chr4.16981_chr4_139587738_139589276_-_2.R 

fitVsDatCorrelation=0.747044581403333
cont.fitVsDatCorrelation=0.284278208155624

fstatistic=11117.3437006681,49,623
cont.fstatistic=5338.37315168565,49,623

residuals=-0.50108511895228,-0.0800574216788877,-0.00528888343019551,0.0743452932379157,0.657958588594495
cont.residuals=-0.507757280603022,-0.134508935540105,-0.0198660442741126,0.109481676212435,0.75874725741537

predictedValues:
Include	Exclude	Both
chr4.16981_chr4_139587738_139589276_-_2.R.tl.Lung	54.6565032908195	54.1356650136604	82.9115435263291
chr4.16981_chr4_139587738_139589276_-_2.R.tl.cerebhem	55.4681680162748	52.5149607137857	61.548673286222
chr4.16981_chr4_139587738_139589276_-_2.R.tl.cortex	53.0279101128497	56.4748318955384	63.7786382291919
chr4.16981_chr4_139587738_139589276_-_2.R.tl.heart	54.8121979740969	60.9107704603884	66.7572131375718
chr4.16981_chr4_139587738_139589276_-_2.R.tl.kidney	54.9847270902868	57.1570681081516	88.4666648353737
chr4.16981_chr4_139587738_139589276_-_2.R.tl.liver	54.596089664853	56.9701842849163	83.4441870068924
chr4.16981_chr4_139587738_139589276_-_2.R.tl.stomach	65.0611993524421	64.0300197662496	74.3856447271046
chr4.16981_chr4_139587738_139589276_-_2.R.tl.testicle	52.9886378310208	55.5911904388033	68.42493198051


diffExp=0.520838277159065,2.95320730248908,-3.4469217826887,-6.09857248629153,-2.17234101786476,-2.37409462006330,1.03117958619247,-2.60255260778251
diffExpScore=1.60734657909906
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	62.9596249099152	56.9794483087004	54.4640622804009
cerebhem	58.3093310987451	57.2420481244545	58.1388098285902
cortex	57.4965919316872	53.8384683876029	61.1145416827087
heart	57.2141639744535	58.4877668506405	62.1809903473034
kidney	60.3935965745748	59.5362170611379	59.7247525554234
liver	58.9413382350456	58.726361052531	57.3840222564681
stomach	57.1447419044956	53.4911674348504	53.5871361512459
testicle	57.289069924712	60.6038855077307	57.1859423350237
cont.diffExp=5.98017660121485,1.06728297429063,3.65812354408428,-1.27360287618708,0.857379513436896,0.214977182514652,3.65357446964519,-3.31481558301874
cont.diffExpScore=1.69043069806746

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=0.713631448375976
cont.tran.correlation=0.175664085423424

tran.covariance=0.00286148682466022
cont.tran.covariance=0.000293108879967618

tran.mean=56.4612577508836
cont.tran.mean=58.0408638300798

weightedLogRatios:
wLogRatio
Lung	0.038264369465491
cerebhem	0.218213135883778
cortex	-0.252052090004133
heart	-0.427966636635772
kidney	-0.156014476866377
liver	-0.171167460460054
stomach	0.0665787738260369
testicle	-0.191503701323056

cont.weightedLogRatios:
wLogRatio
Lung	0.408452972650854
cerebhem	0.0749378100797064
cortex	0.264189359369391
heart	-0.0893372587757535
kidney	0.0585332923037835
liver	0.0148889096921870
stomach	0.265112659656709
testicle	-0.229284740428870

varWeightedLogRatios=0.0420953005298635
cont.varWeightedLogRatios=0.0433000030137431

