fitVsDatCorrelation=0.838269770642116
cont.fitVsDatCorrelation=0.256138320492970

fstatistic=11213.0088069116,62,922
cont.fstatistic=3557.59870463311,62,922

residuals=-0.442448554421595,-0.0979220385360648,-0.00575303968055605,0.0932453915403014,0.73715909767037
cont.residuals=-0.596663533328504,-0.197099335803853,-0.0140937141610345,0.168378634677011,1.22236577490528

predictedValues:
Include	Exclude	Both
Lung	68.8415821571734	107.494099099163	59.8367079548827
cerebhem	76.5366353313729	97.1089172779832	60.9002686916075
cortex	71.8592728625337	96.0644397514995	62.0840940279082
heart	65.6869872219399	91.1023602485925	57.7028701069792
kidney	69.3018950914065	134.719100954021	59.8961767958857
liver	66.547551980769	113.286159507759	55.868869541099
stomach	67.2854545641429	95.3786189776587	58.1160389294604
testicle	66.7729656167835	92.501906523145	57.3326826186312


diffExp=-38.6525169419894,-20.5722819466103,-24.2051668889658,-25.4153730266526,-65.4172058626143,-46.7386075269903,-28.0931644135158,-25.7289409063616
diffExpScore=0.996374489921502
diffExp1.5=-1,0,0,0,-1,-1,0,0
diffExp1.5Score=0.75
diffExp1.4=-1,0,0,0,-1,-1,-1,0
diffExp1.4Score=0.8
diffExp1.3=-1,0,-1,-1,-1,-1,-1,-1
diffExp1.3Score=0.875
diffExp1.2=-1,-1,-1,-1,-1,-1,-1,-1
diffExp1.2Score=0.888888888888889

cont.predictedValues:
Include	Exclude	Both
Lung	66.0101804812774	74.939653700756	60.3719619345196
cerebhem	72.1475099336786	64.1219568125675	81.857715040336
cortex	75.1235845439304	72.9705436098457	68.4064186131544
heart	71.5898722556383	71.7674150988589	70.5393187771404
kidney	70.9678716690785	73.8122889808687	69.0292358228937
liver	68.283400093206	68.9407451477148	68.0293257967172
stomach	72.4461399944861	68.830602276736	76.9592089976324
testicle	68.446817437498	68.2921433773968	66.7381772014112
cont.diffExp=-8.92947321947861,8.02555312111117,2.15304093408467,-0.177542843220621,-2.84441731179014,-0.657345054508809,3.61553771775007,0.154674060101257
cont.diffExpScore=11.3492620710733

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=-0.0138056872602386
cont.tran.correlation=-0.156624615464680

tran.covariance=2.93107270570761e-05
cont.tran.covariance=-0.000346758546675769

tran.mean=86.2804966978715
cont.tran.mean=70.5431703383461

weightedLogRatios:
wLogRatio
Lung	-1.98510427113785
cerebhem	-1.06100256781726
cortex	-1.28312885996115
heart	-1.42230097997819
kidney	-3.03832177592534
liver	-2.3748048661348
stomach	-1.52941336375104
testicle	-1.42244869492048

cont.weightedLogRatios:
wLogRatio
Lung	-0.539627161353882
cerebhem	0.497617907989465
cortex	0.125172048830279
heart	-0.0105819178138853
kidney	-0.168269053694814
liver	-0.040511479579315
stomach	0.217949460269646
testicle	0.00955816420179183

varWeightedLogRatios=0.437107422727721
cont.varWeightedLogRatios=0.0901582730589314

