290de798695f43b8f197789779564f9e0ff6a
# Parent  09799fc16bc636384620c8450cc2ddf30ce33751
planemo upload for repository https://github.com/workflow4metabolomics/univariate.git commit 27bc6157f43574f038b3fb1be1f46ce4786e24b1

diff -r 09799fc16bc6 -r 140290de7986 README.md
--- a/README.md	Sat Aug 06 12:42:42 2016 -0400
+++ b/README.md	Sun Oct 30 14:17:09 2016 -0400
@@ -7,8 +7,8 @@
 
 ### Description
 
-**Version:** 2.1.4   
-**Date:** 2016-08-05  
+**Version:** 2.2.0   
+**Date:** 2016-10-30  
 **Author:** Marie Tremblay-Franco (INRA, MetaToul, MetaboHUB, W4M Core Development Team) and Etienne A. Thevenot (CEA, LIST, MetaboHUB, W4M Core Development Team)    
 **Email:** [marie.tremblay-franco(at)toulouse.inra.fr](mailto:marie.tremblay-franco@toulouse.inra.fr); [etienne.thevenot(at)cea.fr](mailto:etienne.thevenot@cea.fr)  
 **Citation:** Thevenot E.A., Roux A., Xu Y., Ezan E. and Junot C. (2015). Analysis of the human adult urinary metabolome variations with age, body mass index and gender by implementing a comprehensive workflow for univariate and OPLS statistical analyses. *Journal of Proteome Research*, **14**:3322-3335. [doi:10.1021/acs.jproteome.5b00354](http://dx.doi.org/10.1021/acs.jproteome.5b00354)  
@@ -50,11 +50,25 @@
 
 ### News
 
+###### CHANGES IN VERSION 2.2.0  
+
+MAJOR MODIFICATION  
+
+ * ANOVA and Kruskal-Wallis: The p-values of the post-hoc tests (i.e. from pairwise comparisons) are now further corrected for multiple testing over all variables (previously, only the p-value of the -first- omnibus test was corrected over all variables)  
+
+MINOR MODIFICATION  
+
+ * All values in the 'dif', adjusted p-value, and 'sig' columns are now displayed (previously, the values were set to NA when the p-value of the omnibus test was not significant)  
+
+NEW FEATURE  
+
+ * Graphic: a single pdf file containing the graphics of all significant tests is now produced as '_figure.pdf' output: boxplots (respectively scatterplots with the regression line in red and the R2 value) are displayed when the factor of interest is qualitative (respectively quantitative). The corrected p-value is indicated in the title of each plot  
+
 ###### CHANGES IN VERSION 2.1.4
 
 NEW FEATURE  
 
-Level names are now separated by '.' instead of '-' previously in the column names of the output variableMetadata table (e.g., 'jour_ttest_J3.J10_fdr' instead of 'jour_ttest_J3-J10_fdr' previously)  
+ * Level names are now separated by '.' instead of '-' previously in the column names of the output variableMetadata table (e.g., 'jour_ttest_J3.J10_fdr' instead of 'jour_ttest_J3-J10_fdr' previously)  
 
