fitVsDatCorrelation=0.919638454353032
cont.fitVsDatCorrelation=0.267241330028147

fstatistic=9896.26173515064,69,1083
cont.fstatistic=1630.97541333937,69,1083

residuals=-1.15241399940346,-0.0930345492643996,-0.000105222326560419,0.0864549617523516,1.00915397592984
cont.residuals=-0.830500073133831,-0.272744571248839,-0.064920588309313,0.166234129631343,1.63580524285863

predictedValues:
Include	Exclude	Both
Lung	112.342337468556	43.8726059630785	90.6602514982365
cerebhem	74.1299527085561	43.1871232916154	68.1953363878037
cortex	81.7976639596978	42.763826606644	72.1982749693217
heart	89.5265707151322	43.865223979446	77.267537719923
kidney	83.9358425755727	42.9157012237859	75.8821730661137
liver	80.2361354261288	47.6201936273931	74.6077981166898
stomach	91.194673769727	43.8553781707321	78.5599908928935
testicle	87.7644628524053	46.2651615941235	78.2718741968924


diffExp=68.469731505477,30.9428294169407,39.0338373530538,45.6613467356862,41.0201413517868,32.6159417987356,47.3392955989948,41.4993012582818
diffExpScore=0.997122984569932
diffExp1.5=1,1,1,1,1,1,1,1
diffExp1.5Score=0.888888888888889
diffExp1.4=1,1,1,1,1,1,1,1
diffExp1.4Score=0.888888888888889
diffExp1.3=1,1,1,1,1,1,1,1
diffExp1.3Score=0.888888888888889
diffExp1.2=1,1,1,1,1,1,1,1
diffExp1.2Score=0.888888888888889

cont.predictedValues:
Include	Exclude	Both
Lung	68.3177326392291	63.399934870723	73.8482067736488
cerebhem	78.8715206066639	57.0523980749031	69.0158490818163
cortex	72.569032203415	63.3351022064107	74.1872115104715
heart	72.8627974400301	70.661796595905	71.9578684164741
kidney	66.1175512162196	74.6588274379294	70.0233325332055
liver	72.455347815223	67.132027670333	72.0710536694737
stomach	73.981196353015	69.4266770834897	65.645668115945
testicle	75.7906788758764	60.2578592572936	73.5431764974476
cont.diffExp=4.91779776850611,21.8191225317608,9.23392999700429,2.20100084412509,-8.54127622170984,5.32332014488993,4.5545192695253,15.5328196185828
cont.diffExpScore=1.28697712932228

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=0,1,0,0,0,0,0,0
cont.diffExp1.3Score=0.5
cont.diffExp1.2=0,1,0,0,0,0,0,1
cont.diffExp1.2Score=0.666666666666667

tran.correlation=-0.0590183932291042
cont.tran.correlation=-0.698484960407728

tran.covariance=-0.000188082410657765
cont.tran.covariance=-0.00342141777552162

tran.mean=65.9545533707871
cont.tran.mean=69.1806550216662

weightedLogRatios:
wLogRatio
Lung	3.99744348182501
cerebhem	2.18038681484860
cortex	2.64608989125461
heart	2.95198331303385
kidney	2.74674933830376
liver	2.15162143649934
stomach	3.03597625060297
testicle	2.66000576865263

cont.weightedLogRatios:
wLogRatio
Lung	0.312782491524337
cerebhem	1.36207955547784
cortex	0.573858099360922
heart	0.131073644824691
kidney	-0.51661679814875
liver	0.323918660824711
stomach	0.271444498318102
testicle	0.966288897104966

varWeightedLogRatios=0.337568803518949
cont.varWeightedLogRatios=0.316088261745031

