chr5.18373_chr5_92277754_92282711_-_2.R 

fitVsDatCorrelation=0.780062533304448
cont.fitVsDatCorrelation=0.259857558786724

fstatistic=15261.0349609565,56,784
cont.fstatistic=6399.27608737538,56,784

residuals=-0.411642228077270,-0.0721496582379556,-0.00515682612110968,0.0699454219576517,0.456219013268076
cont.residuals=-0.596635179134327,-0.119287619545219,-0.0252225088526689,0.0742832165447044,0.761290959111043

predictedValues:
Include	Exclude	Both
chr5.18373_chr5_92277754_92282711_-_2.R.tl.Lung	44.8093832834673	42.222887752209	58.7413076252666
chr5.18373_chr5_92277754_92282711_-_2.R.tl.cerebhem	47.9729112560837	45.2664342646905	55.4315933347053
chr5.18373_chr5_92277754_92282711_-_2.R.tl.cortex	45.4006981254137	43.8801909736427	56.7025286336929
chr5.18373_chr5_92277754_92282711_-_2.R.tl.heart	48.1007157121972	45.0457926380989	58.3555641249671
chr5.18373_chr5_92277754_92282711_-_2.R.tl.kidney	45.2473709161032	44.2720306152522	66.0102038813875
chr5.18373_chr5_92277754_92282711_-_2.R.tl.liver	48.4571274742836	49.7043630724272	62.174632412182
chr5.18373_chr5_92277754_92282711_-_2.R.tl.stomach	46.524867180188	44.09107068112	61.6228065040707
chr5.18373_chr5_92277754_92282711_-_2.R.tl.testicle	46.5959750914279	44.7118082960443	59.0825392492036


diffExp=2.58649553125832,2.70647699139325,1.52050715177105,3.05492307409830,0.975340300851023,-1.24723559814362,2.43379649906802,1.88416679538356
diffExpScore=1.10020276426638
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=0,0,0,0,0,0,0,0
diffExp1.2Score=0

cont.predictedValues:
Include	Exclude	Both
Lung	50.5244112315942	50.5312637713715	44.4329238751618
cerebhem	49.2276319973807	47.5581839578747	48.456747795093
cortex	50.1207498628973	50.8825168412858	43.6804212401249
heart	49.0104338860692	45.7928463254108	46.5484611801568
kidney	50.8625667216885	49.626640641591	53.4426928974921
liver	51.0158164216423	49.4534123481685	48.8404602978133
stomach	48.0706881045558	50.5296862118674	47.3610392961696
testicle	47.6541635642653	46.7908567742895	51.4834691748653
cont.diffExp=-0.0068525397772703,1.66944803950598,-0.761766978388486,3.21758756065837,1.23592608009754,1.56240407347384,-2.45899810731159,0.86330678997571
cont.diffExpScore=1.8630260805389

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=0.775079833030167
cont.tran.correlation=0.477065385224614

tran.covariance=0.00110958007449523
cont.tran.covariance=0.000490610693434373

tran.mean=45.7689767082906
cont.tran.mean=49.228241791372

weightedLogRatios:
wLogRatio
Lung	0.224305766844526
cerebhem	0.223084507684456
cortex	0.129393820277829
heart	0.252003127767660
kidney	0.0828347338824112
liver	-0.098943710601768
stomach	0.204877772544853
testicle	0.157712504615275

cont.weightedLogRatios:
wLogRatio
Lung	-0.000531969226945821
cerebhem	0.133837229121668
cortex	-0.0591602528587388
heart	0.261984220240422
kidney	0.0963517080845546
liver	0.121823950886916
stomach	-0.194445916418868
testicle	0.0704747090416133

varWeightedLogRatios=0.0130243836986397
cont.varWeightedLogRatios=0.0191143707648869

