chr19.12074_chr19_11182384_11186315_+_2.R 

fitVsDatCorrelation=0.86740256110151
cont.fitVsDatCorrelation=0.250749347801052

fstatistic=10859.5049989110,53,715
cont.fstatistic=2859.43836009138,53,715

residuals=-0.611862889026077,-0.0878888562475529,-0.00805172540517867,0.0731521628045815,0.612593320784375
cont.residuals=-0.529252808663105,-0.190918740224061,-0.0823201744048856,0.127655946072202,0.998058208842581

predictedValues:
Include	Exclude	Both
chr19.12074_chr19_11182384_11186315_+_2.R.tl.Lung	51.0340612791613	68.6028823497387	48.9288751029074
chr19.12074_chr19_11182384_11186315_+_2.R.tl.cerebhem	54.979515565586	62.2590733899999	55.9642398883696
chr19.12074_chr19_11182384_11186315_+_2.R.tl.cortex	50.4293465505991	68.9375486551659	50.3901742803111
chr19.12074_chr19_11182384_11186315_+_2.R.tl.heart	50.6599779636279	75.5103722644437	52.4256796774992
chr19.12074_chr19_11182384_11186315_+_2.R.tl.kidney	49.1241135604919	74.1906144432629	48.0195219844718
chr19.12074_chr19_11182384_11186315_+_2.R.tl.liver	51.5651420965497	80.346471660198	54.6444797597279
chr19.12074_chr19_11182384_11186315_+_2.R.tl.stomach	53.429717458518	89.1773018542135	53.2912292803628
chr19.12074_chr19_11182384_11186315_+_2.R.tl.testicle	52.4291522250233	84.3135791049237	55.3195621388181


diffExp=-17.5688210705774,-7.27955782441389,-18.5082021045668,-24.8503943008158,-25.0665008827710,-28.7813295636483,-35.7475843956955,-31.8844268799004
diffExpScore=0.994755798981727
diffExp1.5=0,0,0,0,-1,-1,-1,-1
diffExp1.5Score=0.8
diffExp1.4=0,0,0,-1,-1,-1,-1,-1
diffExp1.4Score=0.833333333333333
diffExp1.3=-1,0,-1,-1,-1,-1,-1,-1
diffExp1.3Score=0.875
diffExp1.2=-1,0,-1,-1,-1,-1,-1,-1
diffExp1.2Score=0.875

cont.predictedValues:
Include	Exclude	Both
Lung	57.1119570406688	58.8923633726789	49.944159131186
cerebhem	55.9005021040593	54.5135247532082	53.4427487261628
cortex	54.5464363745789	53.0912557172059	60.0769483968058
heart	60.2708685042907	59.29302648955	55.8712604363158
kidney	57.64419943593	54.1372959990796	56.9305786647839
liver	54.8968262118854	52.4681963997905	62.8199875834209
stomach	51.9260950869729	52.7063984008922	61.6092799068261
testicle	58.0665808280961	53.6628776450843	60.0718862763652
cont.diffExp=-1.78040633201003,1.38697735085116,1.45518065737298,0.977842014740688,3.50690343685046,2.42862981209491,-0.780303313919283,4.40370318301186
cont.diffExpScore=1.32713501779564

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=0.0199253136677577
cont.tran.correlation=0.697107807826213

tran.covariance=-8.93563388490186e-05
cont.tran.covariance=0.00153611260045079

tran.mean=63.561804401344
cont.tran.mean=55.5705252727482

weightedLogRatios:
wLogRatio
Lung	-1.20715484510762
cerebhem	-0.505970623346328
cortex	-1.27454794919945
heart	-1.64630850477804
kidney	-1.69058287324309
liver	-1.84700826494506
stomach	-2.16916099465538
testicle	-1.99391277080883

cont.weightedLogRatios:
wLogRatio
Lung	-0.124644715112801
cerebhem	0.100774771893182
cortex	0.107769365716086
heart	0.0669118125332667
kidney	0.252502900472814
liver	0.180216481381126
stomach	-0.0590244040966306
testicle	0.317222714731282

varWeightedLogRatios=0.282935965621287
cont.varWeightedLogRatios=0.0219385234286271

