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changeset 2:c415b7dc6f37 draft default tip

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planemo upload for repository https://github.com/HegemanLab/w4mkmeans_galaxy_wrapper/tree/master commit 3e916537da6bb37e6f3927d7a11e98e0ab6ef5ec
author eschen42
date Mon, 05 Mar 2018 12:40:17 -0500
parents 02cafb660b72
children
files README test-data/input_sampleMetadata.tsv test-data/input_variableMetadata.tsv w4m_general_purpose_routines.R w4mkmeans.xml w4mkmeans_routines.R w4mkmeans_wrapper.R
diffstat 7 files changed, 389 insertions(+), 312 deletions(-) [+]
line wrap: on
line diff
--- a/README	Wed Aug 09 18:06:55 2017 -0400
+++ b/README	Mon Mar 05 12:40:17 2018 -0500
@@ -1,2 +1,5 @@
-# w4mkmeans_galaxy_wrapper
-Planemo-based galaxy-tool-wrapper to wrap the stats::kmeans R package for the W4M dataMatrix
+# Kmeans for W4m
+
+Kmeans for W4m is Galaxy tool-wrapper to wrap the R stats::kmeans package 
+for use with the Workflow4Metabolomics flavor of Galaxy. 
+This tool is built with Planemo.
--- a/test-data/input_sampleMetadata.tsv	Wed Aug 09 18:06:55 2017 -0400
+++ b/test-data/input_sampleMetadata.tsv	Mon Mar 05 12:40:17 2018 -0500
@@ -1,25 +1,25 @@
-sampleMetadata	class	polarity	sampleType	injectionOrder	batch	tissue	hotelling_pval	missing_pval	decile_pval	PCA_XSCOR.p1	PCA_XSCOR.p2	class_PLSDA_XSCOR.p1	class_PLSDA_XSCOR.p2	class_PLSDA_predictions
-Y11_1_RA5_01_213	y1	positive	sample	213	1	2	0.0955561581467602	1	0.0306775551319138	-2.26882060894901	1.94958116765736	-3.05527623038242	2.32594165405491	y1
-Y2_1_RB1_01_218	y2	positive	sample	218	1	1	0.090775969547078	1	0.0334308308237932	-5.43790231069006	3.28509884002914	-5.09396184849057	3.13632363820691	y2
-Y4_1_RB3_01_220	y4	positive	sample	220	1	1	0.0922380872343134	1	0.0343065627030201	-5.96519534532645	2.76065569212045	-5.61271725241037	2.60836215684335	y4
-Y12_1_RB4_01_221	y2	positive	sample	221	1	2	0.0731025791938841	1	0.0335814402688999	-3.90074024447077	2.32567583717618	-4.06427508572245	2.47867307479093	y2
-Y1_1_RC1_01_228	y1	positive	sample	228	1	1	0.948646526283138	1	0.0150153992414774	-5.79172889541087	3.18442356006801	-5.71894211121787	3.14075608795096	y1
-Y14_1_RC6_01_234	y4	positive	sample	234	1	2	0.961424772615561	1	0.0889762542416943	-3.50543091786719	1.90332246047248	-3.60257250798236	1.95341548985651	y4
-Y1_2_RD1_01_239	y1	positive	sample	239	1	1	0.391486624975171	1	0.419632697534464	-10.0290510611276	3.46916578350898	-9.30301404180818	3.22425994348737	y1
-Y14_2_RD2_01_240	y4	positive	sample	240	1	2	0.334478686842038	1	0.471265236704114	-0.955577667004931	1.62643379077323	-1.17127923245195	1.73770179646644	y4
-Y4_2_RD3_01_241	y4	positive	sample	241	1	1	0.243979127543208	1	0.115904447650611	-6.29053214398527	3.48768497975009	-5.86080882473825	3.19393908077519	y4
-Y11_2_RD7_01_246	y1	positive	sample	246	1	2	0.639085015503201	1	0.496291025606805	-0.737703114796199	1.89206669195622	-1.33734677265265	2.19119862732508	y1
-Y2_2_RE4_01_253	y2	positive	sample	253	1	1	0.681339414372971	1	0.713644697663014	-4.43122798643441	2.59136016132011	-4.21959323228049	2.4942150403311	y2
-Y12_2_RE6_01_255	y2	positive	sample	255	1	2	0.581861317126264	1	0.446040669279691	-3.42333388673909	2.19844489197916	-3.40077262161465	2.21882800112511	y2
-Y14_3_GA2_01_260	y4	positive	sample	260	1	2	0.792323381194401	1	0.812319191661791	-2.61403564986014	1.9025507158402	-2.90132077481451	2.05744453719897	y4
-Y2_3_GA4_01_264	y2	positive	sample	264	1	1	0.278347988263537	1	0.668405316795454	-6.19672954480257	4.11371745717593	-5.72942704887795	3.94423530839635	y2
-Y1_3_GA6_01_266	y1	positive	sample	266	1	1	0.303133108610158	1	0.521065147801524	-5.91283168480956	2.57721868167528	-5.8128281040434	2.56623732055011	y1
-Y4_3_GA7_01_267	y4	positive	sample	267	1	1	0.204620420161485	1	0.53551459376182	-5.81862869528986	3.42191037440281	-5.37442797934098	3.22465790741629	y4
-Y12_3_GB1_01_270	y2	positive	sample	270	1	2	0.7747649633382	1	0.698966513767803	-3.0854085700971	1.67899209632345	-3.14572560562774	1.75648836353689	y2
-Y11_3_GC3_01_283	y1	positive	sample	283	1	2	0.918803505111851	1	0.396638581468035	-1.16946485386388	1.66851916844539	-1.7032576378218	1.94860768310552	y1
-Y14_4_GC7_01_287	y4	positive	sample	287	1	2	0.577273975934045	1	0.14919566995266	-1.24666389579168	2.84891525888206	-1.58652468539139	2.90364189714377	y4
-Y11_4_GD8_01_299	y1	positive	sample	299	1	2	0.31302025978985	1	0.426766355892969	-2.15936901108787	1.66989335813642	-2.66042240568943	1.95509478589954	y1
-Y2_4_GE1_01_300	y2	positive	sample	300	1	1	0.0338929937565918	1	0.419149865807458	-5.76080121045973	3.47845733452933	-5.39212267305567	3.34452446158071	y2
-Y12_4_GE2_01_304	y2	positive	sample	304	1	2	0.130905883509031	1	0.59698349195307	-4.15585900988913	3.22702525356271	-4.28382960930699	3.34874792551519	y2
-Y1_4_GE3_01_305	y1	positive	sample	305	1	1	0.129479197101219	1	0.618449187175638	-2.9921645121991	3.14523577730793	-2.85639638001866	3.08197032598192	y1
-Y4_4_GE7_01_309	y4	positive	sample	309	1	1	0.758837157578886	1	0.339564008612217	-5.95949084754462	3.20317151028856	-5.46675286145534	3.04585537553442	y4
+sampleMetadata	class	polarity	sampleType	injectionOrder	batch	tissue	hotelling_pval	missing_pval	decile_pval
+Y11_1_RA5_01_213	y1	positive	sample	213	1	2	0.0955561581467602	1	0.0306775551319138
+Y2_1_RB1_01_218	y2	positive	sample	218	1	1	0.090775969547078	1	0.0334308308237932
+Y4_1_RB3_01_220	y4	positive	sample	220	1	1	0.0922380872343134	1	0.0343065627030201
+Y12_1_RB4_01_221	y2	positive	sample	221	1	2	0.0731025791938841	1	0.0335814402688999
+Y1_1_RC1_01_228	y1	positive	sample	228	1	1	0.948646526283138	1	0.0150153992414774
+Y14_1_RC6_01_234	y4	positive	sample	234	1	2	0.961424772615561	1	0.0889762542416943
+Y1_2_RD1_01_239	y1	positive	sample	239	1	1	0.391486624975171	1	0.419632697534464
+Y14_2_RD2_01_240	y4	positive	sample	240	1	2	0.334478686842038	1	0.471265236704114
+Y4_2_RD3_01_241	y4	positive	sample	241	1	1	0.243979127543208	1	0.115904447650611
+Y11_2_RD7_01_246	y1	positive	sample	246	1	2	0.639085015503201	1	0.496291025606805
+Y2_2_RE4_01_253	y2	positive	sample	253	1	1	0.681339414372971	1	0.713644697663014
+Y12_2_RE6_01_255	y2	positive	sample	255	1	2	0.581861317126264	1	0.446040669279691
+Y14_3_GA2_01_260	y4	positive	sample	260	1	2	0.792323381194401	1	0.812319191661791
+Y2_3_GA4_01_264	y2	positive	sample	264	1	1	0.278347988263537	1	0.668405316795454
+Y1_3_GA6_01_266	y1	positive	sample	266	1	1	0.303133108610158	1	0.521065147801524
+Y4_3_GA7_01_267	y4	positive	sample	267	1	1	0.204620420161485	1	0.53551459376182
+Y12_3_GB1_01_270	y2	positive	sample	270	1	2	0.7747649633382	1	0.698966513767803
+Y11_3_GC3_01_283	y1	positive	sample	283	1	2	0.918803505111851	1	0.396638581468035
+Y14_4_GC7_01_287	y4	positive	sample	287	1	2	0.577273975934045	1	0.14919566995266
+Y11_4_GD8_01_299	y1	positive	sample	299	1	2	0.31302025978985	1	0.426766355892969
+Y2_4_GE1_01_300	y2	positive	sample	300	1	1	0.0338929937565918	1	0.419149865807458
+Y12_4_GE2_01_304	y2	positive	sample	304	1	2	0.130905883509031	1	0.59698349195307
+Y1_4_GE3_01_305	y1	positive	sample	305	1	1	0.129479197101219	1	0.618449187175638
+Y4_4_GE7_01_309	y4	positive	sample	309	1	1	0.758837157578886	1	0.339564008612217
--- a/test-data/input_variableMetadata.tsv	Wed Aug 09 18:06:55 2017 -0400
+++ b/test-data/input_variableMetadata.tsv	Mon Mar 05 12:40:17 2018 -0500