 INTERNAL MODIFICATION  
 
diff -r 09799fc16bc6 -r 140290de7986 build.xml
--- a/build.xml	Sat Aug 06 12:42:42 2016 -0400
+++ b/build.xml	Sun Oct 30 14:17:09 2016 -0400
@@ -36,7 +36,7 @@
 			<arg value="--conda_prefix"/>
 			<arg value="${conda.dir}"/>
 			<arg value="--galaxy_branch"/>
-			<arg value="release_16.01"/>
+			<arg value="release_16.07"/>
 			<arg value="--conda_dependency_resolution"/>
 			<arg value="${tool.xml}"/>
 		</exec>
diff -r 09799fc16bc6 -r 140290de7986 runit/input/dataMatrix.tsv
--- a/runit/input/dataMatrix.tsv	Sat Aug 06 12:42:42 2016 -0400
+++ b/runit/input/dataMatrix.tsv	Sun Oct 30 14:17:09 2016 -0400
@@ -1,4 +1,7 @@
-profile	HU_017	HU_021	HU_027	HU_032	HU_041	HU_048	HU_049	HU_050	HU_052	HU_059	HU_060	HU_066	HU_072	HU_077	HU_090	HU_109	HU_110	HU_125	HU_126	HU_131	HU_134	HU_149	HU_150	HU_173	HU_179	HU_180	HU_182	HU_202	HU_204	HU_209
-HMDB01032	2569204.92420381	6222035.77434915	17070707.9912636	1258838.24348419	13039543.0754619	1909391.77026598	3495.09386434063	2293521.90928998	128503.275117713	81872.5276382213	8103557.56578035	149574887.036181	1544036.41049333	7103429.53933206	14138796.50382	4970265.57952158	263054.73056162	1671332.30008058	88433.1944958815	23602331.2894815	18648126.5206986	1554657.98756878	34152.3646391152	209372.71275317	33187733.370626	202438.591636003	13581070.0886437	354170.810678102	9120781.48986975	43419175.4051586
-HMDB03072	3628416.30251025	65626.9834353751	112170.118946651	3261804.34422417	42228.2787747563	343254.201250707	1958217.69317664	11983270.0435677	5932111.41638028	5511385.83359531	9154521.47755199	2632133.21209418	9500411.14556502	6551644.51726592	7204319.80891836	1273412.04795188	3260583.81592376	8932005.5351622	8340827.52597275	9256460.69197759	11217839.169041	5919262.81433556	11790077.0657915	9567977.80797097	73717.5811684739	9991787.29074293	4208098.14739633	623970.649925847	10904221.2642849	2171793.93621067
-HMDB00792	429568.609438384	3887629.50527037	1330692.11658995	1367446.73023821	844197.447472453	2948090.71886592	1614157.90566884	3740009.19379795	3292251.66531919	2310688.79492013	4404239.59008605	3043289.12780863	825736.467181043	2523241.91730649	6030501.02648005	474901.604069803	2885792.42617652	2955990.64049134	1917716.3427982	1767962.67737699	5926203.40397675	1639065.69474684	346810.763557826	1054776.22313737	2390258.27543894	1831346.37315857	1026696.36904362	7079792.50047866	4368341.01359769	3495986.87280275
+dataMatrix	s1	s2	s3	s4	s5	s6	s7	s8	s9	s10	s11	s12	s13	s14	s15	s16	s17	s18	s19	s20	s21	s22	s23	s24	s25	s26	s27	s28	s29	s30	s31	s32
+v1	7.416640524	6.9916690517	7.3404441347	7.0128372669	7.1702617447	6.820858055	7.2648178466	6.9561684785	7.2966652122	7.2695129676	7.2695129676	6.7986507145	6.9749720403	6.7788745443	7.0791812822	7.3617278549	7.4281348102	6.967548023	7.2304489469	6.6608655728	6.9614211415	6.8959747875	7.1398791179	7.2201081142	7.1673173643	6.6857418281	7.1789769761	6.7788745443	6.7291648707	6.7466342768	6.8954226013	6.9454686344
+v2	7.4563660483	6.9014583759	7.3873898441	6.9599948859	7.2095150414	6.7007038037	7.3159703664	6.8876173566	7.3010300174	7.2966652122	7.3138672415	6.7275413384	6.9863238219	6.7435098431	7.1038037552	7.3838153839	7.4409090978	6.7737865181	7.2174839705	6.48995862	6.8494194752	6.8344207673	7.1238516736	7.212187631	7.2041200098	6.6910815806	7.2900346336	6.7427252098	6.7185017719	6.6180482014	6.8512584099	6.9329808726
+v3	6.8785218529	6.7839036507	6.9025468337	7.0086002143	6.9314579215	6.8129134235	6.8305887328	7.0374265378	6.8767950339	6.8457180799	6.7888751864	6.9929951426	7.0374265378	6.8215135939	7.0718820441	6.809559782	6.8122447637	6.7795965634	6.8129134235	6.9599948859	6.8014037786	6.7355989795	6.8075350957	6.9929951426	6.7686381752	6.9339932144	6.7611758884	6.9740509489	6.9768083831	6.7058637978	6.9790929462	6.7032914641
+v4	7.1303338007	7.1398791179	7.0899051467	7.110589744	6.9947569885	6.9978231244	7.0374265378	7.1003705796	6.9698816903	6.8048207468	6.8543061025	6.6655810848	6.705008045	6.7275413384	6.7442930614	6.9498777528	7.1583625223	7.2013971516	7.1673173643	6.9169800999	7.1003705796	7.0453230179	7.0606978781	7.1003705796	7.0492180614	6.7693773999	6.7774268949	6.9211661027	6.8561245047	6.9656720182	7.0086002143	6.9758911823
+v5	7.0530784819	6.99122612	7.0569048894	6.9698816903	6.8603380665	6.8375885014	6.9633155586	7.0170333811	7.0681858989	6.9633155586	6.95375974	6.7481881046	6.7686381752	6.8175654357	6.8394781102	7.0492180614	7.1038037552	7.0681858989	7.0755469979	6.7259117139	6.9339932144	6.8948697121	6.9464523141	7.0043214168	6.9009131223	6.6875290504	6.7634280684	6.7209858267	6.6454223676	6.7923917595	6.7902852344	6.8312297579
+v6	6.9876663096	7.0969100478	7.1072100036	7.0569048894	7.0000000434	6.9973864281	7.0644580267	7.0863598663	7.0043214168	6.9772662582	6.9434945654	6.9680157607	6.9556877984	7.1553360678	6.9454686344	6.9309490821	7.0086002143	7.0334237957	7.0086002143	6.9206450535	7.0043214168	6.9749720403	6.9489018098	6.9400182049	6.8750613213	6.9434945654	6.9057959343	6.9614211415	6.9518230838	6.9415114823	6.9304396457	6.9304396457
diff -r 09799fc16bc6 -r 140290de7986 runit/input/sampleMetadata.tsv
--- a/runit/input/sampleMetadata.tsv	Sat Aug 06 12:42:42 2016 -0400
+++ b/runit/input/sampleMetadata.tsv	Sun Oct 30 14:17:09 2016 -0400
@@ -1,31 +1,33 @@
-sample	age	ageGroup
-HU_017	41	experienced
-HU_021	34	junior
-HU_027	37	experienced
-HU_032	38	experienced
-HU_041	28	junior
-HU_048	39	experienced
-HU_049	50	senior
-HU_050	30	junior
-HU_052	51	senior
-HU_059	81	senior
-HU_060	55	senior
-HU_066	25	junior
-HU_072	47	experienced
-HU_077	27	junior
-HU_090	46	experienced
-HU_109	32	junior
-HU_110	50	senior
-HU_125	58	senior
-HU_126	45	experienced
-HU_131	42	experienced
-HU_134	48	experienced
-HU_149	35	experienced
-HU_150	49	experienced
-HU_173	55	senior
-HU_179	33	junior
-HU_180	53	senior
-HU_182	43	experienced
-HU_202	42	experienced
-HU_204	31	junior
-HU_209	17.5	junior
+sampleMetadata	quant	qual
+s1	56	A
+s2	24	A
+s3	35	A
+s4	32	A
+s5	54	A
+s6	53	A
+s7	30	A
+s8	20	A
+s9	41	B
+s10	34	B
+s11	44	B
+s12	43	B
+s13	52	B
+s14	46	B
+s15	51	B
+s16	36	B
+s17	31	C
+s18	25	C
+s19	40	C
+s20	50	C
+s21	22	C
+s22	29	C
+s23	49	C
+s24	19	C
+s25	55	D
+s26	39	D
+s27	45	D
+s28	26	D
+s29	23	D
+s30	42	D
+s31	33	D
+s32	21	D
diff -r 09799fc16bc6 -r 140290de7986 runit/input/variableMetadata.tsv
--- a/runit/input/variableMetadata.tsv	Sat Aug 06 12:42:42 2016 -0400
+++ b/runit/input/variableMetadata.tsv	Sun Oct 30 14:17:09 2016 -0400
@@ -1,4 +1,7 @@
-variable	name
-HMDB01032	Dehydroepiandrosterone sulfate
-HMDB03072	Quinic acid
-HMDB00792	Sebacic acid
+variableMetadata	sample_mean
+v1	12620948.56
+v2	12661823.42
+v3	7680621.526
+v4	9901182.687
+v5	8311866.237
+v6	9792909.819
diff -r 09799fc16bc6 -r 140290de7986 runit/output/figure.pdf
Binary file runit/output/figure.pdf has changed
diff -r 09799fc16bc6 -r 140290de7986 runit/output/information.txt
--- a/runit/output/information.txt	Sat Aug 06 12:42:42 2016 -0400
+++ b/runit/output/information.txt	Sun Oct 30 14:17:09 2016 -0400
@@ -1,9 +1,11 @@
 
-Start of the 'Univariate' Galaxy module call: Sat 06 Aug 2016 06:22:18 PM
+Start of the 'Univariate' Galaxy module call: Sun 30 Oct 2016 07:06:06 PM
 
 Performing 'kruskal'
 
-The following 1 variable (33%) was found significant at the 0.05 level:
-HMDB01032
+The following 3 variables (50%) were found significant at the 0.05 level:
+v4
+v5
+v6
 