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.0139327108401	0.0750831325415395	53.4598461061731	5.37063471378032e-252	***
df.mm.trans1	-0.0595743663944952	0.0666752748022234	-0.893500125364441	0.371890042699341	   
df.mm.trans2	0.121720395802384	0.0606498329149431	2.00693703432106	0.0451323417371452	*  
df.mm.exp2	-0.156908073267352	0.081764987510225	-1.91901299132138	0.0553804771528126	.  
df.mm.exp3	-0.0364820335808767	0.081764987510225	-0.446181607699927	0.655601154588217	   
df.mm.exp4	0.0195495824126203	0.081764987510225	0.239094788709845	0.811100599027236	   
df.mm.exp5	0.0589199278947068	0.081764987510225	0.720600952667408	0.471390587503344	   
df.mm.exp6	0.0578857702930872	0.081764987510225	0.707953025564254	0.479205139129605	   
df.mm.exp7	0.222761764265234	0.081764987510225	2.72441507114982	0.00659891825727464	** 
df.mm.exp8	0.110419004336520	0.081764987510225	1.35044360304846	0.17730100346284	   
df.mm.trans1:exp2	0.231375466844712	0.0776396916658537	2.98011831165568	0.00297907935463537	** 
df.mm.trans2:exp2	0.059877804650485	0.0653784711794312	0.915864252716317	0.360046881658589	   
df.mm.trans1:exp3	0.0245620340140522	0.0776396916658537	0.316359242122734	0.751822184263843	   
df.mm.trans2:exp3	0.0413484857717806	0.0653784711794312	0.632448037187956	0.527296342995063	   
df.mm.trans1:exp4	-0.0269066508432647	0.0776396916658537	-0.346557930176562	0.72902541948237	   
df.mm.trans2:exp4	0.0763858947832647	0.0653784711794312	1.16836465284763	0.243049120873487	   
df.mm.trans1:exp5	-0.097063180456674	0.0776396916658537	-1.25017472859649	0.211644735040303	   
df.mm.trans2:exp5	0.0193831736859148	0.0653784711794312	0.296476398671325	0.766952384595602	   
df.mm.trans1:exp6	-0.0475331448690346	0.0776396916658537	-0.61222737815096	0.540581972075391	   
df.mm.trans2:exp6	0.100127858185121	0.0653784711794312	1.5315111592365	0.126085417754753	   
df.mm.trans1:exp7	-0.176887944396146	0.0776396916658537	-2.27831848118920	0.0230017796998795	*  
df.mm.trans2:exp7	0.0395302288124102	0.0653784711794312	0.60463678791019	0.545612179063688	   
df.mm.trans1:exp8	-0.0834495055555892	0.0776396916658537	-1.07483046061981	0.282813211631751	   
df.mm.trans2:exp8	0.0957893778126845	0.0653784711794312	1.46515169419901	0.143319110574672	   
df.mm.trans1:probe2	0.0299756109304646	0.0425250104831339	0.704893675272659	0.481105953821138	   
df.mm.trans1:probe3	0.0194152086102468	0.0425250104831339	0.456559760707105	0.648126165505542	   
df.mm.trans1:probe4	-0.11518826636144	0.0425250104831339	-2.70871811794439	0.00691596722605987	** 
df.mm.trans1:probe5	-0.183392244166044	0.0425250104831339	-4.31257375559685	1.84039713095899e-05	***
df.mm.trans1:probe6	0.111416594673557	0.0425250104831339	2.62002509600194	0.00897891184743974	** 
df.mm.trans1:probe7	-0.153050168529069	0.0425250104831339	-3.59906245266586	0.000341405827272458	***
df.mm.trans1:probe8	-0.00636066569866849	0.0425250104831339	-0.149574700309392	0.881142349768057	   
df.mm.trans1:probe9	-0.116815380479825	0.0425250104831339	-2.74698063922068	0.00616606633199989	** 
df.mm.trans1:probe10	-0.0698041546769002	0.0425250104831339	-1.64148471414453	0.101136588518759	   
df.mm.trans1:probe11	-0.0539963791462537	0.0425250104831339	-1.26975581035235	0.204584721032190	   
df.mm.trans1:probe12	-0.146850180753327	0.0425250104831339	-3.45326618582658	0.000586470390948872	***
df.mm.trans1:probe13	-0.108923910186528	0.0425250104831339	-2.56140819129788	0.0106288923520014	*  
