chr11.3331_chr11_83941271_83942331_-_0.R 

fitVsDatCorrelation=0.871480104443928
cont.fitVsDatCorrelation=0.228098390919819

fstatistic=11071.0317909521,59,853
cont.fstatistic=2798.19009793923,59,853

residuals=-0.576713194262687,-0.0781008220742439,-0.00363550887072735,0.0703650649817355,1.32067521576567
cont.residuals=-0.611342602571162,-0.189192296030459,-0.0712925552450909,0.111726687952276,1.31834409424159

predictedValues:
Include	Exclude	Both
chr11.3331_chr11_83941271_83942331_-_0.R.tl.Lung	47.8748830380099	51.2207561369119	70.5269846256915
chr11.3331_chr11_83941271_83942331_-_0.R.tl.cerebhem	53.6263822802872	66.7170799309342	68.4587995162061
chr11.3331_chr11_83941271_83942331_-_0.R.tl.cortex	49.6523378957113	53.8138741171538	69.9702235591576
chr11.3331_chr11_83941271_83942331_-_0.R.tl.heart	51.1673770574397	53.4568023964619	68.6085708400971
chr11.3331_chr11_83941271_83942331_-_0.R.tl.kidney	48.1132662023013	51.3656242435831	71.3822325110216
chr11.3331_chr11_83941271_83942331_-_0.R.tl.liver	50.6958634813085	52.1651830221941	72.753750536352
chr11.3331_chr11_83941271_83942331_-_0.R.tl.stomach	50.7537357075962	55.4893031669123	77.1695434056285
chr11.3331_chr11_83941271_83942331_-_0.R.tl.testicle	49.4658506939486	55.036968589983	78.730208229953


diffExp=-3.34587309890202,-13.0906976506471,-4.16153622144255,-2.28942533902217,-3.25235804128179,-1.4693195408856,-4.73556745931609,-5.57111789603442
diffExpScore=0.97430355915907
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	61.8564078144522	54.8362056924192	59.3989218968168
cerebhem	63.739766210426	55.7840558709736	61.4622052545818
cortex	53.932920188672	55.1844773417067	58.4032689246311
heart	59.7630716562691	54.8889424128447	58.7314716918928
kidney	61.1124455453146	56.2773446482445	60.6690347967777
liver	59.7902449996802	61.9399530711737	56.3955247828722
stomach	64.4336204890649	54.3422456463996	58.6172442933347
testicle	56.2889208530219	60.5013486621813	58.0994840461306
cont.diffExp=7.02020212203293,7.95571033945242,-1.25155715303469,4.87412924342443,4.83510089707006,-2.14970807149351,10.0913748426653,-4.21242780915939
cont.diffExpScore=1.50518321102196

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=0.839713428350876
cont.tran.correlation=-0.327319370788406

tran.covariance=0.00260638330084236
cont.tran.covariance=-0.000943227938282303

tran.mean=52.5384554975461
cont.tran.mean=58.4169981939278

weightedLogRatios:
wLogRatio
Lung	-0.263619933535920
cerebhem	-0.893610393850267
cortex	-0.317539851586328
heart	-0.173204226616108
kidney	-0.255513448856228
liver	-0.112573296065072
stomach	-0.354285648617116
testicle	-0.422049749505297

cont.weightedLogRatios:
wLogRatio
Lung	0.489640144265644
cerebhem	0.545034187214415
cortex	-0.091744510629931
heart	0.344374979132735
kidney	0.335587539195577
liver	-0.145124259802787
stomach	0.695041033188666
testicle	-0.293476777367532

varWeightedLogRatios=0.0579693768032904
cont.varWeightedLogRatios=0.132175052606354