-End of 'Univariate' Galaxy module call: 2016-08-06 18:22:18
+End of 'Univariate' Galaxy module call: 2016-10-30 19:06:07
diff -r 09799fc16bc6 -r 140290de7986 runit/output/variableMetadata.tsv
--- a/runit/output/variableMetadata.tsv	Sat Aug 06 12:42:42 2016 -0400
+++ b/runit/output/variableMetadata.tsv	Sun Oct 30 14:17:09 2016 -0400
@@ -1,4 +1,7 @@
-variableMetadata	name	ageGroup_kruskal_fdr	ageGroup_kruskal_sig	ageGroup_kruskal_junior.experienced_dif	ageGroup_kruskal_senior.experienced_dif	ageGroup_kruskal_senior.junior_dif	ageGroup_kruskal_junior.experienced_pva	ageGroup_kruskal_senior.experienced_pva	ageGroup_kruskal_senior.junior_pva	ageGroup_kruskal_junior.experienced_sig	ageGroup_kruskal_senior.experienced_sig	ageGroup_kruskal_senior.junior_sig
-HMDB01032	Dehydroepiandrosterone sulfate	0.0117826825222329	1	7211389.71960377	-1703486.11807139	-8914875.83767516	0.204550960009346	0.123124593762726	0.00251932966039092	0	0	1
-HMDB03072	Quinic acid	0.461634758626427	0	-3747468.87812489	1512795.66143568	5260264.53956057	NA	NA	NA	NA	NA	NA
-HMDB00792	Sebacic acid	0.469555338459932	0	1404223.43306179	959174.915801485	-445048.517260305	NA	NA	NA	NA	NA	NA
+variableMetadata	sample_mean	qual_kruskal_fdr	qual_kruskal_sig	qual_kruskal_B.A_dif	qual_kruskal_C.A_dif	qual_kruskal_D.A_dif	qual_kruskal_C.B_dif	qual_kruskal_D.B_dif	qual_kruskal_D.C_dif	qual_kruskal_B.A_fdr	qual_kruskal_C.A_fdr	qual_kruskal_D.A_fdr	qual_kruskal_C.B_fdr	qual_kruskal_D.B_fdr	qual_kruskal_D.C_fdr	qual_kruskal_B.A_sig	qual_kruskal_C.A_sig	qual_kruskal_D.A_sig	qual_kruskal_C.B_sig	qual_kruskal_D.B_sig	qual_kruskal_D.C_sig
+v1	12620948.56	0.147579228303049	0	0.0827976191000008	-0.0378359353499995	-0.254400932999999	-0.12063355445	-0.3371985521	-0.21656499765	0.999945671392641	0.99999320349658	0.185342280966466	0.997174540126794	0.401201067449129	0.580170916884995	0	0	0	0	0	0
+v2	12661823.42	0.246364577097618	0	0.11547952005	-0.0981193892499999	-0.2877631538	-0.2135989093	-0.40324267385	-0.18964376455	0.999945671392641	0.99999320349658	0.291618067987092	0.997174540126794	0.401201067449129	0.890397531402986	0	0	0	0	0	0
+v3	7680621.526	0.293234285978368	0	-0.0292777863999998	-0.0806444136	-0.0392186484999995	-0.0513666272000002	-0.00994086209999967	0.0414257651000005	0.999945671392641	0.99999320349658	0.593281055056761	0.997174540126794	0.593281055056761	0.999155846185138	0	0	0	0	0	0
+v4	9901182.687	0.000819466212247517	1	-0.32058095905	0.00523271645000012	-0.1517188027	0.3258136755	0.16886215635	-0.15695151915	0.0133536911280914	0.99999320349658	0.185342280966466	0.00430289478770107	0.593281055056761	0.143731083414931	1	0	0	1	0	0
+v5	8311866.237	0.0115079177714078	1	-0.0839349800499996	-0.00516703969999988	-0.203697253750001	0.0787679403499997	-0.119762273700001	-0.198530214050001	0.999945671392641	0.99999320349658	0.0336157236420893	0.997174540126794	0.401201067449129	0.073221827596323	0	0	1	0	0	0
+v6	9792909.819	0.00429379409695042	1	-0.0988296785000005	-0.0710347295000009	-0.124705894050001	0.0277949489999996	-0.0258762155500003	-0.0536711645499999	0.535700151485478	0.99999320349658	0.00294595180681068	0.997174540126794	0.401201067449129	0.302648332248575	0	0	1	0	0	0
diff -r 09799fc16bc6 -r 140290de7986 runit/univariate_runtests.R
--- a/runit/univariate_runtests.R	Sat Aug 06 12:42:42 2016 -0400
+++ b/runit/univariate_runtests.R	Sun Oct 30 14:17:09 2016 -0400
@@ -83,6 +83,7 @@
         defaultArgLs[["variableMetadata_in"]] <- file.path(dirname(scriptPathC), testInDirC, "variableMetadata.tsv")
 
     defaultArgLs[["variableMetadata_out"]] <- file.path(dirname(scriptPathC), testOutDirC, "variableMetadata.tsv")
+    defaultArgLs[["figure"]] <- file.path(dirname(scriptPathC), testOutDirC, "figure.pdf")
     defaultArgLs[["information"]] <- file.path(dirname(scriptPathC), testOutDirC, "information.txt")
 
     defaultArgLs
diff -r 09799fc16bc6 -r 140290de7986 runit/univariate_tests.R
--- a/runit/univariate_tests.R	Sat Aug 06 12:42:42 2016 -0400
+++ b/runit/univariate_tests.R	Sun Oct 30 14:17:09 2016 -0400
@@ -1,7 +1,24 @@
+test_input_anova <- function() {
+
+    testDirC <- "input"
+    argLs <- list(facC = "qual",
+                  tesC = "anova",
+                  adjC = "fdr",
+                  thrN = "0.05")
+
+    argLs <- c(defaultArgF(testDirC), argLs)
+    outLs <- wrapperCallF(argLs)
+
+    checkEqualsNumeric(outLs[["varDF"]]["v6", "qual_anova_fdr"], 1.924156e-03, tolerance = 1e-6)
+
+    checkEqualsNumeric(outLs[["varDF"]]["v4", "qual_anova_D.C_fdr"], 0.01102016, tolerance = 1e-6)
+
+}
+
 test_input_kruskal <- function() {
 
     testDirC <- "input"
-    argLs <- list(facC = "ageGroup",
+    argLs <- list(facC = "qual",
                   tesC = "kruskal",
                   adjC = "fdr",
                   thrN = "0.05")
@@ -9,7 +26,9 @@
     argLs <- c(defaultArgF(testDirC), argLs)
     outLs <- wrapperCallF(argLs)
 
-    checkEqualsNumeric(outLs[["varDF"]]["HMDB01032", "ageGroup_kruskal_senior.experienced_pva"], 0.1231246, tolerance = 1e-6)
+    checkEqualsNumeric(outLs[["varDF"]]["v4", "qual_kruskal_fdr"], 0.0008194662, tolerance = 1e-7)
+
+    checkEqualsNumeric(outLs[["varDF"]]["v6", "qual_kruskal_D.A_fdr"], 0.002945952, tolerance = 1e-7)
 
