chr19.12026_chr19_10299691_10301518_-_2.R 

fitVsDatCorrelation=0.891077536388846
cont.fitVsDatCorrelation=0.276540182429074

fstatistic=12483.2716221863,52,692
cont.fstatistic=2773.89810711653,52,692

residuals=-0.397987144762416,-0.0809067296986467,-0.00312742697420795,0.0710074434570656,0.674006304053667
cont.residuals=-0.633666348034303,-0.199244915334932,-0.0599968111951256,0.154320715784383,1.26279209111545

predictedValues:
Include	Exclude	Both
chr19.12026_chr19_10299691_10301518_-_2.R.tl.Lung	56.0796937555604	51.5693353558539	57.1011866082128
chr19.12026_chr19_10299691_10301518_-_2.R.tl.cerebhem	53.4646058499517	52.7663155319708	58.850046388922
chr19.12026_chr19_10299691_10301518_-_2.R.tl.cortex	52.726024627575	53.737822950994	53.5373348611826
chr19.12026_chr19_10299691_10301518_-_2.R.tl.heart	56.3312914419199	57.4020973751516	53.3934052916659
chr19.12026_chr19_10299691_10301518_-_2.R.tl.kidney	59.3106145891002	50.888993389675	58.2960147434634
chr19.12026_chr19_10299691_10301518_-_2.R.tl.liver	59.7901309858026	52.0661394655656	53.1098795800784
chr19.12026_chr19_10299691_10301518_-_2.R.tl.stomach	58.2520800585763	60.1350404375336	57.4388596675727
chr19.12026_chr19_10299691_10301518_-_2.R.tl.testicle	55.3170178727849	56.3510424378242	52.3677006965051


diffExp=4.51035839970646,0.698290317980906,-1.01179832341904,-1.07080593323168,8.42162119942524,7.72399152023698,-1.88296037895729,-1.03402456503930
diffExpScore=1.51854499344925
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=0,0,0,0,0,0,0,0
diffExp1.3Score=0
diffExp1.2=0,0,0,0,0,0,0,0
diffExp1.2Score=0

cont.predictedValues:
Include	Exclude	Both
Lung	56.5963125950809	57.5718812047509	56.1023924258578
cerebhem	56.1971160010405	52.196671257286	50.1878770154201
cortex	54.5473927555992	63.0884500315871	52.7884292002055
heart	53.5639241613162	51.0360729331862	49.6251193438332
kidney	54.4236323328972	49.1930703885217	52.9812449561739
liver	56.997283479191	51.1423730413076	51.4426290496349
stomach	54.7494128525534	65.6145737520207	69.1125860674845
testicle	55.955679324687	57.8623950787974	74.299878946049
cont.diffExp=-0.97556860966995,4.00044474375449,-8.5410572759879,2.52785122813003,5.2305619443755,5.8549104378834,-10.8651608994672,-1.90671575411039
cont.diffExpScore=7.03156651781239

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=-0.0286266982075375
cont.tran.correlation=-0.0853986223150708

tran.covariance=-9.73301968219892e-05
cont.tran.covariance=-0.000140067614250466

tran.mean=55.386765382865
cont.tran.mean=55.671015074364

weightedLogRatios:
wLogRatio
Lung	0.334116014879957
cerebhem	0.052225111241208
cortex	-0.0755491759658418
heart	-0.0760884116979534
kidney	0.61351866544208
liver	0.556301749415557
stomach	-0.129818489195689
testicle	-0.0744943170103611

cont.weightedLogRatios:
wLogRatio
Lung	-0.0691220990892705
cerebhem	0.294791809328865
cortex	-0.592316321810975
heart	0.191279307862865
kidney	0.398754220552881
liver	0.432347896829335
stomach	-0.741011744967453
testicle	-0.135415358525409

varWeightedLogRatios=0.0934391115677445
cont.varWeightedLogRatios=0.198054813726804

