fitVsDatCorrelation=0.973177011684715
cont.fitVsDatCorrelation=0.226481368709311

fstatistic=9355.09436887828,71,1129
cont.fstatistic=506.888524032614,71,1129

residuals=-0.783573561466243,-0.104365809195845,-0.00296326860524897,0.0992282390607815,1.26025253431291
cont.residuals=-1.08429560727642,-0.5028738001874,-0.239345425299888,0.214131318375455,3.02259419313976

predictedValues:
Include	Exclude	Both
Lung	57.0310960345037	110.354223426758	141.153301675203
cerebhem	59.7775038766274	105.55528804267	139.025863011060
cortex	55.8910464044548	86.9456300169678	133.800170351740
heart	56.0249332561121	77.5478124671458	114.275833829574
kidney	59.6016606533276	112.593141916381	134.389085757862
liver	58.9019428494018	99.584720532991	124.678337758738
stomach	57.0024326207521	90.0414250514463	120.821487486187
testicle	56.8451632866673	88.0601797052644	137.969640210996


diffExp=-53.323127392254,-45.7777841660426,-31.054583612513,-21.5228792110337,-52.9914812630533,-40.6827776835891,-33.0389924306942,-31.2150164185972
diffExpScore=0.996780493833008
diffExp1.5=-1,-1,-1,0,-1,-1,-1,-1
diffExp1.5Score=0.875
diffExp1.4=-1,-1,-1,0,-1,-1,-1,-1
diffExp1.4Score=0.875
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	92.4678350000039	76.5693603981498	95.6907049865009
cerebhem	75.0638217177447	79.5285973463348	97.7513715334821
cortex	71.8563382178827	68.0012607442794	79.6999699387376
heart	81.424445775387	73.1651069987219	95.6139606302293
kidney	81.9498246116314	75.2526352234241	104.522343965359
liver	73.9712666872763	89.6381319937791	93.3704152938795
stomach	74.2432561064	89.7735103226003	114.521518847080
testicle	74.1171985868129	83.3241640948364	93.5690087437052
cont.diffExp=15.8984746018541,-4.46477562859012,3.85507747360327,8.2593387766651,6.69718938820733,-15.6668653065028,-15.5302542162002,-9.20696550802346
cont.diffExpScore=7.13150881293817

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=1,0,0,0,0,-1,-1,0
cont.diffExp1.2Score=1.5

tran.correlation=0.760460454829949
cont.tran.correlation=-0.280791704437827

tran.covariance=0.00273164719158540
cont.tran.covariance=-0.00209880092942234

tran.mean=76.9848875088419
cont.tran.mean=78.771672114079

weightedLogRatios:
wLogRatio
Lung	-2.88703824270801
cerebhem	-2.48761035307958
cortex	-1.87548538914476
heart	-1.36162221178826
kidney	-2.80247461691829
liver	-2.27826599320203
stomach	-1.95291000698669
testicle	-1.86419568256862

cont.weightedLogRatios:
wLogRatio
Lung	0.836257983416612
cerebhem	-0.251173965456339
cortex	0.234195906452471
heart	0.464855843575047
kidney	0.372013827082005
liver	-0.845205847318086
stomach	-0.836189571876477
testicle	-0.511007678342536

varWeightedLogRatios=0.272118011565799
cont.varWeightedLogRatios=0.401502505087624

