# HG changeset patch
# User eschen42
# Date 1502220638 14400
# Node ID 6ccbe18131a6017857050f5fcb811d0aaacea6f4

planemo upload for repository https://github.com/HegemanLab/w4mkmeans_galaxy_wrapper/tree/master commit 299e5c7fdb0d6eb0773f3660009f6d63c2082a8d

diff -r 000000000000 -r 6ccbe18131a6 LICENSE
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/LICENSE	Tue Aug 08 15:30:38 2017 -0400
@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2017 Hegeman Lab
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.
diff -r 000000000000 -r 6ccbe18131a6 README
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/README	Tue Aug 08 15:30:38 2017 -0400
@@ -0,0 +1,2 @@
+# w4mkmeans_galaxy_wrapper
+Planemo-based galaxy-tool-wrapper to wrap the stats::kmeans R package for the W4M dataMatrix
diff -r 000000000000 -r 6ccbe18131a6 test-data/input_dataMatrix.tsv
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/input_dataMatrix.tsv	Tue Aug 08 15:30:38 2017 -0400
@@ -0,0 +1,50 @@
+	Y11_1_RA5_01_213	Y2_1_RB1_01_218	Y4_1_RB3_01_220	Y12_1_RB4_01_221	Y1_1_RC1_01_228	Y14_1_RC6_01_234	Y1_2_RD1_01_239	Y14_2_RD2_01_240	Y4_2_RD3_01_241	Y11_2_RD7_01_246	Y2_2_RE4_01_253	Y12_2_RE6_01_255	Y14_3_GA2_01_260	Y2_3_GA4_01_264	Y1_3_GA6_01_266	Y4_3_GA7_01_267	Y12_3_GB1_01_270	Y11_3_GC3_01_283	Y14_4_GC7_01_287	Y11_4_GD8_01_299	Y2_4_GE1_01_300	Y12_4_GE2_01_304	Y1_4_GE3_01_305	Y4_4_GE7_01_309
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+M229T1015	418917.335454547	163204.836363634	173677.360000002	267090.970833331	244282.969999999	200608.134545453	336866.566666665	257400.199999998	266119.544545456	442615.87466667	327720.029090909	252660.875000001	309365.040833335	344560.122500001	252079.805454545	460836.719999996	183110.105454544	408233.730000003	380762.459999995	311279.266666668	243038.487272727	318411.516666665	235092.845454541	202601.280000001
+M237T589	532865.734307696	226635.78133333	893815.050615387	117684.107666665	1230127.28142858	489271.364999995	1265523.30457144	1216228.17830767	4681682.64999998	1859733.21600001	2007864.38714287	155056.536153844	514583.381000006	292040.242999999	495335.002615389	3588519.19453846	507224.674666664	1056575.73	691304.085769224	1500803.19257143	101600.927083334	188361.358999999	2470535.57353847	654847.74642857
+M237T507	703355.073999997	550011.560000002	965632.896307698	915403.287272722	724118.229166667	386659.543	1636376.28627274	439630.258833334	1701594.46984615	524515.044428569	756075.009499999	871363.231857143	1399470.64800001	592975.050307695	1201274.85642856	1413997.779	1236487.384	506445.215357144	235979.998250002	369735.080727271	813289.588999999	700891.467166672	232961.595333333	861136.780000002
+M245T458	212603.254500001	271358.777666667	503074.115083331	44970.0660833335	131609.623090909	280617.921818184	773743.02866666	2334917.91500002	99518.4412499995	1016073.89891667	338413.903833333	55441.1083333332	220399.192666668	188045.522166668	310482.079249999	1530415.97333333	60800.495	1328460.313	1203955.012	778120.18475	119439.517666666	84655.04375	452362.827999998	1983764.26125001
+M246T512	146788.109999999	407066.762249999	633426.109	533463.833615383	1956593.89342856	165702.49815	120181.589833332	353320.577749999	642874.945142854	119179.247615384	99590.2682500005	758379.92076923	52436.3834166666	147546.816500001	294954.356923078	1609162.86685714	584555.170230768	170359.68353846	51917.8174615381	229662.194400003	211933.249999999	60445.8974166666	634458.219230768	100737.631500001
+M246T490	75256.4203333334	34590.6509090912	150163.741636363	34411.6830909094	74755.9112727271	103711.803166667	45954.4614545453	35152.0549999998	24719.0138181817	71617.0521818181	11311.7707272727	63841.0036363632	27595.3401818182	31920.2162500003	98890.9431818186	135013.109083333	47742.2570909091	27862.1563636363	36125.5670909093	62861.4490833336	20261.5554545455	22206.8672727271	73136.961545455	235336.321846153
+M247T433	4836161.03384619	1044492.01435715	951798.914285713	1424537.325	3787406.10666667	9887497.97830775	30488070.5421429	3441749.28	8499038.62799999	17388611.7989999	20526331.7456	1106400.24907692	3016821.11076925	1076241.15692308	35494666.1914286	2461932.47007691	1529940.06230769	38365027.5439287	18163699.0110001	9636879.56399995	10746013.236	18833821.2581429	4966775.00357139	1639208.92369231
+M247T452	5195507.70825003	369661.240285716	490699.764615384	436494.736999998	7412698.44276928	12170776.00775	34440641.0083078	5016978.85769229	8803133.89476923	27844873.7553571	36488147.0250003	506057.533076924	3027637.58846154	653070.954888888	18741495.8692308	552111.216461536	608777.786	42140646.1521431	16718584.3028572	10782086.7869286	551145.999000004	1022681.34276923	5037210.42115387	1667076.72061537
+M248T433	1132061.80500001	198989.691727273	242631.120000001	385296.71625	533583.706545452	1700043.58833333	7857412.81114289	725243.771999994	876190.803333335	4407848.9363846	2654510.973	270592.230999999	537803.304363637	294857.264307691	3564818.11400002	422974.383666665	327743.053846153	4390963.79250004	3575371.16159998	1580044.82999999	1915246.6695	3058478.66324999	981666.862307692	318243.068