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.70397475046969	0.0728097116322015	50.8719876433563	5.23901619001695e-236	***
df.mm.trans1	0.218901106627995	0.0653937686666288	3.34743066642835	0.000859783676689547	***
df.mm.trans2	0.303718952179807	0.0601384515771741	5.05032876993935	5.64751935830186e-07	***
df.mm.exp2	-0.0549758401500845	0.0823747853301867	-0.66738674862365	0.504747616591577	   
df.mm.exp3	0.0439708888338036	0.0823747853301868	0.533790633354042	0.593657811398559	   
df.mm.exp4	0.178767675398145	0.0823747853301868	2.17017470432952	0.0303330740479084	*  
df.mm.exp5	0.0220251111527172	0.0823747853301868	0.267376856454714	0.789258683987387	   
df.mm.exp6	0.146116365803688	0.0823747853301868	1.77379965505224	0.0765359937657779	.  
df.mm.exp7	0.185775332245322	0.0823747853301868	2.25524511536716	0.0244295936557645	*  
df.mm.exp8	0.161515231817697	0.0823747853301868	1.96073630019535	0.0503105311372545	.  
df.mm.trans1:exp2	0.0072219203560986	0.0788678524707299	0.0915698872208908	0.927066277867282	   
df.mm.trans2:exp2	0.0779216440812352	0.0686456544418223	1.13512857754564	0.256714463213929	   
df.mm.trans1:exp3	-0.105635511090135	0.07886785247073	-1.33939885239476	0.180880594707206	   
df.mm.trans2:exp3	-0.00278101702742381	0.0686456544418223	-0.0405126449742094	0.96769611143635	   
df.mm.trans1:exp4	-0.174291278255173	0.07886785247073	-2.20991535581443	0.0274376253741499	*  
df.mm.trans2:exp4	-0.0716140528895666	0.0686456544418223	-1.04324233590431	0.297200378660864	   
df.mm.trans1:exp5	0.0339893951006303	0.07886785247073	0.430966408185701	0.666627004570909	   
df.mm.trans2:exp5	-0.0353056706541359	0.0686456544418223	-0.514317635125144	0.607194141622095	   
df.mm.trans1:exp6	-0.0820495340108205	0.07886785247073	-1.04034193198390	0.298544409947446	   
df.mm.trans2:exp6	-0.136528762404949	0.0686456544418223	-1.9888915549732	0.047106544952347	*  
df.mm.trans1:exp7	-0.147769313138892	0.07886785247073	-1.87363175881749	0.061402428369053	.  
df.mm.trans2:exp7	-0.0321098448232522	0.0686456544418223	-0.467762236143666	0.640101902919619	   
df.mm.trans1:exp8	-0.175208415191033	0.07886785247073	-2.22154413619995	0.0266369061646442	*  
df.mm.trans2:exp8	-0.0728417119489262	0.0686456544418223	-1.06112633845832	0.289002687544012	   
df.mm.trans1:probe2	0.967802430418348	0.039433926235365	24.5423807064488	3.60107529521469e-96	***
df.mm.trans1:probe3	-0.0965477877777747	0.039433926235365	-2.44834326669681	0.0145987019092827	*  
df.mm.trans1:probe4	-0.0637622391013183	0.039433926235365	-1.61693864112712	0.106347259168927	   
df.mm.trans1:probe5	0.505625325867958	0.039433926235365	12.8220893565121	6.41286613506215e-34	***
df.mm.trans1:probe6	0.0719579223892736	0.039433926235365	1.82477194788534	0.0684664485306513	.  
df.mm.trans1:probe7	-0.124660716958643	0.039433926235365	-3.16125551928547	0.00163945613913198	** 
df.mm.trans1:probe8	0.349418422957815	0.039433926235365	8.86085805588517	6.64937325389808e-18	***
df.mm.trans1:probe9	-0.0483152995089056	0.039433926235365	-1.22522163328428	0.220908583832580	   
df.mm.trans1:probe10	-0.103331723831430	0.039433926235365	-2.62037625202942	0.00897598634768359	** 
df.mm.trans1:probe11	-0.120581128502047	0.039433926235365	-3.05780174620065	0.00231562516940103	** 
df.mm.trans1:probe12	-0.130768251837623	0.039433926235365	-3.31613573188531	0.00096038458401504	***