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.63217368911734	0.070074805576512	51.8328043757682	8.2182716429408e-266	***
df.mm.trans1	0.240277880931415	0.0592484877635719	4.05542639147571	5.46257521136212e-05	***
df.mm.trans2	0.296307130925841	0.0529168229264727	5.59948830143405	2.89930748014488e-08	***
df.mm.exp2	0.407529596986171	0.067418366809692	6.0447859577575	2.23459578765189e-09	***
df.mm.exp3	0.0937665698350566	0.067418366809692	1.39081639428822	0.164643811209761	   
df.mm.exp4	0.136818141355163	0.067418366809692	2.0293897320499	0.0427286417446105	*  
df.mm.exp5	-0.00426234099253960	0.067418366809692	-0.0632222522472444	0.949604337078747	   
df.mm.exp6	0.0444386906900329	0.067418366809692	0.659148134149765	0.509978546232735	   
df.mm.exp7	0.0484301775517384	0.067418366809692	0.718352873905217	0.472736445167169	   
df.mm.exp8	-0.00547971030274926	0.067418366809692	-0.08127919085043	0.935238989046761	   
df.mm.trans1:exp2	-0.294079447709170	0.05975999498957	-4.92100857372054	1.03334082574085e-06	***
df.mm.trans2:exp2	-0.143213449111469	0.0441356813051018	-3.2448450975858	0.00122090268194420	** 
df.mm.trans1:exp3	-0.0573120973530797	0.05975999498957	-0.959037854054078	0.337811423920458	   
df.mm.trans2:exp3	-0.0443800958898675	0.0441356813051017	-1.00553780020016	0.314923110202522	   
df.mm.trans1:exp4	-0.07030698366118	0.05975999498957	-1.17648911572785	0.239727689548929	   
df.mm.trans2:exp4	-0.0940890886385594	0.0441356813051017	-2.13181457397562	0.0333072755144819	*  
df.mm.trans1:exp5	0.00922928027126483	0.05975999498957	0.154439107179906	0.877300053109828	   
df.mm.trans2:exp5	0.0070866574843015	0.0441356813051017	0.160565267709651	0.872473828921342	   
df.mm.trans1:exp6	0.0128146240112034	0.05975999498957	0.214434824056461	0.830259238227908	   
df.mm.trans2:exp6	-0.0261682534463556	0.0441356813051017	-0.592904712752917	0.553402076710525	   
df.mm.trans1:exp7	0.00996404323543436	0.05975999498957	0.166734338534891	0.867618600577177	   
df.mm.trans2:exp7	0.0316152456985620	0.0441356813051017	0.716319421467899	0.47399019760145	   
df.mm.trans1:exp8	0.0381712523989808	0.05975999498957	0.638742563576903	0.523161882186455	   
df.mm.trans2:exp8	0.0773399827260775	0.0441356813051018	1.75232330031207	0.0800775174221735	.  
df.mm.trans1:probe2	0.0219891224477981	0.0441356813051017	0.498216449765245	0.618459917748374	   
df.mm.trans1:probe3	0.0253729304474434	0.0441356813051017	0.574884757573923	0.565520830376622	   
df.mm.trans1:probe4	0.125038908496720	0.0441356813051017	2.83305717277478	0.0047192305438042	** 
df.mm.trans1:probe5	0.0536939700388951	0.0441356813051017	1.21656601758833	0.224105891975749	   
df.mm.trans1:probe6	0.143691820350132	0.0441356813051017	3.25568374841246	0.00117579679553617	** 
df.mm.trans1:probe7	-0.0519233643110239	0.0441356813051017	-1.17644868676859	0.239743827481657	   
