chr15.8894_chr15_50872885_51031137_-_2.R 

fitVsDatCorrelation=0.821148381615502
cont.fitVsDatCorrelation=0.252251220609636

fstatistic=8183.48717294471,52,692
cont.fstatistic=2837.94098342523,52,692

residuals=-0.602087962276743,-0.0975574821282559,-0.00993503792314776,0.0854019841429411,1.10207489234899
cont.residuals=-0.848212937518993,-0.201400642860298,-0.00272712503773404,0.176922923152614,1.37186935661802

predictedValues:
Include	Exclude	Both
chr15.8894_chr15_50872885_51031137_-_2.R.tl.Lung	90.709530839214	80.7568637363126	69.8415004649034
chr15.8894_chr15_50872885_51031137_-_2.R.tl.cerebhem	84.7876469759145	75.5894995471943	60.0985400480369
chr15.8894_chr15_50872885_51031137_-_2.R.tl.cortex	83.9023841854612	79.809015829588	71.5161846770336
chr15.8894_chr15_50872885_51031137_-_2.R.tl.heart	88.4514684513916	69.0564637664845	63.6713409792876
chr15.8894_chr15_50872885_51031137_-_2.R.tl.kidney	91.6165513670826	84.5654983364232	74.9014574411935
chr15.8894_chr15_50872885_51031137_-_2.R.tl.liver	93.8581618324617	69.0651664921842	58.2482839436035
chr15.8894_chr15_50872885_51031137_-_2.R.tl.stomach	95.2539497981573	68.121630290572	61.3561935047183
chr15.8894_chr15_50872885_51031137_-_2.R.tl.testicle	95.1514970034784	98.9825376912456	93.4558742192788


diffExp=9.95266710290142,9.19814742872022,4.09336835587320,19.3950046849071,7.05105303065933,24.7929953402775,27.1323195075852,-3.8310406877672
diffExpScore=1.06744054360653
diffExp1.5=0,0,0,0,0,0,0,0
diffExp1.5Score=0
diffExp1.4=0,0,0,0,0,0,0,0
diffExp1.4Score=0
diffExp1.3=0,0,0,0,0,1,1,0
diffExp1.3Score=0.666666666666667
diffExp1.2=0,0,0,1,0,1,1,0
diffExp1.2Score=0.75

cont.predictedValues:
Include	Exclude	Both
Lung	74.2349416461757	84.7729130522899	77.7217639071489
cerebhem	80.5301532506285	80.9957884481784	76.033026951482
cortex	74.3532506347367	78.7677683498234	79.372583743108
heart	79.7225827370025	82.165375873599	90.6717268271197
kidney	78.692287839404	82.7336621860547	78.6887223973498
liver	80.1967612915152	83.3801435406082	87.1818992772902
stomach	77.431778978822	71.9066094768203	75.9379604026166
testicle	79.4040181453174	79.9231731603328	84.8176525899812
cont.diffExp=-10.5379714061142,-0.465635197549958,-4.41451771508675,-2.44279313659646,-4.04137434665077,-3.18338224909303,5.52516950200173,-0.519155015015329
cont.diffExpScore=1.47677899984294

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=0.151339867057462
cont.tran.correlation=0.0887953615638543

tran.covariance=0.00067112736975697
cont.tran.covariance=0.000148839279326086

tran.mean=84.3548666339479
cont.tran.mean=79.3257005382068

weightedLogRatios:
wLogRatio
Lung	0.517124674673319
cerebhem	0.503280175371926
cortex	0.220309571568367
heart	1.07890433340929
kidney	0.358588744193424
liver	1.34607787661413
stomach	1.47139307187741
testicle	-0.180597694651868

cont.weightedLogRatios:
wLogRatio
Lung	-0.58055729048767
cerebhem	-0.0253191447653159
cortex	-0.250181326304312
heart	-0.132605004524800
kidney	-0.219886698603129
liver	-0.171432708372782
stomach	0.31924177363005
testicle	-0.0285295773347858

varWeightedLogRatios=0.334306277466958
cont.varWeightedLogRatios=0.0642882221625461

