chr3.15474_chr3_104078762_104091527_+_2.R 

fitVsDatCorrelation=0.787626773203883
cont.fitVsDatCorrelation=0.205059756000484

fstatistic=13948.2418300707,59,853
cont.fstatistic=5519.0809175675,59,853

residuals=-0.421457450417243,-0.0807961053712053,-0.00330873320557426,0.077364228526676,0.694210504089825
cont.residuals=-0.478165990574759,-0.144014407724117,-0.0336991676382888,0.106999687249284,0.888070971519923

predictedValues:
Include	Exclude	Both
chr3.15474_chr3_104078762_104091527_+_2.R.tl.Lung	49.6082750382799	48.405656114021	55.9351399155102
chr3.15474_chr3_104078762_104091527_+_2.R.tl.cerebhem	57.4420539395256	54.3187102908058	59.5626144235409
chr3.15474_chr3_104078762_104091527_+_2.R.tl.cortex	60.4565200765101	50.3195231037548	63.1294515408487
chr3.15474_chr3_104078762_104091527_+_2.R.tl.heart	51.9316537071894	52.8460592912618	52.618365191474
chr3.15474_chr3_104078762_104091527_+_2.R.tl.kidney	47.605911283214	49.4482509305491	53.9443538775633
chr3.15474_chr3_104078762_104091527_+_2.R.tl.liver	49.2662569166137	55.02629261127	49.2961682933897
chr3.15474_chr3_104078762_104091527_+_2.R.tl.stomach	48.998955463991	50.2803378784202	56.0912111221
chr3.15474_chr3_104078762_104091527_+_2.R.tl.testicle	55.5107135836644	49.9358821554089	62.7908584160653


diffExp=1.20261892425886,3.12334364871980,10.1369969727554,-0.914405584072483,-1.84233964733516,-5.7600356946563,-1.28138241442924,5.57483142825543
diffExpScore=2.65453227521219
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=0,0,1,0,0,0,0,0
diffExp1.2Score=0.5

cont.predictedValues:
Include	Exclude	Both
Lung	55.2501477311765	55.9578115458322	53.2926923840891
cerebhem	52.4366983985659	54.9594554648696	53.298838398185
cortex	53.8168941377158	53.8515250403553	55.0868222722346
heart	53.5992856602384	54.0701539081784	57.4249345464503
kidney	52.0588786802756	48.9008431179136	55.7053433205614
liver	54.3036258010977	50.0471137108418	54.8699756954585
stomach	55.180692377642	53.4968758203145	55.2055738692985
testicle	54.4545881635653	50.7082594809873	55.9290497432386
cont.diffExp=-0.707663814655746,-2.52275706630368,-0.034630902639492,-0.470868247939997,3.15803556236202,4.25651209025587,1.68381655732753,3.74632868257801
cont.diffExpScore=1.64022014858562

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=0.140053480970210
cont.tran.correlation=0.310048118366181

tran.covariance=0.000629217506080337
cont.tran.covariance=0.000332053164057558

tran.mean=51.96256577403
cont.tran.mean=53.3183030649731

weightedLogRatios:
wLogRatio
Lung	0.0955107503408456
cerebhem	0.224907738559536
cortex	0.735989635206479
heart	-0.069096925085134
kidney	-0.147396347870020
liver	-0.43703749923596
stomach	-0.100800478941134
testicle	0.419498549393374

cont.weightedLogRatios:
wLogRatio
Lung	-0.0511401805273272
cerebhem	-0.187162113271452
cortex	-0.00256408768829437
heart	-0.0348631883189125
kidney	0.245384331600953
liver	0.322731717675291
stomach	0.123808153337513
testicle	0.282385247367213

varWeightedLogRatios=0.134277752116183
cont.varWeightedLogRatios=0.0339006255118931