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.60199068933925	0.0764808556700014	47.0966316705592	1.90722565135063e-207	***
df.mm.trans1	0.232272748698126	0.0684393108216636	3.39384990745119	0.000732903451849097	***
df.mm.trans2	0.422482837076823	0.0632782197001413	6.67659171005216	5.41840807845659e-11	***
df.mm.exp2	0.282291985932758	0.0867764987392632	3.25309260034746	0.00120326200428656	** 
df.mm.exp3	0.274408037628688	0.0867764987392632	3.16223910408277	0.00164168555520971	** 
df.mm.exp4	0.337473301794042	0.0867764987392632	3.88899421729431	0.00011147717970539	***
df.mm.exp5	-0.00455441476040585	0.0867764987392632	-0.0524844263893439	0.958159528259269	   
df.mm.exp6	0.0435252026390293	0.0867764987392633	0.501578229951512	0.616141431134109	   
df.mm.exp7	0.45063029300286	0.0867764987392632	5.19299925152392	2.80603828456141e-07	***
df.mm.exp8	0.187577873867885	0.0867764987392633	2.16162067602541	0.0310275781366989	*  
df.mm.trans1:exp2	-0.267550885438809	0.0825409145112611	-3.24143350025891	0.00125271751697810	** 
df.mm.trans2:exp2	-0.312687101997704	0.0725200653428202	-4.31173221534695	1.88273477095160e-05	***
df.mm.trans1:exp3	-0.304657863241808	0.0825409145112611	-3.69099209823078	0.000242949951126689	***
df.mm.trans2:exp3	-0.232106162940646	0.0725200653428202	-3.20057851359376	0.00144124569503314	** 
df.mm.trans1:exp4	-0.334628748301366	0.0825409145112611	-4.05409547837893	5.67047158362826e-05	***
df.mm.trans2:exp4	-0.219556498909083	0.0725200653428202	-3.02752759351754	0.00256749695515219	** 
df.mm.trans1:exp5	0.0105416657027239	0.0825409145112611	0.127714428234081	0.898416183970486	   
df.mm.trans2:exp5	0.0588642627427225	0.0725200653428202	0.811696217653096	0.417275975958449	   
df.mm.trans1:exp6	-0.0446311467725241	0.0825409145112611	-0.540715438359179	0.588896998625466	   
df.mm.trans2:exp6	0.00750963457495887	0.0725200653428202	0.103552507012493	0.917557803730604	   
df.mm.trans1:exp7	-0.276370144027889	0.0825409145112611	-3.34828061530847	0.000862158569805452	***
df.mm.trans2:exp7	-0.282771471813554	0.0725200653428202	-3.8992170025885	0.000106982064331228	***
df.mm.trans1:exp8	-0.218568570305172	0.0825409145112611	-2.64800277049696	0.00830189805559794	** 
df.mm.trans2:exp8	-0.161046341554706	0.0725200653428202	-2.22071423672055	0.0267292163288422	*  
df.mm.trans1:probe2	0.113666723894489	0.0412704572556306	2.75419104737399	0.00605548544911957	** 
df.mm.trans1:probe3	0.320982639896369	0.0412704572556306	7.77754018832872	3.07675264413779e-14	***
df.mm.trans1:probe4	0.351103058800227	0.0412704572556306	8.50737021461824	1.32362459243797e-16	***
df.mm.trans1:probe5	0.419189943712879	0.0412704572556306	10.1571431863815	1.56393636556270e-22	***
df.mm.trans1:probe6	-0.00973678767602637	0.0412704572556306	-0.235926333835275	0.813567371662375	   
df.mm.trans1:probe7	0.285004367301739	0.0412704572556306	6.90577197961275	1.23441835163841e-11	***
df.mm.trans1:probe8	0.241636400289703	0.0412704572556306	5.85494846332812	7.72211073026013e-09	***
df.mm.trans1:probe9	0.125388181435693	0.0412704572556306	3.03820674093903	0.00247952260979522	** 
df.mm.trans1:probe10	0.152484013011788	0.0412704572556306	3.6947497835388	0.000239461506680531	***