 }
 
diff -r 09799fc16bc6 -r 140290de7986 test-data/dataMatrix.tsv
--- a/test-data/dataMatrix.tsv	Sat Aug 06 12:42:42 2016 -0400
+++ b/test-data/dataMatrix.tsv	Sun Oct 30 14:17:09 2016 -0400
@@ -1,4 +1,7 @@
-profile	HU_017	HU_021	HU_027	HU_032	HU_041	HU_048	HU_049	HU_050	HU_052	HU_059	HU_060	HU_066	HU_072	HU_077	HU_090	HU_109	HU_110	HU_125	HU_126	HU_131	HU_134	HU_149	HU_150	HU_173	HU_179	HU_180	HU_182	HU_202	HU_204	HU_209
-HMDB01032	2569204.92420381	6222035.77434915	17070707.9912636	1258838.24348419	13039543.0754619	1909391.77026598	3495.09386434063	2293521.90928998	128503.275117713	81872.5276382213	8103557.56578035	149574887.036181	1544036.41049333	7103429.53933206	14138796.50382	4970265.57952158	263054.73056162	1671332.30008058	88433.1944958815	23602331.2894815	18648126.5206986	1554657.98756878	34152.3646391152	209372.71275317	33187733.370626	202438.591636003	13581070.0886437	354170.810678102	9120781.48986975	43419175.4051586
-HMDB03072	3628416.30251025	65626.9834353751	112170.118946651	3261804.34422417	42228.2787747563	343254.201250707	1958217.69317664	11983270.0435677	5932111.41638028	5511385.83359531	9154521.47755199	2632133.21209418	9500411.14556502	6551644.51726592	7204319.80891836	1273412.04795188	3260583.81592376	8932005.5351622	8340827.52597275	9256460.69197759	11217839.169041	5919262.81433556	11790077.0657915	9567977.80797097	73717.5811684739	9991787.29074293	4208098.14739633	623970.649925847	10904221.2642849	2171793.93621067
-HMDB00792	429568.609438384	3887629.50527037	1330692.11658995	1367446.73023821	844197.447472453	2948090.71886592	1614157.90566884	3740009.19379795	3292251.66531919	2310688.79492013	4404239.59008605	3043289.12780863	825736.467181043	2523241.91730649	6030501.02648005	474901.604069803	2885792.42617652	2955990.64049134	1917716.3427982	1767962.67737699	5926203.40397675	1639065.69474684	346810.763557826	1054776.22313737	2390258.27543894	1831346.37315857	1026696.36904362	7079792.50047866	4368341.01359769	3495986.87280275
+dataMatrix	s1	s2	s3	s4	s5	s6	s7	s8	s9	s10	s11	s12	s13	s14	s15	s16	s17	s18	s19	s20	s21	s22	s23	s24	s25	s26	s27	s28	s29	s30	s31	s32
+v1	7.416640524	6.9916690517	7.3404441347	7.0128372669	7.1702617447	6.820858055	7.2648178466	6.9561684785	7.2966652122	7.2695129676	7.2695129676	6.7986507145	6.9749720403	6.7788745443	7.0791812822	7.3617278549	7.4281348102	6.967548023	7.2304489469	6.6608655728	6.9614211415	6.8959747875	7.1398791179	7.2201081142	7.1673173643	6.6857418281	7.1789769761	6.7788745443	6.7291648707	6.7466342768	6.8954226013	6.9454686344
+v2	7.4563660483	6.9014583759	7.3873898441	6.9599948859	7.2095150414	6.7007038037	7.3159703664	6.8876173566	7.3010300174	7.2966652122	7.3138672415	6.7275413384	6.9863238219	6.7435098431	7.1038037552	7.3838153839	7.4409090978	6.7737865181	7.2174839705	6.48995862	6.8494194752	6.8344207673	7.1238516736	7.212187631	7.2041200098	6.6910815806	7.2900346336	6.7427252098	6.7185017719	6.6180482014	6.8512584099	6.9329808726
+v3	6.8785218529	6.7839036507	6.9025468337	7.0086002143	6.9314579215	6.8129134235	6.8305887328	7.0374265378	6.8767950339	6.8457180799	6.7888751864	6.9929951426	7.0374265378	6.8215135939	7.0718820441	6.809559782	6.8122447637	6.7795965634	6.8129134235	6.9599948859	6.8014037786	6.7355989795	6.8075350957	6.9929951426	6.7686381752	6.9339932144	6.7611758884	6.9740509489	6.9768083831	6.7058637978	6.9790929462	6.7032914641
+v4	7.1303338007	7.1398791179	7.0899051467	7.110589744	6.9947569885	6.9978231244	7.0374265378	7.1003705796	6.9698816903	6.8048207468	6.8543061025	6.6655810848	6.705008045	6.7275413384	6.7442930614	6.9498777528	7.1583625223	7.2013971516	7.1673173643	6.9169800999	7.1003705796	7.0453230179	7.0606978781	7.1003705796	7.0492180614	6.7693773999	6.7774268949	6.9211661027	6.8561245047	6.9656720182	7.0086002143	6.9758911823
+v5	7.0530784819	6.99122612	7.0569048894	6.9698816903	6.8603380665	6.8375885014	6.9633155586	7.0170333811	7.0681858989	6.9633155586	6.95375974	6.7481881046	6.7686381752	6.8175654357	6.8394781102	7.0492180614	7.1038037552	7.0681858989	7.0755469979	6.7259117139	6.9339932144	6.8948697121	6.9464523141	7.0043214168	6.9009131223	6.6875290504	6.7634280684	6.7209858267	6.6454223676	6.7923917595	6.7902852344	6.8312297579
+v6	6.9876663096	7.0969100478	7.1072100036	7.0569048894	7.0000000434	6.9973864281	7.0644580267	7.0863598663	7.0043214168	6.9772662582	6.9434945654	6.9680157607	6.9556877984	7.1553360678	6.9454686344	6.9309490821	7.0086002143	7.0334237957	7.0086002143	6.9206450535	7.0043214168	6.9749720403	6.9489018098	6.9400182049	6.8750613213	6.9434945654	6.9057959343	6.9614211415	6.9518230838	6.9415114823	6.9304396457	6.9304396457
diff -r 09799fc16bc6 -r 140290de7986 test-data/output-variableMetadata.tsv
--- a/test-data/output-variableMetadata.tsv	Sat Aug 06 12:42:42 2016 -0400
+++ b/test-data/output-variableMetadata.tsv	Sun Oct 30 14:17:09 2016 -0400
@@ -1,4 +1,7 @@
-variableMetadata	name	ageGroup_kruskal_fdr	ageGroup_kruskal_sig	ageGroup_kruskal_junior.experienced_dif	ageGroup_kruskal_senior.experienced_dif	ageGroup_kruskal_senior.junior_dif	ageGroup_kruskal_junior.experienced_pva	ageGroup_kruskal_senior.experienced_pva	ageGroup_kruskal_senior.junior_pva	ageGroup_kruskal_junior.experienced_sig	ageGroup_kruskal_senior.experienced_sig	ageGroup_kruskal_senior.junior_sig
-HMDB01032	Dehydroepiandrosterone sulfate	0.0117826825222329	1	7211389.71960377	-1703486.11807139	-8914875.83767516	0.204550960009346	0.123124593762726	0.00251932966039092	0	0	1
-HMDB03072	Quinic acid	0.461634758626427	0	-3747468.87812489	1512795.66143568	5260264.53956057	NA	NA	NA	NA	NA	NA
-HMDB00792	Sebacic acid	0.469555338459932	0	1404223.43306179	959174.915801485	-445048.517260305	NA	NA	NA	NA	NA	NA
+variableMetadata	sample_mean	qual_kruskal_fdr	qual_kruskal_sig	qual_kruskal_B.A_dif	qual_kruskal_C.A_dif	qual_kruskal_D.A_dif	qual_kruskal_C.B_dif	qual_kruskal_D.B_dif	qual_kruskal_D.C_dif	qual_kruskal_B.A_fdr	qual_kruskal_C.A_fdr	qual_kruskal_D.A_fdr	qual_kruskal_C.B_fdr	qual_kruskal_D.B_fdr	qual_kruskal_D.C_fdr	qual_kruskal_B.A_sig	qual_kruskal_C.A_sig	qual_kruskal_D.A_sig	qual_kruskal_C.B_sig	qual_kruskal_D.B_sig	qual_kruskal_D.C_sig
+v1	12620948.56	0.147579228303049	0	0.0827976191000008	-0.0378359353499995	-0.254400932999999	-0.12063355445	-0.3371985521	-0.21656499765	0.999945671392641	0.99999320349658	0.185342280966466	0.997174540126794	0.401201067449129	0.580170916884995	0	0	0	0	0	0
+v2	12661823.42	0.246364577097618	0	0.11547952005	-0.0981193892499999	-0.2877631538	-0.2135989093	-0.40324267385	-0.18964376455	0.999945671392641	0.99999320349658	0.291618067987092	0.997174540126794	0.401201067449129	0.890397531402986	0	0	0	0	0	0
+v3	7680621.526	0.293234285978368	0	-0.0292777863999998	-0.0806444136	-0.0392186484999995	-0.0513666272000002	-0.00994086209999967	0.0414257651000005	0.999945671392641	0.99999320349658	0.593281055056761	0.997174540126794	0.593281055056761	0.999155846185138	0	0	0	0	0	0
+v4	9901182.687	0.000819466212247517	1	-0.32058095905	0.00523271645000012	-0.1517188027	0.3258136755	0.16886215635	-0.15695151915	0.0133536911280914	0.99999320349658	0.185342280966466	0.00430289478770107	0.593281055056761	0.143731083414931	1	0	0	1	0	0
+v5	8311866.237	0.0115079177714078	1	-0.0839349800499996	-0.00516703969999988	-0.203697253750001	0.0787679403499997	-0.119762273700001	-0.198530214050001	0.999945671392641	0.99999320349658	0.0336157236420893	0.997174540126794	0.401201067449129	0.073221827596323	0	0	1	0	0	0
+v6	9792909.819	0.00429379409695042	1	-0.0988296785000005	-0.0710347295000009	-0.124705894050001	0.0277949489999996	-0.0258762155500003	-0.0536711645499999	0.535700151485478	0.99999320349658	0.00294595180681068	0.997174540126794	0.401201067449129	0.302648332248575	0	0	1	0	0	0
diff -r 09799fc16bc6 -r 140290de7986 test-data/sampleMetadata.tsv
--- a/test-data/sampleMetadata.tsv	Sat Aug 06 12:42:42 2016 -0400
+++ b/test-data/sampleMetadata.tsv	Sun Oct 30 14:17:09 2016 -0400
@@ -1,31 +1,33 @@
-sample	age	ageGroup
-HU_017	41	experienced
-HU_021	34	junior
-HU_027	37	experienced
-HU_032	38	experienced
-HU_041	28	junior
-HU_048	39	experienced
-HU_049	50	senior
-HU_050	30	junior
-HU_052	51	senior
-HU_059	81	senior
-HU_060	55	senior
-HU_066	25	junior
-HU_072	47	experienced
-HU_077	27	junior
-HU_090	46	experienced
-HU_109	32	junior
-HU_110	50	senior
-HU_125	58	senior
-HU_126	45	experienced
-HU_131	42	experienced
-HU_134	48	experienced
-HU_149	35	experienced
-HU_150	49	experienced
-HU_173	55	senior
-HU_179	33	junior
-HU_180	53	senior
-HU_182	43	experienced
-HU_202	42	experienced
-HU_204	31	junior
-HU_209	17.5	junior
+sampleMetadata	quant	qual
+s1	56	A
+s2	24	A
+s3	35	A
+s4	32	A
+s5	54	A
+s6	53	A
+s7	30	A
+s8	20	A
+s9	41	B
+s10	34	B
+s11	44	B
+s12	43	B
+s13	52	B
+s14	46	B
+s15	51	B
+s16	36	B
+s17	31	C
+s18	25	C
+s19	40	C
+s20	50	C
+s21	22	C
+s22	29	C
+s23	49	C
+s24	19	C
+s25	55	D
+s26	39	D
+s27	45	D
+s28	26	D
+s29	23	D
+s30	42	D
+s31	33	D
+s32	21	D
diff -r 09799fc16bc6 -r 140290de7986 test-data/variableMetadata.tsv
--- a/test-data/variableMetadata.tsv	Sat Aug 06 12:42:42 2016 -0400
+++ b/test-data/variableMetadata.tsv	Sun Oct 30 14:17:09 2016 -0400
@@ -1,4 +1,7 @@
-variable	name
-HMDB01032	Dehydroepiandrosterone sulfate
-HMDB03072	Quinic acid
-HMDB00792	Sebacic acid
+variableMetadata	sample_mean
+v1	12620948.56
+v2	12661823.42
+v3	7680621.526
+v4	9901182.687
+v5	8311866.237
+v6	9792909.819
diff -r 09799fc16bc6 -r 140290de7986 univariate_config.xml
--- a/univariate_config.xml	Sat Aug 06 12:42:42 2016 -0400
+++ b/univariate_config.xml	Sun Oct 30 14:17:09 2016 -0400
@@ -1,4 +1,4 @@
-<tool id="Univariate" name="Univariate" version="2.1.4">
+<tool id="Univariate" name="Univariate" version="2.2.0">
   <description>Univariate statistics</description>
   
