chr12.5652_chr12_107293671_107323441_+_2.R 

fitVsDatCorrelation=0.860198819935128
cont.fitVsDatCorrelation=0.236363484341900

fstatistic=8936.67228775944,62,922
cont.fstatistic=2450.80078185575,62,922

residuals=-0.592209892290989,-0.0960499854434505,-0.00439274874735109,0.0829532251727908,1.20885113660702
cont.residuals=-0.780427545531883,-0.231609805681318,-0.0211555661945758,0.196881258409118,1.8040266557027

predictedValues:
Include	Exclude	Both
chr12.5652_chr12_107293671_107323441_+_2.R.tl.Lung	69.9828086002455	83.6032942919	87.3134656116614
chr12.5652_chr12_107293671_107323441_+_2.R.tl.cerebhem	73.7255005479343	118.584791085373	118.710028770925
chr12.5652_chr12_107293671_107323441_+_2.R.tl.cortex	60.8894514835334	94.1459936601056	111.807650211520
chr12.5652_chr12_107293671_107323441_+_2.R.tl.heart	63.45912130701	79.977579857163	88.064024712218
chr12.5652_chr12_107293671_107323441_+_2.R.tl.kidney	68.00031704975	68.8301738638802	77.4341208932064
chr12.5652_chr12_107293671_107323441_+_2.R.tl.liver	67.1396268717341	66.7767908222162	70.2996616515765
chr12.5652_chr12_107293671_107323441_+_2.R.tl.stomach	69.5680285850932	72.3542213190007	78.7775245506868
chr12.5652_chr12_107293671_107323441_+_2.R.tl.testicle	64.6641384889078	69.7477169124237	72.0130007735854


diffExp=-13.6204856916545,-44.8592905374383,-33.2565421765722,-16.5184585501531,-0.829856814130238,0.362836049517853,-2.78619273390754,-5.08357842351589
diffExpScore=0.997667112501499
diffExp1.5=0,-1,-1,0,0,0,0,0
diffExp1.5Score=0.666666666666667
diffExp1.4=0,-1,-1,0,0,0,0,0
diffExp1.4Score=0.666666666666667
diffExp1.3=0,-1,-1,0,0,0,0,0
diffExp1.3Score=0.666666666666667
diffExp1.2=0,-1,-1,-1,0,0,0,0
diffExp1.2Score=0.75

cont.predictedValues:
Include	Exclude	Both
Lung	77.6644643344409	84.7263773100266	76.3702997161222
cerebhem	83.3240046289624	81.9208668969133	86.1412231534078
cortex	79.3734739932791	96.275625878618	82.5444723850007
heart	80.2607252413897	74.149156523028	84.5635585011399
kidney	75.315481234038	82.7778882371762	88.7485575733597
liver	81.3970414285666	80.2910775493428	79.7828088274728
stomach	76.5791298097227	97.7142283194374	79.213055017892
testicle	78.2236237337482	85.8273178615768	81.2358066216474
cont.diffExp=-7.06191297558568,1.40313773204917,-16.9021518853389,6.11156871836161,-7.46240700313824,1.10596387922379,-21.1350985097147,-7.60369412782858
cont.diffExpScore=1.30909631933046

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,-1,0,0,0,-1,0
cont.diffExp1.2Score=0.666666666666667

tran.correlation=0.345318462494929
cont.tran.correlation=-0.39129857166086

tran.covariance=0.00320920316668884
cont.tran.covariance=-0.00119357225790861

tran.mean=74.4655971716419
cont.tran.mean=82.2387801862667

weightedLogRatios:
wLogRatio
Lung	-0.771292609371187
cerebhem	-2.15681288917191
cortex	-1.88562893658837
heart	-0.986957281931649
kidney	-0.0512556387617199
liver	0.0227812257448652
stomach	-0.167360799457028
testicle	-0.318380443456330

cont.weightedLogRatios:
wLogRatio
Lung	-0.382572586183713
cerebhem	0.0749668395031647
cortex	-0.863070889926225
heart	0.344185241281758
kidney	-0.412755202256307
liver	0.060091251138584
stomach	-1.08704809233957
testicle	-0.408721235436529

varWeightedLogRatios=0.703454324080281
cont.varWeightedLogRatios=0.234805474184918