+M251T497	1063427.48939999	865129.956142858	495022.085833334	488440.844615385	1129829.22	729926.57776923	1940279.044	1221215.83823077	736401.295384619	760975.140166666	273042.048615386	561850.154166666	1121087.55323077	721610.410153842	2108837.44992856	530356.117499997	934878.418692311	1242937.12242857	328026.640384617	1036373.51330769	718361.729800002	536136.268499999	1419632.77730769	2391210.31999999
+M257T1014	1566972.91999999	1893127.35857143	2151729.80857143	1146142.15333334	1970881.65571426	1402985.23499999	2812863.02500003	1880269.89428571	2527579.56705883	1461081.88615383	1791326.51	1478730.04615386	1823267.80461538	2234492.50153846	2529937.4047059	3148326.66461539	1868762.54266667	1421995.43307692	1517500.28	1805290.20999998	1642292.79999997	1683944.028	2379338.33999998	2487090.41230771
+M261T332	1930046.88	1414066.78508333	3490063.85953848	1047626.07700001	4522749.77961537	1593664.43775	5263343.87100001	2824216.94399999	1630093.88518182	4023852.75425	1510231.9848	1225828.25025001	2182490.55266666	1439822.48483333	8412854.24699993	3388651.75384613	991437.89583333	3719176.7	1188956.48025	2188517.84249999	1310827.23100001	1540731.44238462	1604828.60533333	2495122.53646153
+M263T323	1171838.28333332	1092287.00399999	1784798.51050001	230456.097454544	3162077.20061541	901831.286555555	2870354.68692308	3141344.14099999	795229.416727274	6039038.19407691	3336169.10769232	134580.004545455	1114891.535	919091.57784615	2457832.58563637	5292105.64400001	263116.985833333	6938595.35746151	4367916.80500001	3698151.2689231	268597.518	276733.936000001	1068827.01066667	9589520.52215389
diff -r 000000000000 -r 6ccbe18131a6 test-data/input_sampleMetadata.tsv
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/input_sampleMetadata.tsv	Tue Aug 08 15:30:38 2017 -0400
@@ -0,0 +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
diff -r 000000000000 -r 6ccbe18131a6 test-data/input_variableMetadata.tsv
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/input_variableMetadata.tsv	Tue Aug 08 15:30:38 2017 -0400
@@ -0,0 +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
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diff -r 000000000000 -r 6ccbe18131a6 w4m_general_purpose_routines.R
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/w4m_general_purpose_routines.R	Tue Aug 08 15:30:38 2017 -0400
@@ -0,0 +1,283 @@
+# prepare.data.matrix - Prepare x.datamatrix for multivariate statistical analaysis (MVA)
+#   - Motivation:
+#     - Selection:
+#       - You may want to exclude several samples from your analysis:
+#         - If so, set the argument 'exclude.samples' to a vector of sample names
+#       - You may want to exclude several features or features from your analysis:
+#         - 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 
+#           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.
+#       - For a different imputation, set the 'data.imputation' argument to a function
+#         accepting a single matrix argument and returning  a matrix of the same
+#         dimensions as the argument.
+#     - Transformation
+#       - It may be desirable to transform the intensity data to reduce the range.
+#       - 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 
+#           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
+#           to a function accepting a single matrix argument.
+#         - The function should be written to return a matrix of the same dimensions
+#           as the argument.
+# arguments:
+#   - x.matrix - matrix of intensities (or data.frame of sample metadata)
+#     - one row per sample
+#     - one column per feature or metadata attribute
+#   - exclude.samples - vector of labels of matrix rows (samples) to omit from analysis
+#   - exclude.features - vector of labels of matrix columnss (features) to omit from analysis
+#   - sample.rename.function - function to be used to rename rows if necessary, or NULL
+#     - e.g., sample.rename.function = function(x) {
+#               sub("(.*)_.*","\\1", row.names(x))
+#             }
+#   - data.imputation - function applied to matrix to impute missing values
+#     - e.g., data.imputation = function(m) {
+#               m[is.na(m)] <- min(m, na.rm = TRUE) / 100
+#               return (m)
+#             }
+#   - data.transformation - function applied to matrix cells
+#     - e.g., data.transformation = function(x) { return( log10(x) ) }
+#         or, data.transformation = log10
+# result value:
+#   transformed, imputed x.datamatrix with renamed rows and with neither excluded values nor features
+#
+################################
+##
+##  Notes regarding the effectiveness and performance of the data transformation method.
+##
+##  The two transformations that I tried (log10 and 8th root) required different imputation methods.