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	5.0099601429583	0.0860611310208006	58.2139705060047	7.55681906073858e-269	***
df.mm.trans1	-0.528182611904505	0.0720350719424828	-7.33229797183015	6.33840865215979e-13	***
df.mm.trans2	-0.639227457615288	0.0661217829485745	-9.66742621130529	8.00590762806297e-21	***
df.mm.exp2	0.0166046939745075	0.0855904193903968	0.194001783059034	0.846231419734016	   
df.mm.exp3	-0.113510269968364	0.0855904193903967	-1.32620298833470	0.185210014369387	   
df.mm.exp4	-0.0892330424900194	0.0855904193903968	-1.04255877147894	0.297516772947334	   
df.mm.exp5	-0.0139120345604567	0.0855904193903967	-0.162541960414995	0.870926539179081	   
df.mm.exp6	0.0592435998064895	0.0855904193903968	0.692175598956542	0.489059245917344	   
df.mm.exp7	0.00826815407762576	0.0855904193903968	0.0966013969380485	0.923070916664657	   
df.mm.exp8	-0.0399526123062828	0.0855904193903968	-0.466788369432449	0.640798222114419	   
df.mm.trans1:exp2	-0.0841172667596928	0.0738808468206975	-1.13855309433361	0.255283605458525	   
df.mm.trans2:exp2	-0.08273027363517	0.0599252798279887	-1.38055715171696	0.167860915063292	   
df.mm.trans1:exp3	0.0355018675098297	0.0738808468206976	0.480528703142638	0.631003372028105	   
df.mm.trans2:exp3	0.101703790042001	0.0599252798279887	1.69717672297793	0.0901129097515285	.  
df.mm.trans1:exp4	0.0640246324908406	0.0738808468206976	0.86659310560182	0.386465362236534	   
df.mm.trans2:exp4	-0.0672854304149504	0.0599252798279887	-1.12282213129565	0.261902450197031	   
df.mm.trans1:exp5	0.0238615491801834	0.0738808468206976	0.322973412014257	0.746812943518354	   
df.mm.trans2:exp5	0.0599954385875522	0.0599252798279887	1.00117077066248	0.317094206298978	   
df.mm.trans1:exp6	-0.0251213062916142	0.0738808468206976	-0.340024612232469	0.733941175987828	   
df.mm.trans2:exp6	-0.215636057319158	0.0599252798279887	-3.5984155257702	0.000342991129331046	***
df.mm.trans1:exp7	0.0406158942486703	0.0738808468206976	0.54974862899503	0.582669173746169	   
df.mm.trans2:exp7	-0.178416324263194	0.0599252798279887	-2.97731316024432	0.00300942883903727	** 
df.mm.trans1:exp8	0.0877605064963664	0.0738808468206976	1.18786546544808	0.235293960873176	   
df.mm.trans2:exp8	0.243453101604767	0.0599252798279887	4.06261100997078	5.4083989044461e-05	***
df.mm.trans1:probe2	0.416957697731695	0.0529245741288576	7.87833826903381	1.28799012747241e-14	***
df.mm.trans1:probe3	-0.0402186908931528	0.0529245741288576	-0.759924695761005	0.447558410469113	   
df.mm.trans1:probe4	-0.0702467378072378	0.0529245741288576	-1.32729906595385	0.184847503730773	   
df.mm.trans1:probe5	-0.0322797970376788	0.0529245741288576	-0.609920770625115	0.54211451608608	   
df.mm.trans1:probe6	0.343576183833537	0.0529245741288576	6.49180819097417	1.61810566844232e-10	***
df.mm.trans1:probe7	0.196831741335291	0.0529245741288576	3.7190992006106	0.000216127748312188	***
df.mm.trans1:probe8	-0.184235300459948	0.0529245741288576	-3.48109178944705	0.000530735055889467	***
df.mm.trans1:probe9	-0.0481982393574966	0.0529245741288576	-0.910696782181872	0.362772268100881	   
df.mm.trans1:probe10	-0.0523845885998882	0.0529245741288576	-0.989797073706165	0.322619274323946	   
df.mm.trans1:probe11	0.219815192957027	0.0529245741288576	4.15336725094536	3.68584055814756e-05	***
df.mm.trans1:probe12	-0.102494925098342	0.0529245741288576	-1.93662257628741	0.05319827172098	.  
