chr2.13257_chr2_119670687_119672114_-_2.R 

fitVsDatCorrelation=0.680069261566951
cont.fitVsDatCorrelation=0.244388486614073

fstatistic=9901.42916509003,53,715
cont.fstatistic=5654.352360603,53,715

residuals=-0.505708995761472,-0.0856939418064254,-0.00464624713727966,0.0720510525322733,0.740168319757562
cont.residuals=-0.507537001155986,-0.121686096267901,-0.0295519299445570,0.082696338316532,0.94732944439775

predictedValues:
Include	Exclude	Both
chr2.13257_chr2_119670687_119672114_-_2.R.tl.Lung	54.5415382283911	53.6742481839352	62.8022811627658
chr2.13257_chr2_119670687_119672114_-_2.R.tl.cerebhem	61.1826469677015	49.044174214994	56.6636173639384
chr2.13257_chr2_119670687_119672114_-_2.R.tl.cortex	50.9300980550535	53.9749107294779	59.6968700447651
chr2.13257_chr2_119670687_119672114_-_2.R.tl.heart	53.1130418514664	53.9664645734078	53.9590906138098
chr2.13257_chr2_119670687_119672114_-_2.R.tl.kidney	52.6398360539149	50.8494684533962	63.4149394572439
chr2.13257_chr2_119670687_119672114_-_2.R.tl.liver	53.5452394217171	52.5696583654314	62.2666598833675
chr2.13257_chr2_119670687_119672114_-_2.R.tl.stomach	53.8532197953494	55.8211122191087	56.2843446505457
chr2.13257_chr2_119670687_119672114_-_2.R.tl.testicle	55.287137667957	51.6006452624539	60.314017498516


diffExp=0.867290044455928,12.1384727527075,-3.04481267442437,-0.853422721941428,1.79036760051874,0.975581056285726,-1.96789242375926,3.68649240550312
diffExpScore=1.73548517779868
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=0,1,0,0,0,0,0,0
diffExp1.2Score=0.5

cont.predictedValues:
Include	Exclude	Both
Lung	56.0187676921854	60.3487724164476	54.9643861014208
cerebhem	53.1852525773262	53.1121452788919	57.9789548444312
cortex	54.7735541647875	53.9474794885873	54.2742512224339
heart	56.1900696925082	53.679876199219	56.932832353505
kidney	53.2420607694287	58.2342646033975	53.987515235783
liver	52.1012190684559	56.3888385175281	59.2304382415136
stomach	54.7857071691635	54.1493318732735	51.4592065611134
testicle	53.7291458267284	57.6701116642314	54.3311724694185
cont.diffExp=-4.33000472426215,0.073107298434266,0.826074676200214,2.51019349328912,-4.99220383396876,-4.28761944907222,0.636375295889991,-3.94096583750294
cont.diffExpScore=1.48889903242824

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=-0.660920460408696
cont.tran.correlation=0.00922217428971049

tran.covariance=-0.00146617355569455
cont.tran.covariance=-3.9254288285629e-06

tran.mean=53.5370900027348
cont.tran.mean=55.097287312635

weightedLogRatios:
wLogRatio
Lung	0.0639718780126427
cerebhem	0.885296897544542
cortex	-0.229908609685757
heart	-0.0634487323755353
kidney	0.136551355872145
liver	0.0730239860966958
stomach	-0.143710832348025
testicle	0.274508883193534

cont.weightedLogRatios:
wLogRatio
Lung	-0.302499502654775
cerebhem	0.00546508523695274
cortex	0.0607193476688209
heart	0.183076579694348
kidney	-0.360263031043842
liver	-0.315756623778976
stomach	0.0467066406289305
testicle	-0.284503217328018

varWeightedLogRatios=0.120091497450921
cont.varWeightedLogRatios=0.0463508033010756