df.mm.trans1:probe8	0.100117721419369	0.0441356813051017	2.26840774762881	0.0235535511525536	*  
df.mm.trans1:probe9	-0.108343172790434	0.0441356813051017	-2.45477512948034	0.0142959953586612	*  
df.mm.trans1:probe10	-0.0169919636724515	0.0441356813051017	-0.384993800254022	0.700337982617167	   
df.mm.trans1:probe11	-0.108783289100619	0.0441356813051017	-2.46474702290467	0.0139070673184597	*  
df.mm.trans1:probe12	-0.0636935963833141	0.0441356813051017	-1.44313160009952	0.149350521616370	   
df.mm.trans1:probe13	-0.107351819694072	0.0441356813051017	-2.43231364101914	0.0152074434167952	*  
df.mm.trans1:probe14	-0.0525984390238382	0.0441356813051017	-1.19174412784602	0.233693051557397	   
df.mm.trans1:probe15	-0.0444828418013129	0.0441356813051017	-1.00786575591326	0.313804721575961	   
df.mm.trans1:probe16	-0.0392740863022295	0.0441356813051017	-0.889848873765765	0.373797929661671	   
df.mm.trans2:probe2	-0.101941365727853	0.0441356813051017	-2.30972679504258	0.0211412628845072	*  
df.mm.trans2:probe3	0.215394406962441	0.0441356813051017	4.88027828263168	1.26420048955575e-06	***
df.mm.trans2:probe4	0.0329335490943699	0.0441356813051017	0.746188755231995	0.455758990572826	   
df.mm.trans2:probe5	0.0380445565552036	0.0441356813051017	0.861990920502817	0.3889347719991	   
df.mm.trans2:probe6	-0.0158226375471821	0.0441356813051017	-0.358499904823111	0.720057944754464	   
df.mm.trans3:probe2	0.00521220763876762	0.0441356813051017	0.118095098673941	0.90602010955215	   
df.mm.trans3:probe3	0.8112211973497	0.0441356813051017	18.3801670974983	8.09827106625445e-64	***
df.mm.trans3:probe4	-0.0195451736917792	0.0441356813051017	-0.442842913348659	0.65799163849737	   
df.mm.trans3:probe5	0.226943596738175	0.0441356813051017	5.14195295115886	3.37378906397003e-07	***
df.mm.trans3:probe6	-0.116806567968716	0.0441356813051017	-2.64653370050537	0.00828200773142866	** 
df.mm.trans3:probe7	0.198408729809795	0.0441356813051017	4.49542691860203	7.90169481120181e-06	***
df.mm.trans3:probe8	0.362064548239537	0.0441356813051017	8.20344305408253	8.5637117939633e-16	***
df.mm.trans3:probe9	-0.189642000774566	0.0441356813051017	-4.29679558957311	1.93238076242241e-05	***
df.mm.trans3:probe10	0.0126120171213074	0.0441356813051017	0.285755577989673	0.775134710633417	   
df.mm.trans3:probe11	-0.0226822222689633	0.0441356813051017	-0.513920293020183	0.607440820055691	   
df.mm.trans3:probe12	0.458611234099676	0.0441356813051017	10.3909404032846	6.7735613374844e-24	***
df.mm.trans3:probe13	-0.295890133844958	0.0441356813051017	-6.70410255592352	3.68312362655062e-11	***
df.mm.trans3:probe14	0.278229004830848	0.0441356813051017	6.30394720560679	4.64942642338614e-10	***
df.mm.trans3:probe15	-0.292872394732821	0.0441356813051017	-6.63572841910492	5.7346803262212e-11	***
df.mm.trans3:probe16	-0.0200769024249493	0.0441356813051017	-0.454890506530565	0.649303759939137	   