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.21276142849554	0.0583022143142164	55.1053071703339	7.6018954544467e-272	***
df.mm.trans1	0.575988788504161	0.0508156062439333	11.3348797953764	1.10018167127823e-27	***
df.mm.trans2	0.53159778005867	0.0453474448917778	11.7227725030007	2.33680344959307e-29	***
df.mm.exp2	0.195815770313138	0.0593285398077062	3.30053244101085	0.00100864063437965	** 
df.mm.exp3	0.0869348464340651	0.0593285398077062	1.46531242325929	0.143236606856358	   
df.mm.exp4	0.142185114433284	0.0593285398077061	2.39657195161266	0.0167825296487517	*  
df.mm.exp5	-0.0595484694433355	0.0593285398077062	-1.00370697873809	0.315829551340224	   
df.mm.exp6	0.184588260482311	0.0593285398077061	3.11128945833814	0.00193023754677969	** 
df.mm.exp7	0.0329753764786196	0.0593285398077062	0.555809675840639	0.578499544298325	   
df.mm.exp8	0.0905795469884964	0.0593285398077061	1.52674492381036	0.127227760184064	   
df.mm.trans1:exp2	-0.127596833322966	0.0553999986741208	-2.30319199235957	0.0215289379678556	*  
df.mm.trans2:exp2	-0.126212415746409	0.0432117009551111	-2.92079258526574	0.0035916216657999	** 
df.mm.trans1:exp3	-0.0738249300746247	0.0553999986741208	-1.33257999713835	0.183056836657500	   
df.mm.trans2:exp3	-0.0484342966691676	0.0432117009551111	-1.12086068353296	0.262690380700785	   
df.mm.trans1:exp4	-0.0713056235421987	0.0553999986741208	-1.28710514889431	0.198437506218546	   
df.mm.trans2:exp4	-0.0774679657136083	0.0432117009551111	-1.79275436979634	0.0733976125870503	.  
df.mm.trans1:exp5	0.0692754708183494	0.0553999986741208	1.25045979199112	0.211504714389452	   
df.mm.trans2:exp5	0.106939146201660	0.0432117009551111	2.47477289340566	0.0135425686282543	*  
df.mm.trans1:exp6	-0.106326388854191	0.0553999986741208	-1.91924894221811	0.0553155519130276	.  
df.mm.trans2:exp6	-0.0214579808675781	0.0432117009551111	-0.496578019223751	0.619625858098638	   
df.mm.trans1:exp7	0.00459400545902902	0.0553999986741208	0.0829242882486751	0.933932908307846	   
df.mm.trans2:exp7	0.0103194687740929	0.0432117009551111	0.23881190848777	0.811313845318795	   
df.mm.trans1:exp8	-0.0514829467970981	0.0553999986741208	-0.929295090780345	0.353022207909483	   
df.mm.trans2:exp8	-0.0333043503706768	0.0432117009551111	-0.770725281221255	0.441101979365553	   
df.mm.trans1:probe2	0.106940313717001	0.0352060847327627	3.03755201774773	0.00246417049968376	** 
df.mm.trans1:probe3	0.0655606233026346	0.0352060847327627	1.86219580507980	0.0629494068989201	.  
df.mm.trans1:probe4	-0.0459442674519292	0.0352060847327627	-1.30500928463578	0.192272576173174	   
df.mm.trans1:probe5	0.0234026632920738	0.0352060847327627	0.664733482002198	0.506416447026307	   
df.mm.trans1:probe6	0.00811480918074533	0.0352060847327627	0.230494508047176	0.817767661360099	   
