fitVsDatCorrelation=0.88758284123876
cont.fitVsDatCorrelation=0.241099157754801

fstatistic=8728.42555671588,57,807
cont.fstatistic=1955.48229062833,57,807

residuals=-0.825595571287838,-0.088778880071562,-0.00341408003222285,0.0889550467649052,0.884554836939468
cont.residuals=-0.772244616342354,-0.244172276956908,-0.0804723498524977,0.141957358106994,1.60506719618432

predictedValues:
Include	Exclude	Both
Lung	59.2269673850047	67.0542798421182	81.585137900263
cerebhem	66.6351772190666	79.3281002249016	114.178614236038
cortex	57.9324962869248	67.8455732200696	106.828529479567
heart	58.7694072250543	75.8761237089976	101.98362052138
kidney	58.5709837658781	73.8540682021026	90.6040638336784
liver	58.4360923082397	74.7413763279248	100.929899935587
stomach	60.323880666951	69.8065698612092	84.8472844144121
testicle	59.520077998303	123.196668724422	231.459627822165


diffExp=-7.82731245711356,-12.6929230058350,-9.91307693314482,-17.1067164839433,-15.2830844362245,-16.3052840196851,-9.48268919425821,-63.6765907261193
diffExpScore=0.993476318397546
diffExp1.5=0,0,0,0,0,0,0,-1
diffExp1.5Score=0.5
diffExp1.4=0,0,0,0,0,0,0,-1
diffExp1.4Score=0.5
diffExp1.3=0,0,0,0,0,0,0,-1
diffExp1.3Score=0.5
diffExp1.2=0,0,0,-1,-1,-1,0,-1
diffExp1.2Score=0.8

cont.predictedValues:
Include	Exclude	Both
Lung	80.067246036141	74.1574329408348	79.0865416626943
cerebhem	74.4259053246193	78.1352582324781	64.94279030322
cortex	69.5155916136295	74.8717378435851	68.2256752809457
heart	71.1536170663216	81.8288518773964	66.659660972926
kidney	74.4182489805951	74.8743760835231	72.8808327716615
liver	75.2780131106059	79.147490257071	68.7376299024103
stomach	68.3199046591687	70.0273489508143	73.6427844773532
testicle	73.7267307066798	78.4329113748642	68.0826907004734
cont.diffExp=5.9098130953062,-3.70935290785887,-5.35614622995564,-10.6752348110748,-0.456127102928008,-3.86947714646512,-1.70744429164559,-4.70618066818443
cont.diffExpScore=1.42313502908809

cont.diffExp1.5=0,0,0,0,0,0,0,0
cont.diffExp1.5Score=0
cont.diffExp1.4=0,0,0,0,0,0,0,0
cont.diffExp1.4Score=0
cont.diffExp1.3=0,0,0,0,0,0,0,0
cont.diffExp1.3Score=0
cont.diffExp1.2=0,0,0,0,0,0,0,0
cont.diffExp1.2Score=0

tran.correlation=0.077926240214455
cont.tran.correlation=0.180774237328061

tran.covariance=0.00103687147640166
cont.tran.covariance=0.000522460165687509

tran.mean=69.444865185448
cont.tran.mean=74.8987915661455

weightedLogRatios:
wLogRatio
Lung	-0.514306400493177
cerebhem	-0.747378007316547
cortex	-0.653661026713838
heart	-1.07336652512947
kidney	-0.970569440799751
liver	-1.03140161432668
stomach	-0.609218606710821
testicle	-3.23726879407495

cont.weightedLogRatios:
wLogRatio
Lung	0.33312346962117
cerebhem	-0.210800174034176
cortex	-0.317585617105628
heart	-0.605947189053448
kidney	-0.0263532271499361
liver	-0.217855704428638
stomach	-0.104577852460267
testicle	-0.268013317569832

varWeightedLogRatios=0.785339522944042
cont.varWeightedLogRatios=0.0718599911604428