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.26011091705437	0.061816392566779	52.7386148185942	1.05059557468016e-270	***
df.mm.trans1	0.56857793050001	0.0533831245408176	10.6508926817363	5.89086392526745e-25	***
df.mm.trans2	0.633422643159654	0.0471637492443931	13.430286041879	1.91509641754528e-37	***
df.mm.exp2	0.199035799455609	0.0606676616724746	3.28075607281751	0.00107730314934207	** 
df.mm.exp3	0.115548640423678	0.0606676616724745	1.90461668108272	0.0571660502893408	.  
df.mm.exp4	0.194664920528194	0.0606676616724745	3.20870979961496	0.00138310548494421	** 
df.mm.exp5	0.0163490752971410	0.0606676616724745	0.269485832261089	0.787621017671671	   
df.mm.exp6	0.247622645196365	0.0606676616724745	4.08162501026001	4.89225548459763e-05	***
df.mm.exp7	0.0228524283640689	0.0606676616724745	0.376682201589405	0.706503471511723	   
df.mm.exp8	0.0279248486956601	0.0606676616724745	0.460292154433402	0.645423842441412	   
df.mm.trans1:exp2	-0.0524167726194304	0.0560763812668886	-0.934738858592876	0.350187428289566	   
df.mm.trans2:exp2	-0.083783728047233	0.041415152273201	-2.02302112749801	0.0433824575479657	*  
df.mm.trans1:exp3	0.0822181345555158	0.0560763812668886	1.46618117464122	0.142967469837764	   
df.mm.trans2:exp3	-0.0767721741014856	0.041415152273201	-1.85372188408356	0.0641240274064262	.  
df.mm.trans1:exp4	-0.148894073514084	0.0560763812668886	-2.65520117650676	0.00807390968891623	** 
df.mm.trans2:exp4	-0.106898443772011	0.041415152273201	-2.58114332326585	0.0100132862585174	*  
df.mm.trans1:exp5	-0.0575497904127322	0.0560763812668886	-1.02627503973252	0.305052894757323	   
df.mm.trans2:exp5	0.004960942927198	0.041415152273201	0.119785698105670	0.904681117724486	   
df.mm.trans1:exp6	-0.254540897633031	0.0560763812668886	-4.53918194937678	6.45822725414905e-06	***
df.mm.trans2:exp6	-0.119428195469261	0.041415152273201	-2.88368360163052	0.00402940974849915	** 
df.mm.trans1:exp7	-0.0352111028066498	0.0560763812668886	-0.627913249948618	0.530228834290235	   
df.mm.trans2:exp7	0.0151450065174483	0.041415152273201	0.365687572933261	0.714688885325524	   
df.mm.trans1:exp8	0.0844935357062954	0.0560763812668886	1.50675799324780	0.132242930435629	   
df.mm.trans2:exp8	0.00319830816276036	0.041415152273201	0.0772255560395446	0.938462221444822	   
df.mm.trans1:probe2	0.178948940436251	0.0383928737039188	4.66099364731809	3.65022945511236e-06	***
df.mm.trans1:probe3	0.0064934352802635	0.0383928737039188	0.169131264576340	0.865733491997184	   
df.mm.trans1:probe4	-0.00600428069094491	0.0383928737039188	-0.156390499373639	0.87576223037869	   
df.mm.trans1:probe5	0.161442967101574	0.0383928737039188	4.20502430598457	2.88638538345396e-05	***
df.mm.trans1:probe6	0.0439446017283083	0.0383928737039188	1.14460308616656	0.252694680195889	   
df.mm.trans1:probe7	0.222068450325213	0.0383928737039188	5.78410597856723	1.02287578093291e-08	***
df.mm.trans1:probe8	0.0299574316968621	0.0383928737039188	0.780286256451918	0.435438864330707	   
df.mm.trans1:probe9	0.203537762410018	0.0383928737039188	5.30144640851001	1.46421903203457e-07	***
df.mm.trans1:probe10	0.36784844619232	0.0383928737039188	9.58116469814485	1.01284691337415e-20	***
df.mm.trans1:probe11	0.178093218624422	0.0383928737039188	4.63870508881039	4.05591485976106e-06	***
df.mm.trans1:probe12	0.215335507019821	0.0383928737039188	5.60873636812035	2.7538082288055e-08	***
df.mm.trans1:probe13	0.129808685886833	0.0383928737039187	3.38106198790697	0.000754882009717612	***
df.mm.trans1:probe14	0.1786416898161	0.0383928737039188	4.65299084391972	3.79119109306115e-06	***
df.mm.trans1:probe15	0.150633785438482	0.0383928737039187	3.92348295155377	9.42995708647729e-05	***
df.mm.trans1:probe16	0.154109214355725	0.0383928737039187	4.01400571221098	6.49477899004899e-05	***
df.mm.trans1:probe17	0.0480488921157127	0.0383928737039188	1.2515054873532	0.211093211456394	   