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.37584246105805	0.0781065853016144	43.2209710362062	1.32301447064632e-201	***
df.mm.trans1	0.613134836172527	0.0693601593669158	8.83987063710506	7.39057181081793e-18	***
df.mm.trans2	0.589834035494597	0.063092084570111	9.34878027114708	1.11165368153688e-19	***
df.mm.exp2	0.127548153426162	0.0850575056670628	1.49955200808990	0.134171883499721	   
df.mm.exp3	-0.0122107227803142	0.0850575056670628	-0.143558439488106	0.885889610290005	   
df.mm.exp4	0.130654635868277	0.0850575056670628	1.53607415175909	0.124962570879436	   
df.mm.exp5	-0.099261150542469	0.0850575056670628	-1.16698873031856	0.243603972610713	   
df.mm.exp6	-0.0306646392613543	0.0850575056670628	-0.360516559013424	0.718567353620888	   
df.mm.exp7	0.136093399948423	0.0850575056670628	1.60001635224442	0.110036694705164	   
df.mm.exp8	0.0146054072125424	0.0850575056670628	0.171712150479838	0.863712419413387	   
df.mm.trans1:exp2	-0.012647131088048	0.0807660921250862	-0.156589612735760	0.875612492783785	   
df.mm.trans2:exp2	-0.21776008369076	0.0680111359663934	-3.20182982678546	0.00142604425100397	** 
df.mm.trans1:exp3	-0.0562977917282925	0.0807660921250863	-0.697047365385729	0.485999719539283	   
df.mm.trans2:exp3	0.0177967084095717	0.0680111359663934	0.261673447394934	0.793648574632126	   
df.mm.trans1:exp4	-0.157194709215843	0.0807660921250862	-1.94629584123481	0.0520105837473264	.  
df.mm.trans2:exp4	-0.125225145485882	0.0680111359663934	-1.8412447271541	0.0659997924737577	.  
df.mm.trans1:exp5	0.0637717423834494	0.0807660921250862	0.789585588524985	0.430031588378267	   
df.mm.trans2:exp5	0.0451974829084648	0.0680111359663934	0.664560035150691	0.506546204100754	   
df.mm.trans1:exp6	0.0122289518215462	0.0807660921250863	0.151411953949767	0.879693469614389	   
df.mm.trans2:exp6	0.0098704185646335	0.0680111359663934	0.14512944717628	0.884649569661255	   
df.mm.trans1:exp7	-0.148793786898928	0.0807660921250862	-1.84228038009421	0.0658480407439343	.  
df.mm.trans2:exp7	-0.096874583784995	0.0680111359663934	-1.42439296754084	0.154769041304691	   
df.mm.trans1:exp8	-0.00102769833138842	0.0807660921250862	-0.0127243785646677	0.98985123827959	   
df.mm.trans2:exp8	-0.0540045665636214	0.0680111359663934	-0.794054764653642	0.427427006158634	   
df.mm.trans1:probe2	-0.159130649970811	0.0442374105384501	-3.59719631040562	0.000343819518295365	***
df.mm.trans1:probe3	-0.087680313935464	0.0442374105384501	-1.98203992657424	0.0478572462900771	*  
df.mm.trans1:probe4	0.0133880699478283	0.0442374105384501	0.302641356826067	0.762251215554585	   
df.mm.trans1:probe5	0.0321052429593581	0.0442374105384501	0.725748694794262	0.468230322916226	   
df.mm.trans1:probe6	-0.0139849314295706	0.0442374105384501	-0.316133590536797	0.75199337507947	   
df.mm.trans1:probe7	-0.0594269886162317	0.0442374105384501	-1.34336499114430	0.179580021613952	   
df.mm.trans1:probe8	-0.0300473156104874	0.0442374105384501	-0.679228626738244	0.497212823277665	   
df.mm.trans1:probe9	-0.115654426067436	0.0442374105384501	-2.61440316374106	0.00912657396007624	** 
df.mm.trans1:probe10	0.139671930754053	0.0442374105384501	3.157326096939	0.00165913929404749	** 
df.mm.trans1:probe11	-0.0243640484957162	0.0442374105384501	-0.550756660463648	0.581972534759352	   
df.mm.trans1:probe12	0.138785753538284	0.0442374105384501	3.13729379385023	0.00177512195088749	** 
df.mm.trans1:probe13	0.151805827915057	0.0442374105384501	3.43161649986523	0.00063450669829908	***