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.78977887880842	0.0835569454359163	45.3556417008443	3.52103979276331e-224	***
df.mm.trans1	0.344783040443457	0.072235374208663	4.77304982801776	2.15407768845323e-06	***
df.mm.trans2	0.439955217921237	0.064152674417117	6.85794040417822	1.38973407249145e-11	***
df.mm.exp2	-0.0501712647575066	0.0829895963555042	-0.604548846611893	0.545648733177299	   
df.mm.exp3	-0.279944699476742	0.0829895963555043	-3.37325052501204	0.000778269694356819	***
df.mm.exp4	-0.107321018580979	0.0829895963555043	-1.29318641485188	0.196316654605135	   
df.mm.exp5	-0.0194008503280241	0.0829895963555042	-0.233774487164828	0.815219346205068	   
df.mm.exp6	-0.117690953955134	0.0829895963555042	-1.41814105771739	0.156535613708069	   
df.mm.exp7	0.0193709042863380	0.0829895963555043	0.233413646252217	0.815499410737809	   
df.mm.exp8	-0.429542047132062	0.0829895963555042	-5.17585415516451	2.86574335231345e-07	***
df.mm.trans1:exp2	0.168026921015241	0.0766771118286529	2.19135693830936	0.0287111422932813	*  
df.mm.trans2:exp2	0.218261245737130	0.0579624483283727	3.76556291239841	0.000178237378292231	***
df.mm.trans1:exp3	0.257846206773806	0.0766771118286529	3.36275324701853	0.00080804296320056	***
df.mm.trans2:exp3	0.291676401708153	0.0579624483283727	5.03216151353249	5.98191067026697e-07	***
df.mm.trans1:exp4	0.0995654844823086	0.0766771118286529	1.29850332267083	0.194485370178030	   
df.mm.trans2:exp4	0.230920639557847	0.0579624483283727	3.98396972898073	7.39020262970641e-05	***
df.mm.trans1:exp5	0.0082632983814188	0.0766771118286529	0.107767470426957	0.914206934105588	   
df.mm.trans2:exp5	0.115989506859441	0.0579624483283727	2.00111469071025	0.0457144837917037	*  
df.mm.trans1:exp6	0.104247703551238	0.0766771118286529	1.35956742585971	0.174346600695477	   
df.mm.trans2:exp6	0.226222354423768	0.0579624483283727	3.90291233286348	0.000102972294005473	***
df.mm.trans1:exp7	-0.00101981631142036	0.0766771118286529	-0.013300139860501	0.989391623862506	   
df.mm.trans2:exp7	0.0208547868422953	0.0579624483283727	0.359798239096929	0.719092154655133	   
df.mm.trans1:exp8	0.434478779791696	0.0766771118286529	5.66634252947096	2.02990931981902e-08	***
df.mm.trans2:exp8	1.03782161999502	0.0579624483283727	17.9050687113057	1.25994771753884e-60	***
df.mm.trans1:probe2	-0.160966701978523	0.0514365703101663	-3.1294213632029	0.00181451399812961	** 
df.mm.trans1:probe3	-0.140960062846988	0.0514365703101663	-2.74046387612916	0.00627081909728143	** 
df.mm.trans1:probe4	0.00483436468819407	0.0514365703101663	0.0939869174605246	0.925142878695708	   
df.mm.trans1:probe5	-0.25395142703959	0.0514365703101663	-4.93717651679037	9.63364410201536e-07	***
df.mm.trans1:probe6	-0.305627825839814	0.0514365703101663	-5.94183912334853	4.18946470712012e-09	***