df.mm.trans1:probe13	-0.200995471467341	0.039433926235365	-5.09701900509935	4.45734682796924e-07	***
df.mm.trans1:probe14	-0.123179530059355	0.039433926235365	-3.12369428608622	0.00186049191921867	** 
df.mm.trans1:probe15	-0.140969983891031	0.039433926235365	-3.57484017821706	0.000374806949465537	***
df.mm.trans1:probe16	-0.112708585537139	0.039433926235365	-2.85816291445158	0.00438909564951223	** 
df.mm.trans1:probe17	0.271391089496922	0.039433926235365	6.8821726722594	1.32252156649642e-11	***
df.mm.trans1:probe18	0.321113757868007	0.039433926235365	8.14308359637866	1.79606292086381e-15	***
df.mm.trans1:probe19	0.506876261051458	0.039433926235365	12.8538116652682	4.60631004991982e-34	***
df.mm.trans1:probe20	0.424896617283125	0.039433926235365	10.7749001392124	3.92158261480156e-25	***
df.mm.trans1:probe21	0.234783982111158	0.039433926235365	5.95385761767237	4.164081645202e-09	***
df.mm.trans1:probe22	0.209403028134686	0.039433926235365	5.31022518236823	1.47704804254661e-07	***
df.mm.trans2:probe2	-0.0663452452286861	0.039433926235365	-1.68244077023166	0.0929345158031533	.  
df.mm.trans2:probe3	-0.114129660237342	0.039433926235365	-2.89419976991763	0.00392089021122887	** 
df.mm.trans2:probe4	-0.0128209639434802	0.039433926235365	-0.325125220018852	0.745184573117387	   
df.mm.trans2:probe5	-0.171298896267501	0.039433926235365	-4.34394727131881	1.60902292850834e-05	***
df.mm.trans2:probe6	-0.218303580092902	0.039433926235365	-5.53593316551684	4.39746047567452e-08	***
df.mm.trans3:probe2	-0.271920850433404	0.039433926235365	-6.89560681354474	1.21068077665051e-11	***
df.mm.trans3:probe3	-0.273388884372021	0.039433926235365	-6.9328345024605	9.47037781650385e-12	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.96012888768206	0.154170175750629	25.6867378427822	1.03964155855791e-102	***
df.mm.trans1	0.0183338479000566	0.138467363519557	0.132405553439076	0.894702011775624	   
df.mm.trans2	0.0502101974974406	0.127339546348692	0.394301683468785	0.693479802777804	   
df.mm.exp2	0.0063110066853002	0.174423642767986	0.0361820598695724	0.97114762529366	   
df.mm.exp3	0.115515951170950	0.174423642767986	0.662272323509528	0.508017204325677	   
df.mm.exp4	-0.0528883162364567	0.174423642767986	-0.303217587920734	0.761815179934524	   
df.mm.exp5	-0.139186389154884	0.174423642767986	-0.797978914704961	0.425156472578387	   
df.mm.exp6	-0.0246498598196987	0.174423642767986	-0.141321780857927	0.887656873279955	   
df.mm.exp7	-0.110972032584162	0.174423642767986	-0.636221276101738	0.524842576745861	   
df.mm.exp8	-0.287281344853691	0.174423642767986	-1.64703213563671	0.100005435701034	   
df.mm.trans1:exp2	-0.0133894023999433	0.166997923819678	-0.080177059053746	0.936119616632715	   
df.mm.trans2:exp2	-0.104326557825271	0.145353035639989	-0.71774598559928	0.47315614698281	   
df.mm.trans1:exp3	-0.152389870121912	0.166997923819678	-0.912525537062728	0.361809926267434	   
df.mm.trans2:exp3	-0.0240125156444264	0.145353035639989	-0.165201335759514	0.868833761429635	   
df.mm.trans1:exp4	-0.00217973357306566	0.166997923819678	-0.0130524591157152	0.989589702144774	   
df.mm.trans2:exp4	-0.0676132636290614	0.145353035639989	-0.465165817358822	0.641959058065151	   
df.mm.trans1:exp5	0.100041031978713	0.166997923819678	0.599055543269721	0.549331903980343	   