@@ -1,50 +1,50 @@
-variableMetadata	namecustom	mz	mzmin	mzmax	rt	rtmin	rtmax	npeaks	my_blank	pool	y0	y1	y2	y3	y4	y5	y6	y7	y8	y9	isotopes	adduct	pcgroup	CV.samp	CV.pool	CV.ind	blank_mean	blank_sd	blank_CV	sample_mean	sample_sd	sample_CV	blankMean_over_sampleMean	pool_mean	pool_sd	pool_CV	poolCV_over_sampleCV	class_kruskal_fdr	class_kruskal_sig	class_kruskal_y1.y0_dif	class_kruskal_y2.y0_dif	class_kruskal_y3.y0_dif	class_kruskal_y4.y0_dif	class_kruskal_y5.y0_dif	class_kruskal_y6.y0_dif	class_kruskal_y7.y0_dif	class_kruskal_y8.y0_dif	class_kruskal_y9.y0_dif	class_kruskal_y2.y1_dif	class_kruskal_y3.y1_dif	class_kruskal_y4.y1_dif	class_kruskal_y5.y1_dif	class_kruskal_y6.y1_dif	class_kruskal_y7.y1_dif	class_kruskal_y8.y1_dif	class_kruskal_y9.y1_dif	class_kruskal_y3.y2_dif	class_kruskal_y4.y2_dif	class_kruskal_y5.y2_dif	class_kruskal_y6.y2_dif	class_kruskal_y7.y2_dif	class_kruskal_y8.y2_dif	class_kruskal_y9.y2_dif	class_kruskal_y4.y3_dif	class_kruskal_y5.y3_dif	class_kruskal_y6.y3_dif	class_kruskal_y7.y3_dif	class_kruskal_y8.y3_dif	class_kruskal_y9.y3_dif	class_kruskal_y5.y4_dif	class_kruskal_y6.y4_dif	class_kruskal_y7.y4_dif	class_kruskal_y8.y4_dif	class_kruskal_y9.y4_dif	class_kruskal_y6.y5_dif	class_kruskal_y7.y5_dif	class_kruskal_y8.y5_dif	class_kruskal_y9.y5_dif	class_kruskal_y7.y6_dif	class_kruskal_y8.y6_dif	class_kruskal_y9.y6_dif	class_kruskal_y8.y7_dif	class_kruskal_y9.y7_dif	class_kruskal_y9.y8_dif	class_kruskal_y1.y0_fdr	class_kruskal_y2.y0_fdr	class_kruskal_y3.y0_fdr	class_kruskal_y4.y0_fdr	class_kruskal_y5.y0_fdr	class_kruskal_y6.y0_fdr	class_kruskal_y7.y0_fdr	class_kruskal_y8.y0_fdr	class_kruskal_y9.y0_fdr	class_kruskal_y2.y1_fdr	class_kruskal_y3.y1_fdr	class_kruskal_y4.y1_fdr	class_kruskal_y5.y1_fdr	class_kruskal_y6.y1_fdr	class_kruskal_y7.y1_fdr	class_kruskal_y8.y1_fdr	class_kruskal_y9.y1_fdr	class_kruskal_y3.y2_fdr	class_kruskal_y4.y2_fdr	class_kruskal_y5.y2_fdr	class_kruskal_y6.y2_fdr	class_kruskal_y7.y2_fdr	class_kruskal_y8.y2_fdr	class_kruskal_y9.y2_fdr	class_kruskal_y4.y3_fdr	class_kruskal_y5.y3_fdr	class_kruskal_y6.y3_fdr	class_kruskal_y7.y3_fdr	class_kruskal_y8.y3_fdr	class_kruskal_y9.y3_fdr	class_kruskal_y5.y4_fdr	class_kruskal_y6.y4_fdr	class_kruskal_y7.y4_fdr	class_kruskal_y8.y4_fdr	class_kruskal_y9.y4_fdr	class_kruskal_y6.y5_fdr	class_kruskal_y7.y5_fdr	class_kruskal_y8.y5_fdr	class_kruskal_y9.y5_fdr	class_kruskal_y7.y6_fdr	class_kruskal_y8.y6_fdr	class_kruskal_y9.y6_fdr	class_kruskal_y8.y7_fdr	class_kruskal_y9.y7_fdr	class_kruskal_y9.y8_fdr	class_kruskal_y1.y0_sig	class_kruskal_y2.y0_sig	class_kruskal_y3.y0_sig	class_kruskal_y4.y0_sig	class_kruskal_y5.y0_sig	class_kruskal_y6.y0_sig	class_kruskal_y7.y0_sig	class_kruskal_y8.y0_sig	class_kruskal_y9.y0_sig	class_kruskal_y2.y1_sig	class_kruskal_y3.y1_sig	class_kruskal_y4.y1_sig	class_kruskal_y5.y1_sig	class_kruskal_y6.y1_sig	class_kruskal_y7.y1_sig	class_kruskal_y8.y1_sig	class_kruskal_y9.y1_sig	class_kruskal_y3.y2_sig	class_kruskal_y4.y2_sig	class_kruskal_y5.y2_sig	class_kruskal_y6.y2_sig	class_kruskal_y7.y2_sig	class_kruskal_y8.y2_sig	class_kruskal_y9.y2_sig	class_kruskal_y4.y3_sig	class_kruskal_y5.y3_sig	class_kruskal_y6.y3_sig	class_kruskal_y7.y3_sig	class_kruskal_y8.y3_sig	class_kruskal_y9.y3_sig	class_kruskal_y5.y4_sig	class_kruskal_y6.y4_sig	class_kruskal_y7.y4_sig	class_kruskal_y8.y4_sig	class_kruskal_y9.y4_sig	class_kruskal_y6.y5_sig	class_kruskal_y7.y5_sig	class_kruskal_y8.y5_sig	class_kruskal_y9.y5_sig	class_kruskal_y7.y6_sig	class_kruskal_y8.y6_sig	class_kruskal_y9.y6_sig	class_kruskal_y8.y7_sig	class_kruskal_y9.y7_sig	class_kruskal_y9.y8_sig	PCA_XLOAD.p1	PCA_XLOAD.p2	class_PLSDA_XLOAD.p1	class_PLSDA_XLOAD.p2	class_PLSDA_VIP	class_PLSDA_y0.COEFF	class_PLSDA_y1.COEFF	class_PLSDA_y2.COEFF	class_PLSDA_y3.COEFF	class_PLSDA_y4.COEFF	class_PLSDA_y5.COEFF	class_PLSDA_y6.COEFF	class_PLSDA_y7.COEFF	class_PLSDA_y8.COEFF	class_PLSDA_y9.COEFF
-M118T229	M118T229.46	118	118	118	229.455291748047	228.736724853516	230.594131469727	55	0	8	7	5	7	6	5	2	3	7	3	2			118	0.554571338812749	0.182791107722623	1	222439.554974048	195365.680228179	0.878286599031237	176244.197127339	110977.214194002	0.629678684477871	1.26210995085037	155437.851425	114292.160673818	0.7352916913482	1.16772523744853	1.66164992620511e-08	1	-223469.361382575	-126938.739412878	-133449.455499242	-180267.373249242	-294391.240395454	-239458.682065909	-76554.566924243	-261291.811240909	-260891.311868686	96530.6219696975	90019.905883333	43201.988133333	-70921.879012879	-15989.3206833335	146914.794458332	-37822.4498583335	-37421.950486111	-6510.71608636447	-53328.6338363645	-167452.500982576	-112519.942653031	50384.1724886345	-134353.071828031	-133952.572455808	-46817.91775	-160941.784896212	-106009.226566667	56894.888574999	-127842.355741667	-127441.856369444	-114123.867146212	-59191.3088166665	103712.806324999	-81024.4379916665	-80623.938619444	54932.5583295455	217836.673471211	33099.4291545455	33499.928526768	162904.115141666	-21833.129175	-21432.6298027775	-184737.244316666	-184336.744944443	400.499372222504	0.052228974501798	1	1	0.968678467026688	6.82548419440413e-05	0.0252028502127163	1	0.0103342950150846	0.090998598707947	1	1	1	1	1	0.227514794354078	1	1	1	1	0.292043497476492	1	1	0.563138922804635	1	1	0.390408587868349	1	1	1	1	0.459462762339069	1	1	1	1	1	0.00223728896288522	1	1	0.116839984773705	1	1	0.0900823169726959	0.296718843529397	1	0	0	0	0	1	1	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0.0751465404017426	0.114470509909381	0.0775649916703844	0.113255054281766	0.926381557758783	0.0182402372008762	-0.0113981265999673	0.0426448134069729	0.0197016024646982	-0.0232348488731026	-0.0424516830523853	-0.0147138462348906	0.0214688799695989	-0.00513169620476044	-0.00704409517333393
-M144T249	M143.875T248.69	143.875	143.875	143.875	248.692764282227	248.002151489258	249.422805786133	62	0	8	2	7	3	5	7	8	6	5	8	3			142	0.470618443098571	0.216046830280697	1	292413.434736192	203604.182450115	0.696288741431463	271524.560161574	158685.604562455	0.584424497246317	1.07693180521934	383231.735361819	301728.874896368	0.787327475923931	1.34718424644013	6.45954352514962e-07	1	235618.806762501	169118.08305	229322.632631251	259605.281649999	416619.815033332	275136.827786364	150532.487104546	457817.763728431	195173.60565	-66500.7237125005	-6296.17413124954	23986.4748874989	181001.008270831	39518.021023863	-85086.319657955	222198.95696593	-40445.201112501	60204.549581251	90487.1985999995	247501.731983331	106018.744736364	-18585.5959454545	288699.680678431	26055.5225999995	30282.6490187485	187297.182402081	45814.1951551125	-78790.1455267055	228495.13109718	-34149.0269812515	157014.533383332	15531.546136364	-109072.794545454	198212.482078431	-64431.676	-141482.987246968	-266087.327928786	41197.9486950994	-221446.209383332	-124604.340681818	182680.935942067	-79963.222136364	307285.276623885	44641.118545454	-262644.158078431	0.41166209367422	0.695786868243422	0.294551379678528	0.0999587113644674	6.82548419440413e-05	0.0979696628294609	1	6.99294016548446e-05	1	1	1	1	1	1	1	0.660361761700837	1	1	1	0.78294591403514	1	1	0.359071250151712	1	1	1	1	1	0.800695257846844	1	1	1	1	1	1	1	0.0211144277007564	1	1	1	1	1	0.0290403766541771	1	1	0	0	0	0	1	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	1	0	0	-0.0425445507499967	-0.137386022704088	-0.0414455664421757	-0.138961517651778	0.94030081229378	-0.0175777629069553	-0.0012658955204704	-0.0374855225818242	-0.0115638894382932	0.0369249335046923	0.0613915623367941	-0.0282052479473143	-0.0129882503144432	0.0212574166388364	-0.0144134753666958