df.mm.trans1:probe7	-0.245512228523649	0.0514365703101663	-4.77310650852482	2.15348799110645e-06	***
df.mm.trans1:probe8	-0.0663588937549471	0.0514365703101663	-1.29011116710928	0.197381611965969	   
df.mm.trans1:probe9	-0.0740964328749315	0.0514365703101663	-1.44053991990766	0.150102553352531	   
df.mm.trans1:probe10	-0.051447440394024	0.0514365703101663	-1.00021132987274	0.317508057611251	   
df.mm.trans1:probe11	-0.168419040640381	0.0514365703101663	-3.27430541392636	0.00110434804903635	** 
df.mm.trans1:probe12	-0.132523371946760	0.0514365703101663	-2.57644261947548	0.0101589346794145	*  
df.mm.trans1:probe13	-0.211857171107742	0.0514365703101663	-4.1188043804287	4.20106135432826e-05	***
df.mm.trans1:probe14	-0.0921160968922396	0.0514365703101663	-1.79086778797211	0.0736892016530869	.  
df.mm.trans1:probe15	0.0563880318884147	0.0514365703101663	1.09626344735644	0.273290532666513	   
df.mm.trans1:probe16	-0.0189488514629357	0.0514365703101663	-0.368392592054111	0.712677134618744	   
df.mm.trans1:probe17	0.0343727222014224	0.0514365703101663	0.66825455107431	0.504162200188946	   
df.mm.trans1:probe18	-0.00333111682960591	0.0514365703101663	-0.064761643506148	0.948379819857878	   
df.mm.trans1:probe19	-0.00570667035447559	0.0514365703101663	-0.110945778850805	0.911686929437328	   
df.mm.trans1:probe20	0.103376635887166	0.0514365703101663	2.00978866327589	0.0447859740430264	*  
df.mm.trans1:probe21	0.137303049856413	0.0514365703101663	2.6693663482706	0.0077521651590738	** 
df.mm.trans2:probe2	-0.068399959094714	0.0514365703101663	-1.32979237694615	0.183962422081288	   
df.mm.trans2:probe3	0.0400455272425284	0.0514365703101663	0.77854194012258	0.436477803966297	   
df.mm.trans2:probe4	-0.204338154926173	0.0514365703101663	-3.97262402399691	7.74417018229e-05	***
df.mm.trans2:probe5	-0.0229769372192291	0.0514365703101663	-0.446704301641351	0.655208424129732	   
df.mm.trans2:probe6	-0.107805352501331	0.0514365703101663	-2.09588920589488	0.0364029783697073	*  
df.mm.trans3:probe2	-0.145714452987317	0.0514365703101663	-2.83289597476364	0.00472782437145043	** 
df.mm.trans3:probe3	-0.0899619073167014	0.0514365703101663	-1.74898728228232	0.0806733849666012	.  
df.mm.trans3:probe4	-0.0776856731687566	0.0514365703101663	-1.51031985026035	0.131353263482306	   
df.mm.trans3:probe5	-0.440263087488713	0.0514365703101663	-8.55933987888177	5.68850134214401e-17	***
df.mm.trans3:probe6	-0.374073546865338	0.0514365703101663	-7.27252117724114	8.35445524702285e-13	***
df.mm.trans3:probe7	-0.114982481911401	0.0514365703101663	-2.23542279778858	0.0256624006075735	*  
df.mm.trans3:probe8	-0.231340623334256	0.0514365703101663	-4.49759037080534	7.88065234860143e-06	***
df.mm.trans3:probe9	-0.0818084552268381	0.0514365703101663	-1.5904725904843	0.112119958768213	   