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.44922756194105	0.0847371404782462	40.7050266562457	3.84570905072926e-208	***
df.mm.trans1	0.621859871309142	0.0725645518329394	8.56974728846682	4.32183414417838e-17	***
df.mm.trans2	0.932343771585298	0.0637878020207914	14.6163332494417	1.13691382131129e-43	***
df.mm.exp2	0.0944653499629658	0.0810242036582299	1.16589050799478	0.243960222788168	   
df.mm.exp3	-0.267701184271379	0.0810242036582299	-3.30396563230137	0.00098998988892366	***
df.mm.exp4	-0.150749669189931	0.0810242036582299	-1.86055107466174	0.0631257897832288	.  
df.mm.exp5	-0.103100780775300	0.0810242036582299	-1.27246891817896	0.20352736202688	   
df.mm.exp6	-0.0494648233885746	0.0810242036582299	-0.610494409759623	0.541684796308068	   
df.mm.exp7	-0.0475766841342529	0.0810242036582299	-0.58719101189735	0.557219186792679	   
df.mm.exp8	-0.0675830561549999	0.0810242036582299	-0.834109477213422	0.404435343398543	   
df.mm.trans1:exp2	-0.0423662263543323	0.0739646400791752	-0.572790272608391	0.566926359095215	   
df.mm.trans2:exp2	0.255079966603607	0.0523008985680071	4.87716222068202	1.26722215061237e-06	***
df.mm.trans1:exp3	0.128511513071773	0.0739646400791752	1.73747229668404	0.0826377587765543	.  
df.mm.trans2:exp3	0.386464961015485	0.0523008985680071	7.38926044478879	3.30687267780130e-13	***
df.mm.trans1:exp4	0.0528959883803071	0.0739646400791752	0.715152379889698	0.474695937103909	   
df.mm.trans2:exp4	0.106413088080695	0.0523008985680071	2.03463211903187	0.0421736014091302	*  
df.mm.trans1:exp5	0.0743635279750779	0.0739646400791752	1.00539295392333	0.314971452168167	   
df.mm.trans2:exp5	-0.0913399215475693	0.0523008985680071	-1.74643120956707	0.0810690646078538	.  
df.mm.trans1:exp6	0.00798963699725831	0.0739646400791752	0.108019683306859	0.91400358012263	   
df.mm.trans2:exp6	-0.175262523942032	0.0523008985680071	-3.35104230980157	0.00083778167426141	***
df.mm.trans1:exp7	0.0416321660654091	0.0739646400791752	0.562865796694801	0.573663110092115	   
df.mm.trans2:exp7	-0.096932443404966	0.0523008985680071	-1.85336095667504	0.0641499337757432	.  
df.mm.trans1:exp8	-0.0114597902081677	0.0739646400791752	-0.154936063988152	0.876905632244234	   
df.mm.trans2:exp8	-0.113615180837399	0.0523008985680071	-2.17233707160241	0.0300844186617315	*  
df.mm.trans1:probe2	-0.217927792133919	0.0535924732641815	-4.06638803661204	5.18261899145494e-05	***
df.mm.trans1:probe3	0.221102434502589	0.0535924732641815	4.12562475728028	4.03161024823769e-05	***
df.mm.trans1:probe4	0.0566981909407994	0.0535924732641815	1.05795063163643	0.290355047762278	   
df.mm.trans1:probe5	0.0062588241822329	0.0535924732641815	0.116785507386090	0.907055461740342	   
df.mm.trans1:probe6	-0.140976670993390	0.0535924732641815	-2.63053116243493	0.00866733657592638	** 
df.mm.trans1:probe7	0.220066751738290	0.0535924732641815	4.1062996039291	4.37735978589913e-05	***
df.mm.trans1:probe8	0.0151586826217862	0.0535924732641815	0.282850962056971	0.777354575456952	   