df.mm.trans1:probe14	-0.0723040944378454	0.0425250104831339	-1.70027223077399	0.0895146076535135	.  
df.mm.trans1:probe15	0.320810643629465	0.0425250104831339	7.5440461973949	1.38774247773232e-13	***
df.mm.trans1:probe16	-0.0594151508510859	0.0425250104831339	-1.3971813334332	0.162792476353635	   
df.mm.trans1:probe17	-0.0989993425228784	0.0425250104831339	-2.32802629318911	0.0201887761303127	*  
df.mm.trans1:probe18	-0.133284906085695	0.0425250104831339	-3.13427097539594	0.00179325821060238	** 
df.mm.trans1:probe19	-0.119813106741659	0.0425250104831339	-2.81747389078666	0.0049739022849498	** 
df.mm.trans1:probe20	-0.0747411888628578	0.0425250104831339	-1.75758190330139	0.0792465799560956	.  
df.mm.trans1:probe21	0.572576598859088	0.0425250104831339	13.4644669655327	5.40807483868309e-37	***
df.mm.trans1:probe22	-0.109747244308759	0.0425250104831339	-2.58076936517833	0.0100562244825627	*  
df.mm.trans2:probe2	0.135691226543414	0.0425250104831339	3.19085698044051	0.00148052437060191	** 
df.mm.trans2:probe3	-0.00367923201033574	0.0425250104831339	-0.0865192499316369	0.931077876979554	   
df.mm.trans2:probe4	-0.0417348250656029	0.0425250104831339	-0.981418336913888	0.326718447753691	   
df.mm.trans2:probe5	0.316031938606895	0.0425250104831339	7.43167220928114	3.06901138331887e-13	***
df.mm.trans2:probe6	0.520505331592818	0.0425250104831339	12.2399812646550	2.07228211293544e-31	***
df.mm.trans3:probe2	-0.335054114813933	0.0425250104831339	-7.87898958771146	1.22997248154251e-14	***
df.mm.trans3:probe3	-0.172529177577367	0.0425250104831339	-4.05712251724887	5.51510164274983e-05	***
df.mm.trans3:probe4	-0.235260808158701	0.0425250104831339	-5.53229277279095	4.43689968648551e-08	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.33295182167092	0.146067820095881	29.6639726589109	1.08516596480459e-126	***
df.mm.trans1	-0.294759616335923	0.129711051137437	-2.27243256261648	0.0233564668085592	*  
df.mm.trans2	-0.231746174873600	0.117989068692145	-1.96413258823380	0.0499017159724215	*  
df.mm.exp2	-0.166408134050048	0.159066798114449	-1.04615253480061	0.295844143259012	   
df.mm.exp3	-0.334380956647249	0.159066798114449	-2.10214174554931	0.0358900276319371	*  
df.mm.exp4	-0.0515289080117161	0.159066798114449	-0.323945088620196	0.746074343018833	   
df.mm.exp5	-0.205839008513827	0.159066798114449	-1.29404131442770	0.196068970593165	   
df.mm.exp6	-0.384429920981477	0.159066798114449	-2.41678292100202	0.0159076981732983	*  
df.mm.exp7	-0.416073551945947	0.159066798114449	-2.61571589343604	0.00909190156501877	** 
df.mm.exp8	-0.261049545459769	0.159066798114449	-1.64113157839477	0.101209877927719	   
df.mm.trans1:exp2	0.144967996348896	0.151041387468396	0.959789887915518	0.337485416139558	   
df.mm.trans2:exp2	0.0891455374042114	0.127188230473703	0.700894548750272	0.483596854824753	   
df.mm.trans1:exp3	0.288419839098242	0.151041387468396	1.90954177482375	0.056592172523933	.  
df.mm.trans2:exp3	0.230681767438158	0.127188230473703	1.81370372540762	0.0701424403504257	.  
df.mm.trans1:exp4	0.105364285622943	0.151041387468396	0.697585525324902	0.48566320801594	   
df.mm.trans2:exp4	0.0583091818218911	0.127188230473703	0.45844793661114	0.646769968866392	   
df.mm.trans1:exp5	0.215115133265009	0.151041387468396	1.42421317011551	0.154821048901052	   
df.mm.trans2:exp5	0.121650918709994	0.127188230473703	0.95646364649398	0.339161317814623	   