df.mm.trans2:exp5	-0.0180951180060272	0.145353035639989	-0.124490815938962	0.900962824735314	   
df.mm.trans1:exp6	0.0317096336686730	0.166997923819678	0.18988040655471	0.849458530003704	   
df.mm.trans2:exp6	-0.0937710435301954	0.145353035639989	-0.64512614488801	0.519059264536522	   
df.mm.trans1:exp7	0.0777948424789156	0.166997923819678	0.465843171576896	0.641474346059949	   
df.mm.trans2:exp7	0.241735589736387	0.145353035639989	1.66309281861247	0.0967466563651126	.  
df.mm.trans1:exp8	0.275897447013692	0.166997923819678	1.65210106031978	0.0989675884221044	.  
df.mm.trans2:exp8	0.292314762946723	0.145353035639989	2.01106747898083	0.0447056711340963	*  
df.mm.trans1:probe2	0.120080960666778	0.083498961909839	1.43811321626297	0.150854003896219	   
df.mm.trans1:probe3	0.0209312129497049	0.083498961909839	0.250676325441102	0.80213877518932	   
df.mm.trans1:probe4	0.122170538281761	0.083498961909839	1.46313840899818	0.143883552446849	   
df.mm.trans1:probe5	0.0740230477745146	0.083498961909839	0.886514587504018	0.375648273549852	   
df.mm.trans1:probe6	0.10320376192537	0.083498961909839	1.23598856278965	0.216882198304564	   
df.mm.trans1:probe7	0.0910965222848694	0.083498961909839	1.09098987821231	0.275657056690182	   
df.mm.trans1:probe8	0.0951829617555785	0.083498961909839	1.13992988150386	0.254709914619395	   
df.mm.trans1:probe9	0.010543381691965	0.083498961909839	0.126269614026455	0.899555185806566	   
df.mm.trans1:probe10	0.0597401183687983	0.083498961909839	0.715459414133853	0.474566535757346	   
df.mm.trans1:probe11	0.00994190310647387	0.083498961909839	0.119066188118710	0.905257473701204	   
df.mm.trans1:probe12	0.00933944917475289	0.083498961909839	0.111851081272573	0.91097391989668	   
df.mm.trans1:probe13	0.0679641507314925	0.083498961909839	0.8139520441569	0.415952413311839	   
df.mm.trans1:probe14	0.0983863548286273	0.083498961909839	1.17829434735804	0.239084303504386	   
df.mm.trans1:probe15	0.167936916243816	0.083498961909839	2.01124555806037	0.0446868181104903	*  
df.mm.trans1:probe16	0.0139407402343631	0.083498961909839	0.166957048512963	0.86745261852345	   
df.mm.trans1:probe17	0.0438427991683478	0.083498961909839	0.525069990878313	0.599702715821507	   
df.mm.trans1:probe18	0.0391918639139992	0.083498961909839	0.469369474992013	0.638953414320973	   
df.mm.trans1:probe19	0.0763742721704456	0.083498961909839	0.914673313578598	0.360681755779828	   
df.mm.trans1:probe20	0.0990157294592241	0.083498961909839	1.18583186179177	0.236095718523111	   
df.mm.trans1:probe21	0.0221858340320153	0.083498961909839	0.26570191442585	0.790547926303841	   
df.mm.trans1:probe22	0.0919349564361094	0.083498961909839	1.10103113060710	0.271265937756798	   
df.mm.trans2:probe2	0.0973166718345043	0.083498961909839	1.16548361331229	0.244224856380466	   
df.mm.trans2:probe3	0.128582876046802	0.083498961909839	1.53993382798752	0.124033607151935	   
df.mm.trans2:probe4	-0.0349186689029527	0.083498961909839	-0.418192850596842	0.675935864199285	   
df.mm.trans2:probe5	0.0706264763550268	0.083498961909839	0.845836579756384	0.397936292128694	   
df.mm.trans2:probe6	0.122649352850630	0.083498961909839	1.46887278650321	0.142321724761475	   
df.mm.trans3:probe2	-0.0213906751185487	0.083498961909839	-0.256178934794975	0.797888809514675	   
df.mm.trans3:probe3	0.0169072479495629	0.083498961909839	0.202484528703711	0.839597468057123