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.60822611039614	0.0788627509256561	58.4334943469105	0	***
df.mm.trans1	0.277965095326035	0.0672583316491664	4.1327979524672	3.8601926905007e-05	***
df.mm.trans2	-0.82357651423628	0.0585852379430401	-14.0577480463083	2.35141648932205e-41	***
df.mm.exp2	-0.146736021261958	0.0734499599046645	-1.99776856859304	0.045991518860478	*  
df.mm.exp3	-0.115196785225927	0.0734499599046644	-1.56837097495285	0.117086693768470	   
df.mm.exp4	-0.067338502858045	0.0734499599046644	-0.916794276612922	0.359454557104982	   
df.mm.exp5	-0.135613189571658	0.0734499599046644	-1.84633442615461	0.0651163831415375	.  
df.mm.exp6	-0.0597360369302082	0.0734499599046645	-0.813288897744036	0.416231198972315	   
df.mm.exp7	-0.0656905835618729	0.0734499599046644	-0.894358331129615	0.371328910838583	   
df.mm.exp8	-0.0468643264642758	0.0734499599046644	-0.63804427565521	0.523579741489156	   
df.mm.trans1:exp2	-0.268995102000855	0.0667784801111191	-4.02817047577676	6.01355124547664e-05	***
df.mm.trans2:exp2	0.130988284773171	0.0443239836713257	2.95524621036964	0.00319181818385803	** 
df.mm.trans1:exp3	-0.202105323501462	0.066778480111119	-3.0265037953119	0.0025324494994761	** 
df.mm.trans2:exp3	0.089599242358634	0.0443239836713257	2.02146185737809	0.0434775609502277	*  
df.mm.trans1:exp4	-0.159676830400859	0.066778480111119	-2.39114202861697	0.0169659803984670	*  
df.mm.trans2:exp4	0.0671702291805496	0.0443239836713257	1.51543754908482	0.129953433052589	   
df.mm.trans1:exp5	-0.155884876253338	0.0667784801111191	-2.33435795474749	0.0197590742669219	*  
df.mm.trans2:exp5	0.113560829084685	0.0443239836713257	2.56206278584454	0.0105396314416783	*  
df.mm.trans1:exp6	-0.276840777239705	0.0667784801111191	-4.14565855316029	3.6529723904576e-05	***
df.mm.trans2:exp6	0.141702828721363	0.0443239836713257	3.19697863288027	0.00142895038126073	** 
df.mm.trans1:exp7	-0.142863716689416	0.0667784801111191	-2.13936759943759	0.0326289645661981	*  
df.mm.trans2:exp7	0.0652978287815609	0.0443239836713257	1.47319404469061	0.140989291259569	   
df.mm.trans1:exp8	-0.200029799918451	0.0667784801111191	-2.99542307021068	0.0028029814732563	** 
df.mm.trans2:exp8	0.0999634396001361	0.0443239836713257	2.25529005563651	0.0243141194369226	*  
df.mm.trans1:probe2	-0.644400513708203	0.0507218967619079	-12.7045823371524	1.42776562660091e-34	***
df.mm.trans1:probe3	-0.369675938399939	0.050721896761908	-7.28829089604482	6.04050649318911e-13	***
df.mm.trans1:probe4	-0.298296478376955	0.0507218967619079	-5.88101978475252	5.42707293012893e-09	***
df.mm.trans1:probe5	-0.665312620792847	0.0507218967619079	-13.1168718692810	1.37869615770306e-36	***
df.mm.trans1:probe6	-0.426835010105833	0.0507218967619079	-8.4152020597618	1.22757875046462e-16	***
df.mm.trans1:probe7	-0.181703277656417	0.050721896761908	-3.58234390384383	0.000355654196208598	***
df.mm.trans1:probe8	-0.647988748991004	0.050721896761908	-12.7753256553616	6.48916035742527e-35	***
df.mm.trans1:probe9	0.742258941793355	0.050721896761908	14.6338955989278	2.19084864281646e-44	***
df.mm.trans1:probe10	-0.589610031415822	0.0507218967619079	-11.6243687451889	1.61375324486383e-29	***
df.mm.trans1:probe11	-0.217743934112983	0.050721896761908	-4.2928980975433	1.92155107481224e-05	***
df.mm.trans1:probe12	-0.491678572766211	0.050721896761908	-9.69361566019867	2.29898428541752e-21	***