df.mm.trans1:probe7	-0.133778231182359	0.0352060847327627	-3.79986108077123	0.000155990340977242	***
df.mm.trans1:probe8	-0.00161884431039133	0.0352060847327627	-0.0459819466628972	0.963336348709765	   
df.mm.trans1:probe9	-0.00188629552463012	0.0352060847327627	-0.0535786793376299	0.957284492759829	   
df.mm.trans1:probe10	0.00909249428938548	0.0352060847327627	0.258264852749275	0.796270291872681	   
df.mm.trans1:probe11	-0.0239744435602094	0.0352060847327627	-0.680974432181005	0.496088742232865	   
df.mm.trans1:probe12	0.0879591291979912	0.0352060847327627	2.49840701872017	0.0126789851049392	*  
df.mm.trans1:probe13	-0.0575356245046699	0.0352060847327627	-1.63425228739302	0.102607337362262	   
df.mm.trans1:probe14	0.0875136006182262	0.0352060847327627	2.48575214433845	0.0131351164938938	*  
df.mm.trans1:probe15	-0.0207483899053874	0.0352060847327627	-0.589341020533276	0.555802313413816	   
df.mm.trans1:probe16	-0.0272042858214184	0.0352060847327627	-0.772715456089956	0.439923683257137	   
df.mm.trans1:probe17	0.0386897500475440	0.0352060847327627	1.0989506598426	0.272126943244888	   
df.mm.trans1:probe18	0.00173706649219448	0.0352060847327627	0.0493399509028042	0.96066094873718	   
df.mm.trans1:probe19	0.0324570477830643	0.0352060847327627	0.921915857143291	0.356856076187513	   
df.mm.trans1:probe20	0.121399642574001	0.0352060847327627	3.44825741048754	0.00059431558245247	***
df.mm.trans1:probe21	-0.0782019981713637	0.0352060847327627	-2.22126370384461	0.0266180328799577	*  
df.mm.trans1:probe22	0.204378354606555	0.0352060847327627	5.80519975901669	9.33427563436582e-09	***
df.mm.trans2:probe2	0.00398544053288972	0.0352060847327627	0.113203173915584	0.909898459533417	   
df.mm.trans2:probe3	-0.0123732741684065	0.0352060847327627	-0.351452717969856	0.725343169021111	   
df.mm.trans2:probe4	0.0387049676040105	0.0352060847327627	1.09938290206953	0.271938558353796	   
df.mm.trans2:probe5	-0.0470530427340099	0.0352060847327627	-1.33650313834024	0.181772608709884	   
df.mm.trans2:probe6	-0.00142211087922298	0.0352060847327627	-0.0403938946922879	0.96778938165504	   
df.mm.trans3:probe2	-0.102124848406735	0.0352060847327627	-2.90077266989300	0.00382660634302123	** 
df.mm.trans3:probe3	-0.351533639204648	0.0352060847327627	-9.98502508509592	3.50315233418890e-22	***
df.mm.trans3:probe4	-0.32803313729332	0.0352060847327627	-9.3175125772521	1.17812749017671e-19	***
df.mm.trans3:probe5	-0.503611674600227	0.0352060847327627	-14.3046771154183	2.06210991776185e-41	***
df.mm.trans3:probe6	-0.389892730448505	0.0352060847327627	-11.074583652458	1.38610115988297e-26	***
df.mm.trans3:probe7	-0.0552376421128124	0.0352060847327627	-1.56897997979901	0.117056094149107	   