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	2.22290849691940	0.0867896265734847	25.6126058456682	1.93817603169142e-114	***
df.mm.trans1	1.70020898026258	0.0743645262392331	22.8631723517333	2.21019462585407e-95	***
df.mm.trans2	2.43367649627647	0.0643664020780794	37.8097333034757	8.55605896858186e-203	***
df.mm.exp2	0.0177587702498383	0.0805611206015245	0.220438471029687	0.825569541280978	   
df.mm.exp3	-0.205105767083283	0.0805611206015245	-2.54596467318010	0.0110296171371087	*  
df.mm.exp4	-0.159369134175727	0.0805611206015245	-1.97823879541108	0.0481445590679389	*  
df.mm.exp5	0.113279503760088	0.0805611206015245	1.40613118231556	0.159960333775128	   
df.mm.exp6	0.0537001745933005	0.0805611206015245	0.666576807675194	0.505178677883973	   
df.mm.exp7	-0.0483958818065443	0.0805611206015245	-0.600734963034122	0.548137103566417	   
df.mm.exp8	-0.2061276062248	0.0805611206015245	-2.55864869661333	0.0106375348580639	*  
df.mm.trans1:exp2	0.02927396703148	0.0740872181525512	0.39512844133522	0.692822740129004	   
df.mm.trans2:exp2	-0.0622193023085785	0.0481509924451175	-1.29217071443535	0.196562452154942	   
df.mm.trans1:exp3	0.184913299215693	0.0740872181525512	2.49588665665571	0.0127060934002596	*  
df.mm.trans2:exp3	-0.0333066569349890	0.0481509924451175	-0.691712781890257	0.48925993602982	   
df.mm.trans1:exp4	0.141569298994463	0.0740872181525512	1.91084646616048	0.0562773724765721	.  
df.mm.trans2:exp4	-0.193431589612522	0.0481509924451175	-4.01718801191888	6.27940201881788e-05	***
df.mm.trans1:exp5	-0.0691927303103382	0.0740872181525512	-0.93393613683625	0.350536532480472	   
df.mm.trans2:exp5	-0.0931941014947542	0.0481509924451175	-1.93545546545021	0.0531838834571885	.  
df.mm.trans1:exp6	-0.0214227624654112	0.0740872181525511	-0.289155983982286	0.772515099958514	   
df.mm.trans2:exp6	-0.156386835055605	0.0481509924451175	-3.24784240395159	0.00119706987627803	** 
df.mm.trans1:exp7	0.0478931627432441	0.0740872181525512	0.646442988919202	0.518123892581918	   
df.mm.trans2:exp7	-0.155029680390670	0.0481509924451175	-3.21965701054599	0.00132001546767745	** 
df.mm.trans1:exp8	0.202862080747029	0.0740872181525512	2.73815221850172	0.00627569615257626	** 
df.mm.trans2:exp8	-0.0195473577170888	0.0481509924451174	-0.405959601754189	0.684849210916464	   
df.mm.trans1:probe2	0.0517133015561383	0.0559346230821547	0.924531152738506	0.355407398059905	   
df.mm.trans1:probe3	0.11476810162325	0.0559346230821547	2.05182577979801	0.0404168514750441	*  
df.mm.trans1:probe4	0.0202942329420738	0.0559346230821547	0.362820589892354	0.716806822087395	   
df.mm.trans1:probe5	0.0915380444274427	0.0559346230821547	1.63651848145995	0.102009822992577	   
df.mm.trans1:probe6	0.00975999211711603	0.0559346230821547	0.174489280150881	0.861512238558841	   
df.mm.trans1:probe7	-0.111112228839194	0.0559346230821547	-1.98646603331888	0.0472230479618712	*  
df.mm.trans1:probe8	0.00986348365559727	0.0559346230821547	0.176339503371823	0.860058854248444	   
df.mm.trans1:probe9	-0.0575156294417198	0.0559346230821547	-1.02826525454982	0.304045312846888	   
df.mm.trans1:probe10	-0.0825253158084058	0.0559346230821547	-1.47538878892946	0.140386863446469	   
df.mm.trans1:probe11	-0.0434493364715666	0.0559346230821547	-0.776787865500584	0.437446651020438	   
df.mm.trans1:probe12	0.183483740631855	0.0559346230821547	3.2803249672812	0.00106855755300229	** 
df.mm.trans1:probe13	0.0217120355814529	0.0559346230821547	0.388168085973567	0.697964827680975	   