+##
+##  For the LCMS resin data set that I was working with, separation in MVA was nearly equivalent for:
+##    data.imputation <- function(x.matrix) {
+##      x.matrix[is.na(x.matrix)] <- 0
+##      return (x.matrix)
+##    }
+##    data.transformation <- function(x) {
+##      sqrt( sqrt( sqrt(x) ) )
+##    }
+##  and
+##    data.imputation <- function(x.matrix) {
+##      x.matrix[is.na(x.matrix)] <- min(x.matrix, na.rm = TRUE) / 100
+##      return (x.matrix)
+##    }
+##    data.transformation <- function(x) {
+##      log10(x)
+##    }
+##
+##  Note further that triple application of the square root:
+##  - may be four times faster than log10:
+##  - may be three times faster than log2:
+##
+##      system.time( junk <- sqrt( sqrt( sqrt(1:100000000) ) ) )
+##         user  system elapsed
+##        0.832   0.236   1.069
+##      system.time( junk <- log10(1:100000000) )
+##         user  system elapsed
+##        3.936   0.400   4.337
+##      system.time( junk <- log2(1:100000000) )
+##         user  system elapsed
+##        2.784   0.320   3.101
+##
+################################
+#
+prepare.data.matrix <- function(
+  x.matrix
+, exclude.samples = NULL
+, exclude.features = NULL
+, sample.rename.function = NULL
+, data.imputation =
+    function(m) {
+      # replace NA values with zero
+      m[is.na(m)] <- 0
+      # replace negative values with zero, if applicable (It should never be applicable!)
+      if (min(m < 0)) {
+        m <- matrix(lapply(X = m, FUN = function(z) {max(z,0)}), nrow = nrow(m) )
+      }
+      # return matrix as the result
+      return (m)
+    }
+, data.transformation = function(x) {
+    sqrt( sqrt( sqrt(x) ) )
+  }
+, en = new.env()
+) {
+  # 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
+  MatVar <- function(x, dim = 1) {
+    if (dim == 1) {
+      dim.x.2 <- dim(x)[2]
+      if ( dim.x.2 == 0 )
+        stop("MatVar: there are zero columns")
+      if ( dim.x.2 == 1 ) {
+        stop("MatVar: a single column is insufficient to calculate a variance")
+        # return ( rep.int(x = 0, times = nrow(x)) )
+      } else {
+        return ( rowSums( (x    - rowMeans(x))^2 ) / ( dim(x)[2] - 1 ) )
+      }
+    } else if (dim == 2) {
+      dim.x.1 <- dim(x)[1]
+      if ( dim.x.1 == 0 ) {
+        stop("MatVar: there are zero rows")
+      }
+      if ( dim.x.1 == 1 ) {
+        stop("MatVar: a single row is insufficient to calculate a variance")
+        # return ( rep.int(x = 0, times = ncol(x)) )
+      } else {
+        return ( rowSums( (t(x) - colMeans(x))^2 ) / ( dim(x)[1] - 1 ) )
+      }
+    } else stop("Please enter valid dimension, for rows, dim = 1; for colums, dim = 2")
+  }
+
+  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) ) {
+      # exclude any rows with zero variance
+      row.vars <- MatVar(x, dim = 1)
+      nonzero.row.vars <- row.vars > 0
+      nonzero.rows <- row.vars[nonzero.row.vars]
+      if ( length(rownames(x)) != length(rownames(nonzero.rows)) ) {
+        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
+      nonzero.columns <- column.vars[nonzero.column.vars]
+      if ( length(colnames(x)) != length(colnames(nonzero.columns)) ) {
+        column.names <- attr(nonzero.columns,"names")
+        x <- x[ , column.names, drop = FALSE ]
+      }
+    }
+    return (x)
+  }
+
+  if (is.null(x.matrix)) {
+    stop("FATAL ERROR - prepare.data.matrix was called with null x.matrix")
+  }
+
+  en$xpre <- x <- x.matrix
+
+  # exclude any samples as indicated
+  if ( !is.null(exclude.features) ) {
+    my.colnames <- colnames(x)
+    my.col.diff <- setdiff(my.colnames, exclude.features)
+    x <- x[ , my.col.diff , drop = FALSE ]
+  }
+
+  # exclude any features as indicated
+  if ( !is.null(exclude.samples) ) {
+    my.rownames <- rownames(x)
+    my.row.diff <- setdiff(my.rownames, exclude.samples)
+    x <- x[ my.row.diff, , drop = FALSE ]
+  }
+
+  # rename rows if desired
+  if ( !is.null(sample.rename.function) ) {
+    renamed <- sample.rename.function(x)
+    rownames(x) <- renamed
+  }
+
+  # save redacted x.datamatrix to environment
+  en$redacted.data.matrix <- x
+
+  # impute values missing from the x.datamatrix
+  if ( !is.null(data.imputation) ) {
+    x <- data.imputation(x)
+  }
+
+  # perform transformation if desired
+  if ( !is.null(data.transformation) ) {
+    x <- data.transformation(x)
+  } else {
+    x <- x
+  }
+
+  # purge rows and columns that have zero variance
+  if ( is.numeric(x) ) {
+    x <- nonzero.var(x)
+  }
+
+  # save imputed, transformed x.datamatrix to environment
+  en$imputed.transformed.data.matrix <- x
+
+  return(x)
+}
+
+
+##-----------------------------------------------
+## helper functions for error detection/reporting
+##-----------------------------------------------
+
+# log-printing to stderr
+log_print <- function(x, ...) { 
+  cat(
+    format(Sys.time(), "%Y-%m-%dT%H:%M:%S%z")
+  , " "
+  , c(x, ...)