df.mm.trans1:probe13	-0.0477462952722086	0.050721896761908	-0.941334972079869	0.346743144683045	   
df.mm.trans1:probe14	0.0389406276452543	0.050721896761908	0.767728143686035	0.442816061772582	   
df.mm.trans1:probe15	-0.446616399394953	0.050721896761908	-8.80519909362619	5.08471497503174e-18	***
df.mm.trans1:probe16	-0.259117108250727	0.050721896761908	-5.10858474924627	3.83371911568012e-07	***
df.mm.trans1:probe17	-0.170493007725128	0.050721896761908	-3.36132949691204	0.000802767874582734	***
df.mm.trans1:probe18	-0.394601535782572	0.050721896761908	-7.77970779828795	1.68777260501335e-14	***
df.mm.trans1:probe19	-0.434630832951564	0.050721896761908	-8.5688994437994	3.55165580885524e-17	***
df.mm.trans1:probe20	-0.481629165125197	0.0507218967619079	-9.49548806082701	1.35053347327047e-20	***
df.mm.trans1:probe21	-0.40623187376728	0.0507218967619079	-8.00900399435298	2.96762017672332e-15	***
df.mm.trans1:probe22	-0.521785518081098	0.0507218967619079	-10.2871846557788	9.56100533275995e-24	***
df.mm.trans2:probe2	-0.0363586308710689	0.050721896761908	-0.716823170902673	0.473637769013443	   
df.mm.trans2:probe3	0.0128843419715512	0.050721896761908	0.254019324869320	0.799528846695355	   
df.mm.trans2:probe4	-0.0159314199251702	0.050721896761908	-0.314093536366618	0.753510490139488	   
df.mm.trans2:probe5	-0.0452885343772323	0.050721896761908	-0.892879353266693	0.372120138354512	   
df.mm.trans2:probe6	-0.00265225777035689	0.050721896761908	-0.0522901929871979	0.95830711052405	   
df.mm.trans3:probe2	0.245970787473428	0.050721896761908	4.84940042025699	1.41934459772668e-06	***
df.mm.trans3:probe3	0.473437861651635	0.050721896761908	9.33399363738277	5.59227576863511e-20	***
df.mm.trans3:probe4	0.0392895255946597	0.050721896761908	0.774606789235178	0.438741120766759	   
df.mm.trans3:probe5	0.449859266941302	0.050721896761908	8.86913336567385	2.98222445171296e-18	***
df.mm.trans3:probe6	0.306989867648372	0.050721896761908	6.05241300595291	1.96385579626666e-09	***
df.mm.trans3:probe7	0.307783820242641	0.050721896761908	6.06806606005684	1.78744309582384e-09	***
df.mm.trans3:probe8	0.201212265781543	0.050721896761908	3.96697045313677	7.75782852195488e-05	***
df.mm.trans3:probe9	1.59903739309436	0.050721896761908	31.5255835285566	2.62516212050439e-155	***
df.mm.trans3:probe10	0.75522471532636	0.050721896761908	14.8895203756168	9.33566059034797e-46	***
df.mm.trans3:probe11	0.490560242227325	0.050721896761908	9.67156738104744	2.80384875652169e-21	***
df.mm.trans3:probe12	0.244546844132744	0.050721896761908	4.82132687743646	1.62961099963158e-06	***
df.mm.trans3:probe13	0.642980280192358	0.050721896761908	12.6765819348309	1.94905558365830e-34	***
df.mm.trans3:probe14	0.159677909216203	0.050721896761908	3.14810603329252	0.00168823892740870	** 
df.mm.trans3:probe15	0.162545320455848	0.050721896761908	3.20463805245389	0.00139181854067592	** 
df.mm.trans3:probe16	0.232659161654433	0.050721896761908	4.58695704434222	5.02220467418764e-06	***
df.mm.trans3:probe17	0.369563355679954	0.050721896761908	7.28607128819945	6.1360816612168e-13	***
df.mm.trans3:probe18	0.279234412699916	0.050721896761908	5.50520446841058	4.60295698721661e-08	***
df.mm.trans3:probe19	0.334145003564955	0.050721896761908	6.58778604304674	6.95230649117005e-11	***