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.47656382192464	0.176039043562586	25.4293805017926	3.11966353090807e-105	***
df.mm.trans1	-0.0435389930925836	0.152186585097599	-0.286089559501329	0.774882950449074	   
df.mm.trans2	-0.110875510666359	0.135157830237256	-0.82034100778126	0.412263971556712	   
df.mm.exp2	0.176224297154410	0.174843743890478	1.00789592600348	0.31380653140808	   
df.mm.exp3	0.0159921446472816	0.174843743890478	0.0914653523851501	0.927145534082107	   
df.mm.exp4	0.151356621758747	0.174843743890478	0.865667929494555	0.386929565739588	   
df.mm.exp5	0.0181727923008384	0.174843743890478	0.103937332251486	0.917244905122733	   
df.mm.exp6	0.143689941488325	0.174843743890478	0.821819175745471	0.411422548559372	   
df.mm.exp7	-0.144653594238210	0.174843743890478	-0.827330684069658	0.408294226049071	   
df.mm.exp8	0.123371605111487	0.174843743890478	0.705610634766359	0.48063393486322	   
df.mm.trans1:exp2	-0.249287083112296	0.161544505475130	-1.54314801595449	0.123186889330182	   
df.mm.trans2:exp2	-0.123973197855240	0.122116167758887	-1.01520707806700	0.310311396194215	   
df.mm.trans1:exp3	-0.157307934512432	0.161544505475130	-0.973774589545854	0.330460260640310	   
df.mm.trans2:exp3	-0.00640596270635364	0.122116167758887	-0.0524579408600663	0.958176789632536	   
df.mm.trans1:exp4	-0.269382318361442	0.161544505475130	-1.66754243710823	0.0957945610529003	.  
df.mm.trans2:exp4	-0.0529170334141647	0.122116167758887	-0.433333557589581	0.66488826271937	   
df.mm.trans1:exp5	-0.091338455581205	0.161544505475130	-0.565407379920247	0.571953862056604	   
df.mm.trans2:exp5	-0.00855137418757728	0.122116167758887	-0.0700265521307676	0.944189874439335	   
df.mm.trans1:exp6	-0.205368696922973	0.161544505475130	-1.27128246373313	0.203994468667057	   
df.mm.trans2:exp6	-0.0785671698688137	0.122116167758887	-0.643380572046291	0.52015996430692	   
df.mm.trans1:exp7	-0.0140121088263688	0.161544505475130	-0.0867383807648348	0.930900984429524	   
df.mm.trans2:exp7	0.0873491537684996	0.122116167758887	0.71529556955117	0.474633313971963	   
df.mm.trans1:exp8	-0.205873032558731	0.161544505475130	-1.27440442467061	0.202886969503692	   
df.mm.trans2:exp8	-0.0673182834100554	0.122116167758887	-0.551264297312148	0.581605101073399	   
df.mm.trans1:probe2	-0.0345239862439402	0.108367348690193	-0.318582918759408	0.75012524054826	   
df.mm.trans1:probe3	-0.148267534791081	0.108367348690193	-1.36819380175994	0.171632230526869	   
df.mm.trans1:probe4	-0.0830778136288921	0.108367348690193	-0.766631412810512	0.44352489342211	   
df.mm.trans1:probe5	0.0341342619000523	0.108367348690193	0.314986592480336	0.752853260014494	   
df.mm.trans1:probe6	-0.130715858290690	0.108367348690193	-1.20622918130430	0.228082639079164	   
df.mm.trans1:probe7	-0.194022450173607	0.108367348690193	-1.79041429469950	0.0737620861160727	.  
df.mm.trans1:probe8	-0.000354545081568799	0.108367348690193	-0.00327169655670356	0.997390377053932	   
df.mm.trans1:probe9	0.0215011565441898	0.108367348690193	0.198409915939335	0.84277433120744	   
df.mm.trans1:probe10	-0.099546299515235	0.108367348690193	-0.918600489154937	0.358579044296747	   
df.mm.trans1:probe11	0.0301111729008138	0.108367348690193	0.277862042993203	0.781189432425453	   
df.mm.trans1:probe12	0.0265552280211662	0.108367348690193	0.245048239549385	0.806481377991266	   
df.mm.trans1:probe13	-0.113750143555852	0.108367348690193	-1.04967174089538	0.294183299374897	   
df.mm.trans1:probe14	-0.172181272426776	0.108367348690193	-1.58886670669611	0.112482165338600	   
df.mm.trans1:probe15	-0.155168664186542	0.108367348690193	-1.43187653903158	0.152566340466657	   
df.mm.trans1:probe16	-0.0573667888152025	0.108367348690193	-0.529373372224931	0.596692052477723	   
df.mm.trans1:probe17	-0.000724742572141267	0.108367348690193	-0.00668783153690696	0.994665575100472	   
df.mm.trans1:probe18	-0.0827811202282595	0.108367348690193	-0.763893564148358	0.445153950433132	   
df.mm.trans1:probe19	-0.212001936978981	0.108367348690193	-1.95632669380022	0.0507712508558353	.  
df.mm.trans1:probe20	-0.0171603565481846	0.108367348690193	-0.158353570107576	0.874217814313949	   
df.mm.trans1:probe21	-0.115397460092630	0.108367348690193	-1.06487296669530	0.287251923689183	   
df.mm.trans2:probe2	-0.166301688802130	0.108367348690193	-1.53461066282579	0.125271429032611	   
df.mm.trans2:probe3	-0.144217581376035	0.108367348690193	-1.33082135088801	0.183623682484316	   
df.mm.trans2:probe4	-0.127703188470419	0.108367348690193	-1.17842865045545	0.238973117185865	   
df.mm.trans2:probe5	-0.235610176409007	0.108367348690193	-2.17418050046221	0.0299815084302184	*  
df.mm.trans2:probe6	-0.218637451116892	0.108367348690193	-2.01755836752955	0.0439678418601254	*  
df.mm.trans3:probe2	0.073406742472489	0.108367348690193	0.677388008101485	0.498353945935636	   
df.mm.trans3:probe3	-0.0316291209816834	0.108367348690193	-0.291869473268250	0.770461436381476	   
df.mm.trans3:probe4	0.00383520271021155	0.108367348690193	0.0353907589007816	0.971776905291344	   
df.mm.trans3:probe5	0.148705512066505	0.108367348690193	1.37223539990476	0.170371458782962	   
df.mm.trans3:probe6	0.0469815621513204	0.108367348690193	0.433539832054339	0.664738497726003	   
df.mm.trans3:probe7	0.149486810777195	0.108367348690193	1.37944512423716	0.168139677259652	   
df.mm.trans3:probe8	-0.0126009502413419	0.108367348690193	-0.116279953266793	0.90745960224828	   
df.mm.trans3:probe9	0.0573548302733655	0.108367348690193	0.529263020334056	0.596768555863586