+  , "\n"
+  , sep=""
+  , file=stderr()
+  )
+}
+
+# tryCatchFunc produces a list
+#   On success of expr(), tryCatchFunc produces
+#     list(success TRUE, value = expr(), msg = "")
+#   On failure of expr(), tryCatchFunc produces
+#     list(success = FALSE, value = NA, msg = "the error message")
+tryCatchFunc <- function(expr) {
+  # format error for logging
+  format_error <- function(e) {
+    paste(c("Error { message:", e$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)
+}
+
diff -r 000000000000 -r 6ccbe18131a6 w4mkmeans.xml
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/w4mkmeans.xml	Tue Aug 08 15:30:38 2017 -0400
@@ -0,0 +1,319 @@
+<tool id="w4mkmeans" name="Kmeans_for_W4M" version="0.98.1">
+  <description>Calculate K-means for dataMatrix features or samples</description>
+
+  <requirements>
+    <requirement type="package" version="3.3.2">r-base</requirement>
+    <requirement type="package" version="1.1_4">r-batch</requirement>
+  </requirements>
+
+  <stdio>
+    <exit_code range="1:" level="fatal" />
+  </stdio>
+
+
+  <command detect_errors="aggressive"><![CDATA[
+    Rscript $__tool_directory__/w4mkmeans_wrapper.R
+      tool_directory $__tool_directory__
+      data_matrix_path '$dataMatrix_in'
+      variable_metadata_path '$variableMetadata_in'
+      sample_metadata_path '$sampleMetadata_in'
+      ksamples '$ksamples'
+      kfeatures '$kfeatures'
+      iter_max '$iter_max'
+      nstart '$nstart'
+      algorithm '$algorithm'
+      scores_out '$scores_out'
+      sampleMetadata_out '$sampleMetadata_out'
+      variableMetadata_out '$variableMetadata_out'
+      slots "\${GALAXY_SLOTS:-1}"
+    ; echo exit code $?
+  ]]></command>
+
+  <inputs>
+    <param name="dataMatrix_in" label="Data matrix file" type="data" format="tabular" help="variable x sample, decimal: '.', missing: NA, mode: numerical, separator: tab" />
+    <param name="sampleMetadata_in" label="Sample metadata file" type="data" format="tabular" help="sample x metadata columns, separator: tab" />
+    <param name="variableMetadata_in" label="Variable metadata file" type="data" format="tabular" help="variable x metadata columns, separator: tab" />
+    <param name="ksamples" label="K value(s) for samples" type="text" value = "0" help="[ksamples] Single K or comma-separated Ks for samples, or 0 for none." />
+    <param name="kfeatures" label="K value(s) for features" type="text" value = "0" help="[kfeatures] Single K or comma-separated Ks for features (variables), or 0 for none." />
+    <param name="iter_max" label="Max number of iterations" type="text" value = "10" help="[iter_max] The maximum number of iterations allowed; default 10." />
+    <param name="nstart" label="Number of random sets" type="text" value = "1" help="[nstart] How many random sets should be chosen; default 1." />
+    <param name="algorithm" label="Algorithm for clustering" type="select" value = "Hartigan-Wong" help="[algorithm] K-means clustering algorithm, default 'Hartigan-Wong'; alternatives 'Lloyd', 'MacQueen'; 'Forgy' is a synonym for 'Lloyd', see references for further info.">
+      <option value="Forgy">Forgy</option>
+      <option value="Hartigan-Wong" selected="True">Hartigan-Wong</option>
+      <option value="Lloyd">Lloyd</option>
+      <option value="MacQueen">MacQueen</option>
+    </param>
+  </inputs>
+
+  <outputs>
+    <data name="sampleMetadata_out" label="${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>
+  </outputs>
+
+  <tests>
+        <test>
+      <param name="dataMatrix_in" value="input_dataMatrix.tsv"/>
+      <param name="sampleMetadata_in" value="input_sampleMetadata.tsv"/>
+      <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="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" />
+        </assert_contents>
+      </output>
+    </test>
+  </tests>
+
+  <help>
+    <![CDATA[
+
+**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
+
+----------------------------------------------------------------------------------------------------------------------------------------------------------------------
+
+
+**Tool updates**
+
+See the **NEWS** section at the bottom of this page
+
+---------------------------------------------------
+
+===========================
+K-means for W4M data matrix
+===========================
+
+-----------
+Description
+-----------
+
+Calculate K-means for sample-clusters (or feature-clusters, or both) using W4M dataMatrix (i.e., XCMS-preprocessed data files) as input.
+
+*Please note that XCMS refers to features as 'variables'.  This documentation does not use either term consistently.*
+
+
+-----------------
+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.
+
+
+-----------
+Input files
+-----------
+
++--------------------------------------------+------------+
+| File                                       |   Format   |
++============================================+============+
+|     Data matrix                            |   tabular  |
++--------------------------------------------+------------+
+|     Sample metadata                        |   tabular  |
++--------------------------------------------+------------+
+|     Variable (i.e., feature) metadata      |   tabular  |
++--------------------------------------------+------------+
+
+
+----------
+Parameters
+----------
+
+**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)
+
+**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
+
+**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
+
+**kfeatures** - K or K's for features (default = 0)
+
+  - integer or comma-separated integers ; zero (the default) or less will result in no calculation.
+
+**ksamples** - K or K-range for samples (default = 0)
+
+  - integer or comma-separated integers ; zero (the default) or less will result in no calculation.
+
+**iter_max** - maximum_iterations (default = 10)
+
+  - maximum number of iterations per calculation (see https://stat.ethz.ch/R-manual/R-devel/library/stats/html/kmeans.html).
+
+**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).