df.mm.trans1:exp6	0.344871957472312	0.151041387468396	2.28329442183172	0.0227055942517736	*  
df.mm.trans2:exp6	0.26892569601974	0.127188230473703	2.11439136324287	0.0348273818751418	*  
df.mm.trans1:exp7	0.32088151126316	0.151041387468396	2.12446082919029	0.0339741362642048	*  
df.mm.trans2:exp7	0.305098983767476	0.127188230473703	2.39879887180723	0.0167040856669812	*  
df.mm.trans1:exp8	0.277626342927387	0.151041387468396	1.83808125428852	0.0664651178531599	.  
df.mm.trans2:exp8	0.168059588259383	0.127188230473703	1.32134543922389	0.186808875073746	   
df.mm.trans1:probe2	0.0538979055901025	0.0827287750333185	0.651501313399062	0.514932272943253	   
df.mm.trans1:probe3	0.076980238439141	0.0827287750333185	0.930513456873232	0.352419359740327	   
df.mm.trans1:probe4	0.0604115673334223	0.0827287750333185	0.730236454112755	0.465484850299703	   
df.mm.trans1:probe5	-0.0419355149052971	0.0827287750333185	-0.506903612297025	0.612378739438851	   
df.mm.trans1:probe6	-0.0424062710812529	0.0827287750333184	-0.512593968231417	0.608393685645535	   
df.mm.trans1:probe7	0.00260243178071147	0.0827287750333185	0.0314573953218013	0.97491354772723	   
df.mm.trans1:probe8	-0.0146206848481696	0.0827287750333185	-0.176730343732048	0.859770252647653	   
df.mm.trans1:probe9	0.0889692067493805	0.0827287750333184	1.07543242014098	0.282543930914664	   
df.mm.trans1:probe10	-0.0183576908935715	0.0827287750333184	-0.221902123972923	0.824453440576055	   
df.mm.trans1:probe11	0.062268632988616	0.0827287750333184	0.752684092850859	0.45188750383316	   
df.mm.trans1:probe12	0.0293729544541635	0.0827287750333185	0.355051243564694	0.722655907417643	   
df.mm.trans1:probe13	0.00574390744946584	0.0827287750333184	0.0694305874485935	0.944666295290435	   
df.mm.trans1:probe14	-0.042821706132932	0.0827287750333185	-0.517615619422454	0.604886585067255	   
df.mm.trans1:probe15	-0.00105452032786132	0.0827287750333185	-0.012746717540984	0.989833422031248	   
df.mm.trans1:probe16	-0.0419297186955362	0.0827287750333184	-0.506833549495316	0.612427877630664	   
df.mm.trans1:probe17	-0.0716650877921294	0.0827287750333185	-0.866265549843652	0.386635142798482	   
df.mm.trans1:probe18	0.00765665357951565	0.0827287750333185	0.0925512746493827	0.926285980036393	   
df.mm.trans1:probe19	0.0104132053346209	0.0827287750333185	0.125871624841865	0.899868928462115	   
df.mm.trans1:probe20	0.0889964077984409	0.0827287750333184	1.07576121805984	0.282396919948956	   
df.mm.trans1:probe21	-0.00374972593726160	0.0827287750333185	-0.0453255343833076	0.96386049054924	   
df.mm.trans1:probe22	-0.0314185294533352	0.0827287750333185	-0.37977752530098	0.70422334165488	   
df.mm.trans2:probe2	-0.0151526172641742	0.0827287750333185	-0.183160179249258	0.854724262687082	   
df.mm.trans2:probe3	-0.0828435692053504	0.0827287750333185	-1.00138759666133	0.316978229448954	   
df.mm.trans2:probe4	-0.0374691134471459	0.0827287750333185	-0.452915124538656	0.650747261095915	   
df.mm.trans2:probe5	-0.0228986741348374	0.0827287750333185	-0.276792133397541	0.782019718352006	   
df.mm.trans2:probe6	-0.0965782125456563	0.0827287750333185	-1.16740774303451	0.243434908034902	   
df.mm.trans3:probe2	0.0753650759371025	0.0827287750333185	0.910989869084242	0.362607728739953	   
df.mm.trans3:probe3	0.217364122322614	0.0827287750333185	2.6274306882348	0.0087876655907379	** 
df.mm.trans3:probe4	0.125108935681168	0.0827287750333185	1.51227835333934	0.130904914614089