df.mm.trans3:probe20	1.59536852851297	0.050721896761908	31.4532505754218	8.63118803270543e-155	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.03747164747115	0.193485403819384	20.8670606039103	1.51410964973082e-81	***
df.mm.trans1	0.212575176834713	0.165014602034677	1.28822040118632	0.197944408463606	   
df.mm.trans2	0.097081705553096	0.143735645640230	0.675418440016552	0.499554134186837	   
df.mm.exp2	0.105833673060994	0.180205419996935	0.587294616681308	0.55712827182452	   
df.mm.exp3	0.0547657370700297	0.180205419996935	0.30390726910967	0.761256917569205	   
df.mm.exp4	0.198781997188561	0.180205419996935	1.10308556308652	0.270235060831947	   
df.mm.exp5	0.183914027353350	0.180205419996935	1.02057988797717	0.307681444630432	   
df.mm.exp6	0.140358764569463	0.180205419996935	0.778882036799169	0.43621935564218	   
df.mm.exp7	0.288190064139226	0.180205419996935	1.59923083414543	0.110060994483827	   
df.mm.exp8	0.0571151849362452	0.180205419996935	0.316944878446034	0.751346513786096	   
df.mm.trans1:exp2	0.0378171725104177	0.163837312788199	0.230821489115278	0.817497082380313	   
df.mm.trans2:exp2	-0.211326397186273	0.108746445931297	-1.94329474748796	0.0522393745194635	.  
df.mm.trans1:exp3	0.00560317852416672	0.163837312788199	0.0341996485953736	0.972724248734249	   
df.mm.trans2:exp3	-0.0557888585597607	0.108746445931297	-0.513017764231177	0.608043520237579	   
df.mm.trans1:exp4	-0.134373173179859	0.163837312788199	-0.820162213924799	0.412304110377606	   
df.mm.trans2:exp4	-0.090339763822156	0.108746445931297	-0.830737621340101	0.406304968455446	   
df.mm.trans1:exp5	-0.216649152098419	0.163837312788199	-1.32234317330688	0.186333092662120	   
df.mm.trans2:exp5	-0.0204480935157992	0.108746445931297	-0.188034591297978	0.850884759813307	   
df.mm.trans1:exp6	-0.081557646216856	0.163837312788199	-0.497796532602373	0.618728519739495	   
df.mm.trans2:exp6	-0.083160356112612	0.108746445931297	-0.764717921587526	0.444606125474157	   
df.mm.trans1:exp7	-0.208548468127103	0.163837312788199	-1.27289971117083	0.203326811122709	   
df.mm.trans2:exp7	-0.197381708638977	0.108746445931297	-1.81506353562743	0.0697906589255882	.  
df.mm.trans1:exp8	0.0466907686107191	0.163837312788199	0.284982509882096	0.775712028655955	   
df.mm.trans2:exp8	-0.107945011135106	0.108746445931297	-0.992630243781047	0.321111903262415	   
df.mm.trans1:probe2	-0.0080438808936913	0.124443372343357	-0.0646388854803539	0.948473452149059	   
df.mm.trans1:probe3	-0.0148792203427766	0.124443372343357	-0.119566193543219	0.90484897651086	   
df.mm.trans1:probe4	0.0109941728527800	0.124443372343357	0.0883467929689776	0.929617382202396	   
df.mm.trans1:probe5	-0.242326232418742	0.124443372343357	-1.94728114366854	0.0517592219951912	.  
df.mm.trans1:probe6	-0.207746952859115	0.124443372343357	-1.66940953903043	0.095325245608232	.  
df.mm.trans1:probe7	-0.107300965245923	0.124443372343357	-0.862247327642844	0.388742313260819	   
df.mm.trans1:probe8	0.00882050084267644	0.124443372343357	0.0708796352636556	0.943506637347713	   
df.mm.trans1:probe9	-0.0954661379838829	0.124443372343357	-0.767145217830308	0.443162382907097	   
df.mm.trans1:probe10	-0.0531503477756037	0.124443372343357	-0.427104688459858	0.669387974184075	   
df.mm.trans1:probe11	-0.0864628353288346	0.124443372343357	-0.694796626776323	0.487331792289874	   
df.mm.trans1:probe12	-0.114906755536138	0.124443372343357	-0.923365811873804	0.356022272741214	   