df.mm.trans1:probe14	0.26193783022583	0.0442374105384501	5.92118360992581	4.96246354283409e-09	***
df.mm.trans1:probe15	0.000394885067573659	0.0442374105384501	0.00892649598534784	0.992880271241222	   
df.mm.trans1:probe16	0.0215123294607324	0.0442374105384501	0.48629269206511	0.626908669743126	   
df.mm.trans1:probe17	-0.145951090859542	0.0442374105384501	-3.29926840389279	0.00101741468693814	** 
df.mm.trans1:probe18	0.0729545891473911	0.0442374105384501	1.64916047886620	0.0995540118996895	.  
df.mm.trans1:probe19	-0.0082122928755505	0.0442374105384501	-0.185641355938150	0.852778670353323	   
df.mm.trans1:probe20	0.0739225186723465	0.0442374105384501	1.67104081754728	0.0951512120860657	.  
df.mm.trans1:probe21	0.0221662810894485	0.0442374105384501	0.501075465757249	0.616472234752478	   
df.mm.trans1:probe22	-0.0245758308697813	0.0442374105384501	-0.555544064868366	0.578696355570883	   
df.mm.trans2:probe2	-0.0404886592104128	0.0442374105384501	-0.915258346218549	0.36036458500579	   
df.mm.trans2:probe3	0.0821799352628965	0.0442374105384501	1.85770220866494	0.063622215239287	.  
df.mm.trans2:probe4	0.120037636300337	0.0442374105384501	2.71348695231615	0.00681822527309764	** 
df.mm.trans2:probe5	0.044310755839214	0.0442374105384501	1.00165799263273	0.316847664211708	   
df.mm.trans2:probe6	-0.0334712652512374	0.0442374105384501	-0.756628040471423	0.44952197114994	   
df.mm.trans3:probe2	-0.589522654339716	0.0442374105384501	-13.3263373051033	2.39297927128395e-36	***
df.mm.trans3:probe3	-0.55030320872473	0.0442374105384501	-12.4397699147969	2.67942916039877e-32	***
df.mm.trans3:probe4	-0.615453879587013	0.0442374105384501	-13.9125204684410	4.08783032553145e-39	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.11403376868251	0.103305477582148	39.8239654369831	1.22428892826253e-183	***
df.mm.trans1	-0.0935601016695809	0.0917372633933987	-1.01987020550597	0.308134975939568	   
df.mm.trans2	-0.0240166623194570	0.0834469706107342	-0.287807479932263	0.773577512058571	   
df.mm.exp2	-0.233035247870120	0.112498916844859	-2.07144436947323	0.0386754014394352	*  
df.mm.exp3	-0.121973387369591	0.112498916844859	-1.08421832663329	0.278633462523444	   
df.mm.exp4	-0.149235889573009	0.112498916844859	-1.32655401277163	0.185079789482900	   
df.mm.exp5	-0.0685721283134912	0.112498916844859	-0.609535898092736	0.542362926922169	   
df.mm.exp6	-0.215117909981831	0.112498916844859	-1.91217761037191	0.0562527580802188	.  
df.mm.exp7	-0.0647563395456145	0.112498916844859	-0.575617449143233	0.565054895537733	   
df.mm.exp8	-0.0755454180035691	0.112498916844859	-0.671521292136079	0.502105395841682	   
df.mm.trans1:exp2	0.181129626645963	0.106822999459091	1.69560513712525	0.0903960335978079	.  
df.mm.trans2:exp2	0.105300268347060	0.0899530155464046	1.17061410012139	0.242143931758279	   
df.mm.trans1:exp3	0.0994941046569559	0.106822999459091	0.931392164241352	0.351965117296429	   
df.mm.trans2:exp3	0.0098437523193532	0.0899530155464047	0.109432154770565	0.912890436261806	   
df.mm.trans1:exp4	0.152289163047636	0.106822999459091	1.42562148431299	0.154414040464234	   
df.mm.trans2:exp4	0.0321334696928901	0.0899530155464046	0.357225041291842	0.721028750424342	   
df.mm.trans1:exp5	0.0177340563060024	0.106822999459091	0.166013465225658	0.86819328479104	   
df.mm.trans2:exp5	0.0329054424351012	0.0899530155464046	0.365806996410543	0.714617298500996	   