-M146T229	M145.875T229.42	145.875	145.875	145.875	229.422142028809	228.712783813477	230.594131469727	62	0	9	8	6	6	6	6	2	6	8	4	1			118	0.48059751415742	0.159160327803038	1	542353.504170514	417121.368012766	0.769094999488792	448233.950201589	270767.904818011	0.604077189370051	1.20997863710813	405757.717987176	296615.203178599	0.731015554429883	1.21013600131501	7.70188729663607e-09	1	-582059.466330129	-420926.183282051	-341325.049310082	-484778.717540386	-752503.017731838	-593558.467831296	-221486.367407052	-660228.32603985	-671454.694221447	161133.283048078	240734.417020047	97280.748789743	-170443.551401709	-11499.001501167	360573.098923077	-78168.8597097215	-89395.2278913175	79601.133971969	-63852.5342583345	-331576.834449787	-172632.284549245	199439.815875	-239302.142757799	-250528.510939395	-143453.668230304	-411177.968421756	-252233.418521214	119838.68190303	-318903.276729768	-330129.644911364	-267724.300191452	-108779.75029091	263292.350133334	-175449.608499464	-186675.976681061	158944.549900542	531016.650324786	92274.6916919875	81048.3235103915	372072.100424244	-66669.8582085545	-77896.2263901506	-438741.958632799	-449968.326814395	-11226.368181596	0.027794335864133	1	1	0.975940335684243	4.71856017804362e-05	0.0252028502127163	1	0.00137556615138216	0.0668266484078959	1	1	1	1	1	0.208110883871688	1	1	1	1	0.516072532531189	1	1	0.763113985902735	1	1	0.305615730979035	1	1	0.399116928338267	1	0.410587406257074	1	1	0.753420461486308	1	1	0.00223728896288522	1	1	0.168171506310646	1	1	0.0294571388445663	0.272157453958819	1	1	0	0	0	1	1	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	1	0	0	0.0754996095137967	0.120865516467987	0.0784680705150086	0.119115248373144	0.956609897374724	0.0226314437704627	-0.0153551878413858	0.0159176509359441	0.0285781636368376	-0.00845519747929896	-0.0389253784563034	-0.0108323340963491	0.0184213773838867	-0.00841148941129242	-0.00490518790379316
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+M247T452	M247T451.94	247	247	247	451.937652587891	450.494750976562	452.778442382812	42	0	7	5	4	2	0	4	7	2	7	1	3			2	1.8377037387191	0.244212966980515	1	19304952.0532017	33519245.0029629	1.73630293981507	26880324.4295464	50698463.1885802	1.88608077709261	0.718181512421852	11819323.2613698	17126211.9235465	1.44900105909801	0.768260340011335
+M248T433	M248T433.34	248	248	248	433.344421386719	432.868927001953	434.076232910156	21	0	2	4	4	2	1	2	3	1	1	0	1	[3][M+1]+		3	1.59105092227126	0.195371307889308	1	2480421.37630339	3152657.65535375	1.27101696730747	3650972.15175104	6060161.14033005	1.65987602436889	0.679386550542094	1475786.50491227	2195945.58877151	1.4879832424691	0.896442397277746
+M251T497	M251T497.27	251	251	251	497.265563964844	496.463073730469	498.130004882812	40	0	10	0	7	5	8	6	4	0	0	0	0	[4][M]+		48	1.96330612768638	0.192700519895792	1	594224.888258475	760181.506434167	1.27928250979536	830581.94802374	1732410.35548682	2.08577896450659	0.715431980760423	470765.000605745	589833.527563223	1.25292561427521	0.600699132360655
+M257T1014	M257.125T1013.9	257.125	257.125	257.125	1013.90087890625	1012.74542236328	1014.60961914062	73	0	7	7	6	6	7	5	8	7	8	8	4	[6][M]+		249	0.881053983255579	0.186470621075041	1	2202927.69616102	2148769.26942137	0.975415249972102	2007043.93637984	1762758.42680569	0.878285918336807	1.09759814233788	2962754.71167807	3892125.71725248	1.31368476165481	1.49573701937805
+M261T332	M261T331.57	261	261	261	331.569473266602	330.826965332031	332.320007324219	46	0	9	0	6	5	5	5	5	7	0	0	4	[7][M]+		57	1.04326999694138	0.221252808937921	1	1146683.03090926	1186248.76846245	1.03450451126133	1145403.0063442	1327072.12823314	1.1586071634898	1.0011175320459	919208.931999451	1174249.82975397	1.27745694028425	1.10257987395527
+M263T323	M263T323.29	263	263	263	323.286376953125	317.427062988281	324.498107910156	95	0	10	8	7	3	1	8	7	5	8	2	3	[8][M]+		25	1.24549325872202	0.169688974744568	1	4856126.41100817	6417229.02375924	1.32147075274076	3269419.74893288	4306448.22482372	1.31719037490653	1.48531751317437	2850327.25225239	3833675.62917697	1.34499490405795	1.02110896775524
--- a/w4m_general_purpose_routines.R	Wed Aug 09 18:06:55 2017 -0400
+++ b/w4m_general_purpose_routines.R	Mon Mar 05 12:40:17 2018 -0500
@@ -1,3 +1,48 @@
+##-----------------------------------------------
+## helper functions for error detection/reporting
+##-----------------------------------------------
+
+# ISO 8601 date ref: https://en.wikipedia.org/wiki/ISO_8601
+iso_date <- function() {
+  format(Sys.time(), "%Y-%m-%dT%H:%M:%S%z")
+}
+
+# log-printing to stderr
+log_print <- function(x, ...) {
+  cat(
+    sep=""
+  , file=stderr()
+  , iso_date()
+  , " "
+  , c(x, ...)
+  , "\n"
+  )
+}
+
+# format error for logging
+format_error <- function(e) {
+  paste(c("Error { message:", e$message, ", call:", e$call, "}"), collapse = " ")
+}
+
+# tryCatchFunc produces a list
+#   func - a function that takes no arguments
+#   On success of func(), tryCatchFunc produces
+#     list(success = TRUE, value = func(), msg = "")
+#   On failure of func(), tryCatchFunc produces
+#     list(success = FALSE, value = NA, msg = "the error message")
+tryCatchFunc <- function(func) {
+  retval <- NULL
+  tryCatch(
+    expr = {
+      retval <- ( list( success = TRUE, value = func(), msg = "" ) )
+    }
+  , error = function(e) {
+      retval <<- list( success = FALSE, value = NA, msg = format_error(e) )
+    }
+  )
+  return (retval)
+}
+
 # prepare.data.matrix - Prepare x.datamatrix for multivariate statistical analaysis (MVA)
 #   - Motivation:
 #     - Selection:
@@ -7,7 +52,7 @@
 #         - If so, set the argument 'exclude.features' to a vector of feature names
 #     - Renaming samples:
 #       - You may want to rename several samples from your analysis:
-#         - If so, set the argument 'sample.rename.function' to a function accepting a vector 
+#         - If so, set the argument 'sample.rename.function' to a function accepting a vector
 #           of sample names and producing a vector of strings of equivalent length
 #     - MVA is confounded by missing values.
 #       - By default, this function imputes missing values as zero.
@@ -19,7 +64,7 @@
 #       - By default, this function performs an eigth-root transformation:
 #         - Any root-tranformation has the advantage of never being negative.
 #         - Calculation of the eight-root is four times faster in my hands than log10.
-#         - However, it has the disadvantage that calculation of fold-differences 
+#         - However, it has the disadvantage that calculation of fold-differences
 #           is not additive as with log-transformation.
 #           - Rather, you must divide the values and raise to the eighth power.
 #       - For a different transformation, set the 'data.transformation' argument
@@ -107,6 +152,13 @@
   }
 , en = new.env()
 ) {
+  # log to environment
+  if ( !exists("log", envir = en) ) {
+    en$log <- c()
+  }
+  enlog <- function(s) { en$log <- c(en$log, s); s }
+  #enlog("foo")
+
   # MatVar - Compute variance of rows or columns of a matrix
   # ref: http://stackoverflow.com/a/25100036
   # For row variance, dim == 1, for col variance, dim == 2
@@ -137,11 +189,9 @@
 