df.mm.trans1:probe14	0.116476138565666	0.0559346230821547	2.08236208894427	0.0375341280021789	*  
df.mm.trans1:probe15	-0.0140475069456741	0.0559346230821547	-0.251141532232043	0.801750387048653	   
df.mm.trans1:probe16	0.169708018181800	0.0559346230821547	3.03404240219048	0.00246818317122845	** 
df.mm.trans1:probe17	0.545152528279885	0.0559346230821547	9.74624478078962	1.32240325695699e-21	***
df.mm.trans1:probe18	1.12383248736593	0.0559346230821547	20.0918934541722	5.27023436281544e-77	***
df.mm.trans1:probe19	0.87735008488381	0.0559346230821547	15.6852774996837	2.60791312464833e-50	***
df.mm.trans1:probe20	0.674105138098055	0.0559346230821547	12.0516614031341	1.49644296871449e-31	***
df.mm.trans1:probe21	0.529584695836742	0.0559346230821547	9.46792284733746	1.60770947894169e-20	***
df.mm.trans1:probe22	0.955229165986492	0.0559346230821547	17.0776008373827	2.42974761567606e-58	***
df.mm.trans1:probe23	0.0631246212755832	0.0559346230821547	1.12854289163383	0.259330462413654	   
df.mm.trans1:probe24	0.0615229485156828	0.0559346230821547	1.09990816288009	0.271606608887213	   
df.mm.trans1:probe25	-0.00948454207806011	0.0559346230821547	-0.169564780370998	0.865382799225437	   
df.mm.trans2:probe2	0.391697079622742	0.0559346230821547	7.00276605149251	4.30060194721984e-12	***
df.mm.trans2:probe3	0.466563471581119	0.0559346230821547	8.3412284891211	2.12114502724929e-16	***
df.mm.trans2:probe4	0.230770076507931	0.0559346230821547	4.12571076359957	3.96727735502882e-05	***
df.mm.trans2:probe5	-0.0956032272990738	0.0559346230821547	-1.70919587960136	0.0876894864600005	.  
df.mm.trans2:probe6	0.184332894223032	0.0559346230821547	3.29550614745881	0.00101298739191509	** 
df.mm.trans3:probe2	-1.94671558535933	0.0559346230821547	-34.8034093749066	6.49350529425503e-181	***
df.mm.trans3:probe3	0.86582655308097	0.0559346230821547	15.4792596315394	3.70153164198892e-49	***
df.mm.trans3:probe4	-1.67937041013964	0.0559346230821547	-30.0238084678436	4.61051216599994e-146	***
df.mm.trans3:probe5	-2.14985431085846	0.0559346230821547	-38.4351264457585	2.52522686281644e-207	***
df.mm.trans3:probe6	-2.09956060766341	0.0559346230821547	-37.5359748930398	8.28098963792633e-201	***
df.mm.trans3:probe7	0.0857377111695295	0.0559346230821547	1.53282003963093	0.125600355876322	   
df.mm.trans3:probe8	-1.46318940970119	0.0559346230821547	-26.1589214170999	2.67639313613718e-118	***
df.mm.trans3:probe9	0.658974286443017	0.0559346230821547	11.7811518185282	2.67430254161442e-30	***
df.mm.trans3:probe10	-2.07707679460363	0.0559346230821547	-37.1340089581528	6.86749869713533e-198	***
df.mm.trans3:probe11	-2.05787564497857	0.0559346230821547	-36.7907305276025	2.1474341995448e-195	***
df.mm.trans3:probe12	-1.94339005594691	0.0559346230821547	-34.7439555120006	1.76348990323367e-180	***
df.mm.trans3:probe13	-2.16939514900400	0.0559346230821547	-38.7844778325881	7.5174290042025e-210	***
df.mm.trans3:probe14	-2.30278352889623	0.0559346230821547	-41.1691972164358	5.3351145695518e-227	***
df.mm.trans3:probe15	-2.48600965173958	0.0559346230821547	-44.4449164891702	3.17449698234365e-250	***
df.mm.trans3:probe16	-0.00449429714412791	0.0559346230821547	-0.0803491093079658	0.935973840917132	   
df.mm.trans3:probe17	-2.38048312092564	0.0559346230821547	-42.5583116458883	6.70846687961363e-237	***
df.mm.trans3:probe18	-2.10879576685432	0.0559346230821547	-37.7010812025496	5.25076649299214e-202	***