df.mm.trans1:probe9	0.124181825946696	0.0535924732641815	2.31715049489409	0.0207138070828295	*  
df.mm.trans1:probe10	0.40451947377604	0.0535924732641815	7.54806503857324	1.05999537025791e-13	***
df.mm.trans1:probe11	0.326418923888148	0.0535924732641815	6.09076058645551	1.64809459207594e-09	***
df.mm.trans1:probe12	0.55154426354905	0.0535924732641815	10.2914500853551	1.37356203503783e-23	***
df.mm.trans1:probe13	0.298509210665220	0.0535924732641815	5.56998385190647	3.34268928213855e-08	***
df.mm.trans1:probe14	0.417994774964424	0.0535924732641815	7.79950521977104	1.67772735808690e-14	***
df.mm.trans1:probe15	0.324013911568877	0.0535924732641815	6.04588465196719	2.15596500259838e-09	***
df.mm.trans1:probe16	0.757291101509163	0.0535924732641815	14.1305495974432	3.50585747883839e-41	***
df.mm.trans1:probe17	0.638198414281648	0.0535924732641815	11.9083590551173	1.62493899673998e-30	***
df.mm.trans1:probe18	0.404347807719363	0.0535924732641815	7.54486186383208	1.0848231929617e-13	***
df.mm.trans1:probe19	0.620114136256592	0.0535924732641815	11.5709184235586	5.22628240143672e-29	***
df.mm.trans1:probe20	0.593147390913077	0.0535924732641815	11.0677368441121	8.0638287366837e-27	***
df.mm.trans1:probe21	0.580014896508912	0.0535924732641815	10.8226932101035	8.82750842268006e-26	***
df.mm.trans2:probe2	-0.0182239440761126	0.0535924732641815	-0.340046707422487	0.733898806447563	   
df.mm.trans2:probe3	0.461446238828427	0.0535924732641815	8.61028071150495	3.11755353505374e-17	***
df.mm.trans2:probe4	0.142796666949144	0.0535924732641815	2.66449107965655	0.00784514833991108	** 
df.mm.trans2:probe5	0.0989173798423532	0.0535924732641815	1.84573269001311	0.0652514699924663	.  
df.mm.trans2:probe6	0.205295482324946	0.0535924732641815	3.83067751534719	0.000136440898619523	***
df.mm.trans3:probe2	-0.576181518689832	0.0535924732641815	-10.7511649229095	1.76151560316249e-25	***
df.mm.trans3:probe3	-0.856770746711667	0.0535924732641815	-15.9867737860923	5.8130319133957e-51	***
df.mm.trans3:probe4	-0.586941985106931	0.0535924732641815	-10.9519480881882	2.51084020482557e-26	***
df.mm.trans3:probe5	-0.177930579545345	0.0535924732641815	-3.32006658226511	0.000935260094607147	***
df.mm.trans3:probe6	-0.726047368356574	0.0535924732641815	-13.5475622626625	2.88951303879188e-38	***
df.mm.trans3:probe7	-0.368970829489748	0.0535924732641815	-6.88475091774407	1.06959879705285e-11	***
df.mm.trans3:probe8	-1.06192454858066	0.0535924732641815	-19.8148076381165	4.50621764753132e-73	***
df.mm.trans3:probe9	-0.707595583549483	0.0535924732641815	-13.2032641983404	1.39639004287585e-36	***
df.mm.trans3:probe10	-0.261874508102486	0.0535924732641815	-4.88640460408663	1.21061935173412e-06	***
df.mm.trans3:probe11	-0.569972935058665	0.0535924732641815	-10.6353168708787	5.35343034646255e-25	***
df.mm.trans3:probe12	-0.872691294403072	0.0535924732641815	-16.2838406449574	1.36439295764026e-52	***
df.mm.trans3:probe13	-0.367991507316897	0.0535924732641815	-6.86647741564193	1.20831094335508e-11	***