df.mm.trans1:probe13	-0.126917843352721	0.124443372343357	-1.01988431334484	0.308011100393419	   
df.mm.trans1:probe14	-0.140256035467389	0.124443372343357	-1.12706713765682	0.259963748969444	   
df.mm.trans1:probe15	-0.131261286786979	0.124443372343357	-1.05478728449122	0.291757872852346	   
df.mm.trans1:probe16	0.312792003157025	0.124443372343357	2.51352882252328	0.0120972212664080	*  
df.mm.trans1:probe17	0.150262058522498	0.124443372343357	1.20747337277154	0.227513528391072	   
df.mm.trans1:probe18	-0.106020061774785	0.124443372343357	-0.85195426464545	0.394427826502758	   
df.mm.trans1:probe19	0.0550936114398354	0.124443372343357	0.442720334577757	0.658056440529346	   
df.mm.trans1:probe20	0.0439077404618261	0.124443372343357	0.352833096974248	0.724282227311874	   
df.mm.trans1:probe21	-0.0644611081682798	0.124443372343357	-0.517995510362917	0.604567199998708	   
df.mm.trans1:probe22	-0.169523858471050	0.124443372343357	-1.36225702726305	0.173400027440090	   
df.mm.trans2:probe2	0.0521819271687651	0.124443372343357	0.419322670112054	0.675063489015336	   
df.mm.trans2:probe3	0.0264788900394742	0.124443372343357	0.212778628068799	0.831539677385658	   
df.mm.trans2:probe4	0.0768136795707333	0.124443372343357	0.617258100003858	0.537194249727808	   
df.mm.trans2:probe5	0.111347710967570	0.124443372343357	0.894766100201345	0.371110945407959	   
df.mm.trans2:probe6	0.120824301964842	0.124443372343357	0.970917933913514	0.331805935580055	   
df.mm.trans3:probe2	-0.128122204378565	0.124443372343357	-1.02956229782215	0.303445397135745	   
df.mm.trans3:probe3	-0.109490955216534	0.124443372343357	-0.879845612946209	0.379138138580007	   
df.mm.trans3:probe4	-0.130419494505283	0.124443372343357	-1.0480228239511	0.294861917632207	   
df.mm.trans3:probe5	-0.0344183925963841	0.124443372343357	-0.276578751831145	0.782156355885085	   
df.mm.trans3:probe6	-0.110765891636849	0.124443372343357	-0.890090726014963	0.373614850063783	   
df.mm.trans3:probe7	0.0323269033163211	0.124443372343357	0.259771996753081	0.795089026904533	   
df.mm.trans3:probe8	0.0615418568063092	0.124443372343357	0.494537038392904	0.621027255058928	   
df.mm.trans3:probe9	-0.1833289061421	0.124443372343357	-1.47319140175878	0.140990003601765	   
df.mm.trans3:probe10	-0.138978930308161	0.124443372343357	-1.11680459707166	0.264325589042187	   
df.mm.trans3:probe11	-0.0075004477169707	0.124443372343357	-0.0602719741174796	0.95195014271451	   
df.mm.trans3:probe12	-0.242074203790035	0.124443372343357	-1.94525589616872	0.0520026944140492	.  
df.mm.trans3:probe13	-0.00200552966776667	0.124443372343357	-0.0161160022426355	0.987144815579385	   
df.mm.trans3:probe14	-0.103798897609655	0.124443372343357	-0.834105470263683	0.404405488637865	   
df.mm.trans3:probe15	-0.00388970033006161	0.124443372343357	-0.0312567897897316	0.975070509448527	   
df.mm.trans3:probe16	-0.0526535382385218	0.124443372343357	-0.423112434571791	0.672297233986755	   
df.mm.trans3:probe17	0.0754557402097238	0.124443372343357	0.606345993272591	0.544412093568059	   
df.mm.trans3:probe18	0.00103376261362722	0.124443372343357	0.00830709256877835	0.993373505234576	   
df.mm.trans3:probe19	0.0070080895615737	0.124443372343357	0.0563154905689744	0.955100866339838	   
df.mm.trans3:probe20	0.167744675925335	0.124443372343357	1.34795990149241	0.177953155322401