+
+------------
+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
+
+  - **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
+
+  - **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
+
+---------------
+Working example
+---------------
+
+**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 |
++-------------------+-------------------------------------------------------------------------------------------------------------------+
+
+**Other input parameters**
+
++-----------------+---------------+
+| Input Parameter | Value         |
++=================+===============+
+| ksamples        | 3,4           |
++-----------------+---------------+
+| kfeatures       | 5,6,7         |
++-----------------+---------------+
+| iter_max        | 10            |
++-----------------+---------------+
+| nstart          | 1             |
++-----------------+---------------+
+| algorithm       | Hartigan-Wong |
++-----------------+---------------+
+
+----
+NEWS
+----
+
+August 2017, Version 0.98.1 - First release
+
+---------
+Citations
+---------
+
+    ]]>
+  </help>
+  <citations>
+    <citation type="bibtex"><![CDATA[
+@incollection{RCoreTeam2017,
+  title = {stats::kmeans - K-Means Clustering},
+  booktitle = {R: A Language and Environment for Statistical Computing},
+  author = {{R Core Team}},
+  publisher = {R Foundation for Statistical Computing},
+  address = {Vienna, Austria},
+  year = {2017},
+  url = {https://stat.ethz.ch/R-manual/R-devel/library/stats/html/kmeans.html},
+}
+    ]]></citation>
+    <!-- Forgy algorithm -->
+    <citation type="bibtex"><![CDATA[
+@article{forgy65,
+  added-at = {2006-03-23T12:22:43.000+0100},
+  author = {Forgy, E.},
+  biburl = {https://www.bibsonomy.org/bibtex/21e31409932ce91df646c4731350e1207/hotho},
+  interhash = {c86383cba8cfe00d5e6ef200016aca3f},
+  intrahash = {1e31409932ce91df646c4731350e1207},
+  journal = {Biometrics},
+  keywords = {clustering kmeans},
+  number = 3,
+  pages = {768-769},
+  timestamp = {2006-03-23T12:22:43.000+0100},
+  title = {Cluster Analysis of Multivariate Data: Efficiency versus Interpretability of Classification},
+  volume = 21,
+  year = 1965
+}
+    ]]></citation>
+    <!-- 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 -->
+    <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>
+    <!-- Lloyd algorithm -->
+    <citation type="doi">10.1109/TIT.1982.1056489</citation>
+    <!-- MacQueen algorithm -->
+    <citation type="bibtex"><![CDATA[
+@inproceedings{MacQueen1967,
+  added-at = {2011-01-11T13:35:01.000+0100},
+  author = {MacQueen, J. B.},
+  biburl = {https://www.bibsonomy.org/bibtex/25dcdb8cd9fba78e0e791af619d61d66d/enitsirhc},
+  booktitle = {Proc. of the fifth Berkeley Symposium on Mathematical Statistics and Probability},
+  editor = {Cam, L. M. Le and Neyman, J.},
+  interhash = {8d7d4dfe7d3a06b8c9c3c2bb7aa91e28},
+  intrahash = {5dcdb8cd9fba78e0e791af619d61d66d},
+  keywords = {kmeans clustering},
+  pages = {281-297},
+  publisher = {University of California Press},
+  timestamp = {2011-01-11T13:35:01.000+0100},
+  title = {Some Methods for Classification and Analysis of MultiVariate Observations},
+  volume = 1,
+  year = 1967
+}
+    ]]></citation>
+  </citations>
+  <!--
+     vim:et:sw=2:ts=2:
+--> </tool>
diff -r 000000000000 -r 6ccbe18131a6 w4mkmeans_routines.R
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/w4mkmeans_routines.R	Tue Aug 08 15:30:38 2017 -0400
@@ -0,0 +1,216 @@
+##------------------------------------------------------------------------------------------------------
+## these are the batch-independent and file-structure-independent routines to support the w4mkmeans tool
+##------------------------------------------------------------------------------------------------------
+
+library(parallel)
+
+w4kmeans_usage <- function() {
+  return ( 
+    c(
+     "w4mkmeans: bad input.",
+     "# 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",
+     "      sampleMetdata    - the corresponding W4M data.frame having sample metadata",
+     "      dataMatrix       - the corresponding W4M matrix",
+     "      slots            - the number of parallel slots for calculating kmeans",
+     "    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)",
+     "      algorithm        - string from c('Hartigan-Wong', 'Lloyd', 'Forgy', 'MacQueen') (default = Hartigan-Wong)",
+     "      ",
+     "    this routine will return a list comprising:",
+     "      variableMetadata - the input variableMetadata data.frame with updates, if any",
+     "      sampleMetadata   - the input sampleMetadata data.frame with updates, if any",
+     "      scores           - an array of strings, each representing a line of a tsv having the following header:",
+     "                           clusterOn TAB k TAB totalSS TAB betweenSS TAB proportion"
+    )
+  )
+}
+
+w4mkmeans <- function(env) {
+  # abort if 'env' is null or is not an environment
+  if ( is.null(env) || ! is.environment(env) ) {
+    lapply(w4kmeans_usage(),print)
+  } 
+  # supply default arguments
+  if ( ! exists("iter.max" , env) ) env$iter.max  <- 10
+  if ( ! exists("nstart"   , env) ) env$nstart    <- 1
+  if ( ! exists("algorithm", env) ) env$algorithm <- 'Hartigan-Wong'
+  if ( ! exists("ksamples" , env) ) env$ksamples  <- c()
+  if ( ! exists("kfeatures", env) ) env$kfeatures <- c()
+  # check mandatory arguments
+  expected <- c(
+    "log_print"
+  , "variableMetadata"
+  , "sampleMetadata"
+  , "dataMatrix"
+  , "slots"
+  )
+  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)
+    stop("w4mkmeans: contract has been broken")
+  } 
+  # extract parameters from 'env'
+  failure_action  <- env$log_print
+  scores          <- c( "clusterOn\tk\ttotalSS\tbetweenSS\tproportion" )
+  sampleMetadata  <- env$sampleMetadata
+  featureMetadata <- env$variableMetadata
+  ksamples        <- as.numeric(env$ksamples)
+  kfeatures       <- as.numeric(env$kfeatures)
+  slots           <- env$slots
+
+  myLapply <- parLapply
+  # uncomment the next line to mimic parLapply, but without parallelization (for testing/experimentation)
+  # myLapply <- function(cl, ...) lapply(...)