   nonzero.var <- function(x) {
     if (nrow(x) == 0) {
-      print(str(x))
       stop("matrix has no rows")
     }
     if (ncol(x) == 0) {
-      print(str(x))
       stop("matrix has no columns")
     }
     if ( is.numeric(x) ) {
@@ -153,7 +203,7 @@
         row.names <- attr(nonzero.rows,"names")
         x <- x[ row.names, , drop = FALSE ]
       }
-      
+
       # exclude any columns with zero variance
       column.vars <- MatVar(x, dim = 2)
       nonzero.column.vars <- column.vars > 0
@@ -170,10 +220,13 @@
     stop("FATAL ERROR - prepare.data.matrix was called with null x.matrix")
   }
 
+  enlog("prepare.data.matrix - get matrix")
+
   en$xpre <- x <- x.matrix
 
   # exclude any samples as indicated
   if ( !is.null(exclude.features) ) {
+    enlog("prepare.data.matrix - exclude any samples as indicated")
     my.colnames <- colnames(x)
     my.col.diff <- setdiff(my.colnames, exclude.features)
     x <- x[ , my.col.diff , drop = FALSE ]
@@ -181,6 +234,7 @@
 
   # exclude any features as indicated
   if ( !is.null(exclude.samples) ) {
+    enlog("prepare.data.matrix - exclude any features as indicated")
     my.rownames <- rownames(x)
     my.row.diff <- setdiff(my.rownames, exclude.samples)
     x <- x[ my.row.diff, , drop = FALSE ]
@@ -188,20 +242,25 @@
 
   # rename rows if desired
   if ( !is.null(sample.rename.function) ) {
+    enlog("prepare.data.matrix - rename rows if desired")
     renamed <- sample.rename.function(x)
     rownames(x) <- renamed
   }
 
+  enlog("prepare.data.matrix - save redacted x.datamatrix to environment")
+
   # save redacted x.datamatrix to environment
   en$redacted.data.matrix <- x
 
   # impute values missing from the x.datamatrix
   if ( !is.null(data.imputation) ) {
+    enlog("prepare.data.matrix - impute values missing from the x.datamatrix")
     x <- data.imputation(x)
   }
 
   # perform transformation if desired
   if ( !is.null(data.transformation) ) {
+    enlog("prepare.data.matrix - perform transformation")
     x <- data.transformation(x)
   } else {
     x <- x
@@ -209,6 +268,7 @@
 
   # purge rows and columns that have zero variance
   if ( is.numeric(x) ) {
+    enlog("prepare.data.matrix - purge rows and columns that have zero variance")
     x <- nonzero.var(x)
   }
 
@@ -218,66 +278,4 @@
   return(x)
 }
 
-
-##-----------------------------------------------
-## helper functions for error detection/reporting
-##-----------------------------------------------
-
-# log-printing to stderr
-log_print <- function(x, ...) { 
-  cat(
-    format(Sys.time(), "%Y-%m-%dT%H:%M:%S%z")
-  , " "
-  , c(x, ...)
-  , "\n"
-  , sep=""
-  , file=stderr()
-  )
-}
-
-# tryCatchFunc produces a list
-#   On success of expr(), tryCatchFunc produces
-#     list(success TRUE, value = expr(), msg = "")
-#   On failure of expr(), tryCatchFunc produces
-#     list(success = FALSE, value = NA, msg = "the error message")
-tryCatchFunc <- function(expr) {
-  # format error for logging
-  format_error <- function(e) {
-    paste(c("Error { message:", e$message, ", call:", e$call, "}"), collapse = " ")
-  }
-  my_expr <- expr
-  retval <- NULL
-  tryCatch(
-    expr = {
-      retval <- ( list( success = TRUE, value = my_expr(), msg = "" ) )
-    }
-  , error = function(e) {
-      retval <<- list( success = FALSE, value = NA, msg = format_error(e) )
-    }
-  )
-  return (retval)
-}
-
-# tryCatchProc produces a list
-#   On success of expr(), tryCatchProc produces
-#     list(success TRUE, msg = "")
-#   On failure of expr(), tryCatchProc produces
-#     list(success = FALSE, msg = "the error message")
-tryCatchProc <- function(expr) {
-  # format error for logging
-  format_error <- function(e) {
-    paste(c("Error { message:", e$message, ", call:", e$call, "}"), collapse = " ")
-  }
-  retval <- NULL
-  tryCatch(
-    expr = {
-      expr()
-      retval <- ( list( success = TRUE, msg = "" ) )
-    }
-  , error = function(e) {
-      retval <<- list( success = FALSE, msg = format_error(e) )
-    }
-  )
-  return (retval)
-}
-
+# vim: sw=2 ts=2 et :
--- a/w4mkmeans.xml	Wed Aug 09 18:06:55 2017 -0400
+++ b/w4mkmeans.xml	Mon Mar 05 12:40:17 2018 -0500
@@ -1,9 +1,11 @@
-<tool id="w4mkmeans" name="w4mKmeans" version="0.98.3">
-  <description>Calculate K-means for W4M dataMatrix features or samples</description>
+<tool id="w4mkmeans" name="Kmeans for W4m" version="0.98.4">
+  <description>Calculate K-means for W4m dataMatrix features or samples</description>
 
   <requirements>
-    <requirement type="package" version="3.3.2">r-base</requirement>
+    <requirement type="package" version="3.4.1">r-base</requirement>
     <requirement type="package" version="1.1_4">r-batch</requirement>
+    <requirement type="package" version="1.8.0">libssh2</requirement>
+    <requirement type="package" version="1.13.2">krb5</requirement>
   </requirements>
 
   <stdio>
@@ -27,19 +29,18 @@
       slots "\${GALAXY_SLOTS:-1}"
       variableMetadata_out '$variableMetadata_out'
       variable_metadata_path '$variableMetadata_in'
-    ; echo exit code $?
   ]]></command>
 