df.mm.trans2:probe2	0.0261480558638053	0.0529245741288576	0.494062659817376	0.621418714218281	   
df.mm.trans2:probe3	0.283403988648054	0.0529245741288576	5.35486573700222	1.16632460949882e-07	***
df.mm.trans2:probe4	0.0474681479914168	0.0529245741288576	0.896901841398746	0.370083211091664	   
df.mm.trans2:probe5	-0.046090361147475	0.0529245741288576	-0.870868814839339	0.384127794271137	   
df.mm.trans2:probe6	0.082565355225708	0.0529245741288576	1.56005705449198	0.119203588557389	   
df.mm.trans3:probe2	0.416183759599211	0.0529245741288576	7.86371485174186	1.43386843157999e-14	***
df.mm.trans3:probe3	0.591885913739582	0.0529245741288576	11.1835744260973	8.32639161712164e-27	***
df.mm.trans3:probe4	0.331459783935414	0.0529245741288576	6.26287106493092	6.63613701625621e-10	***
df.mm.trans3:probe5	0.418790077505850	0.0529245741288576	7.91296074459106	9.98416947022909e-15	***
df.mm.trans3:probe6	0.45336072680493	0.0529245741288576	8.566166743281	6.92709995666508e-17	***
df.mm.trans3:probe7	0.415372349667938	0.0529245741288576	7.84838341177038	1.60430600447938e-14	***
df.mm.trans3:probe8	0.343908359838127	0.0529245741288576	6.49808459489535	1.5557592787028e-10	***
df.mm.trans3:probe9	0.18647240149138	0.0529245741288576	3.52336139800328	0.000454133185782179	***
df.mm.trans3:probe10	0.745462524480468	0.0529245741288576	14.0853759666627	8.43747647253378e-40	***
df.mm.trans3:probe11	0.163837863019921	0.0529245741288576	3.09568599684937	0.00204282347799174	** 
df.mm.trans3:probe12	0.412551374794058	0.0529245741288576	7.79508161538727	2.36740244561412e-14	***
df.mm.trans3:probe13	0.768533053488884	0.0529245741288576	14.5212893280484	6.70266510905583e-42	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.41771027289814	0.145918895686867	30.2751076349857	7.091554446287e-129	***
df.mm.trans1	-0.121556968364695	0.122137346138648	-0.995248154702052	0.319963464277948	   
df.mm.trans2	0.0388010437717421	0.112111210185824	0.346094237208122	0.729377091559534	   
df.mm.exp2	0.0577853353394168	0.145120791821849	0.398187844856542	0.690614542365572	   
df.mm.exp3	-0.0928974422678412	0.145120791821849	-0.640138749944822	0.522294285929961	   
df.mm.exp4	-0.114034400301641	0.145120791821849	-0.785789540354978	0.432259765518758	   
df.mm.exp5	0.0215961673278249	0.145120791821849	0.148815115027325	0.8817428789807	   
df.mm.exp6	-0.0541791084438359	0.145120791821849	-0.373338015619061	0.709011224188724	   
df.mm.exp7	-0.09922693381733	0.145120791821849	-0.683754082179634	0.494359352845996	   
df.mm.exp8	-0.0789645786753867	0.145120791821849	-0.544130015307001	0.586527279497214	   
df.mm.trans1:exp2	0.0236114024887828	0.125266905658968	0.188488750197625	0.850548795411612	   
df.mm.trans2:exp2	-0.103364246704411	0.101604877283255	-1.01731579692037	0.309358764004168	   
df.mm.trans1:exp3	0.0944898839248288	0.125266905658968	0.754308437873223	0.450920639950974	   
df.mm.trans2:exp3	0.0194252541432851	0.101604877283255	0.191184268537928	0.848437307048313	   
df.mm.trans1:exp4	0.185352341722833	0.125266905658968	1.47965929826225	0.139419277028900	   
df.mm.trans2:exp4	0.0827923253250406	0.101604877283255	0.81484597529931	0.415440818429061	   
df.mm.trans1:exp5	0.0367140378742871	0.125266905658968	0.293086491449217	0.769543908259285	   
df.mm.trans2:exp5	-0.0459456786004812	0.101604877283255	-0.452199538339025	0.65126695344783	   