df.mm.trans1:probe18	0.207204498029607	0.0383928737039188	5.39695203926497	8.78817202297676e-08	***
df.mm.trans1:probe19	-0.0532828356736671	0.0383928737039188	-1.38783140029001	0.165550789612462	   
df.mm.trans1:probe20	0.0241137271435043	0.0383928737039188	0.628078203508975	0.530120826356853	   
df.mm.trans1:probe21	-0.0289498982920674	0.0383928737039188	-0.754043537228433	0.45103117683899	   
df.mm.trans1:probe22	0.00300760778231831	0.0383928737039188	0.0783376572827713	0.937577835545634	   
df.mm.trans2:probe2	0.0746633978741998	0.0383928737039187	1.94472022203899	0.0521370069460835	.  
df.mm.trans2:probe3	-0.0754814335059956	0.0383928737039188	-1.96602718744367	0.0496194341959012	*  
df.mm.trans2:probe4	-0.04084342528029	0.0383928737039188	-1.06382829259590	0.287707550225528	   
df.mm.trans2:probe5	-0.103742191527206	0.0383928737039187	-2.70212103233672	0.00702671002186624	** 
df.mm.trans2:probe6	-0.0772666109097117	0.0383928737039188	-2.01252481139033	0.0444785067331614	*  
df.mm.trans3:probe2	-0.382516784898829	0.0383928737039187	-9.96322358802191	3.40758896425379e-22	***
df.mm.trans3:probe3	-0.411981147517627	0.0383928737039187	-10.7306671205385	2.75899912864882e-25	***
df.mm.trans3:probe4	-0.69309967566707	0.0383928737039187	-18.0528209743342	6.00295894411926e-62	***
df.mm.trans3:probe5	-0.648223507571443	0.0383928737039187	-16.8839538444156	2.13539069516776e-55	***
df.mm.trans3:probe6	-0.682508181009879	0.0383928737039187	-17.7769496045881	2.20163507643704e-60	***
df.mm.trans3:probe7	-0.640721955327047	0.0383928737039187	-16.6885646609373	2.53488335889582e-54	***
df.mm.trans3:probe8	-0.381482031782657	0.0383928737039187	-9.93627189057533	4.34339623298778e-22	***
df.mm.trans3:probe9	-0.367007587121399	0.0383928737039187	-9.55926326202403	1.22637472505364e-20	***
df.mm.trans3:probe10	-0.171645999340128	0.0383928737039187	-4.47077758919119	8.8460022959316e-06	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.02914548607990	0.0981944365748893	41.0323194125873	4.54603888128843e-204	***
df.mm.trans1	-0.025612504151451	0.0847983135093168	-0.302040254003837	0.762695003171488	   
df.mm.trans2	-0.006443577967811	0.0749189267039407	-0.0860073448899539	0.931480762010372	   
df.mm.exp2	-0.070381937482842	0.096369694330014	-0.73033268365283	0.465387385218933	   
df.mm.exp3	-0.0977622062186148	0.096369694330014	-1.01444968668088	0.310655879573352	   
df.mm.exp4	-0.139330355697589	0.096369694330014	-1.44579015909772	0.148603365551832	   
df.mm.exp5	-0.238575911867250	0.096369694330014	-2.47563213234087	0.0134932313352696	*  
df.mm.exp6	-0.158080311465670	0.096369694330014	-1.64035294046208	0.101300478499187	   
df.mm.exp7	-0.0814973889710813	0.096369694330014	-0.845674457490723	0.397971530863809	   
df.mm.exp8	-0.161297840271440	0.096369694330014	-1.67374029141446	0.0945482680488609	.  
df.mm.trans1:exp2	0.0181176205343740	0.0890765124754305	0.203393914185530	0.838875665455759	   
df.mm.trans2:exp2	0.0523796359434692	0.0657873643910392	0.796195993384464	0.426139650333894	   
df.mm.trans1:exp3	0.0714786272611258	0.0890765124754305	0.802440792468625	0.42252153155854	   
df.mm.trans2:exp3	0.0593948880152189	0.0657873643910392	0.902831243735142	0.366870339081035	   
df.mm.trans1:exp4	0.108995082189443	0.0890765124754305	1.22361191699671	0.221436553354315	   
df.mm.trans2:exp4	0.105014663989101	0.0657873643910392	1.59627407118631	0.110798001123638	   
df.mm.trans1:exp5	0.179080257951791	0.0890765124754305	2.01040939946079	0.0447022138484797	*  
df.mm.trans2:exp5	0.103772508314621	0.0657873643910392	1.57739269957371	0.115075954196786	   
df.mm.trans1:exp6	0.140800295415451	0.0890765124754305	1.58066690648994	0.114324929359786	   
df.mm.trans2:exp6	0.0464471058890366	0.0657873643910392	0.70601864535803	0.480369319772735	   