+  cl <- NULL
+  if ( identical(myLapply, parLapply) ) {
+    failure_action(sprintf("w4mkmeans: using parallel evaluation with %d slots", slots))
+    failure_action(names(cl))
+    cl <- makePSOCKcluster(names = slots)
+    # from ?makePSOCKcluster: "It is good practice to shut down the workers by calling stopCluster."
+    clusterExport(
+      cl = cl
+    , varlist = c(
+        "tryCatchFunc"
+      , "calc_kmeans_one_dimension_one_k"
+      , "prepare.data.matrix"
+      )
+    )
+    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")
+        stopCluster(cl)
+      }
+    }
+  } else {
+    failure_action("w4mkmeans: using sequential evaluation (1 slot)")
+    final <- function(cl) { }
+  }
+
+  tryCatch(
+    expr = {
+      # These myLapply calls produce lists of lists of results:
+      #   - The outer list has no keys and its members are accessed by index
+      #   - The inner list has keys "clusters" and "scores"
+
+      # 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( 
+            cl = cl
+          , ksamples
+          , calc_kmeans_one_dimension_one_k
+          , env = env
+          , dimension = "samples"
+          )
+        for ( i in 1:ksamples_length ) {
+          result <- smpl_result_list[[i]]
+          if (result$success) {
+            sampleMetadata[sprintf("k%d",ksamples[i])] <- result$value$clusters
+            scores <- c(scores, result$value$scores)
+          }
+        }
+      }
+
+      # 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( 
+            cl = cl
+          , kfeatures
+          , calc_kmeans_one_dimension_one_k
+          , env = env
+          , dimension = "features"
+          )
+        for ( i in 1:kfeatures_length ) {
+          result <- feat_result_list[[i]]
+          if (result$success) {
+            featureMetadata[sprintf("k%d",kfeatures[i])] <- result$value$clusters
+            scores <- c(scores, result$value$scores)
+          }
+        }
+      }
+
+      return ( 
+        list(
+          variableMetadata = featureMetadata
+        , sampleMetadata   = sampleMetadata  
+        , scores           = scores          
+        )
+      )
+    }
+  , finally = final(cl)
+  )
+}
+
+# calculate k-means for features or samples
+#   - recall that the dataMatrix has features in rows and samples in columns
+# return value:
+#   list(clusters = km$cluster, scores = scores) 
+# arguments:
+#   env:
+#     environment having dataMatrix
+#   dimension:
+#   - "samples":  produce clusters column to add to the sampleMetadata table
+#     - this is the default case
+#   - "variables":  produce clusters column to add to the variableMetadata table
+#   k:
+#     integer, the number of clusters to make
+calc_kmeans_one_dimension_one_k <- function(k, env, dimension = "samples") {
+  # 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$log_print) ) {
+    stop("calc_kmeans_one_dimension_one_k - argument 'env' - environment does not include log_print or it is not a function")
+  } 
+  # abort if k is not as expected
+  if ( ! is.numeric(k) ) {
+    stop(sprintf("calc_kmeans_one_dimension_one_k - expected numeric argument 'k' but type is %s", typeof(k)))
+  } 
+  k <- as.integer(k)
+  # abort if dimension is not as expected
+  if (   ! is.character(dimension) 
+      || ! Reduce( f =`|`, x = sapply(X = c("features","samples"), FUN = `==`, dimension), init = FALSE) ) {
+    stop("calc_kmeans_one_dimension_one_k - argument 'dimension' is neither 'features' nor 'samples'")
+  } 
+  dm           <- env$dataMatrix
+  iter.max     <- env$iter.max
+  nstart       <- env$nstart
+  algorithm    <- env$algorithm
+  dim_features <- dimension == "features"
+  # tryCatchFunc produces a list
+  #   On success of expr(), tryCatchFunc produces
+  #     list(success TRUE, value = expr(), msg = "")
+  #   On failure of expr(), tryCatchFunc produces
+  #     list(success = FALSE, value = NA, msg = "the error message")
+  result_list <- tryCatchFunc( expr = function() {
+    # kmeans clusters the rows; features are the columns of args_env$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 } )
+    # 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 )
+    scores <-
+      sprintf("%s\t%d\t%0.5e\t%0.5e\t%0.5f"
+             , dimension
+             , k
+             , km$totss
+             , km$betweenss
+             , km$betweenss/km$totss
+             )
+    list(clusters = km$cluster, scores = scores)
+  })
+  return ( result_list )
+}
+
diff -r 000000000000 -r 6ccbe18131a6 w4mkmeans_wrapper.R
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/w4mkmeans_wrapper.R	Tue Aug 08 15:30:38 2017 -0400
@@ -0,0 +1,370 @@
+#!/usr/bin/env Rscript
+
+# references:
+#   what this does:
+#   - [stats::kmeans](https://stat.ethz.ch/R-manual/R-devel/library/stats/html/kmeans.html)
+#   - [stats::p.adjust](https://stat.ethz.ch/R-manual/R-devel/library/stats/html/p.adjust.html)
+#   how this does what it does:
+#   - [parallel::clusterApply](https://stat.ethz.ch/R-manual/R-devel/library/parallel/html/clusterApply.html)
+
+# invocation:
+#   Rscript $__tool_directory__/w4mkmeans_wrapper.R \
+#     tool_directory $__tool_directory__
+#     data_matrix_path '$dataMatrix_in' \
+#     variable_metadata_path '$variableMetadata_in' \
+#     sample_metadata_path '$sampleMetadata_in' \
+#     kfeatures '$kfeatures' \
+#     ksamples '$ksamples' \
+#     iter_max '$iter_max' \
+#     nstart '$nstart' \
+#     algorithm '$algorithm' \
+#     scores '$scores' \
+#     sampleMetadata_out '$sampleMetadata_out' \
+#     variableMetadata_out '$variableMetadata_out' \
+#     slots "\${GALAXY_SLOTS:-1}" \
+# 
+# <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="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="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>
+# 	  <option value="MacQueen">MacQueen</option>
+# 	  <option value="Forgy">Forgy</option>
+# 	</param>
+# </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>
+# </outputs>
+
+##------------------------
+## libraries for this file
+##------------------------
+
+library(batch) ## for 'parseCommandArgs'
+
+##-------------------
+## Pre-initialization
+##-------------------
+
+argVc <- unlist(parseCommandArgs(evaluate=FALSE))
+if ( Reduce( `|`, grepl("tool_directory",names(argVc)) ) ) {
+  tool_directory <- as.character(argVc["tool_directory"])
+} else {
+  tool_directory <- "."
