chr6.19951_chr6_30556379_30559003_+_2.R 

fitVsDatCorrelation=0.859469579208373
cont.fitVsDatCorrelation=0.242882620448229

fstatistic=5126.99337787851,53,715
cont.fstatistic=1413.98965913801,53,715

residuals=-1.24276967878238,-0.111329853307615,-0.0137477743255022,0.0983361350850493,1.49699627332442
cont.residuals=-0.64769782342869,-0.275759124977576,-0.0993247842354775,0.173261922783565,2.92494232391267

predictedValues:
Include	Exclude	Both
chr6.19951_chr6_30556379_30559003_+_2.R.tl.Lung	60.1761762030592	59.3676995607176	55.56678651563
chr6.19951_chr6_30556379_30559003_+_2.R.tl.cerebhem	59.3615679051881	82.4283244504278	55.1606550753224
chr6.19951_chr6_30556379_30559003_+_2.R.tl.cortex	56.7466257601699	55.0800969006394	59.5354413913235
chr6.19951_chr6_30556379_30559003_+_2.R.tl.heart	56.6054427920079	59.2506921037769	77.966869544461
chr6.19951_chr6_30556379_30559003_+_2.R.tl.kidney	60.6026212703383	60.9749880329875	58.7690658183038
chr6.19951_chr6_30556379_30559003_+_2.R.tl.liver	61.7758552159418	60.2530373207027	55.7954441633607
chr6.19951_chr6_30556379_30559003_+_2.R.tl.stomach	61.0810026842037	122.9316290118	271.104320557478
chr6.19951_chr6_30556379_30559003_+_2.R.tl.testicle	56.9964373980823	58.5943302157658	53.2443234191393


diffExp=0.808476642341674,-23.0667565452397,1.66652885953052,-2.64524931176893,-0.37236676264925,1.52281789523916,-61.8506263275964,-1.59789281768354
diffExpScore=1.08084175498062
diffExp1.5=0,0,0,0,0,0,-1,0
diffExp1.5Score=0.5
diffExp1.4=0,0,0,0,0,0,-1,0
diffExp1.4Score=0.5
diffExp1.3=0,-1,0,0,0,0,-1,0
diffExp1.3Score=0.666666666666667
diffExp1.2=0,-1,0,0,0,0,-1,0
diffExp1.2Score=0.666666666666667

cont.predictedValues:
Include	Exclude	Both
Lung	69.5188772590431	69.966806862434	51.2424699479339
cerebhem	64.1351110280113	60.2730798529659	64.6385498901821
cortex	62.2317109884881	60.6625013564046	58.5032939634714
heart	63.4708155964098	58.4802269330079	62.6940259996127
kidney	64.8393024765278	67.0821367351181	77.5961277915783
liver	65.899881917659	55.4567741356095	65.1180351592285
stomach	64.7197195551161	56.2819170606213	56.5192313276113
testicle	72.6285929078874	69.8080207348753	58.2959217640808
cont.diffExp=-0.447929603390975,3.86203117504544,1.56920963208346,4.99058866340199,-2.24283425859025,10.4431077820495,8.43780249449481,2.82057217301214
cont.diffExpScore=1.14397505314365

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=0.423437659738677
cont.tran.correlation=0.697634966877148

tran.covariance=0.00425330854918219
cont.tran.covariance=0.00323956138911145

tran.mean=64.514157926613
cont.tran.mean=64.0909672125112

weightedLogRatios:
wLogRatio
Lung	0.0553292245477561
cerebhem	-1.39447282581807
cortex	0.119937268892326
heart	-0.185381258029091
kidney	-0.0251602969498119
liver	0.102609786479906
stomach	-3.12078553188915
testicle	-0.112167537463092

cont.weightedLogRatios:
wLogRatio
Lung	-0.0272627609758033
cerebhem	0.256495632347439
cortex	0.105171851281014
heart	0.336544159892704
kidney	-0.142447613830275
liver	0.707707066143023
stomach	0.572770523228482
testicle	0.168957615991593

varWeightedLogRatios=1.30858695865211
cont.varWeightedLogRatios=0.0827972332726704