df.mm.trans3:probe14	-0.340077715757519	0.0535924732641815	-6.34562458204013	3.46865136334484e-10	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.5330583321284	0.161456350626037	28.0760608954169	7.68785084546623e-126	***
df.mm.trans1	-0.186427468769089	0.138262958339597	-1.34835440386854	0.177875550535065	   
df.mm.trans2	-0.0905491951920743	0.121539925357490	-0.745016050698885	0.456451878703107	   
df.mm.exp2	-0.0837285656443935	0.154381799541572	-0.542347387405903	0.587710273700377	   
df.mm.exp3	0.0718112965910826	0.154381799541572	0.465153902884421	0.641931086151537	   
df.mm.exp4	-0.202375237516522	0.154381799541572	-1.31087497436527	0.190226410905888	   
df.mm.exp5	-0.204191400794986	0.154381799541572	-1.32263907663546	0.186283399853226	   
df.mm.exp6	-0.0505415344050137	0.154381799541572	-0.327380135191414	0.743454705052501	   
df.mm.exp7	0.0919997342341252	0.154381799541572	0.595923447629923	0.551372627962949	   
df.mm.exp8	-0.0416779851600566	0.154381799541572	-0.26996696037886	0.787246083952828	   
df.mm.trans1:exp2	0.154067435833201	0.140930656795266	1.09321448815086	0.274585180577897	   
df.mm.trans2:exp2	0.050055335515234	0.0996530230970067	0.502296206975155	0.61557911535161	   
df.mm.trans1:exp3	-0.0500448731311882	0.140930656795266	-0.355102816301280	0.722593781153749	   
df.mm.trans2:exp3	0.0559769104331176	0.0996530230970067	0.561718136524841	0.574444588495668	   
df.mm.trans1:exp4	0.23525783017772	0.140930656795266	1.66931621215308	0.0953941976200167	.  
df.mm.trans2:exp4	0.069026957154228	0.0996530230970067	0.692672986819818	0.488689218365288	   
df.mm.trans1:exp5	0.173479300260167	0.140930656795266	1.2309550257201	0.218653601696781	   
df.mm.trans2:exp5	0.1809254026442	0.0996530230970067	1.81555357801919	0.0697636172054184	.  
df.mm.trans1:exp6	0.0974826523799372	0.140930656795266	0.691706507275794	0.489295798441431	   
df.mm.trans2:exp6	-0.00322693837384080	0.0996530230970067	-0.0323817409001185	0.97417463188558	   
df.mm.trans1:exp7	-0.106072959689438	0.140930656795266	-0.752660649581254	0.451845981026729	   
df.mm.trans2:exp7	0.0506204733566836	0.0996530230970067	0.507967262642974	0.611597800826305	   
df.mm.trans1:exp8	0.0488518725854987	0.140930656795266	0.346637656393436	0.72894264689749	   
df.mm.trans2:exp8	0.0545883573716255	0.0996530230970067	0.547784258571732	0.583972632050471	   
df.mm.trans1:probe2	-0.0216265416938214	0.102113962135406	-0.211788292624900	0.832319008955796	   
df.mm.trans1:probe3	0.00142113011842463	0.102113962135406	0.0139170989814318	0.988899130828918	   
df.mm.trans1:probe4	0.047031970844306	0.102113962135406	0.460583154945453	0.645206310277693	   
df.mm.trans1:probe5	0.0499642970617214	0.102113962135406	0.489299367264461	0.624746149369632	   
df.mm.trans1:probe6	-0.0821443416772021	0.102113962135406	-0.804437904076978	0.421351488438681	   
df.mm.trans1:probe7	0.0475130561746228	0.102113962135406	0.465294414015774	0.641830511244608	   
df.mm.trans1:probe8	-0.0325456023361604	0.102113962135406	-0.318718436299671	0.750012218721539	   