+}
+r_path <- function(f) paste( tool_directory, f, sep = "/" )
+
+##----------------------------------------------------------
+## Computation - source general and module-specific routines
+##----------------------------------------------------------
+
+log_print <- function(x, ...) { 
+  cat(
+    format(Sys.time(), "%Y-%m-%dT%H:%M:%S%z")
+  , " "
+  , 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")
+# log_print(sprintf("w4m_general_purpose_routines_path is %s", w4m_general_purpose_routines_path))
+if ( ! file.exists(w4m_general_purpose_routines_path) ) {
+  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)
+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")
+  q(save = "no", status = 1, runLast = TRUE)
+}
+
+w4mkmeans_routines_path <- r_path("w4mkmeans_routines.R")
+# log_print(sprintf("w4mkmeans_routines_path is %s", w4mkmeans_routines_path))
+if ( ! file.exists(w4mkmeans_routines_path) ) {
+  log_print("cannot find file w4mkmeans_routines.R")
+  q(save = "no", status = 1, runLast = TRUE)
+}
+# log_print("sourcing ",w4mkmeans_routines_path)
+source(w4mkmeans_routines_path)
+if ( ! exists("w4mkmeans") ) {
+  log_print("'w4mkmeans' was not read from file w4mkmeans_routines.R")
+  q(save = "no", status = 1, runLast = TRUE)
+}
+
+##-----------------------------------------
+## Computation - W4m data-suppport routines
+##-----------------------------------------
+
+# read_data_frame - read a w4m data frame from a tsv, with error handling
+#   e.g., data_matrix_input_env <- read_data_frame(dataMatrix_in, "data matrix input")
+read_data_frame <- function(file_path, kind_string, failure_action = log_print) {
+  my.env <- new.env()
+  my.env$success <- FALSE
+  my.env$msg <- sprintf("no message reading %s", kind_string)
+  tryCatch(
+    expr = {
+      my.env$data    <- utils::read.delim( fill = FALSE, file = file_path )
+      my.env$success <- TRUE
+    }
+  , error = function(e) {
+     my.env$msg <<- sprintf("%s read failed", kind_string)
+    }
+  )
+  if (!my.env$success) {
+    failure_action(my.env$msg)
+  }
+  return (my.env)
+}
+
+# write_result - write a w4m data frame to a tsv
+write_result <- function(result, file_path, kind_string, failure_action = log_print) {
+  my.env <- new.env()
+  my.env$success <- FALSE
+  my.env$msg <- sprintf("no message writing %s", kind_string)
+  tryCatch(
+    expr = {
+      write.table(
+        x = result
+      , sep = "\t"
+      , file = file_path
+      , quote = FALSE
+      , row.names = FALSE
+      )
+      my.env$success <- TRUE
+    }
+  , error = function(e) {
+     my.env$msg <<- sprintf("%s write failed", kind_string)
+    }
+  )
+  if (!my.env$success) {
+    failure_action(my.env$msg)
+    return (my.env)
+  }
+  return (my.env)
+}
+
+# read the three input files
+read_input_data <- function(env, failure_action = log_print) {
+  kind_string <- "none"
+  tryCatch(
+    expr = {
+      # read in the sample metadata
+      kind_string <- "sample metadata input"
+      smpl_metadata_input_env <- 
+        read_data_frame(
+                         file_path = env$sample_metadata_path
+                       , kind_string = kind_string
+                       , failure_action = failure_action
+                       )
+      if (!smpl_metadata_input_env$success) {
+        failure_action(smpl_metadata_input_env$msg)
+        return ( FALSE )
+      }
+      env$sampleMetadata <- smpl_metadata_input_env$data
+
+      # read in the variable metadata
+      kind_string <- "variable metadata input"
+      vrbl_metadata_input_env <- 
+        read_data_frame(
+                         file_path = env$variable_metadata_path
+                       , kind_string = kind_string
+                       , failure_action = failure_action
+                       )
+      if (!vrbl_metadata_input_env$success) {
+        failure_action(vrbl_metadata_input_env$msg)
+        return ( FALSE )
+      }
+      env$variableMetadata <- vrbl_metadata_input_env$data
+
+      # read in the data matrix
+      kind_string <- "data matrix input"
+      data_matrix_input_env <-
+        read_data_frame(
+                         file_path = env$data_matrix_path
+                       , kind_string = kind_string
+                       , failure_action = failure_action
+                       )
+      if (!data_matrix_input_env$success) {
+        failure_action(data_matrix_input_env$msg)
+        return ( FALSE )
+      }
+      # data frame for dataMatrix has rownames in first column
+      data_matrix_df <- data_matrix_input_env$data
+      rownames(data_matrix_df) <- data_matrix_df[,1]
+      data_matrix <- data_matrix_df[,2:ncol(data_matrix_df)]
+      env$dataMatrix <- as.matrix(data_matrix)
+
+    }
+  , error = function(e) {
+     failure_action( sprintf("read_input_data failed for '%s' - %s", kind_string, format_error(e)) )
+     return ( FALSE )
+    }
+  )
+  return ( TRUE )
+}
+
+
+read_input_failure_action <- function(x, ...) { 
+  log_print("Failure reading input for '", modNamC, "' Galaxy module call")
+  log_print(x, ...)