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.03606411142155	0.113044323849854	35.7033769938087	4.6387177625183e-161	***
df.mm.trans1	-0.0831871911393294	0.100385547357798	-0.828676969233729	0.407563756725074	   
df.mm.trans2	-0.0369255967974442	0.09131370950304	-0.404381740687195	0.686052958312378	   
df.mm.exp2	0.321885032475901	0.123104449891878	2.61473109021333	0.00911790155628459	** 
df.mm.exp3	-0.202628331047319	0.123104449891878	-1.64598705591298	0.10020588595566	   
df.mm.exp4	-0.401842554960071	0.123104449891878	-3.26424069408545	0.00114983253753944	** 
df.mm.exp5	-0.0222549016218149	0.123104449891878	-0.180780643115349	0.856590986547844	   
df.mm.exp6	0.0369321867064126	0.123104449891878	0.300006918830716	0.76425907636743	   
df.mm.exp7	-0.842115630193628	0.123104449891878	-6.84065954507132	1.69436680625684e-11	***
df.mm.exp8	-0.0247056663958570	0.123104449891878	-0.200688654370788	0.840999103496474	   
df.mm.trans1:exp2	-0.335514550320673	0.116893450648478	-2.87025961214569	0.00422249390740804	** 
df.mm.trans2:exp2	0.0062937886753701	0.0984330943401585	0.063939762511381	0.949036062299544	   
df.mm.trans1:exp3	0.143947997818390	0.116893450648478	1.23144621892693	0.218560894897432	   
df.mm.trans2:exp3	0.127666464148707	0.0984330943401586	1.29698720744799	0.195053955247279	   
df.mm.trans1:exp4	0.340671168096697	0.116893450648478	2.91437344185486	0.00367536194512332	** 
df.mm.trans2:exp4	0.399869715955106	0.0984330943401585	4.06235035722094	5.3957327491353e-05	***
df.mm.trans1:exp5	0.0293165191955392	0.116893450648478	0.250796935439093	0.802043163733618	   
df.mm.trans2:exp5	0.0489683494344263	0.0984330943401585	0.497478513326064	0.619004601170904	   
df.mm.trans1:exp6	-0.0106961207337347	0.116893450648478	-0.0915031652705683	0.927118420561745	   
df.mm.trans2:exp6	-0.022129503672804	0.0984330943401586	-0.224817718280097	0.822185315585961	   
df.mm.trans1:exp7	0.85703999647404	0.116893450648478	7.33180509018703	6.16235682087225e-13	***
df.mm.trans2:exp7	1.56999366929659	0.0984330943401586	15.9498558876054	3.18684707280485e-49	***
df.mm.trans1:exp8	-0.0295820993631120	0.116893450648478	-0.253068920448514	0.800287697250486	   
df.mm.trans2:exp8	0.0115933042722975	0.0984330943401585	0.117778521035152	0.9062762264191	   
df.mm.trans1:probe2	-0.0688461515905364	0.0640251797447884	-1.07529806030947	0.282604020369221	   
df.mm.trans1:probe3	0.41547028680655	0.0640251797447884	6.48917017433862	1.61249358652547e-10	***
df.mm.trans1:probe4	-0.101682809091016	0.0640251797447884	-1.58816905311840	0.112690197991857	   
df.mm.trans1:probe5	-0.0522823700204277	0.0640251797447884	-0.816590757399371	0.414434320069152	   
df.mm.trans1:probe6	-0.135542124113344	0.0640251797447884	-2.11701278549519	0.0346035062167234	*  
df.mm.trans1:probe7	0.117684199004898	0.0640251797447884	1.83809244228599	0.0664634674155511	.  
df.mm.trans1:probe8	-0.145159790492632	0.0640251797447884	-2.26722972229451	0.0236739433301339	*  
df.mm.trans1:probe9	0.0459347191808132	0.0640251797447884	0.717447719224126	0.473332204680463	   
df.mm.trans1:probe10	-0.118512504981057	0.0640251797447884	-1.85102963323900	0.0645775010010646	.  
df.mm.trans1:probe11	0.759428236948306	0.0640251797447884	11.8613995302391	9.42760552045374e-30	***
df.mm.trans1:probe12	0.59325359146231	0.0640251797447884	9.26594183455768	2.22839392951987e-19	***
df.mm.trans1:probe13	0.592507083362425	0.0640251797447884	9.25428223277505	2.45661607810031e-19	***