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.68844737833846	0.0760657892421937	61.6367413662196	0	***
df.mm.trans1	-0.372363702195806	0.0667141104497843	-5.58148343259533	3.13575028090223e-08	***
df.mm.trans2	0.000411914145815354	0.0589203069902279	0.00699103869033932	0.994423515917431	   
df.mm.exp2	-0.0132594503117759	0.0768144044408561	-0.172616716985486	0.862990594648757	   
df.mm.exp3	-0.106385534912104	0.0768144044408561	-1.38496855747955	0.166397094389544	   
df.mm.exp4	-0.176046949745821	0.0768144044408561	-2.29184813743326	0.022139042593424	*  
df.mm.exp5	0.231426862292724	0.0768144044408562	3.01280552752202	0.00265916797911758	** 
df.mm.exp6	0.0872018425089097	0.0768144044408562	1.13522773682443	0.25657493430994	   
df.mm.exp7	-0.113267754607204	0.0768144044408562	-1.47456398876874	0.140671164007748	   
df.mm.exp8	-0.137967880865424	0.0768144044408561	-1.79611990576134	0.0728027385648878	.  
df.mm.trans1:exp2	0.119221015544563	0.0727328059950962	1.63916425213405	0.101520200139816	   
df.mm.trans2:exp2	-0.0883432965433502	0.0552445319322697	-1.59913195846532	0.110133885127538	   
df.mm.trans1:exp3	0.149287243551845	0.0727328059950962	2.05254343634026	0.0403987046718517	*  
df.mm.trans2:exp3	-0.00603120535466583	0.0552445319322696	-0.109172892659497	0.91308911568096	   
df.mm.trans1:exp4	0.129139837838699	0.0727328059950963	1.77553768305606	0.0761389521459439	.  
df.mm.trans2:exp4	0.0105947080428698	0.0552445319322697	0.191778401812853	0.84795801656884	   
df.mm.trans1:exp5	-0.224762564461926	0.0727328059950962	-3.09025014760299	0.00205989564921923	** 
df.mm.trans2:exp5	-0.0056709392223112	0.0552445319322697	-0.102651593270151	0.918261814885594	   
df.mm.trans1:exp6	-0.121093037193487	0.0727328059950962	-1.66490259157129	0.0962719354889972	.  
df.mm.trans2:exp6	-0.0347207938162752	0.0552445319322697	-0.628492858964634	0.529836779985555	   
df.mm.trans1:exp7	0.0904038853924647	0.0727328059950962	1.24295885681297	0.214198988054155	   
df.mm.trans2:exp7	-0.00631376574845619	0.0552445319322697	-0.114287614133412	0.90903468283273	   
df.mm.trans1:exp8	0.107458218969234	0.0727328059950962	1.47743810374206	0.139899728334043	   
df.mm.trans2:exp8	-0.0122388177278531	0.0552445319322697	-0.221538988561945	0.824721837859296	   
df.mm.trans1:probe2	-0.416373436129398	0.0462208915523153	-9.00833848386771	1.17913164315181e-18	***
df.mm.trans1:probe3	-0.110287245745616	0.0462208915523153	-2.38609083558649	0.0172294699621253	*  
df.mm.trans1:probe4	-0.127991285130344	0.0462208915523153	-2.76912194533237	0.00573356952451632	** 
df.mm.trans1:probe5	-0.227367090103980	0.0462208915523153	-4.91914116036972	1.02908280519620e-06	***
df.mm.trans1:probe6	0.058774312428928	0.0462208915523153	1.27159625128399	0.203837274638402	   
df.mm.trans1:probe7	-0.106758400428752	0.0462208915523153	-2.30974342647452	0.0211224991232775	*  
df.mm.trans1:probe8	-0.437457137132172	0.0462208915523153	-9.4644893778614	2.38603259754219e-20	***
df.mm.trans1:probe9	-0.44782519181965	0.0462208915523153	-9.68880471102071	3.31208786508949e-21	***