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.07669142619704	0.0899779693801942	45.3076620230374	3.87845903765098e-221	***
df.mm.trans1	-0.138510705974214	0.0784238663391165	-1.76618053202419	0.0777546905388331	.  
df.mm.trans2	-0.136677160508097	0.0699848377276382	-1.95295388181091	0.0511804964265809	.  
df.mm.exp2	-0.173330739504719	0.0915619003665823	-1.89304436464034	0.0587200114910959	.  
df.mm.exp3	0.0159863839714826	0.0915619003665823	0.174596463239389	0.861441804337127	   
df.mm.exp4	-0.175400964934749	0.0915619003665823	-1.91565448327857	0.0557725536232277	.  
df.mm.exp5	-0.196023010781983	0.0915619003665823	-2.14087966716696	0.0325915400173536	*  
df.mm.exp6	-0.106460417685814	0.0915619003665823	-1.16271524793155	0.245298843411542	   
df.mm.exp7	-0.113634445304403	0.0915619003665823	-1.24106691592737	0.214952245667534	   
df.mm.exp8	-0.282671065301665	0.0915619003665823	-3.08721273990544	0.00209157677783766	** 
df.mm.trans1:exp2	0.147329221102875	0.0854989719172184	1.72316950483944	0.0852521730819014	.  
df.mm.trans2:exp2	0.112692397056061	0.066688738174684	1.68982650055359	0.0914587044897859	.  
df.mm.trans1:exp3	-0.0240079029151234	0.0854989719172184	-0.280797562552779	0.77893975686812	   
df.mm.trans2:exp3	-0.00905922925191177	0.066688738174684	-0.135843464726864	0.891979900809984	   
df.mm.trans1:exp4	0.144977566617726	0.0854989719172183	1.69566444328823	0.0903464767901402	.  
df.mm.trans2:exp4	0.0769366207779262	0.066688738174684	1.15366736399179	0.248988143168764	   
df.mm.trans1:exp5	0.202693625703770	0.0854989719172184	2.37071418706674	0.0179946689459012	*  
df.mm.trans2:exp5	0.177958580721447	0.066688738174684	2.66849524510876	0.00777662306800958	** 
df.mm.trans1:exp6	0.116139518035301	0.0854989719172183	1.35837326965463	0.174736002388621	   
df.mm.trans2:exp6	0.0848992500943028	0.066688738174684	1.27306727369647	0.203371529802669	   
df.mm.trans1:exp7	0.0638504315335946	0.0854989719172184	0.746797652671377	0.455409604324852	   
df.mm.trans2:exp7	0.113603225342500	0.066688738174684	1.70348440309259	0.0888737543534091	.  
df.mm.trans1:exp8	0.224184459428180	0.0854989719172183	2.62207198988590	0.00890924761293697	** 
df.mm.trans2:exp8	0.205766651758581	0.066688738174684	3.08547825900675	0.00210366525847001	** 
df.mm.trans1:probe2	-0.06384412216742	0.0543336484101362	-1.17503837926535	0.240336105118416	   
df.mm.trans1:probe3	-0.0496254952991252	0.0543336484101362	-0.913347377752519	0.361340656379309	   
df.mm.trans1:probe4	-0.0261308617431249	0.0543336484101362	-0.48093331678883	0.630698077144187	   
df.mm.trans1:probe5	0.000523430849193529	0.0543336484101362	0.00963364074583073	0.992316036462797	   
df.mm.trans1:probe6	0.0303019014674900	0.0543336484101362	0.557700473908117	0.577208135725159	   
df.mm.trans1:probe7	-0.0813819045013285	0.0543336484101362	-1.49781777742991	0.134583094853736	   
df.mm.trans1:probe8	0.00243002800466337	0.0543336484101362	0.0447241824498949	0.964338546776153	   
df.mm.trans1:probe9	-0.0189317327945777	0.0543336484101362	-0.348434779341008	0.727607229415457	   
df.mm.trans1:probe10	0.0419305608576067	0.0543336484101362	0.771723638749507	0.440510668904483	   
df.mm.trans1:probe11	-0.0352221058390635	0.0543336484101362	-0.648255857460378	0.517009289261515	   
df.mm.trans1:probe12	0.0057672330689015	0.0543336484101362	0.106144778376885	0.91549463642078	   
df.mm.trans1:probe13	-0.0240729256554765	0.0543336484101362	-0.443057412117123	0.65784643238854	   
df.mm.trans1:probe14	-0.0755082387983688	0.0543336484101362	-1.38971412757702	0.165010194673594	   
df.mm.trans1:probe15	-0.0335467113625985	0.0543336484101362	-0.617420555111117	0.537136646580335	   
df.mm.trans1:probe16	-0.0206722993384330	0.0543336484101362	-0.380469560637429	0.703699946703012	   
df.mm.trans1:probe17	-0.0144725416417446	0.0543336484101362	-0.266364252451795	0.790028730715325	   
df.mm.trans1:probe18	-0.0030105963618448	0.0543336484101362	-0.0554094276739782	0.955826402193125	   
df.mm.trans1:probe19	0.00709973564787486	0.0543336484101362	0.130669223503688	0.896070500271795	   
df.mm.trans1:probe20	-0.0570640961789957	0.0543336484101362	-1.05025334850052	0.293925271725458	   
df.mm.trans1:probe21	-0.0302449668666608	0.0543336484101362	-0.556652603895794	0.577923659258237	   
df.mm.trans1:probe22	-0.0103234817105043	0.0543336484101362	-0.190001629056414	0.849357036748693	   
df.mm.trans2:probe2	-0.0540516106331002	0.0543336484101362	-0.994809150769576	0.320136027143232	   
df.mm.trans2:probe3	-0.0109505594260403	0.0543336484101362	-0.201542869777127	0.840326391364014	   
df.mm.trans2:probe4	-0.0703710634777774	0.0543336484101361	-1.29516543683176	0.195644416065774	   
df.mm.trans2:probe5	-0.0712161033422132	0.0543336484101362	-1.31071822758229	0.190336791518103	   
df.mm.trans2:probe6	-0.0198971360585667	0.0543336484101362	-0.3662028345377	0.714312523633955	   
df.mm.trans3:probe2	0.00488334538698366	0.0543336484101362	0.089877001266726	0.928407912795044	   
df.mm.trans3:probe3	-0.0346870495770808	0.0543336484101361	-0.63840825330275	0.523394396290433	   
df.mm.trans3:probe4	-0.0146813476062481	0.0543336484101362	-0.270207284727621	0.787071899877702	   
df.mm.trans3:probe5	0.00901735534416255	0.0543336484101362	0.165962632880738	0.868229145026927	   
df.mm.trans3:probe6	-0.0053232234440722	0.0543336484101362	-0.0979728694802525	0.921978878458487	   
df.mm.trans3:probe7	-0.0118689345766745	0.0543336484101362	-0.218445381894515	0.827138957886088