df.mm.trans1:probe14	0.706907886735111	0.0640251797447884	11.0410917947115	2.79916813375e-26	***
df.mm.trans1:probe15	0.749345747415913	0.0640251797447884	11.7039225880019	4.50799496950937e-29	***
df.mm.trans1:probe16	0.605630750168491	0.0640251797447884	9.45925888193682	4.36513118722033e-20	***
df.mm.trans1:probe17	-0.0633905923906169	0.0640251797447884	-0.990088472118298	0.322465810983618	   
df.mm.trans1:probe18	-0.0375540173026955	0.0640251797447884	-0.586550751632249	0.557690599051269	   
df.mm.trans1:probe19	-0.0140065905467398	0.0640251797447884	-0.218766907060185	0.826894055915979	   
df.mm.trans1:probe20	-0.0584548011390644	0.0640251797447884	-0.912997064156193	0.361551830740833	   
df.mm.trans1:probe21	0.0142378245224767	0.0640251797447884	0.222378517002690	0.824082739735389	   
df.mm.trans1:probe22	-0.050578725120502	0.0640251797447884	-0.789981774703554	0.429800323417859	   
df.mm.trans2:probe2	0.0225552052989566	0.0640251797447884	0.352286481488442	0.724727236513994	   
df.mm.trans2:probe3	0.274423167489814	0.0640251797447884	4.2861756668813	2.06694042410429e-05	***
df.mm.trans2:probe4	0.107941947346867	0.0640251797447884	1.68592962608049	0.0922456380646503	.  
df.mm.trans2:probe5	0.115656145919796	0.0640251797447884	1.80641657517894	0.0712736342331338	.  
df.mm.trans2:probe6	0.325541387987336	0.0640251797447884	5.08458374166196	4.71051039634551e-07	***
df.mm.trans3:probe2	0.187553716570306	0.0640251797447884	2.92937430738837	0.00350456645495003	** 
df.mm.trans3:probe3	-0.0337507714867195	0.0640251797447884	-0.527148406630858	0.598254049680941	   
df.mm.trans3:probe4	0.252734392722428	0.0640251797447884	3.94742183824951	8.68035419036328e-05	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.72997228924686	0.214518975257388	22.0492023308038	1.33755973575263e-82	***
df.mm.trans1	-0.391955966413852	0.190497001675637	-2.05754401888825	0.0399960010550809	*  
df.mm.trans2	-0.489460874775468	0.173281795338618	-2.82465260599931	0.00486504927822895	** 
df.mm.exp2	-0.461984038759316	0.233609610293265	-1.97759004083504	0.0483585841616203	*  
df.mm.exp3	-0.385943223290698	0.233609610293265	-1.65208624254027	0.0989560189448297	.  
df.mm.exp4	-0.472047858141406	0.233609610293265	-2.02066968712809	0.0436862592732596	*  
df.mm.exp5	-0.526738390626513	0.233609610293265	-2.25478048597943	0.0244488635339478	*  
df.mm.exp6	-0.525511619522144	0.233609610293265	-2.24952911338894	0.0247822913916366	*  
df.mm.exp7	-0.38719231705899	0.233609610293265	-1.65743317054861	0.0978705933169847	.  
df.mm.exp8	-0.0874751323676436	0.233609610293265	-0.374450059044363	0.708180530782411	   
df.mm.trans1:exp2	0.381377675742605	0.221823284826909	1.71928603455763	0.0859951016912853	.  
df.mm.trans2:exp2	0.312848663969932	0.186791922054491	1.67485114200318	0.094400716389492	.  
df.mm.trans1:exp3	0.275209585158268	0.221823284826909	1.24067040740568	0.215134468798589	   
df.mm.trans2:exp3	0.243248018474571	0.186791922054491	1.30224056693207	0.193253489109149	   
df.mm.trans1:exp4	0.381029730558055	0.221823284826909	1.71771746530296	0.0862811424904275	.  
df.mm.trans2:exp4	0.292715612191476	0.186791922054491	1.56706783126352	0.117541276325436	   
df.mm.trans1:exp5	0.457051998897475	0.221823284826909	2.06043292188247	0.0397184294163638	*  
df.mm.trans2:exp5	0.484635238701749	0.186791922054491	2.59451925635398	0.0096664155681508	** 