   <requirements>
@@ -24,6 +24,7 @@
   thrN "$thrN"	  
   
   variableMetadata_out "$variableMetadata_out"
+  figure "$figure"
   information "$information"
   ]]></command>
   <inputs>
@@ -54,6 +55,7 @@
   
   <outputs>
     <data name="variableMetadata_out" label="${tool.name}_${variableMetadata_in.name}" format="tabular" ></data>
+    <data name="figure" label="${tool.name}_figure.pdf" format="pdf"/>
     <data name="information" label="${tool.name}_information.txt" format="txt"/>
   </outputs>
   
@@ -62,7 +64,7 @@
       <param name="dataMatrix_in" value="dataMatrix.tsv"/>
       <param name="sampleMetadata_in" value="sampleMetadata.tsv"/>
       <param name="variableMetadata_in" value="variableMetadata.tsv"/>
-      <param name="facC" value="ageGroup"/>
+      <param name="facC" value="qual"/>
       <param name="tesC" value="kruskal"/>
       <param name="adjC" value="fdr"/>
       <param name="thrN" value="0.05"/>
@@ -71,7 +73,7 @@
   </tests>
   
   <help>
-
+    
 .. class:: infomark
 
 | **Tool update: See the 'NEWS' section at the bottom of the page**
@@ -188,12 +190,16 @@
 	
 variableMetadata_out.tabular
 	| **variableMetadata** file identical to the file given as argument, except that (at least) three columns have been added:
-	| 1) [factor]_[test]_[class'a']-[class'b']_dif or [factor]_[test]_cor: difference of the means (ttest) or the medians (wilcoxon) between the two classes, or 'pearson' or 'spearman' correlations
-	| 2) [factor]_[test]_[class'a']-[class'b']_[method] or [factor]_[test]_[method]: adjusted p-values
-	| 3) [factor]_[test]_[class'a']-[class'b']_sig or [factor]_[test]_sig: significance (coded as '1' if below the threshold and '0' otherwise)
-	| In the case of 'anova' and 'kruskal', the columns 2) and 3) appear first to give the results from the ANOVA or Kruskal Wallis test, and, when these tests are significant, the results of the pairwise comparisons are reported in additional columns (otherwise NA in these columns): in the case of ANOVA, the Tukey HSD post-hoc analysis is used (for each comparison, the difference between means, p value, and significance are provided); in the case of Kruskal Wallis, the Nemenyi is performed (PMCMR package) (for each pairwise comparison, the difference between medians, p value and significance are provided)
+	| 1) [factor]_[test]_[class'a'].[class'b']_dif or [factor]_[test]_cor: difference of the means (ttest) or the medians (wilcoxon) between the two classes, or 'pearson' or 'spearman' correlations
+	| 2) [factor]_[test]_[class'a'].[class'b']_[method] or [factor]_[test]_[method]: adjusted p-values
+	| 3) [factor]_[test]_[class'a'].[class'b']_sig or [factor]_[test]_sig: significance (coded as '1' if below the threshold and '0' otherwise)
+	| In the case of 'anova' and 'kruskal', the columns 2) and 3) appear first to give the results from the ANOVA or Kruskal Wallis test, and then the results of the pairwise comparisons are reported in additional columns (otherwise NA in these columns): in the case of ANOVA, the Tukey HSD post-hoc analysis is used (for each comparison, the difference between means, p value, and significance are provided); in the case of Kruskal Wallis, the Nemenyi is performed (PMCMR package) (for each pairwise comparison, the difference between medians, p value and significance are provided); note that since version 2.2.0, the p-values of the post-hoc pairwise comparisons (ANOVA or Kruskal) are further corrected for multiple testing over all variables (as the p-value of the omnibus test in column 2)
 	|
 	