df.mm.trans1:probe11	0.098019995976938	0.0412704572556306	2.37506445275851	0.0178476183166283	*  
df.mm.trans1:probe12	0.206393481248543	0.0412704572556306	5.00099817092248	7.4255370129504e-07	***
df.mm.trans1:probe13	0.0813690712994616	0.0412704572556306	1.97160576136724	0.0490966645008613	*  
df.mm.trans1:probe14	0.326796240667838	0.0412704572556306	7.91840610448415	1.10742009694455e-14	***
df.mm.trans1:probe15	0.128746565074125	0.0412704572556306	3.11958174528246	0.00189472607944235	** 
df.mm.trans1:probe16	0.105778324358586	0.0412704572556306	2.56305191152575	0.0106093574930929	*  
df.mm.trans1:probe17	0.392545948528792	0.0412704572556306	9.51154832371614	4.02599975722722e-20	***
df.mm.trans1:probe18	0.180909845077438	0.0412704572556306	4.38351928007185	1.37052276602943e-05	***
df.mm.trans1:probe19	0.149426892395465	0.0412704572556306	3.62067450500754	0.000317728893376760	***
df.mm.trans2:probe2	-0.0944948460126594	0.0412704572556306	-2.28964863237049	0.0223751975912728	*  
df.mm.trans2:probe3	-0.143722059024297	0.0412704572556306	-3.48244406729195	0.000531643325488601	***
df.mm.trans2:probe4	-0.0689921657614725	0.0412704572556306	-1.67170829569764	0.0950840037785322	.  
df.mm.trans2:probe5	0.0430610225586059	0.0412704572556306	1.04338612707595	0.29717418967447	   
df.mm.trans2:probe6	-0.032674790106151	0.0412704572556306	-0.791723481612094	0.428823261050403	   
df.mm.trans3:probe2	0.308316211194157	0.0412704572556306	7.47062745838836	2.71017893921703e-13	***
df.mm.trans3:probe3	0.174767859996985	0.0412704572556306	4.23469647826936	2.63393146255084e-05	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.18647313435333	0.110301248424411	37.9549025433044	3.64002743875324e-164	***
df.mm.trans1	-0.073566784178667	0.098703673733828	-0.7453297470675	0.456353493700184	   
df.mm.trans2	-0.131076790902419	0.0912603104378895	-1.43629569386166	0.151420160326911	   
df.mm.exp2	-0.137425771901499	0.125149700026099	-1.09809110108006	0.272588916557054	   
df.mm.exp3	-0.262679140060494	0.125149700026099	-2.09891945410747	0.0362258826563683	*  
df.mm.exp4	-0.202073440101232	0.125149700026099	-1.61465381106859	0.106891999147571	   
df.mm.exp5	-0.0899218671801468	0.125149700026099	-0.718514444392548	0.472709522875807	   
df.mm.exp6	-0.08797773445708	0.125149700026099	-0.702979986677817	0.482330822217248	   
df.mm.exp7	-0.143848332524109	0.125149700026099	-1.14941012638552	0.250827968553523	   
df.mm.exp8	-0.0814824595866838	0.125149700026099	-0.651079943217531	0.515234949651223	   
df.mm.trans1:exp2	0.0606942591045911	0.119041109529005	0.509859655582279	0.610330356199184	   
df.mm.trans2:exp2	0.142023860468372	0.104588967697317	1.35792391487592	0.174979419570088	   
df.mm.trans1:exp3	0.171911168856020	0.119041109529005	1.44413278350814	0.149204324118467	   
df.mm.trans2:exp3	0.205976730680058	0.104588967697317	1.96939252021456	0.0493507081825996	*  
df.mm.trans1:exp4	0.106381283266806	0.119041109529005	0.893651644274077	0.371853386177917	   
df.mm.trans2:exp4	0.228200411976569	0.104588967697317	2.18187842370705	0.0294914417421215	*  
df.mm.trans1:exp5	0.0483113030255479	0.119041109529005	0.405837136571517	0.685001616109792	   