   <inputs>
-    <param name="dataMatrix_in" label="Data matrix file" type="data" format="tabular" help="variable x sample, decimal: '.', missing: NA, mode: numerical, separator: tab" />
-    <param name="sampleMetadata_in" label="Sample metadata file" type="data" format="tabular" help="sample x metadata columns, separator: tab" />
-    <param name="variableMetadata_in" label="Variable metadata file" type="data" format="tabular" help="variable x metadata columns, separator: tab" />
-    <param name="categoricalPrefix" label="prefix for cluster names " type="text" value="k" help="[categorical_prefix] Some tools require non-numeric values to discern categorical data; e.g., enter 'k' here to prepend 'k' to cluster numbers in the output; default 'k'." />
+    <param name="dataMatrix_in" label="Data matrix file" type="data" format="tabular" help="Feature (variable) x sample; decimal point: '.'; missing: NA; mode: numerical; separator: tab" />
+    <param name="sampleMetadata_in" label="Sample metadata file" type="data" format="tabular" help="Sample x metadata columns; separator: tab" />
+    <param name="variableMetadata_in" label="Variable (feature) metadata file" type="data" format="tabular" help="Feature (variable) x metadata columns; separator: tab" />
+    <param name="categoricalPrefix" label="Prefix for cluster names " type="text" value="c" help="String prepended to cluster numbers in output; default 'c'; leave blank for no prefix." />
     <param name="ksamples" label="K value(s) for samples" type="text" value = "0" help="[ksamples] Single K or comma-separated Ks for samples, or 0 for none." />
     <param name="kfeatures" label="K value(s) for features" type="text" value = "0" help="[kfeatures] Single K or comma-separated Ks for features (variables), or 0 for none." />
-    <param name="iter_max" label="Max number of iterations" type="text" value = "10" help="[iter_max] The maximum number of iterations allowed; default 10." />
-    <param name="nstart" label="Number of random sets" type="text" value = "1" help="[nstart] How many random sets should be chosen; default 1." />
-    <param name="algorithm" label="Algorithm for clustering" type="select" value = "Hartigan-Wong" help="[algorithm] K-means clustering algorithm, default 'Hartigan-Wong'; alternatives 'Lloyd', 'MacQueen'; 'Forgy' is a synonym for 'Lloyd', see stats::kmeans reference for further info.">
+    <param name="iter_max" label="Maximum number of iterations" type="text" value = "20" help="[iter_max] The maximum number of iterations allowed; default 20." />
+    <param name="nstart" label="Number of random sets of clusters" type="text" value = "20" help="[nstart] How many random sets of clusters should be chosen initially; default 20." />
+    <param name="algorithm" label="Algorithm for clustering" type="select" value = "Hartigan-Wong" help="[algorithm] K-means clustering algorithm, default 'Hartigan-Wong'; &lt;br /&gt;alternatives 'Lloyd', 'MacQueen'; 'Forgy' (synonym for 'Lloyd'); see references.">
       <option value="Forgy">Forgy</option>
       <option value="Hartigan-Wong" selected="True">Hartigan-Wong</option>
       <option value="Lloyd">Lloyd</option>
@@ -48,9 +49,9 @@
   </inputs>
 
   <outputs>
-    <data name="sampleMetadata_out" label="${tool.name}_${sampleMetadata_in.name}" format="tabular" ></data>
-    <data name="variableMetadata_out" label="${tool.name}_${variableMetadata_in.name}" format="tabular" ></data>
-    <data name="scores_out" label="${tool.name}_${dataMatrix_in.name}.kmeans" format="tabular" ></data>
+    <data name="sampleMetadata_out" label="${sampleMetadata_in.name}.kmeans-smpl" format="tabular" ></data>
+    <data name="variableMetadata_out" label="${variableMetadata_in.name}.kmeans-vrbl" format="tabular" ></data>
+    <data name="scores_out" label="${dataMatrix_in.name}.kmeans-score" format="tabular" ></data>
   </outputs>
 
   <tests>
@@ -60,17 +61,17 @@
       <param name="variableMetadata_in" value="input_variableMetadata.tsv"/>
       <param name="ksamples" value="3,4"/>
       <param name="kfeatures" value="5,6,7"/>
-      <param name="iter_max" value="10"/>
-      <param name="nstart" value="1"/>
+      <param name="iter_max" value="20"/>
+      <param name="nstart" value="20"/>
       <param name="algorithm" value="Hartigan-Wong"/>
       <output name="scores_out">
         <assert_contents>
           <has_text     text="proportion" />
           <has_text     text="0.87482" />
-          <has_text     text="0.89248" />
-          <has_text     text="0.95355" />
-          <has_text     text="0.95673" />
-          <has_text     text="0.95963" />
+          <has_text     text="0.91765" />
+          <has_text     text="0.95362" />
+          <has_text     text="0.95719" />
+          <has_text     text="0.97966" />
         </assert_contents>
       </output>
     </test>
@@ -79,57 +80,38 @@
   <help>
     <![CDATA[
 
+===========================
+K-means for W4m data matrix
+===========================
+
 **Author** - Arthur Eschenlauer (University of Minnesota, esch0041@umn.edu)
 
----------------------------------------------------------------------------
-
-
-**Source** - The source code for the w4mkmeans tool is available (from the Hegeman lab github repository) at https://github.com/HegemanLab/w4mkmeans_galaxy_wrapper
-
-**R code used** - The R code invoked by this wrapper is the R 'stats::kmeans' package
-
-----------------------------------------------------------------------------------------------------------------------------------------------------------------------
-
+**Source Code** - The source code for the w4mkmeans tool is available (from the Hegeman lab github repository) at https://github.com/HegemanLab/w4mkmeans_galaxy_wrapper
 
-**Tool updates**
-
-See the **NEWS** section at the bottom of this page
+**R code used** - The R code invoked by this wrapper is the R kmeans package at https://stat.ethz.ch/R-manual/R-devel/library/stats/html/kmeans.html
 
----------------------------------------------------
-
-===========================
-K-means for W4M data matrix
-===========================
+**Tool updates** - See the **NEWS** section at the bottom of this page
 
 -----------
 Description
 -----------
 
-Calculate K-means for sample-clusters (or feature-clusters, or both) using W4M dataMatrix (i.e., XCMS-preprocessed data files) as input.
+This tool calculate K-means clusters for samples, features, or both using W4m dataMatrix (i.e., XCMS-preprocessed data files) as input and writes the results to new columns in sampleMetadata, variableMetadata, or both.
 
-*Please note that XCMS refers to features as 'variables'.  This documentation does not use either term consistently.*
+- If several, comma-separated K's are supplied, then one column is added for each K.
+- For feature-clustering, each feature is assigned to a cluster such that the feature's response for all samples is closer to the mean of all features for that cluster than to the mean for any other cluster.
+- For sample-clustering, each sample is assigned to a cluster such that the sample's response for all features is closer to the mean of all samples for that cluster than to the mean for any other cluster.
+- Clustering is mutually exclusive, **not** hierarchical.
 
+    - Hierarchical clustering is available through the W4m Heat Map tool, https://github.com/workflow4metabolomics/heatmap
 
 -----------------
 Workflow Position
 -----------------
 
-  - Tool category: Statistical Analysis
-  - Upstream tool category: Preprocessing
-  - Downstream tool categories: Statistical Analysis
-
-
-----------
-Motivation
-----------
-
-This tool clusters samples, features (variables), or both from the W4M dataMatrix and writes the results to new columns in sampleMetadata, variableMetadata, or both, respectively.
-
-  - If several, comma-separated K's are supplied, then one column is added for each K.
-  - This clustering is **not** hierarchical; each member of a cluster is not a member of any other cluster.
-  - For feature-clustering, each feature is assigned to a cluster such that the feature's response for all samples is closer to the mean of all features for that cluster than to the mean for any other cluster.
-  - For sample-clustering, each sample is assigned to a cluster such that the sample's response for all features is closer to the mean of all samples for that cluster than to the mean for any other cluster.
-
+- Tool category: Statistical Analysis
+- Upstream tool category: Preprocessing
+- Downstream tool categories: Statistical Analysis
 
 -----------
 Input files
@@ -152,92 +134,117 @@
 
 **Data matrix** - input-file dataset
 
-  - XCMS variable x sample 'dataMatrix' (tabular separated values) file of the numeric data matrix, with . as decimal, and NA for missing values; the table must not contain metadata apart from row and column names; the row and column names must be identical to the rownames of the sample and feature metadata, respectively (see below)
+- W4m variable (i.e. feature) x sample 'dataMatrix' (tabular separated values) file of the numeric data matrix, with . as decimal, and NA for missing values; the table must not contain metadata apart from row and column names; the row and column names must be identical to the rownames of the sample and feature metadata, respectively (see below)
 
 **Sample metadata** - input-file dataset
 
-  - XCMS sample x metadata 'sampleMetadata' (tabular separated values) file of the numeric and/or character sample metadata, with . as decimal and NA for missing values
+- W4m sample x metadata 'sampleMetadata' (tabular separated values) file of the numeric and/or character sample metadata, with . as decimal and NA for missing values
 
 **Feature metadata** - input-file dataset
 
-  - XCMS variable x metadata 'variableMetadata' (tabular separated values) file of the numeric and/or character feature metadata, with . as decimal and NA for missing values
+- W4m variable (i.e. feature) x metadata 'variableMetadata' (tabular separated values) file of the numeric and/or character feature metadata, with . as decimal and NA for missing values
 
-**kfeatures** - K or K's for features (default = 0)
+**Prefix for cluster names** - character(s) to add as prefix to category number (default = 'c')
+
+- Some tools treat only non-numeric data as categorical; this prefix ensures that clusters data will be treated as categorical; an empty string is permitted here if desired (and succeeding tools requiring categorical data accept integers).
 
-  - integer or comma-separated integers ; zero (the default) or less will result in no calculation.
+**K-values for samples** - K or K-range for samples (default = 0)
 
-**ksamples** - K or K-range for samples (default = 0)
+- Integer or comma-separated positive integers ; zero (or less) will result in no calculation.
 