df.mm.trans1:exp7	0.0802394910392303	0.0890765124754305	0.90079291172701	0.367952686362356	   
df.mm.trans2:exp7	0.0365226037260199	0.0657873643910392	0.555161375806607	0.578929836674664	   
df.mm.trans1:exp8	0.146793935632349	0.0890765124754305	1.64795333307237	0.0997305109586013	.  
df.mm.trans2:exp8	0.0627886049356328	0.0657873643910392	0.95441739484224	0.340142791044332	   
df.mm.trans1:probe2	0.0324620631291109	0.0609865190333545	0.532282603494009	0.594668892650765	   
df.mm.trans1:probe3	0.0104387017949361	0.0609865190333545	0.171164086102815	0.864135339456656	   
df.mm.trans1:probe4	0.0188640692196228	0.0609865190333545	0.309315394920405	0.757157160191951	   
df.mm.trans1:probe5	0.0468383343430886	0.0609865190333545	0.76801127668021	0.442693037945845	   
df.mm.trans1:probe6	-0.0234100288119039	0.0609865190333545	-0.383855796050609	0.701180989730716	   
df.mm.trans1:probe7	-0.0138453934868106	0.0609865190333545	-0.227023835861797	0.820459604108913	   
df.mm.trans1:probe8	0.0236745395089606	0.0609865190333545	0.388192995504672	0.69797007615402	   
df.mm.trans1:probe9	-0.0374242742671309	0.0609865190333545	-0.6136483088445	0.539611355384584	   
df.mm.trans1:probe10	-0.0061351901557022	0.0609865190333545	-0.100599120148942	0.91989233458778	   
df.mm.trans1:probe11	-0.0126104173361097	0.0609865190333545	-0.206773849958756	0.836235826322382	   
df.mm.trans1:probe12	0.0244990703940384	0.0609865190333545	0.401712883147823	0.687995966652781	   
df.mm.trans1:probe13	-0.00876455720705219	0.0609865190333545	-0.143713026189586	0.88576102802102	   
df.mm.trans1:probe14	0.0220078326718426	0.0609865190333545	0.360863892884364	0.718290545790268	   
df.mm.trans1:probe15	0.0399472884150142	0.0609865190333545	0.65501833926882	0.512632539262713	   
df.mm.trans1:probe16	0.0490515094638332	0.0609865190333545	0.80430085601387	0.421447340715654	   
df.mm.trans1:probe17	0.0428454352245902	0.0609865190333545	0.70253944484284	0.482534480879197	   
df.mm.trans1:probe18	-0.00377225722067588	0.0609865190333545	-0.0618539519957316	0.950693628016087	   
df.mm.trans1:probe19	0.0656913006498648	0.0609865190333545	1.07714461640182	0.28172019407186	   
df.mm.trans1:probe20	-0.0335013294958571	0.0609865190333545	-0.549323523081136	0.582927221212896	   
df.mm.trans1:probe21	-0.00113219760327602	0.0609865190333545	-0.0185647192399488	0.985192689356671	   
df.mm.trans1:probe22	0.0310925354346566	0.0609865190333545	0.509826366998444	0.610304990812747	   
df.mm.trans2:probe2	0.00474658307475409	0.0609865190333545	0.0778300376868223	0.937981504768219	   
df.mm.trans2:probe3	-0.00298566364894818	0.0609865190333545	-0.0489561249973174	0.960965721397114	   
df.mm.trans2:probe4	-0.0590930165282616	0.0609865190333545	-0.968952113760463	0.332843735081608	   
df.mm.trans2:probe5	0.0240650038770151	0.0609865190333545	0.394595465661084	0.69324009904116	   
df.mm.trans2:probe6	0.0636052283910692	0.0609865190333545	1.04293915113080	0.297271960953723	   
df.mm.trans3:probe2	-0.0333020872421306	0.0609865190333545	-0.546056534623942	0.585169857831476	   
df.mm.trans3:probe3	-0.0913814224061723	0.0609865190333545	-1.49838724778167	0.134402631022990	   
df.mm.trans3:probe4	-0.038392378893684	0.0609865190333545	-0.629522384654986	0.529175686928659	   
df.mm.trans3:probe5	0.00195706261370882	0.0609865190333545	0.0320900855587194	0.974407716832793	   
df.mm.trans3:probe6	-0.00261322462322255	0.0609865190333545	-0.0428492175753356	0.965831757896426	   
df.mm.trans3:probe7	-0.0372480700683995	0.0609865190333545	-0.610759076903995	0.541521790227793	   
df.mm.trans3:probe8	0.0392901593986632	0.0609865190333545	0.644243351176919	0.519590838305445	   
df.mm.trans3:probe9	-0.00447526261572924	0.0609865190333545	-0.0733811781138328	0.941519998318777	   
df.mm.trans3:probe10	-0.0467366329269785	0.0609865190333545	-0.766343671810774	0.443683860188871