+figure.pdf
+	| File containing the graphics of all significant tests: boxplots (respectively scatterplots with the regression line in red and the R2 value) are displayed when the factor of interest is qualitative (respectively quantitative). The variable name and the corrected p-value is indicated in the title of each plot
+	|
+
 information.txt
 	| File with all messages and warnings generated during the computation
 	|
@@ -214,6 +220,22 @@
 NEWS
 ----
 
+CHANGES IN VERSION 2.2.0
+========================
+
+MAJOR MODIFICATION
+
+ANOVA and Kruskal-Wallis: The p-values of the post-hoc tests (i.e. from pairwise comparisons) are now further corrected for multiple testing over all variables (previously, only the p-value of the omnibus test was corrected over all variables)
+
+MINOR MODIFICATION
+
+All values in the 'dif', adjusted p-value, and 'sig' columns are now displayed (previously, the values were set to NA when the p-value of the omnibus test was not significant)
+
+NEW FEATURE
+
+Graphic: a single pdf file containing the graphics of all significant tests is now produced as '_figure.pdf' output: boxplots (respectively scatterplots with the regression line in red and the R2 value) are displayed when the factor of interest is qualitative (respectively quantitative). The corrected p-value is indicated in the title of each plot
+
+
 CHANGES IN VERSION 2.1.4
 ========================
 
@@ -245,127 +267,32 @@
 NEW FEATURE
 
 (corrected) p-value threshold can be set to any value between 0 and 1
-
-
-</help>
-
-<citations>
-  <citation type="bibtex">@Manual{,
-  title = {R: A Language and Environment for Statistical Computing},
-  author = {{R Core Team}},
-  organization = {R Foundation for Statistical Computing},
-  address = {Vienna, Austria},
-  year = {2016},
-  url = {https://www.R-project.org/},
-  }</citation>
-  <citation type="bibtex">@Article{Thevenot2015,
-   Title                    = {Analysis of the human adult urinary metabolome variations with age, body mass index and gender by implementing a comprehensive workflow for univariate and OPLS statistical analyses},
-   Author                   = {Thevenot, Etienne A. and Roux, Aurelie and Xu, Ying and Ezan, Eric and Junot, Christophe},
-   Journal                  = {Journal of Proteome Research},
-   Year                     = {2015},
-   Note                     = {PMID: 26088811},
-   Number                   = {8},
-   Pages                    = {3322-3335},
-   Volume                   = {14},
-   
-   Doi                      = {10.1021/acs.jproteome.5b00354},
-   Url                      = {http://pubs.acs.org/doi/full/10.1021/acs.jproteome.5b00354}
-   }</citation>
-   <citation type="doi">10.1093/bioinformatics/btu813</citation>
- </citations>
-
-
-
-Input files
-===========
-
-| **To generate the "dataMatrix", "sampleMetadata" and "variableMetadata" files:**
-|   **1) copy/paste the values below in three distinct .txt files**
-|   **2) use the "Get Data" / "Upload File" in the "Tools" (left) panel from the Galaxy / ABiMS page by choosing:**
-|     **a) File Format: 'tabular'**
-|     **b) Convert spaces to tabs: 'Yes'**
-| 
-
-**dataMatrix file**::
-
-	dataMatrix HU_017 HU_021 HU_027 HU_032 HU_041 HU_048 HU_049 HU_050 HU_052 HU_059 HU_060 HU_066 HU_072 HU_077 HU_090 HU_109 HU_110 HU_125 HU_126 HU_131 HU_134 HU_149 HU_150 HU_173 HU_179 HU_180 HU_182 HU_202 HU_204 HU_209
-	HMDB01032 2569204.92420381 6222035.77434915 17070707.9912636 1258838.24348419 13039543.0754619 1909391.77026598 3495.09386434063 2293521.90928998 128503.275117713 81872.5276382213 8103557.56578035 149574887.036181 1544036.41049333 7103429.53933206 14138796.50382 4970265.57952158 263054.73056162 1671332.30008058 88433.1944958815 23602331.2894815 18648126.5206986 1554657.98756878 34152.3646391152 209372.71275317 33187733.370626 202438.591636003 13581070.0886437 354170.810678102 9120781.48986975 43419175.4051586
-	HMDB03072 3628416.30251025 65626.9834353751 112170.118946651 3261804.34422417 42228.2787747563 343254.201250707 1958217.69317664 11983270.0435677 5932111.41638028 5511385.83359531 9154521.47755199 2632133.21209418 9500411.14556502 6551644.51726592 7204319.80891836 1273412.04795188 3260583.81592376 8932005.5351622 8340827.52597275 9256460.69197759 11217839.169041 5919262.81433556 11790077.0657915 9567977.80797097 73717.5811684739 9991787.29074293 4208098.14739633 623970.649925847 10904221.2642849 2171793.93621067
-	HMDB00792 429568.609438384 3887629.50527037 1330692.11658995 1367446.73023821 844197.447472453 2948090.71886592 1614157.90566884 3740009.19379795 3292251.66531919 2310688.79492013 4404239.59008605 3043289.12780863 825736.467181043 2523241.91730649 6030501.02648005 474901.604069803 2885792.42617652 2955990.64049134 1917716.3427982 1767962.67737699 5926203.40397675 1639065.69474684 346810.763557826 1054776.22313737 2390258.27543894 1831346.37315857 1026696.36904362 7079792.50047866 4368341.01359769 3495986.87280275
-
-	
-**sampleMetadata file**::
-
-	sampleMetadata age ageGrp
-	HU_017 41 experienced
-	HU_021 34 junior
-	HU_027 37 experienced
-	HU_032 38 experienced
-	HU_041 28 junior
-	HU_048 39 experienced
-	HU_049 50 senior
-	HU_050 30 junior
-	HU_052 51 senior
-	HU_059 81 senior
-	HU_060 55 senior
-	HU_066 25 junior
-	HU_072 47 experienced
-	HU_077 27 junior
-	HU_090 46 experienced
-	HU_109 32 junior
-	HU_110 50 senior
-	HU_125 58 senior
-	HU_126 45 experienced
-	HU_131 42 experienced
-	HU_134 48 experienced
-	HU_149 35 experienced
-	HU_150 49 experienced
-	HU_173 55 senior
-	HU_179 33 junior
-	HU_180 53 senior
-	HU_182 43 experienced
-	HU_202 42 experienced
-	HU_204 31 junior
-	HU_209 17.5 junior
-
-	
-**variableMetadata file**::
-
-	variableMetadata name
-	HMDB01032        Dehydroepiandrosterone sulfate
-	HMDB03072        Quinic acid
-	HMDB00792        Sebacic acid
-
-	
-Parameters
-==========
-
-**Factor of interest:** "ageGroup"
-
-**Test:** "Kruskal-Wallis rank test (qualitative, > 2 levels)"
- 
-**Method for multiple testing correction:** "fdr"
-
-**(Corrected) p-value significance threshold:** 0.05
-
-
-Output files
-============
-
-+------------------------------+------------+
-| File                         |   Format   |
-+==============================+============+
-| 1)  dataMatrix               |   tabular  |
-+------------------------------+------------+
-| 2)  sampleMetadata           |   tabular  |
-+------------------------------+------------+
-| 3)  variableMetadata         |   tabular  |
-+------------------------------+------------+
-| 4)  information              |    text    |
-+------------------------------+------------+
-
-
----------------------------------------------------
-
-
+    
+  </help>
+  
+  <citations>
+    <citation type="bibtex">@Manual{,
+    title = {R: A Language and Environment for Statistical Computing},
+    author = {{R Core Team}},
+    organization = {R Foundation for Statistical Computing},
+    address = {Vienna, Austria},
+    year = {2016},
+    url = {https://www.R-project.org/},
+    }</citation>
+    <citation type="bibtex">@Article{Thevenot2015,
+    Title                    = {Analysis of the human adult urinary metabolome variations with age, body mass index and gender by implementing a comprehensive workflow for univariate and OPLS statistical analyses},
+    Author                   = {Thevenot, Etienne A. and Roux, Aurelie and Xu, Ying and Ezan, Eric and Junot, Christophe},
+    Journal                  = {Journal of Proteome Research},
+    Year                     = {2015},
+    Note                     = {PMID: 26088811},
+    Number                   = {8},
+    Pages                    = {3322-3335},
+    Volume                   = {14},
+    
+    Doi                      = {10.1021/acs.jproteome.5b00354},
+    Url                      = {http://pubs.acs.org/doi/full/10.1021/acs.jproteome.5b00354}
+    }</citation>
+    <citation type="doi">10.1093/bioinformatics/btu813</citation>
+  </citations>
+  
 </tool>
diff -r 09799fc16bc6 -r 140290de7986 univariate_script.R
--- a/univariate_script.R	Sat Aug 06 12:42:42 2016 -0400
+++ b/univariate_script.R	Sun Oct 30 14:17:09 2016 -0400
@@ -4,16 +4,18 @@
                         facC,
                         tesC = c("ttest", "wilcoxon", "anova", "kruskal", "pearson", "spearman")[1],
                         adjC = c("holm", "hochberg", "hommel", "bonferroni", "BH", "BY", "fdr", "none")[7],
-                        thrN = 0.05) {
+                        thrN = 0.05,
+                        pdfC) {
 