+}
+
+##--------------------------
+## Computation - Entry Point
+##--------------------------
+
+##----------
+## Constants
+##----------
+
+modNamC <- "w4mkmeans" ## module name
+
+## options
+##--------
+
+# Set the handler for R error-handling
+options( show.error.messages = F
+       , error = function () { 
+                   log_print( "Fatal error in '", modNamC, "': ", geterrmessage() )
+                   q( "no", 1, F )
+                 }
+       , warn = -1
+       )
+
+# strings as factors? - not by default!
+# save old value
+strAsFacL <- options()$stringsAsFactors
+options(stringsAsFactors = FALSE)
+
+
+## log file
+##---------
+
+log_print("Start of the '", modNamC, "' Galaxy module call")
+
+## arguments
+##----------
+
+args_env <- new.env()
+
+# files
+
+log_print("PARAMETERS (raw):")
+invisible(
+  lapply(
+    X = 1:length(argVc)
+  , FUN = function(i) {
+      log_print(sprintf("  - %s: %s", names(argVc)[i], argVc[i]))
+    }
+  )
+)
+
+# write.table(as.matrix(argVc), col.names=F, quote=F, sep='\t')
+
+## output files
+sampleMetadata_out              <- as.character(argVc["sampleMetadata_out"])
+variableMetadata_out            <- as.character(argVc["variableMetadata_out"])
+scores_out                      <- as.character(argVc["scores_out"])
+## input files
+args_env$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")
+args_env$kfeatures <- strsplit(x = as.character(argVc['kfeatures']), split = ",", fixed = TRUE)[[1]]
+args_env$ksamples  <- strsplit(x = as.character(argVc['ksamples' ]), split = ",", fixed = TRUE)[[1]]
+# numeric args
+args_env$iter_max  <- as.numeric(               argVc['iter_max'  ])
+args_env$nstart    <- as.numeric(               argVc['nstart'   ])
+args_env$slots     <- as.numeric(               argVc['slots'    ])
+# string args
+args_env$algorithm <- as.character(             argVc['algorithm'])
+args_env$log_print <- log_print
+
+log_print("PARAMETERS (parsed):")
+for (member in ls(args_env)) {
+  value <- get(member, args_env)
+  value <- ifelse(length(value) == 1, value, sprintf("c(%s)", paste(value, collapse=", ")))
+  
+  log_print(sprintf("  - %s: %s", member, ifelse( !is.function(value) , value, "function" )))
+}
+log_print("")
+
+##---------------------------------------------------------
+## Computation - attempt to read input data
+##---------------------------------------------------------
+if ( ! read_input_data(args_env, failure_action = read_input_failure_action) ) {
+  result <- -1
+} else {
+  log_print("Input data was read successfully.")
+  result <- w4mkmeans(env = args_env)
+  log_print("returned from call to w4mkmeans.")
+}
+
+if ( length(result) == 0 ) {
+  log_print("no results were produced")
+  # exit with status code non-zero to indicate error
+  q(save = "no", status = 1, runLast = FALSE)
+} else if ( ! setequal(names(result),c("variableMetadata","sampleMetadata","scores")) ) {
+  log_print(sprintf("unexpected result keys %s", names(result)))
+  # exit with status code non-zero to indicate error
+  q(save = "no", status = 1, runLast = FALSE)
+} else if ( ! write_result(result = result$variableMetadata, file_path = variableMetadata_out, kind_string = "clustered variableMetadata")$success ) {
+  log_print("failed to write output file for clustered variableMetadata")
+  # exit with status code non-zero to indicate error
+  q(save = "no", status = 1, runLast = FALSE)
+} else if ( ! write_result(result = result$sampleMetadata, file_path = sampleMetadata_out, kind_string = "clustered sampleMetadata")$success ) {
+  log_print("failed to write output file for clustered sampleMetadata")
+  # exit with status code non-zero to indicate error
+  q(save = "no", status = 1, runLast = FALSE)
+} else {
+  tryCatch(
+    expr = {
+      fileConn<-file(scores_out)
+      writeLines(result$scores, fileConn)
+      close(fileConn)
+    }
+  , error = function(e) {
+      log_print(sprintf("failed to write output file for cluster scores - %s", format_error(e)))
+      # exit with status code non-zero to indicate error
+      q(save = "no", status = 1, runLast = FALSE)
+    }
+  )
+}
+
+##--------
+## Closing
+##--------
+
+
+if (!file.exists(sampleMetadata_out)) {
+  log_print(sprintf("ERROR %s::w4m_kmeans_wrapper - file '%s' was not created", modNamC, sampleMetadata_out))
+}
+
+if (!file.exists(variableMetadata_out)) {
+  log_print(sprintf("ERROR %s::w4m_kmeans_wrapper - file '%s' was not created", modNamC, variableMetadata_out))
+}
+
+if (!file.exists(scores_out)) {
+  log_print(sprintf("ERROR %s::w4m_kmeans_wrapper - file '%s' was not created", modNamC, scores_out))
+}
+
+log_print("Normal termination of '", modNamC, "' Galaxy module call")
+
+# exit with status code zero
+q(save = "no", status = 0, runLast = FALSE)