-  - integer or comma-separated integers ; zero (the default) or less will result in no calculation.
+**K-values for features** - K or K's for features (default = 0)
 
-**iter_max** - maximum_iterations (default = 10)
+- Integer or comma-separated positive integers ; zero (or less) will result in no calculation.
 
-  - maximum number of iterations per calculation (see https://stat.ethz.ch/R-manual/R-devel/library/stats/html/kmeans.html).
+**Maximumn number of iterations** - (default = 20)
 
-**nstart** - how many random sets should be chosen (default = 1)
+- Maximum number of iterations per calculation (see https://stat.ethz.ch/R-manual/R-devel/library/stats/html/kmeans.html for further info).
 
-  - maximum number of iterations per calculation (see https://stat.ethz.ch/R-manual/R-devel/library/stats/html/kmeans.html).
+**Number of random sets** - how many random sets should be chosen to start (default = 20)
+
+- Number of random sets of clusters to be chosen to start calculation (see https://stat.ethz.ch/R-manual/R-devel/library/stats/html/kmeans.html for further info).
 
-**categorical_prefix** - character(s) to add as prefix to category number (default = 'k')
+**Algorithm** - Algorithm for clustering" (default = Hartigan-Wong)
 
-  - some tools treat only non-numeric data as categorical; this prefix ('k' by default) ensures that clusters data will be treated as categorical; an empty string is permitted here if desired (and succeeding tools accept integers as categorical data).
+- K-means clustering algorithm: 'Hartigan-Wong', 'Lloyd', or 'MacQueen'; 'Forgy' is a synonym for 'Lloyd' (see references for further info).
 
 ------------
 Output files
 ------------
 
-**XCMS sampleMetadata** - (tabular separated values) file identical to the Sample metadata file given as an input argument, excepting one column added for each K
+**Sample Metadata** - (tabular separated values) file identical to the Sample metadata file given as an input argument, excepting one column added for each K
 
-  - **k#** - cluster number for clustering samples with K = #
+- **k#** - cluster number for clustering samples with K = #
 
-**XCMS variableMetadata** - (tabular separated values) file identical to the Feature metadata file given as an input argument, excepting one column added for each K
+**Variable Metadata** - (tabular separated values) file identical to the Feature metadata file given as an input argument, excepting one column added for each K
 
-  - **k#** - cluster number for clustering features with K = #
+- **k#** - cluster number for clustering features with K = #
 
 **scores** - (tabular separated values) file with one line for each K.
 
-  - **clusterOn** - what was clustered - either 'sample' or 'feature'
-  - **k** - the chosen K for clustering
-  - **totalSS** - total (*between-treatements* plus total of *within-treatements*) sum of squares
-  - **betweenSS** - *between-treatements* sum of squares
-  - **proportion** - betweenSS / totalSS
+- **clusterOn** - what was clustered - either 'sample' or 'feature'
+- **k** - the chosen K for clustering
+- **totalSS** - total (*between-treatements* plus total of *within-treatements*) sum of squares
+- **betweenSS** - *between-treatements* sum of squares
+- **proportion** - betweenSS / totalSS
 
 ---------------
 Working example
 ---------------
 
+.. class:: infomark
+
 **Input files**
 
-+-------------------+-------------------------------------------------------------------------------------------------------------------+
-| Input File        | Download from URL                                                                                                 |
-+===================+===================================================================================================================+
-| Data matrix       | https://raw.githubusercontent.com/HegemanLab/w4mkmeans_galaxy_wrapper/master/test-data/input_dataMatrix.tsv       |
-+-------------------+-------------------------------------------------------------------------------------------------------------------+
-| Sample metadata   | https://raw.githubusercontent.com/HegemanLab/w4mkmeans_galaxy_wrapper/master/test-data/input_sampleMetadata.tsv   |
-+-------------------+-------------------------------------------------------------------------------------------------------------------+
-| Feature metadata  | https://raw.githubusercontent.com/HegemanLab/w4mkmeans_galaxy_wrapper/master/test-data/input_variableMetadata.tsv |
-+-------------------+-------------------------------------------------------------------------------------------------------------------+
++-------------------------------------------------------------------------------------------------------------------+
+| URL                                                                                                               |
++===================================================================================================================+
+| https://raw.githubusercontent.com/HegemanLab/w4mkmeans_galaxy_wrapper/master/test-data/input_dataMatrix.tsv       |
++-------------------------------------------------------------------------------------------------------------------+
+| https://raw.githubusercontent.com/HegemanLab/w4mkmeans_galaxy_wrapper/master/test-data/input_sampleMetadata.tsv   |
++-------------------------------------------------------------------------------------------------------------------+
+| https://raw.githubusercontent.com/HegemanLab/w4mkmeans_galaxy_wrapper/master/test-data/input_variableMetadata.tsv |
++-------------------------------------------------------------------------------------------------------------------+
+
+.. class:: infomark
 
 **Other input parameters**
 
 +-----------------+---------------+
 | Input Parameter | Value         |
 +=================+===============+
+| prefix          | c             |
++-----------------+---------------+
 | ksamples        | 3,4           |
 +-----------------+---------------+
 | kfeatures       | 5,6,7         |
 +-----------------+---------------+
-| iter_max        | 10            |
+| iter_max        | 20            |
 +-----------------+---------------+
-| nstart          | 1             |
+| nstart          | 20            |
 +-----------------+---------------+
 | algorithm       | Hartigan-Wong |
 +-----------------+---------------+
 
+.. class:: infomark
+
+**Expected output files**
+
++-------------------------------------------------------------------------------------------------------------------+
+| URL                                                                                                               |
++===================================================================================================================+
+| https://raw.githubusercontent.com/HegemanLab/w4mkmeans_galaxy_wrapper/master/test-data/output_kmeans-score.tsv    |
++-------------------------------------------------------------------------------------------------------------------+
+| https://raw.githubusercontent.com/HegemanLab/w4mkmeans_galaxy_wrapper/master/test-data/output_kmeans-vrbl.tsv     |
++-------------------------------------------------------------------------------------------------------------------+
+| https://raw.githubusercontent.com/HegemanLab/w4mkmeans_galaxy_wrapper/master/test-data/output_kmeans-smpl.tsv     |
++-------------------------------------------------------------------------------------------------------------------+
+
 ----
 NEWS
 ----
 
+- February 2018, Version 0.98.4 - Renamed output datasets to append '``.kmeans-smpl``', '``.kmeans-vrbl``', or '``.kmeans-score``'; refactored multi-threading.
 - August 2017, Version 0.98.3 - Add (optional) prefix to category numbers for downstream tools that treat only non-numeric data as categorical.
 - August 2017, Version 0.98.1 - First release
 
@@ -277,28 +284,12 @@
   year = 1965
 }
     ]]></citation>
-    <!-- W4M 3.0 - Guitton et al. 2017-->
+    <!-- W4m 3.0 - Guitton et al. 2017-->
     <citation type="doi">10.1016/j.biocel.2017.07.002</citation>
-    <!-- W4M 2.5 - Giacomini et al. 2014 -->
+    <!-- W4m 2.5 - Giacomini et al. 2014 -->
     <citation type="doi">10.1093/bioinformatics/btu813</citation>
     <!-- Hartigan and Wong algorithm -->
-    <citation type="bibtex"><![CDATA[
-@article{Hartigan79,
-  added-at = {2007-02-27T16:22:09.000+0100},
-  author = {Hartigan, J. and Wong, M.},
-  biburl = {https://www.bibsonomy.org/bibtex/23d8bfc440c5725783876929c022f67ce/pierpaolo.pk81},
-  description = {WSD},
-  interhash = {10d6d33920d9af578a4d0a556dc1477d},
-  intrahash = {3d8bfc440c5725783876929c022f67ce},
-  journal = {Applied Statistics},
-  keywords = {imported},
-  pages = {100-108},
-  timestamp = {2007-02-27T16:22:11.000+0100},
-  title = {Algorithm AS136: A k-means clustering algorithm},
-  volume = 28,
-  year = 1979
-}
-    ]]></citation>
+    <citation type="doi">10.2307/2346830</citation>
     <!-- Lloyd algorithm -->
     <citation type="doi">10.1109/TIT.1982.1056489</citation>
     <!-- MacQueen algorithm -->
--- a/w4mkmeans_routines.R	Wed Aug 09 18:06:55 2017 -0400
+++ b/w4mkmeans_routines.R	Mon Mar 05 12:40:17 2018 -0500
@@ -4,11 +4,11 @@
 
 library(parallel)
 