 
     ## Option
-    ##---------------
 
     strAsFacL <- options()$stringsAsFactors
     options(stingsAsFactors = FALSE)
     options(warn = -1)
 
+    ## Getting the response (either a factor or a numeric)
+
     if(mode(samDF[, facC]) == "character") {
         facFcVn <- factor(samDF[, facC])
         facLevVc <- levels(facFcVn)
@@ -24,8 +26,10 @@
 
     varPfxC <- paste0(make.names(facC), "_", tesC, "_")
 
+    
     if(tesC %in% c("ttest", "wilcoxon", "pearson", "spearman")) {
 
+        
         switch(tesC,
                ttest = {
                    staF <- function(y) diff(tapply(y, facFcVn, function(x) mean(x, na.rm = TRUE)))
@@ -46,61 +50,168 @@
 
         staVn <- apply(datMN, 2, staF)
 
-        fdrVn <- p.adjust(apply(datMN,
+        adjVn <- p.adjust(apply(datMN,
                                 2,
                                 tesF),
                           method = adjC)
 
-        sigVn <- as.numeric(fdrVn < thrN)
+        sigVn <- as.numeric(adjVn < thrN)
 
         if(tesC %in% c("ttest", "wilcoxon"))
             varPfxC <- paste0(varPfxC, paste(rev(facLevVc), collapse = "."), "_")
 
         varDF[, paste0(varPfxC, ifelse(tesC %in% c("ttest", "wilcoxon"), "dif", "cor"))] <- staVn
 
-        varDF[, paste0(varPfxC, adjC)] <- fdrVn
+        varDF[, paste0(varPfxC, adjC)] <- adjVn
 
         varDF[, paste0(varPfxC, "sig")] <- sigVn
 
+        ## graphic
+
+        pdf(pdfC, onefile = TRUE)
+
+        varVi <- which(sigVn > 0)
+
+        if(tesC %in% c("ttest", "wilcoxon")) {
+
+            facVc <- as.character(facFcVn)
+            names(facVc) <- rownames(samDF)
+
+            for(varI in varVi) {
+                
+                varC <- rownames(varDF)[varI]
+                
+                boxF(facFcVn,
+                     datMN[, varI],
+                     paste0(varC, " (", adjC, " = ", signif(adjVn[varI], 2), ")"),
+                     facVc)
+                
+            }
+
+        } else { ## pearson or spearman
+
+            for(varI in varVi) {
+
+                varC <- rownames(varDF)[varI]
+
+                mod <- lm(datMN[, varI] ~  facFcVn)
+
+                plot(facFcVn, datMN[, varI],
+                     xlab = facC,
+                     ylab = "",
+                     pch = 18,
+                     main = paste0(varC, " (", adjC, " = ", signif(adjVn[varI], 2), ", R2 = ", signif(summary(mod)$r.squared, 2), ")"))
+            
+                abline(mod, col = "red")
+
+                }
+
+        }
+
+        dev.off()
+
+        
     } else if(tesC == "anova") {
 
+        
         ## getting the names of the pairwise comparisons 'class1Vclass2'
         prwVc <- rownames(TukeyHSD(aov(datMN[, 1] ~ facFcVn))[["facFcVn"]])
 
         prwVc <- gsub("-", ".", prwVc, fixed = TRUE) ## 2016-08-05: '-' character in dataframe column names seems not to be converted to "." by write.table on ubuntu R-3.3.1
 
+        ## omnibus and post-hoc tests
+        
         aovMN <- t(apply(datMN, 2, function(varVn) {
 
             aovMod <- aov(varVn ~ facFcVn)
             pvaN <- summary(aovMod)[[1]][1, "Pr(>F)"]
             hsdMN <- TukeyHSD(aovMod)[["facFcVn"]]
-            c(pvaN, c(hsdMN[, c("diff", "p adj")]), as.numeric(hsdMN[, "p adj"] < thrN))
+            c(pvaN, c(hsdMN[, c("diff", "p adj")]))
 
         }))
 
-        aovMN[, 1] <- p.adjust(aovMN[, 1], method = adjC)
-        sigVn <-  as.numeric(aovMN[, 1] < thrN)
-        aovMN <- cbind(aovMN[, 1], sigVn, aovMN[, 2:ncol(aovMN)])
-        ## aovMN[which(aovMN[, 2] < 1), (3 + length(prwVc)):ncol(aovMN)] <- NA
-        colnames(aovMN) <- paste0(varPfxC,
-                                  c(adjC,
-                                    "sig",
-                                    paste0(prwVc, "_dif"),
-                                    paste0(prwVc, "_pva"),
-                                    paste0(prwVc, "_sig")))
-        aovMN[which(aovMN[, paste0(varPfxC, "sig")] < 1), paste0(varPfxC, c(paste0(prwVc, "_pva"), paste0(prwVc, "_sig")))] <- NA
+        difVi <- 1:length(prwVc) + 1
+
+        ## difference of the means for each pairwise comparison
+        
+        difMN <- aovMN[, difVi]
+        colnames(difMN) <- paste0(varPfxC, prwVc, "_dif")
+
+        ## correction for multiple testing
+        
+        aovMN <- aovMN[, -difVi, drop = FALSE]
+        aovMN <- apply(aovMN, 2, function(pvaVn) p.adjust(pvaVn, method = adjC))
+
+        ## significance coding (0 = not significant, 1 = significant)
+        
+        adjVn <- aovMN[, 1]
+        sigVn <-  as.numeric(adjVn < thrN)
+
+        aovMN <- aovMN[, -1, drop = FALSE]
+        colnames(aovMN) <- paste0(varPfxC, prwVc, "_", adjC)
+
+        aovSigMN <- aovMN < thrN
+        mode(aovSigMN) <- "numeric"
+        colnames(aovSigMN) <- paste0(varPfxC, prwVc, "_sig")
+
+        ## final aggregated table
+
+        resMN <- cbind(adjVn, sigVn, difMN, aovMN, aovSigMN)
+        colnames(resMN)[1:2] <- paste0(varPfxC, c(adjC, "sig"))
+
+        varDF <- cbind.data.frame(varDF, as.data.frame(resMN))
+
+        ## graphic
 