df.mm.trans3:probe19	-1.47354534471051	0.0559346230821547	-26.3440649728919	1.30295527889173e-119	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.05135934963837	0.367449011178964	11.0256368268336	6.41883915924647e-27	***
df.mm.trans1	0.347769982293397	0.314843751635019	1.10457959062992	0.269577147906510	   
df.mm.trans2	0.281251517966868	0.272513798370964	1.03206340247039	0.30226344708316	   
df.mm.exp2	-0.191908538936387	0.341078827887747	-0.562651572731294	0.573783869520362	   
df.mm.exp3	-0.188010829113714	0.341078827887747	-0.551223980327476	0.581589211628148	   
df.mm.exp4	-0.171861358623335	0.341078827887747	-0.503875774663729	0.61444698063067	   
df.mm.exp5	-0.226379467880973	0.341078827887747	-0.663715978159388	0.507007617253195	   
df.mm.exp6	-0.041053691867563	0.341078827887747	-0.120364234044671	0.904216038347347	   
df.mm.exp7	-0.240062538715412	0.341078827887747	-0.703833011864399	0.481681904893246	   
df.mm.exp8	-0.114249717232531	0.341078827887747	-0.334965726075884	0.737713164100045	   
df.mm.trans1:exp2	-0.0166136074653848	0.313669687564621	-0.0529652947799176	0.957768929574542	   
df.mm.trans2:exp2	0.229828209037899	0.203861167051612	1.12737610777885	0.259823035251439	   
df.mm.trans1:exp3	-0.0641812019843095	0.313669687564621	-0.204613976194582	0.837910615163815	   
df.mm.trans2:exp3	0.0693400725499369	0.203861167051612	0.340133795723744	0.733819043157604	   
df.mm.trans1:exp4	0.0446760488800666	0.313669687564621	0.142430239998446	0.886765590836132	   
df.mm.trans2:exp4	0.126382983672015	0.203861167051612	0.619946336518412	0.535418217741428	   
df.mm.trans1:exp5	0.105625778564295	0.313669687564621	0.336742065783882	0.736373932596917	   
df.mm.trans2:exp5	0.20903338855211	0.203861167051612	1.02537129349009	0.305407667826427	   
df.mm.trans1:exp6	-0.182130432658046	0.313669687564621	-0.580644033767287	0.561596266234475	   
df.mm.trans2:exp6	0.198637499677492	0.203861167051612	0.974376349112154	0.330078426155117	   
df.mm.trans1:exp7	0.0205486311066909	0.313669687564621	0.0655104140480821	0.947779212750624	   
df.mm.trans2:exp7	0.399155483310077	0.203861167051612	1.95797703448358	0.0504787129487371	.  
df.mm.trans1:exp8	-0.106963532026000	0.313669687564621	-0.341006913535322	0.733161825527529	   
df.mm.trans2:exp8	0.1987913075858	0.203861167051612	0.975130822906905	0.329704254500901	   
df.mm.trans1:probe2	-0.0615123722082265	0.236815420847599	-0.259748170064534	0.795105391105881	   
df.mm.trans1:probe3	0.357128464895264	0.236815420847600	1.50804564845079	0.131822610012009	   
df.mm.trans1:probe4	0.359305402364949	0.236815420847599	1.51723819791354	0.129486498752041	   
df.mm.trans1:probe5	0.271966137103613	0.236815420847599	1.14843085864174	0.25103400298944	   
df.mm.trans1:probe6	0.362308500774565	0.236815420847599	1.52991937551113	0.126316810335564	   
df.mm.trans1:probe7	0.228813951834618	0.236815420847600	0.966212212936376	0.334144922874972	   
df.mm.trans1:probe8	0.398123886848103	0.236815420847599	1.68115693405081	0.0930090212196099	.  
df.mm.trans1:probe9	0.184971958723514	0.236815420847599	0.781080717047355	0.434918769372577	   
df.mm.trans1:probe10	0.258922368383656	0.236815420847599	1.09335096277486	0.274472962743027	   
df.mm.trans1:probe11	-0.0808425498337244	0.236815420847599	-0.34137367213831	0.732885815520572	   
df.mm.trans1:probe12	0.343722613671935	0.236815420847599	1.45143678752717	0.146936074923811	   
df.mm.trans1:probe13	0.528954602660301	0.236815420847599	2.23361553384948	0.0257036575894003	*  