df.mm.trans1:probe10	0.168962775036746	0.0462208915523153	3.65554989015096	0.00027113723698511	***
df.mm.trans1:probe11	-0.368366190444827	0.0462208915523153	-7.9696902866508	4.68035669561657e-15	***
df.mm.trans1:probe12	-0.178850988243302	0.0462208915523153	-3.86948373855723	0.000116742673955390	***
df.mm.trans1:probe13	-0.00608513445700454	0.0462208915523153	-0.131653333647125	0.895287220867286	   
df.mm.trans1:probe14	-0.250336618418331	0.0462208915523153	-5.41609237751258	7.77442377292854e-08	***
df.mm.trans1:probe15	-0.0309027427006150	0.0462208915523153	-0.668588200330096	0.503925551764368	   
df.mm.trans1:probe16	-0.176069404925422	0.0462208915523153	-3.80930352081454	0.000148589810122701	***
df.mm.trans1:probe17	-0.0205042158836661	0.0462208915523153	-0.443613595390265	0.657426055461229	   
df.mm.trans1:probe18	0.065603469906181	0.0462208915523153	1.41934670022380	0.156135947260167	   
df.mm.trans1:probe19	0.00559665053528814	0.0462208915523153	0.121084867628603	0.903650197425008	   
df.mm.trans1:probe20	-0.00371404992133929	0.0462208915523153	-0.0803543548513236	0.935972866840087	   
df.mm.trans1:probe21	0.138070742480431	0.0462208915523153	2.98719340634430	0.00289005753566967	** 
df.mm.trans1:probe22	0.166193031905965	0.0462208915523153	3.59562583767686	0.000340783656177894	***
df.mm.trans1:probe23	-0.0898967105945871	0.0462208915523153	-1.94493674992913	0.0520863156051177	.  
df.mm.trans1:probe24	-0.168730107340427	0.0462208915523153	-3.65051606911236	0.000276429258236061	***
df.mm.trans1:probe25	-0.237974172311099	0.0462208915523153	-5.14862791086033	3.20791279484748e-07	***
df.mm.trans1:probe26	0.022138042628373	0.0462208915523153	0.478961826240759	0.63207935466064	   
df.mm.trans1:probe27	-0.276318373165425	0.0462208915523153	-5.97821383113463	3.22203226186665e-09	***
df.mm.trans1:probe28	0.106819201543607	0.0462208915523153	2.31105887307915	0.0210494083206642	*  
df.mm.trans2:probe2	0.0606457281040905	0.0462208915523153	1.31208477524602	0.189818100308273	   
df.mm.trans2:probe3	-0.175017084285289	0.0462208915523153	-3.78653631306949	0.000162648906839914	***
df.mm.trans2:probe4	0.0553725152338418	0.0462208915523153	1.19799755855354	0.231225834560657	   
df.mm.trans2:probe5	-0.0290423873968756	0.0462208915523153	-0.628338970138727	0.529937519972489	   
df.mm.trans2:probe6	-0.0604621724677591	0.0462208915523153	-1.30811350532529	0.191160839748803	   
df.mm.trans3:probe2	-0.197611904984336	0.0462208915523153	-4.27538064168814	2.10677385226379e-05	***
df.mm.trans3:probe3	-0.440429782781805	0.0462208915523153	-9.52880327466862	1.35969314866396e-20	***
df.mm.trans3:probe4	-0.173658868881284	0.0462208915523153	-3.75715099923434	0.000182652570080544	***
df.mm.trans3:probe5	-0.302159228231685	0.0462208915523153	-6.5372868865951	1.03579430195915e-10	***
df.mm.trans3:probe6	-0.439098344551240	0.0462208915523153	-9.49999729136	1.74983432666852e-20	***
df.mm.trans3:probe7	-0.0211757763793689	0.0462208915523153	-0.458142966701562	0.646957688315745	   