df.mm.trans1:probe9	-0.0930963978460275	0.102113962135406	-0.911691172286301	0.362169615267109	   
df.mm.trans1:probe10	0.0363814806471800	0.102113962135406	0.356283116298407	0.721710062843787	   
df.mm.trans1:probe11	0.0266732613012105	0.102113962135406	0.261210717353626	0.793988348421803	   
df.mm.trans1:probe12	0.120420556035070	0.102113962135406	1.1792761099152	0.23859246021517	   
df.mm.trans1:probe13	0.0561909822376885	0.102113962135406	0.550277171335077	0.582262564357631	   
df.mm.trans1:probe14	0.0421163902183823	0.102113962135406	0.412444971653677	0.680109188048455	   
df.mm.trans1:probe15	0.0863134116596003	0.102113962135406	0.845265523485869	0.398181898110904	   
df.mm.trans1:probe16	0.0922752376823221	0.102113962135406	0.903649567137179	0.366417284170644	   
df.mm.trans1:probe17	-0.105308402894930	0.102113962135406	-1.03128309481604	0.302678526086513	   
df.mm.trans1:probe18	-0.115441461384632	0.102113962135406	-1.13051593504474	0.258552881510708	   
df.mm.trans1:probe19	0.00873003388337023	0.102113962135406	0.0854930481670463	0.93188798278045	   
df.mm.trans1:probe20	0.0235491227245474	0.102113962135406	0.230616090415927	0.817664226466846	   
df.mm.trans1:probe21	0.0134248897004023	0.102113962135406	0.131469677795878	0.895432453614665	   
df.mm.trans2:probe2	-0.0101761606841081	0.102113962135406	-0.0996549391611517	0.92063993898694	   
df.mm.trans2:probe3	-0.0556562005907326	0.102113962135406	-0.545040065304006	0.585857774846872	   
df.mm.trans2:probe4	-0.0293095428919581	0.102113962135406	-0.287027770532425	0.774155548140042	   
df.mm.trans2:probe5	0.0853363958885806	0.102113962135406	0.835697627474507	0.403541541137974	   
df.mm.trans2:probe6	-0.0518377584068344	0.102113962135406	-0.50764613695134	0.611822939037756	   
df.mm.trans3:probe2	0.0442665521437315	0.102113962135406	0.433501464618842	0.664751905614295	   
df.mm.trans3:probe3	0.0746405837826976	0.102113962135406	0.73095375227652	0.464993111711682	   
df.mm.trans3:probe4	0.0590226822725854	0.102113962135406	0.578007953450278	0.563399887260779	   
df.mm.trans3:probe5	0.195428817690314	0.102113962135406	1.91383052428393	0.0559511808247891	.  
df.mm.trans3:probe6	0.041296917888649	0.102113962135406	0.40441989542907	0.68599776868624	   
df.mm.trans3:probe7	0.0515212512515094	0.102113962135406	0.50454658867502	0.613997889140326	   
df.mm.trans3:probe8	0.0783602727160439	0.102113962135406	0.767380592010873	0.443051727418216	   
df.mm.trans3:probe9	-0.0132961763222722	0.102113962135406	-0.130209190244141	0.89642932585802	   
df.mm.trans3:probe10	0.125451333650014	0.102113962135406	1.22854241502902	0.219556907711559	   
df.mm.trans3:probe11	0.261854284122827	0.102113962135406	2.56433379576047	0.0104947920865310	*  
df.mm.trans3:probe12	0.0444506787862307	0.102113962135406	0.435304613166295	0.663443216386001	   
df.mm.trans3:probe13	0.0658788807974603	0.102113962135406	0.645150569224832	0.518990115865354	   
df.mm.trans3:probe14	0.0842947947522545	0.102113962135406	0.825497248265394	0.409302880679302