df.mm.trans1:exp6	0.142619485144579	0.106822999459091	1.33510092271091	0.182268232945138	   
df.mm.trans2:exp6	0.147248544008916	0.0899530155464047	1.63694950207594	0.102081049049931	   
df.mm.trans1:exp7	0.0424989094500543	0.106822999459091	0.397844187724101	0.69086379578039	   
df.mm.trans2:exp7	-0.0436386317942315	0.0899530155464047	-0.485126946875054	0.627734898976409	   
df.mm.trans1:exp8	0.033814253157207	0.106822999459091	0.316544689143993	0.751681503843451	   
df.mm.trans2:exp8	0.0301438556240238	0.0899530155464046	0.335106671420852	0.7376429105601	   
df.mm.trans1:probe2	0.056245261602334	0.0585093664641065	0.961303548498316	0.336724538302160	   
df.mm.trans1:probe3	0.00178712745169967	0.0585093664641065	0.0305442967459921	0.975641489897096	   
df.mm.trans1:probe4	-0.00426405312802115	0.0585093664641065	-0.072878128506775	0.941923480357092	   
df.mm.trans1:probe5	-0.0256838271700680	0.0585093664641065	-0.438969497060341	0.660816257371543	   
df.mm.trans1:probe6	0.000986181708198247	0.0585093664641065	0.0168551082979720	0.986556908154585	   
df.mm.trans1:probe7	0.00785710388776398	0.0585093664641065	0.134287967253654	0.893212658088786	   
df.mm.trans1:probe8	0.0331050400738845	0.0585093664641065	0.565807529196088	0.571702146630676	   
df.mm.trans1:probe9	-0.0140824427022441	0.0585093664641065	-0.240686979765593	0.809866727158776	   
df.mm.trans1:probe10	-0.064634756170317	0.0585093664641065	-1.10469075425673	0.269665214922291	   
df.mm.trans1:probe11	-0.00927265195324259	0.0585093664641065	-0.158481496444352	0.874122150237008	   
df.mm.trans1:probe12	0.0749620467533166	0.0585093664641065	1.28119737545446	0.20053971688678	   
df.mm.trans1:probe13	-0.035315104539664	0.0585093664641065	-0.603580361126087	0.546314102495862	   
df.mm.trans1:probe14	-0.0402763349242656	0.0585093664641065	-0.68837414175342	0.491440479990664	   
df.mm.trans1:probe15	0.00676965632049453	0.0585093664641065	0.115702095743038	0.907921132041905	   
df.mm.trans1:probe16	-0.040040988336504	0.0585093664641065	-0.684351767183598	0.493974817440323	   
df.mm.trans1:probe17	0.0260055975194919	0.0585093664641065	0.444468964391291	0.656838062507484	   
df.mm.trans1:probe18	0.0347257473460073	0.0585093664641065	0.593507491955333	0.553029270549796	   
df.mm.trans1:probe19	-0.0111135241294403	0.0585093664641065	-0.189944359357541	0.849406642853119	   
df.mm.trans1:probe20	0.0147367063220862	0.0585093664641065	0.251869182879064	0.801214558241172	   
df.mm.trans1:probe21	0.100719756036006	0.0585093664641065	1.72142961243297	0.0856054456618815	.  
df.mm.trans1:probe22	0.0223241863865844	0.0585093664641065	0.381548933712683	0.702909326307007	   
df.mm.trans2:probe2	-0.0293273779846965	0.0585093664641065	-0.501242446415615	0.616354785860185	   
df.mm.trans2:probe3	-0.00163988811271580	0.0585093664641065	-0.0280277878879754	0.97764780922748	   
df.mm.trans2:probe4	0.0632428843046269	0.0585093664641065	1.08090188164017	0.280105202185448	   
df.mm.trans2:probe5	0.00852011905141467	0.0585093664641065	0.145619745458045	0.884262621233557	   
df.mm.trans2:probe6	0.0604392617811064	0.0585093664641065	1.03298438239258	0.301960473699033	   
df.mm.trans3:probe2	0.0079684255020714	0.0585093664641065	0.136190596200691	0.891708944265277	   
df.mm.trans3:probe3	0.0325392007195518	0.0585093664641065	0.556136610016338	0.578291462028578	   
df.mm.trans3:probe4	0.000406155278996707	0.0585093664641065	0.00694171384073813	0.994463294494816