df.mm.trans1:exp6	0.131427288599324	0.125266905658968	1.04917805631063	0.294462454131037	   
df.mm.trans2:exp6	0.0376132321995091	0.101604877283255	0.370191207402875	0.711353301509046	   
df.mm.trans1:exp7	0.141389260266622	0.125266905658968	1.12870402220636	0.259413852231294	   
df.mm.trans2:exp7	-0.065380949935222	0.101604877283255	-0.643482396548272	0.52012431789082	   
df.mm.trans1:exp8	0.146278601009461	0.125266905658968	1.16773540657016	0.243315691328446	   
df.mm.trans2:exp8	0.0200543462171197	0.101604877283255	0.197375822434311	0.84359140794945	   
df.mm.trans1:probe2	0.0539296711821013	0.0897349979018294	0.600988158946642	0.548044670575027	   
df.mm.trans1:probe3	-0.0286854124397183	0.0897349979018294	-0.319668057173192	0.74931646053104	   
df.mm.trans1:probe4	-0.0587193756803738	0.0897349979018294	-0.654364262030887	0.513094566555311	   
df.mm.trans1:probe5	0.0968205049596711	0.0897349979018295	1.07896035241003	0.280981335658245	   
df.mm.trans1:probe6	0.0125664628077566	0.0897349979018294	0.140039706932455	0.888669367322075	   
df.mm.trans1:probe7	0.128797011040477	0.0897349979018294	1.43530410711528	0.151652277586781	   
df.mm.trans1:probe8	-0.0567478540255946	0.0897349979018294	-0.632393774474448	0.527338488512228	   
df.mm.trans1:probe9	-0.0343604051531007	0.0897349979018294	-0.382909744876699	0.701904273749916	   
df.mm.trans1:probe10	0.0106446210489343	0.0897349979018294	0.118622848362682	0.905608587942538	   
df.mm.trans1:probe11	0.0121652353024115	0.0897349979018294	0.135568458091684	0.892201866972492	   
df.mm.trans1:probe12	0.140630702690160	0.0897349979018294	1.56717786792630	0.117530261964106	   
df.mm.trans2:probe2	-0.0755204723606983	0.0897349979018294	-0.841594407160048	0.400305755845796	   
df.mm.trans2:probe3	-0.018413888957627	0.0897349979018295	-0.205202979753472	0.837473887099506	   
df.mm.trans2:probe4	-0.0293767897130067	0.0897349979018295	-0.327372712987022	0.743485011162712	   
df.mm.trans2:probe5	-0.0290343480746453	0.0897349979018295	-0.323556569382317	0.7463715297179	   
df.mm.trans2:probe6	-0.161824183465748	0.0897349979018295	-1.80335640775056	0.0717671047846054	.  
df.mm.trans3:probe2	0.00634788702967607	0.0897349979018294	0.0707403708486257	0.94362482825977	   
df.mm.trans3:probe3	-0.0204992132362198	0.0897349979018294	-0.228441675104801	0.819370382022055	   
df.mm.trans3:probe4	0.101545101194320	0.0897349979018294	1.13161089395032	0.258190048695348	   
df.mm.trans3:probe5	-0.102827388974326	0.0897349979018294	-1.14590061156317	0.252232384509098	   
df.mm.trans3:probe6	0.0576828421451965	0.0897349979018295	0.642813211053972	0.520558234388481	   
df.mm.trans3:probe7	0.0429570840256619	0.0897349979018294	0.4787104811955	0.632295824810633	   
df.mm.trans3:probe8	0.0717891821572654	0.0897349979018294	0.800013192576247	0.423977696170245	   
df.mm.trans3:probe9	-0.0523993634181386	0.0897349979018294	-0.583934525473147	0.55945465191055	   
df.mm.trans3:probe10	0.0546443516772913	0.0897349979018294	0.608952504095142	0.542755772733523	   
df.mm.trans3:probe11	0.0191434458246452	0.0897349979018294	0.213333106059558	0.831129987279333	   
df.mm.trans3:probe12	0.0398610938538835	0.0897349979018294	0.444209001904605	0.657030361983936	   
df.mm.trans3:probe13	0.0181076086812211	0.0897349979018294	0.201789815619441	0.840140347704513