-w4kmeans_usage <- function() {
-  return ( 
+w4mkmeans_usage <- function() {
+  return (
     c(
      "w4mkmeans: bad input.",
-     "# contract:",
+     "  contract:",
      "    required - caller will provide an environment comprising:",
      "      log_print          - a logging function with the signature function(x, ...) expecting strings as x and ...",
      "      variableMetadata   - the corresponding W4M data.frame having feature metadata",
@@ -18,8 +18,8 @@
      "    optional - environment may comprise:",
      "      kfeatures          - an array of integers, the k's to apply for clustering by feature (default, empty array)",
      "      ksamples           - an array of integers, the k's to apply for clustering by sample (default, empty array)",
-     "      iter.max           - the maximum number of iterations when calculating a cluster (default = 10)",
-     "      nstart             - how many random sets of centers should be chosen (default = 1)",
+     "      iter_max           - the maximum number of iterations when calculating a cluster (default = 20)",
+     "      nstart             - how many random sets of centers should be chosen (default = 20)",
      "      algorithm          - string from c('Hartigan-Wong', 'Lloyd', 'Forgy', 'MacQueen') (default = Hartigan-Wong)",
      "      categorical_prefix - string from c('Hartigan-Wong', 'Lloyd', 'Forgy', 'MacQueen') (default = Hartigan-Wong)",
      "      ",
@@ -35,13 +35,15 @@
 w4mkmeans <- function(env) {
   # abort if 'env' is null or is not an environment
   if ( is.null(env) || ! is.environment(env) ) {
-    lapply(w4kmeans_usage(),print)
-  } 
+    lapply(w4mkmeans_usage(),print)
+  }
+  # extract parameters from 'env'
+  log_action  <- env$log_print
   # supply default arguments
-  if ( ! exists("iter.max"          , env) ) env$iter.max  <- 10
-  if ( ! exists("nstart"            , env) ) env$nstart    <- 1
+  if ( ! exists("iter_max"          , env) ) env$iter_max  <- 20
+  if ( ! exists("nstart"            , env) ) env$nstart    <- 20
   if ( ! exists("algorithm"         , env) ) env$algorithm <- 'Hartigan-Wong'
-  if ( ! exists("categorical_prefix", env) ) env$categorical_prefix <- 'k'
+  if ( ! exists("categorical_prefix", env) ) env$categorical_prefix <- 'c'
   if ( ! exists("ksamples"          , env) ) env$ksamples  <- c()
   if ( ! exists("kfeatures"         , env) ) env$kfeatures <- c()
   # check mandatory arguments
@@ -55,11 +57,11 @@
   missing_from_env <- setdiff(expected, (ls(env)))
   if ( length(missing_from_env) > 0 ) {
     print(paste(c('expected environment members not found: ', as.character(missing_from_env)), collapse = ", "))
-    lapply(w4kmeans_usage(),print)
+    lapply(w4mkmeans_usage(),log_action)
     stop("w4mkmeans: contract has been broken")
-  } 
+  }
   # extract parameters from 'env'
-  failure_action  <- env$log_print
+  log_action  <- env$log_print
   scores          <- c( "clusterOn\tk\ttotalSS\tbetweenSS\tproportion" )
   sampleMetadata  <- env$sampleMetadata
   featureMetadata <- env$variableMetadata
@@ -70,39 +72,79 @@
     i <- i[i > 0]         # eliminate non-positive integers
     i <- unique(sort(i))  # eliminate redundancy and disorder
     if (length(a)!=length(i)) {
-      failure_action("Some values for '", what, "' were skipped where not unique, not positive, or not convertible to an integer.")
+      log_action("Some values for '", what, "' were skipped where not unique, not positive, or not convertible to an integer.")
     }
     return (i)            # return results, if any
   }
   ksamples        <- positive_ints(env$ksamples , "ksamples")
   kfeatures       <- positive_ints(env$kfeatures, "kfeatures")
 
+  log_action("w4mkmeans: preparing data matrix")
+  # prepare data matrix (normalize, eliminate zero-variance rows, etc.; no transformation)
+  dm_en <- new.env()
+  dm_en$log <- c()
+  preparation_result <- tryCatchFunc(function(){
+    dm <- prepare.data.matrix(
+            x.matrix = env$dataMatrix
+          , data.transformation = function(x) { x }
+          , en = dm_en
+          )
+    my_log <- dm_en$log
+    for ( i in 1:length(my_log) ) {
+      log_action(my_log[i])
+    }
+    dm
+  })
+  if ( !preparation_result$success ) {
+    postmortem <- paste("prepare.data.matrix failed:", preparation_result$msg)
+    log_action(postmortem)
+    stop(postmortem)
+  }
+
+  env$preparedDataMatrix <- preparation_result$value
+
+  log_action("w4mkmeans: determining evaluation mode")
+
   myLapply <- parLapply
-  # uncomment the next line to mimic parLapply, but without parallelization (for testing/experimentation)
-  # myLapply <- function(cl, ...) lapply(...)
   cl <- NULL
+  tryCatch(
+    expr = {
+      outfile <- ""
+      # outfile: Where to direct the stdout and stderr connection output from the workers.
+      #   - "" indicates no redirection (which may only be useful for workers on the local machine).
+      #   - Defaults to ‘/dev/null’ (‘nul:’ on Windows).
+      #   - The other possibility is a file path on the worker's host.
+      #     - Files will be opened in append mode, as all workers log to the same file.
+      cl <- makePSOCKcluster( names = slots, outfile = outfile )
+    }
+    , error = function(e) {
+      log_action(sprintf("w4kmeans: falling back to serial evaluation because makePSOCKcluster(names = %d) threw an exception", slots))
+      # mimic parLapply, but without parallelization (as a last resort)
+      myLapply <<- function(cl, ...) lapply(...)
+    }
+  )
   if ( identical(myLapply, parLapply) ) {
-    failure_action(sprintf("w4mkmeans: using parallel evaluation with %d slots", slots))
-    failure_action(names(cl))
-    cl <- makePSOCKcluster(names = slots)
+    log_action(sprintf("w4mkmeans: using parallel evaluation with %d slots", slots))
     # from ?makePSOCKcluster: "It is good practice to shut down the workers by calling stopCluster."
+    # note varlist: names of references must be passed to the cluster so that they can be resolved
     clusterExport(
       cl = cl
     , varlist = c(
-        "tryCatchFunc"
-      , "calc_kmeans_one_dimension_one_k"
-      , "prepare.data.matrix"
+        "tryCatchFunc"                     # required by calc_kmeans_one_dimension_one_k
+      , "format_error"                     # required by tryCatchFunc when errors are caught
+      , "iso_date"                         # required by log_print
+      , "log_print"                        # required by calc_kmeans_one_dimension_one_k
       )
     )
     final <- function(cl) {
       # from ?makePSOCKcluster: "It is good practice to shut down the workers by calling stopCluster."
       if ( !is.null(cl) ) {
-        failure_action("w4mkmeans: stopping cluster used for parallel evaluation")
+        log_action("w4mkmeans: stopping cluster used for parallel evaluation")
         stopCluster(cl)
       }
     }
   } else {
-    failure_action("w4mkmeans: using sequential evaluation (1 slot)")
+    log_action("w4mkmeans: using sequential evaluation (one slot)")
     final <- function(cl) { }
   }
 
@@ -115,7 +157,7 @@
       # for each $i in ksamples, append column 'k$i' to data frame sampleMetadata
       ksamples_length <- length(ksamples)
       if ( ksamples_length > 0 ) {
-        smpl_result_list <- myLapply( 
+        smpl_result_list <- myLapply(
             cl = cl
           , ksamples
           , calc_kmeans_one_dimension_one_k
@@ -134,7 +176,7 @@
       # for each $i in kfeatures, append column 'k$i' to data frame featureMetadata
       kfeatures_length <- length(kfeatures)
       if ( kfeatures_length > 0 ) {
-        feat_result_list <- myLapply( 
+        feat_result_list <- myLapply(
             cl = cl
           , kfeatures
           , calc_kmeans_one_dimension_one_k
@@ -150,22 +192,24 @@
         }
       }
 
-      return ( 
+      return (
         list(
           variableMetadata = featureMetadata
-        , sampleMetadata   = sampleMetadata  
-        , scores           = scores          
+        , sampleMetadata   = sampleMetadata
+        , scores           = scores
         )
       )
     }
-  , finally = final(cl)
+  , finally = {
+      final(cl)
+    }
   )
 }
 