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.01333496991368	0.139117468585081	28.8485336222115	2.93615790621650e-128	***
df.mm.trans1	0.109628014946425	0.117624295456127	0.932018462013366	0.351590701089678	   
df.mm.trans2	-0.0195226928058304	0.105054225845236	-0.185834435966345	0.852618780110928	   
df.mm.exp2	0.0129840121385587	0.133843718145806	0.097008752584221	0.922742231468405	   
df.mm.exp3	-0.113839384467155	0.133843718145806	-0.850539614740391	0.395263825196924	   
df.mm.exp4	-0.0221661567319441	0.133843718145806	-0.165612230734630	0.868501363726566	   
df.mm.exp5	-0.00731612913207763	0.133843718145806	-0.0546617296159363	0.956420758726992	   
df.mm.exp6	0.139727846650466	0.133843718145806	1.04396267965486	0.296798418638984	   
df.mm.exp7	0.0450182753830051	0.133843718145806	0.336349557578513	0.736690007147229	   
df.mm.exp8	0.0261163181774795	0.133843718145806	0.195125468264630	0.845341191497552	   
df.mm.trans1:exp2	0.0170089330117842	0.118639775839674	0.143366193095050	0.886034841041281	   
df.mm.trans2:exp2	0.0041534157250357	0.0876212813853558	0.047401905785526	0.962204005707785	   
df.mm.trans1:exp3	-0.0232352558594399	0.118639775839674	-0.195847098454058	0.844776489060312	   
df.mm.trans2:exp3	0.120170427012435	0.0876212813853557	1.37147534380294	0.170587473938178	   
df.mm.trans1:exp4	-0.0122615998631214	0.118639775839674	-0.103351509022500	0.91770828902179	   
df.mm.trans2:exp4	0.0231274081022433	0.0876212813853557	0.263947385116746	0.79188421510615	   
df.mm.trans1:exp5	-0.0047840299893086	0.118639775839674	-0.0403239972045596	0.967844258232278	   
df.mm.trans2:exp5	0.0332575155612935	0.0876212813853558	0.379559794555251	0.704366683264026	   
df.mm.trans1:exp6	-0.173701022065535	0.118639775839674	-1.46410443576924	0.143533835390108	   
df.mm.trans2:exp6	-0.0179130933691867	0.0876212813853558	-0.204437701503193	0.838060238494407	   
df.mm.trans1:exp7	-0.00419841718291041	0.118639775839674	-0.0353879392741269	0.971778681063886	   
df.mm.trans2:exp7	-0.0540670100077451	0.0876212813853557	-0.617053404754035	0.537364166308118	   
df.mm.trans1:exp8	-0.120434286056959	0.118639775839674	-1.01512570471905	0.310333752668754	   
df.mm.trans2:exp8	0.0721986749340083	0.0876212813853558	0.823985609346212	0.410178062746905	   
df.mm.trans1:probe2	-0.0175735150504578	0.0876212813853557	-0.200562178190136	0.841088743225919	   
df.mm.trans1:probe3	-0.0976030071750178	0.0876212813853557	-1.11391896616717	0.265627725453218	   
df.mm.trans1:probe4	-0.0723846861736261	0.0876212813853558	-0.826108509590044	0.408973539001913	   
df.mm.trans1:probe5	0.0311515198676315	0.0876212813853557	0.355524586893772	0.722284525978168	   
df.mm.trans1:probe6	0.0131425480815389	0.0876212813853558	0.149992648746352	0.880805873022854	   
df.mm.trans1:probe7	0.0266368461613348	0.0876212813853558	0.303999733171976	0.761202235015475	   
df.mm.trans1:probe8	0.141264364123512	0.0876212813853558	1.61221522773943	0.107285012907168	   
df.mm.trans1:probe9	-0.000895620598535405	0.0876212813853558	-0.0102214962435495	0.991846958005737	   
df.mm.trans1:probe10	0.0388968854552945	0.0876212813853558	0.44392052752832	0.6572126293883	   
df.mm.trans1:probe11	0.0860675453190448	0.0876212813853558	0.982267594792667	0.326246550445101	   
df.mm.trans1:probe12	-0.0352034984679087	0.0876212813853557	-0.401768815878015	0.687954814326062	   
df.mm.trans1:probe13	-0.0777493493325337	0.0876212813853558	-0.887334082579715	0.375149176658515	   
df.mm.trans1:probe14	0.0179770850153779	0.0876212813853558	0.205168022324567	0.837489801179676	   
df.mm.trans1:probe15	-0.0165365320108112	0.0876212813853558	-0.188727347390459	0.85035140829346	   
df.mm.trans1:probe16	0.0220961702804620	0.0876212813853558	0.252178123066743	0.800964135873237	   
df.mm.trans2:probe2	0.148094372939676	0.0876212813853557	1.69016442807269	0.0913618701427916	.  
df.mm.trans2:probe3	0.0157335716538709	0.0876212813853557	0.179563359552746	0.85753801650058	   
df.mm.trans2:probe4	0.0170862584359350	0.0876212813853558	0.195001239034500	0.845438413526278	   
df.mm.trans2:probe5	0.068955035316929	0.0876212813853557	0.786966753130063	0.431519918945468	   
df.mm.trans2:probe6	-0.0180247352171291	0.0876212813853558	-0.205711842284717	0.83706509109211	   
df.mm.trans3:probe2	-0.0312319988718611	0.0876212813853557	-0.356443073852158	0.721596923123203	   
df.mm.trans3:probe3	-0.0304138933261471	0.0876212813853557	-0.34710623772309	0.728597125666315	   
df.mm.trans3:probe4	-0.00238984817761754	0.0876212813853557	-0.0272747458132581	0.978246978835924	   
df.mm.trans3:probe5	0.0378862306099317	0.0876212813853557	0.432386173894321	0.66557005168064	   
df.mm.trans3:probe6	-0.0800197110161988	0.0876212813853557	-0.913245158607925	0.361371664823313	   
df.mm.trans3:probe7	-0.0543474552045647	0.0876212813853557	-0.620254056380963	0.535256203201464	   
df.mm.trans3:probe8	0.0467201336634712	0.0876212813853557	0.533205323236457	0.594030348837927	   
df.mm.trans3:probe9	0.0561646155153425	0.0876212813853558	0.640992857298357	0.521699495197306	   
df.mm.trans3:probe10	-0.107008105094991	0.0876212813853557	-1.22125702116102	0.222326154294683	   
df.mm.trans3:probe11	-0.0324591927436978	0.0876212813853557	-0.370448733806383	0.711140136285802	   
df.mm.trans3:probe12	-0.0312011298345169	0.0876212813853557	-0.356090773168396	0.721860637827294	   
df.mm.trans3:probe13	-0.0410063721332745	0.0876212813853557	-0.467995576929875	0.639907302131277	   
df.mm.trans3:probe14	0.00214385046675670	0.0876212813853557	0.0244672348185382	0.980485641165035	   
df.mm.trans3:probe15	-0.0215673792852901	0.0876212813853557	-0.246143162303657	0.805630628381954	   
df.mm.trans3:probe16	-0.017897196337376	0.0876212813853557	-0.204256272613324	0.838201961790566