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.442411124279	0.134850939742048	32.9431232201773	6.22781826721029e-158	***
df.mm.trans1	-0.204584803867582	0.118272098111852	-1.72978079474080	0.0840041112036573	.  
df.mm.trans2	-0.140775117512748	0.104455088768275	-1.34770951968694	0.178082882848168	   
df.mm.exp2	-0.371450301535887	0.136178099613135	-2.72768016730392	0.00649935248051597	** 
df.mm.exp3	-0.0222436221461810	0.136178099613135	-0.163342139517091	0.870284852426147	   
df.mm.exp4	-0.117753469551529	0.136178099613135	-0.86470195931689	0.387427307644611	   
df.mm.exp5	-0.0767449858137223	0.136178099613135	-0.5635633485248	0.573188371528691	   
df.mm.exp6	-0.168992172641914	0.136178099613135	-1.24096439238027	0.214934555848637	   
df.mm.exp7	-0.234751053880887	0.136178099613135	-1.72385320802526	0.0850695645757858	.  
df.mm.exp8	-0.156892737583451	0.136178099613135	-1.15211431228049	0.249572743542982	   
df.mm.trans1:exp2	0.46035409395899	0.128942160940260	3.57023715596234	0.000375077588278336	***
df.mm.trans2:exp2	0.215553974451944	0.0979386018457792	2.20090924711556	0.0279896787025714	*  
df.mm.trans1:exp3	0.151569193631214	0.128942160940260	1.17548203416133	0.240105373574457	   
df.mm.trans2:exp3	-0.00438370266706898	0.0979386018457791	-0.0447597023487416	0.964308537703714	   
df.mm.trans1:exp4	0.198898104722198	0.128942160940260	1.54253739251625	0.123286189902049	   
df.mm.trans2:exp4	0.0745008415527965	0.0979386018457792	0.76068924968023	0.447037186216939	   
df.mm.trans1:exp5	0.149163268890115	0.128942160940260	1.15682308875857	0.247644275630692	   
df.mm.trans2:exp5	0.0615870485788221	0.0979386018457792	0.628833242645237	0.529613989814066	   
df.mm.trans1:exp6	0.202849885807851	0.128942160940260	1.57318509577200	0.116019033394961	   
df.mm.trans2:exp6	0.0855563699784883	0.0979386018457792	0.873571486278834	0.382579124615732	   
df.mm.trans1:exp7	0.32778546318181	0.128942160940260	2.54211237652265	0.0111811051493204	*  
df.mm.trans2:exp7	0.149716328124911	0.0979386018457792	1.5286753670495	0.126687926405396	   
df.mm.trans1:exp8	0.193140813685319	0.128942160940260	1.49788720986927	0.134504823597011	   
df.mm.trans2:exp8	0.0640042941311735	0.0979386018457792	0.653514476671405	0.513587599939515	   
df.mm.trans1:probe2	0.0297656213787519	0.0819413132189999	0.363255361788003	0.71649740184411	   
df.mm.trans1:probe3	-0.0593964642777002	0.0819413132189999	-0.724865906395162	0.468718211189844	   
df.mm.trans1:probe4	-0.0728835391225236	0.0819413132189998	-0.889460227806356	0.373987797164669	   
df.mm.trans1:probe5	-0.11402847120662	0.0819413132189999	-1.39158706063036	0.164383115133406	   
df.mm.trans1:probe6	-0.0836185864683916	0.0819413132189998	-1.02046920147483	0.307773671714341	   
df.mm.trans1:probe7	-0.0980212489907906	0.0819413132189999	-1.19623722320406	0.231911528842829	   
df.mm.trans1:probe8	-0.138609452583926	0.0819413132189999	-1.69156982160479	0.0910659458875632	.  
df.mm.trans1:probe9	-0.0596940838581487	0.0819413132189999	-0.728498012945042	0.46649376941898	   
df.mm.trans1:probe10	-0.0891582481522126	0.0819413132189999	-1.08807443583342	0.276846587088591	   
df.mm.trans1:probe11	-0.0442395592738376	0.0819413132189998	-0.539893217913179	0.589401044666893	   
df.mm.trans1:probe12	-0.127013097562872	0.0819413132189998	-1.55004957295976	0.121472783119873	   
df.mm.trans1:probe13	-0.0766961460261647	0.0819413132189999	-0.935988734049005	0.349524075290545	   
df.mm.trans1:probe14	-0.0243387324212905	0.0819413132189999	-0.297026389560559	0.766513264302439	   
df.mm.trans1:probe15	-0.0633534038902347	0.0819413132189999	-0.773155828256177	0.439628330531025	   
df.mm.trans1:probe16	-0.0251274582874197	0.0819413132189999	-0.306651886579643	0.759177593079553	   
df.mm.trans1:probe17	-0.159003697444029	0.0819413132189999	-1.94045825234786	0.0526285807464642	.  
df.mm.trans1:probe18	-0.00968834895720529	0.0819413132189999	-0.118235217091430	0.905907039580955	   
df.mm.trans1:probe19	0.00963976760118926	0.0819413132189998	0.117642337210603	0.906376679856979	   
df.mm.trans1:probe20	-0.171042800466787	0.0819413132189999	-2.08738173392035	0.0371277173024336	*  
df.mm.trans1:probe21	-0.0107870618538970	0.0819413132189999	-0.131643751242637	0.895294798425988	   
df.mm.trans1:probe22	-0.0920944837761977	0.0819413132189999	-1.12390783303731	0.261344663032184	   
df.mm.trans1:probe23	-0.0138067514238524	0.0819413132189998	-0.168495608399049	0.866230353063729	   
df.mm.trans1:probe24	0.0681488626838792	0.0819413132189998	0.831678917589978	0.405805543711092	   
df.mm.trans1:probe25	-0.136470016592653	0.0819413132189998	-1.66546045250602	0.0961606374521293	.  
df.mm.trans1:probe26	-0.0193007537993235	0.0819413132189999	-0.235543623126218	0.813839131982543	   
df.mm.trans1:probe27	-0.103335970913586	0.0819413132189998	-1.26109732507466	0.207592783417466	   
df.mm.trans1:probe28	0.00354720594809238	0.0819413132189999	0.0432895911566851	0.965480061523704	   
df.mm.trans2:probe2	0.0233340846550272	0.0819413132189998	0.284765812730673	0.775887513870428	   
df.mm.trans2:probe3	0.00522994108028388	0.0819413132189998	0.063825448663559	0.949123049010286	   
df.mm.trans2:probe4	0.0373679395916145	0.0819413132189998	0.456032959732331	0.648473668844331	   
df.mm.trans2:probe5	0.0817052087739766	0.0819413132189998	0.997118615314448	0.318968551294641	   
df.mm.trans2:probe6	0.0479759782901506	0.0819413132189999	0.585491938137823	0.558360258764198	   
df.mm.trans3:probe2	-0.0316537068554515	0.0819413132189998	-0.386297285361445	0.699365636992122	   
df.mm.trans3:probe3	0.0177733637732856	0.0819413132189998	0.216903574949839	0.828331476458571	   
df.mm.trans3:probe4	0.0595434620447164	0.0819413132189998	0.726659845999514	0.467618799409065	   
df.mm.trans3:probe5	0.0144031363588235	0.0819413132189998	0.175773804360800	0.860510246878025	   
df.mm.trans3:probe6	-0.0320597742195649	0.0819413132189998	-0.391252873063928	0.69570072764419	   
df.mm.trans3:probe7	-0.00369136592196545	0.0819413132189998	-0.0450488987417098	0.964078087634272