 # calculate k-means for features or samples
 #   - recall that the dataMatrix has features in rows and samples in columns
 # return value:
-#   list(clusters = km$cluster, scores = scores) 
+#   list(clusters = km$cluster, scores = scores)
 # arguments:
 #   env:
 #     environment having dataMatrix
@@ -179,40 +223,64 @@
   # abort if environment is not as expected
   if ( is.null(env) || ! is.environment(env) ) {
     stop("calc_kmeans_one_dimension_one_k - argument 'env' is not an environment")
-  } 
+  }
   if ( ! exists("log_print", env) || ! is.function(env$log_print) ) {
     stop("calc_kmeans_one_dimension_one_k - argument 'env' - environment does not include log_print or it is not a function")
-  } 
+  }
+  log_action  <- env$log_print
   # abort if k is not as expected
   if ( ! is.numeric(k) ) {
     stop(sprintf("calc_kmeans_one_dimension_one_k - expected numeric argument 'k' but type is %s", typeof(k)))
-  } 
+  }
   k <- as.integer(k)
   # abort if dimension is not as expected
-  if (   ! is.character(dimension) 
+  if (   ! is.character(dimension)
       || ! Reduce( f =`|`, x = sapply(X = c("features","samples"), FUN = `==`, dimension), init = FALSE) ) {
     stop("calc_kmeans_one_dimension_one_k - argument 'dimension' is neither 'features' nor 'samples'")
-  } 
-  dm           <- env$dataMatrix
-  iter.max     <- env$iter.max
+  }
+  dm           <- env$preparedDataMatrix
+  iter_max     <- env$iter_max
   nstart       <- env$nstart
   algorithm    <- env$algorithm
   dim_features <- dimension == "features"
+
   # tryCatchFunc produces a list
-  #   On success of expr(), tryCatchFunc produces
-  #     list(success TRUE, value = expr(), msg = "")
-  #   On failure of expr(), tryCatchFunc produces
+  #   On success of func(), tryCatchFunc produces
+  #     list(success = TRUE, value = func(), msg = "")
+  #   On failure of func(), tryCatchFunc produces
   #     list(success = FALSE, value = NA, msg = "the error message")
-  result_list <- tryCatchFunc( expr = function() {
+  result_list <- tryCatchFunc( func = function() {
     # kmeans clusters the rows; features are the columns of args_env$dataMatrix; samples, the rows
     # - to calculate sample-clusters, no transposition is needed because samples are rows
     # - to calculate feature-clusters, transposition is needed so that features will be the rows
-    if ( ! dim_features ) dm <- t(dm)
-    dm <- prepare.data.matrix( x.matrix = dm, data.transformation = function(x) { x } )
+    if ( ! dim_features ) {
+      dm <- t(dm)
+    }
+
     # need to set.seed to get reproducible results from kmeans
     set.seed(4567)
+
     # do the k-means clustering
-    km <- kmeans( x = dm, centers = k, iter.max, nstart = nstart, algorithm = algorithm )
+    withCallingHandlers(
+      {
+        km <<- kmeans( x = dm, centers = k, iter.max = iter_max, nstart = nstart, algorithm = algorithm )
+      }
+    , warning = function(w) {
+        lw <- list(w)
+        smplwrn <- as.character(w[[1]])
+        log_print(
+            sprintf( "Warning for %s: center = %d, nstart = %d, iter_max = %d: %s"
+          , if (dim_features) "features" else "samples"
+          , k
+          , nstart
+          , iter_max
+          , smplwrn
+          )
+        )
+      }
+    )
+
+    # collect the scores
     scores <-
       sprintf("%s\t%d\t%0.5e\t%0.5e\t%0.5f"
              , dimension
@@ -221,8 +289,16 @@
              , km$betweenss
              , km$betweenss/km$totss
              )
+
+    # return list of results
     list(clusters = km$cluster, scores = scores)
   })
+
+  # return either
+  #     list(success = TRUE, value = func(), msg = "")
+  # or
+  #     list(success = FALSE, value = NA, msg = "the error message")
   return ( result_list )
 }
 
+# vim: sw=2 ts=2 et :
--- a/w4mkmeans_wrapper.R	Wed Aug 09 18:06:55 2017 -0400
+++ b/w4mkmeans_wrapper.R	Mon Mar 05 12:40:17 2018 -0500
@@ -22,7 +22,7 @@
 #     slots "${GALAXY_SLOTS:-1}" \
 #     variableMetadata_out "$variableMetadata_out" \
 #     variable_metadata_path "$variableMetadata_in"
-# 
+#
 # <inputs>
 #   <param name="dataMatrix_in" label="Data matrix file" type="data" format="tabular" help="variable x sample, decimal: '.', missing: NA, mode: numerical, separator: tab" />
 #   <param name="sampleMetadata_in" label="Sample metadata file" type="data" format="tabular" help="sample x metadata columns, separator: tab" />
@@ -30,8 +30,8 @@
 #   <param name="categoricalPrefix" label="prefix for cluster names " type="text" value="k" help="Some tools require non-numeric values to discern categorical; e.g., enter 'k' here to prepend 'k' to cluster numbers in the output; default 'k'." />
 #   <param name="kfeatures" label="K value(s) for features" type="text" value="0" help="Single or min,max value(s) for K for features (variables), or 0 for none." />
 #   <param name="ksamples" label="K value(s) for samples" type="text" value="0" help="Single or min,max value(s) for K for samples, or 0 for none." />
-#   <param name="iter_max" label="Max number of iterations" type="text" value="10" help="The maximum number of iterations allowed; default 10." />
-#   <param name="nstart" label="Number of random sets" type="text" value="1" help="How many random sets should be chosen; default 1." />
+#   <param name="iter_max" label="Max number of iterations" type="text" value="20" help="The maximum number of iterations allowed; default 20." />
+#   <param name="nstart" label="Number of random sets" type="text" value="20" help="How many random sets should be chosen; default 20." />
 # 	<param name="algorithm" label="Algorithm for clustering" type="select" value = "Hartigan-Wong" help="K-means clustering algorithm, default 'Hartigan-Wong'; alternatives 'Lloyd', 'MacQueen'; 'Forgy' is a synonym for 'Lloyd', see stats::kmeans reference for further info and references.">
 # 	  <option value="Hartigan-Wong" selected="TRUE">Hartigan-Wong</option>
 # 	  <option value="Lloyd">Lloyd</option>
@@ -66,7 +66,7 @@
 ## Computation - source general and module-specific routines
 ##----------------------------------------------------------
 
-log_print <- function(x, ...) { 
+log_print <- function(x, ...) {
   cat(
     format(Sys.time(), "%Y-%m-%dT%H:%M:%S%z")
   , " "
@@ -77,6 +77,15 @@
   )
 }
 
+log_cat <- function(x, ...) {
+  cat(
+    c(x, ...)
+  , "\n"
+  , sep=""
+  , file=stderr()
+  )
+}
+
 # log_print(sprintf("tool_directory is %s", tool_directory))
 
 w4m_general_purpose_routines_path <- r_path("w4m_general_purpose_routines.R")
@@ -85,7 +94,7 @@
   log_print("cannot find file w4m_general_purpose_routines.R")
   q(save = "no", status = 1, runLast = TRUE)
 }
-# log_print("sourcing ",w4m_general_purpose_routines_path)
+log_print("sourcing ",w4m_general_purpose_routines_path)
 source(w4m_general_purpose_routines_path)
 if ( ! exists("prepare.data.matrix") ) {
   log_print("'prepare.data.matrix' was not read from file w4m_general_purpose_routines.R")
@@ -164,7 +173,7 @@
     expr = {
       # read in the sample metadata
       kind_string <- "sample metadata input"
-      smpl_metadata_input_env <- 
+      smpl_metadata_input_env <-
         read_data_frame(
                          file_path = env$sample_metadata_path
                        , kind_string = kind_string
@@ -178,7 +187,7 @@
 
       # read in the variable metadata
       kind_string <- "variable metadata input"
-      vrbl_metadata_input_env <- 
+      vrbl_metadata_input_env <-
         read_data_frame(
                          file_path = env$variable_metadata_path
                        , kind_string = kind_string
@@ -218,7 +227,7 @@
 }
 
 
-read_input_failure_action <- function(x, ...) { 
+read_input_failure_action <- function(x, ...) {
   log_print("Failure reading input for '", modNamC, "' Galaxy module call")
   log_print(x, ...)
 }
@@ -238,7 +247,7 @@
 
 # Set the handler for R error-handling
 options( show.error.messages = F
-       , error = function () { 
+       , error = function () {
                    log_print( "Fatal error in '", modNamC, "': ", geterrmessage() )
                    q( "no", 1, F )
                  }
@@ -283,7 +292,7 @@
 args_env$data_matrix_path       <- as.character(argVc["data_matrix_path"])
 args_env$variable_metadata_path <- as.character(argVc["variable_metadata_path"])
 args_env$sample_metadata_path   <- as.character(argVc["sample_metadata_path"])
-  
+
 # other parameters
 
 # multi-string args - split csv: "1,2,3" -> c("1","2","3")
@@ -305,20 +314,21 @@
 for (member in ls(args_env)) {
   value <- get(member, args_env)
   value <- ifelse(length(value) == 1, value, sprintf("c(%s)", paste(value, collapse=", ")))
-  
+
   log_print(sprintf("  - %s: %s", member, ifelse( !is.function(value) , value, "function" )))
 }
 log_print("")
 
 ##---------------------------------------------------------
-## Computation - attempt to read input data
+## Computation - attempt to read input data and process
 ##---------------------------------------------------------
 if ( ! read_input_data(args_env, failure_action = read_input_failure_action) ) {
   result <- -1
 } else {
-  log_print("Input data was read successfully.")
+  log_print("Input data was read.")
+  # attempt to process the data
   result <- w4mkmeans(env = args_env)
-  log_print("returned from call to w4mkmeans.")
+  log_print("Returned from call to w4mkmeans.")
 }
 
 if ( length(result) == 0 ) {
@@ -356,7 +366,6 @@
 ## Closing
 ##--------
 
-
 if (!file.exists(sampleMetadata_out)) {
   log_print(sprintf("ERROR %s::w4m_kmeans_wrapper - file '%s' was not created", modNamC, sampleMetadata_out))
 }