df.mm.trans1:probe14	0.425550700493367	0.236815420847599	1.79697208471583	0.0726072399629806	.  
df.mm.trans1:probe15	0.246137538238016	0.236815420847599	1.03936448630352	0.298857788634279	   
df.mm.trans1:probe16	0.130457804180955	0.236815420847599	0.550883906605516	0.581822250520409	   
df.mm.trans1:probe17	0.233844720054509	0.236815420847599	0.987455627752375	0.323630896820753	   
df.mm.trans1:probe18	0.202636748936799	0.236815420847599	0.855673791054358	0.392359797247972	   
df.mm.trans1:probe19	0.335221832107495	0.236815420847599	1.41554055435952	0.157185852768541	   
df.mm.trans1:probe20	0.157181881533112	0.236815420847599	0.663731614142918	0.506997611648866	   
df.mm.trans1:probe21	0.0707085799662838	0.236815420847599	0.298580978017423	0.765314725775741	   
df.mm.trans1:probe22	0.092854505509549	0.236815420847599	0.392096533144709	0.695060891817256	   
df.mm.trans1:probe23	-0.0914803897574468	0.236815420847599	-0.386294057329646	0.69935168765562	   
df.mm.trans1:probe24	0.235008306431142	0.236815420847599	0.992369101598243	0.321230140395988	   
df.mm.trans1:probe25	0.430201792024357	0.236815420847599	1.81661223954334	0.0695415815310779	.  
df.mm.trans2:probe2	0.0855870859144446	0.236815420847599	0.361408415077511	0.717861779459068	   
df.mm.trans2:probe3	-0.0635063029030997	0.236815420847599	-0.268167937188384	0.788619041387112	   
df.mm.trans2:probe4	0.0470594703213518	0.236815420847599	0.198717930415674	0.842519175506673	   
df.mm.trans2:probe5	0.132157496240605	0.236815420847599	0.558061192837835	0.576913242147063	   
df.mm.trans2:probe6	-0.0616443915775723	0.236815420847599	-0.260305647989212	0.79467548183944	   
df.mm.trans3:probe2	0.200413117714435	0.236815420847599	0.846284067976338	0.397573628250783	   
df.mm.trans3:probe3	-0.241997947075328	0.236815420847599	-1.02188424304963	0.307054602362026	   
df.mm.trans3:probe4	0.070394842206182	0.236815420847599	0.297256158210592	0.766325633135137	   
df.mm.trans3:probe5	-0.286620210683325	0.236815420847599	-1.21031058559222	0.226413064432194	   
df.mm.trans3:probe6	-0.186020333587529	0.236815420847599	-0.78550768747125	0.432320776575556	   
df.mm.trans3:probe7	-0.00546474965407556	0.236815420847599	-0.0230759873428697	0.981593737596811	   
df.mm.trans3:probe8	-0.132507861307537	0.236815420847599	-0.559540678699344	0.575903761629223	   
df.mm.trans3:probe9	-0.253082906331120	0.236815420847599	-1.06869267814274	0.285436613197262	   
df.mm.trans3:probe10	-0.135842017613096	0.236815420847599	-0.573619813806451	0.566339345016575	   
df.mm.trans3:probe11	-0.172616838903650	0.236815420847599	-0.728908777502021	0.466208638341031	   
df.mm.trans3:probe12	-0.292215189394242	0.236815420847599	-1.23393649091076	0.217483370957962	   
df.mm.trans3:probe13	-0.302886539340739	0.236815420847599	-1.27899837880768	0.201160444805573	   
df.mm.trans3:probe14	-0.0211137443568002	0.236815420847599	-0.0891569657129203	0.928972978187115	   
df.mm.trans3:probe15	-0.0387095776698273	0.236815420847599	-0.163458855556279	0.870186420570686	   
df.mm.trans3:probe16	0.0478551950545219	0.236815420847599	0.202078035641601	0.839892124445572	   
df.mm.trans3:probe17	-0.0876953134268749	0.236815420847599	-0.370310823142343	0.71122036991952	   
df.mm.trans3:probe18	-0.0544923409998993	0.236815420847599	-0.230104698439243	0.818052098179288	   
df.mm.trans3:probe19	-0.373331556589513	0.236815420847599	-1.57646641106944	0.115198498101475