df.mm.trans2:exp5	0.133816037827309	0.104588967697317	1.27944697011042	0.201215991224435	   
df.mm.trans1:exp6	0.0220267700507379	0.119041109529005	0.185034986131164	0.853261814598707	   
df.mm.trans2:exp6	0.118175794616605	0.104588967697317	1.12990688423858	0.258950296518613	   
df.mm.trans1:exp7	0.0469420668635803	0.119041109529005	0.394334923870501	0.693468728861778	   
df.mm.trans2:exp7	0.0806742312898512	0.104588967697317	0.77134551631989	0.440794612399905	   
df.mm.trans1:exp8	-0.0129013331362116	0.119041109529005	-0.108377124400609	0.913731441262094	   
df.mm.trans2:exp8	0.143150821064914	0.104588967697317	1.36869905322325	0.171586679332686	   
df.mm.trans1:probe2	0.0691462391583142	0.0595205547645027	1.16172034067720	0.245793956401520	   
df.mm.trans1:probe3	0.0673544334910368	0.0595205547645027	1.13161635938256	0.258231147807291	   
df.mm.trans1:probe4	0.0265899233672742	0.0595205547645027	0.446735140028166	0.65522155568464	   
df.mm.trans1:probe5	0.0109960986850337	0.0595205547645027	0.184744559733028	0.853489516759358	   
df.mm.trans1:probe6	0.0706718004816422	0.0595205547645027	1.18735117240187	0.235541643487685	   
df.mm.trans1:probe7	0.0247687646724080	0.0595205547645027	0.416138000904181	0.677452337524454	   
df.mm.trans1:probe8	0.0266593942101407	0.0595205547645027	0.447902314009345	0.654379407069474	   
df.mm.trans1:probe9	-0.00986109329764099	0.0595205547645027	-0.16567542652546	0.868466133372434	   
df.mm.trans1:probe10	0.0258806394737647	0.0595205547645027	0.434818519016888	0.663844735281905	   
df.mm.trans1:probe11	0.059663454712546	0.0595205547645027	1.00240085040552	0.316539202439543	   
df.mm.trans1:probe12	0.0681596470341526	0.0595205547645027	1.14514468663525	0.252588946224733	   
df.mm.trans1:probe13	0.0391733752530515	0.0595205547645027	0.658148691793007	0.510685712595932	   
df.mm.trans1:probe14	0.0833568458078643	0.0595205547645027	1.40047158729739	0.161869984503477	   
df.mm.trans1:probe15	0.0830348967983566	0.0595205547645027	1.39506254817164	0.163494105277407	   
df.mm.trans1:probe16	0.00488896016739839	0.0595205547645027	0.0821390221704402	0.934562544824162	   
df.mm.trans1:probe17	0.0274205692125568	0.0595205547645027	0.460690753321239	0.645181257347045	   
df.mm.trans1:probe18	-0.00201378537567210	0.0595205547645027	-0.0338334443225501	0.973020803244117	   
df.mm.trans1:probe19	-0.0249696447402949	0.0595205547645027	-0.419512970587876	0.67498589612814	   
df.mm.trans2:probe2	0.0801132784221076	0.0595205547645027	1.34597667543727	0.178799612591286	   
df.mm.trans2:probe3	-0.05307130670004	0.0595205547645027	-0.891646707763737	0.372926563729006	   
df.mm.trans2:probe4	-0.109788363537700	0.0595205547645027	-1.84454536709353	0.0655782343022223	.  
df.mm.trans2:probe5	0.0639134845872929	0.0595205547645027	1.07380525669109	0.283325700302787	   
df.mm.trans2:probe6	-0.0955183623517816	0.0595205547645027	-1.60479623769817	0.109045420958696	   
df.mm.trans3:probe2	0.0501819803857065	0.0595205547645027	0.843103371335416	0.399494205878363	   
df.mm.trans3:probe3	-0.00304742880667875	0.0595205547645027	-0.0511996035442902	0.959182878903073