-        varDF <- cbind.data.frame(varDF, as.data.frame(aovMN))
+        pdf(pdfC, onefile = TRUE)
+        
+        for(varI in 1:nrow(varDF)) {
+            
+            if(sum(aovSigMN[varI, ]) > 0) {
+                
+                varC <- rownames(varDF)[varI]
 
+                boxplot(datMN[, varI] ~ facFcVn,
+                        main = paste0(varC, " (", adjC, " = ", signif(adjVn[varI], 2), ")"))
+                
+                for(prwI in 1:length(prwVc)) {
+                    
+                    if(aovSigMN[varI, paste0(varPfxC, prwVc[prwI], "_sig")] == 1) {
+                        
+                        claVc <- unlist(strsplit(prwVc[prwI], ".", fixed = TRUE))
+                        aovClaVl <- facFcVn %in% claVc
+                        aovFc <- facFcVn[aovClaVl, drop = TRUE]
+                        aovVc <- as.character(aovFc)
+                        names(aovVc) <- rownames(samDF)[aovClaVl]
+                        boxF(aovFc,
+                             datMN[aovClaVl, varI],
+                             paste0(varC, " (", adjC, " = ", signif(aovMN[varI, paste0(varPfxC, prwVc[prwI], "_", adjC)], 2), ")"),
+                             aovVc)
+                        
+                    }
+                       
+                }
+                
+            }
+            
+        }
+
+        dev.off()
+
+        
     } else if(tesC == "kruskal") {
+        
 
-        ## getting the names of the pairwise comparisons 'class1Vclass2'
+        ## getting the names of the pairwise comparisons 'class1.class2'
+        
         nemMN <- posthoc.kruskal.nemenyi.test(datMN[, 1], facFcVn, "Tukey")[["p.value"]]
         nemVl <- c(lower.tri(nemMN, diag = TRUE))
         nemClaMC <- cbind(rownames(nemMN)[c(row(nemMN))][nemVl],
                           colnames(nemMN)[c(col(nemMN))][nemVl])
         nemNamVc <- paste0(nemClaMC[, 1], ".", nemClaMC[, 2])
-        nemNamVc <- paste0(varPfxC, nemNamVc)
+        pfxNemVc <- paste0(varPfxC, nemNamVc)
+
+        ## omnibus and post-hoc tests
         
         nemMN <- t(apply(datMN, 2, function(varVn) {
 
@@ -109,17 +220,24 @@
             c(pvaN, c(varNemMN))
 
         }))
-        pvaVn <- nemMN[, 1]
-        fdrVn <- p.adjust(pvaVn, method = adjC)
-        sigVn <- as.numeric(fdrVn < thrN)
+
+        ## correction for multiple testing
+        
+        nemMN <- apply(nemMN, 2,
+                       function(pvaVn) p.adjust(pvaVn, method = adjC))
+        adjVn <- nemMN[, 1]
+        sigVn <- as.numeric(adjVn < thrN)
         nemMN <- nemMN[, c(FALSE, nemVl)]
-        colnames(nemMN) <- paste0(nemNamVc, "_pva")
+        colnames(nemMN) <- paste0(pfxNemVc, "_", adjC)
+
+        ## significance coding (0 = not significant, 1 = significant)
+        
         nemSigMN <- nemMN < thrN
         mode(nemSigMN) <- "numeric"
-        colnames(nemSigMN) <- paste0(nemNamVc, "_sig")
-        nemMN[sigVn < 1, ] <- NA
-        nemSigMN[sigVn < 1, ] <- NA
+        colnames(nemSigMN) <- paste0(pfxNemVc, "_sig")
 
+        ## difference of the medians for each pairwise comparison
+        
         difMN <- sapply(1:nrow(nemClaMC), function(prwI) {
             prwVc <- nemClaMC[prwI, ]
             prwVi <- which(facFcVn %in% prwVc)
@@ -128,13 +246,52 @@
         })
         colnames(difMN) <- gsub("_sig", "_dif", colnames(nemSigMN))
 
-        nemMN <- cbind(fdrVn, sigVn, difMN, nemMN, nemSigMN)
-        colnames(nemMN)[1:2] <- paste0(varPfxC, c(adjC, "sig"))
+        ## final aggregated table
+        
+        resMN <- cbind(adjVn, sigVn, difMN, nemMN, nemSigMN)
+        colnames(resMN)[1:2] <- paste0(varPfxC, c(adjC, "sig"))
+
+        varDF <- cbind.data.frame(varDF, as.data.frame(resMN))
+
+        ## graphic
+
+        pdf(pdfC, onefile = TRUE)
+        
+        for(varI in 1:nrow(varDF)) {
+            
+            if(sum(nemSigMN[varI, ]) > 0) {
+                
+                varC <- rownames(varDF)[varI]
 
-        varDF <- cbind.data.frame(varDF, as.data.frame(nemMN))
+                boxplot(datMN[, varI] ~ facFcVn,
+                        main = paste0(varC, " (", adjC, " = ", signif(adjVn[varI], 2), ")"))
+                
+                for(nemI in 1:length(nemNamVc)) {
+                    
+                    if(nemSigMN[varI, paste0(varPfxC, nemNamVc[nemI], "_sig")] == 1) {
+                        
+                        nemClaVc <- nemClaMC[nemI, ]
+                        nemClaVl <- facFcVn %in% nemClaVc
+                        nemFc <- facFcVn[nemClaVl, drop = TRUE]
+                        nemVc <- as.character(nemFc)
+                        names(nemVc) <- rownames(samDF)[nemClaVl]
+                        boxF(nemFc,
+                             datMN[nemClaVl, varI],
+                             paste0(varC, " (", adjC, " = ", signif(nemMN[varI, paste0(varPfxC, nemNamVc[nemI], "_", adjC)], 2), ")"),
+                             nemVc)
+                        
+                    }
+                       
+                }
+                
+            }
+            
+        }
 
+        dev.off()
+        
     }
-
+    
     names(sigVn) <- rownames(varDF)
     sigSumN <- sum(sigVn, na.rm = TRUE)
     if(sigSumN) {
@@ -148,3 +305,28 @@
     return(varDF)
 
 }
+
+
+boxF <- function(xFc,
+                 yVn,
+                 maiC,
+                 xVc) {
+    
+    boxLs <- boxplot(yVn ~  xFc,
+                     main = maiC)
+    
+    outVn <- boxLs[["out"]]
+    
+    if(length(outVn)) {
+        
+        for(outI in 1:length(outVn)) {
+            levI <- which(levels(xFc) == xVc[names(outVn)[outI]])
+            text(levI,
+                 outVn[outI],
+                 labels = names(outVn)[outI],
+                 pos = ifelse(levI == 2, 2, 4))
+            }
+        
+    }
+    
+}
diff -r 09799fc16bc6 -r 140290de7986 univariate_wrapper.R
--- a/univariate_wrapper.R	Sat Aug 06 12:42:42 2016 -0400
+++ b/univariate_wrapper.R	Sun Oct 30 14:17:09 2016 -0400
@@ -119,7 +119,8 @@
                      facC = argVc["facC"],
                      tesC = tesC,
                      adjC = argVc["adjC"],
-                     thrN = as.numeric(argVc["thrN"]))
+                     thrN = as.numeric(argVc["thrN"]),
+                     pdfC = argVc["figure"])
 
 
 ##------------------------------