df.mm.trans1:exp6	0.472049938259674	0.221823284826909	2.12804502750024	0.0336747782632946	*  
df.mm.trans2:exp6	0.293094550506909	0.186791922054491	1.56909649669651	0.117067874533235	   
df.mm.trans1:exp7	0.315659925669023	0.221823284826909	1.42302430475383	0.155165270890315	   
df.mm.trans2:exp7	0.169544669728564	0.186791922054491	0.907665962552192	0.364360541915161	   
df.mm.trans1:exp8	0.131235487441509	0.221823284826909	0.591621783727135	0.554290886693646	   
df.mm.trans2:exp8	0.0852031038687464	0.186791922054491	0.456139124923673	0.648428449958431	   
df.mm.trans1:probe2	-0.158925542333999	0.121497616879592	-1.30805481140835	0.191275088476703	   
df.mm.trans1:probe3	-0.186261093331683	0.121497616879592	-1.53304318319490	0.125707547265832	   
df.mm.trans1:probe4	-0.0309149176341911	0.121497616879592	-0.254448757335124	0.799222048398785	   
df.mm.trans1:probe5	-0.161945023839028	0.121497616879592	-1.33290699849299	0.182986890901262	   
df.mm.trans1:probe6	-0.151115811982108	0.121497616879592	-1.24377593456725	0.213989660786493	   
df.mm.trans1:probe7	-0.208330794362658	0.121497616879592	-1.71469037593652	0.0868353327835128	.  
df.mm.trans1:probe8	-0.0580093967787708	0.121497616879592	-0.477452959725622	0.633185523627566	   
df.mm.trans1:probe9	-0.232077598488628	0.121497616879592	-1.91014115707821	0.0565148408809345	.  
df.mm.trans1:probe10	-0.0123586216370426	0.121497616879592	-0.101719045644249	0.91900820260393	   
df.mm.trans1:probe11	-0.147359543859509	0.121497616879592	-1.2128595411509	0.225584087848132	   
df.mm.trans1:probe12	-0.237390922165021	0.121497616879592	-1.95387307390797	0.0511057595752411	.  
df.mm.trans1:probe13	-0.105688734308455	0.121497616879592	-0.869883188023316	0.384656174971141	   
df.mm.trans1:probe14	-0.117851814485984	0.121497616879592	-0.969992807371515	0.332378084274505	   
df.mm.trans1:probe15	-0.00805773782659587	0.121497616879592	-0.0663201306621624	0.947141495884833	   
df.mm.trans1:probe16	-0.125370050107268	0.121497616879592	-1.03187250356946	0.302480751697813	   
df.mm.trans1:probe17	-0.171974508475027	0.121497616879592	-1.41545581626889	0.157370304992103	   
df.mm.trans1:probe18	-0.149119132620238	0.121497616879592	-1.22734203723535	0.220098002644946	   
df.mm.trans1:probe19	-0.0197305985921659	0.121497616879592	-0.162394943200570	0.871040735245902	   
df.mm.trans1:probe20	-0.0422642647319376	0.121497616879592	-0.347860853713887	0.728047068550524	   
df.mm.trans1:probe21	-0.182582227700849	0.121497616879592	-1.50276385981952	0.133341470345031	   
df.mm.trans1:probe22	0.000460545731927477	0.121497616879592	0.00379057420018282	0.996976623930309	   
df.mm.trans2:probe2	0.107216585524503	0.121497616879592	0.882458341802363	0.377825577246051	   
df.mm.trans2:probe3	0.0256048108733303	0.121497616879592	0.210743317695734	0.83314761604957	   
df.mm.trans2:probe4	-0.00535998473032988	0.121497616879592	-0.0441159659587546	0.96482428347801	   
df.mm.trans2:probe5	0.000439668509467188	0.121497616879592	0.00361874183839272	0.997113677444228	   
df.mm.trans2:probe6	-0.0528058058177008	0.121497616879592	-0.434624210531085	0.663966355607701	   
df.mm.trans3:probe2	0.058937527705043	0.121497616879592	0.485092047224697	0.627759641540798	   
df.mm.trans3:probe3	0.206214401562379	0.121497616879592	1.69727116348911	0.0900805885941775	.  
df.mm.trans3:probe4	0.086900996015926	0.121497616879592	0.715248564110092	0.474688949240917