chr11.4507_chr11_3288810_3294944_-_2.R 

fitVsDatCorrelation=0.705171539016415
cont.fitVsDatCorrelation=0.255498742527663

fstatistic=6969.23723370915,51,669
cont.fstatistic=3742.2951441267,51,669

residuals=-0.658253489407316,-0.086613469513511,-0.00802534175006777,0.0891980885416677,1.06849957677624
cont.residuals=-0.532100316568316,-0.138667378986409,-0.0414720118098989,0.075984119680945,1.59478084049930

predictedValues:
Include	Exclude	Both
chr11.4507_chr11_3288810_3294944_-_2.R.tl.Lung	59.2407499481285	65.4694149475421	55.3593577090505
chr11.4507_chr11_3288810_3294944_-_2.R.tl.cerebhem	96.4361940675317	105.892097307523	59.1506372880481
chr11.4507_chr11_3288810_3294944_-_2.R.tl.cortex	55.4760326416639	57.0245702534143	54.5031025021572
chr11.4507_chr11_3288810_3294944_-_2.R.tl.heart	54.8228788008414	55.0598178584391	52.6807460967841
chr11.4507_chr11_3288810_3294944_-_2.R.tl.kidney	53.917806266542	56.8506528979109	55.3364281504691
chr11.4507_chr11_3288810_3294944_-_2.R.tl.liver	55.3217871393704	56.0875338788444	56.9306645441832
chr11.4507_chr11_3288810_3294944_-_2.R.tl.stomach	58.275222775375	62.5438292057671	55.0686037681353
chr11.4507_chr11_3288810_3294944_-_2.R.tl.testicle	64.5253375571845	65.0981841541342	61.4495999642752


diffExp=-6.22866499941367,-9.45590323999113,-1.54853761175036,-0.236939057597752,-2.93284663136883,-0.765746739473975,-4.26860643039211,-0.572846596949674
diffExpScore=0.962976800461865
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	56.2119752893975	60.5430758305793	67.0241893357618
cerebhem	59.7597762967941	61.6258698286064	61.3332464286996
cortex	55.2463694421864	73.7548118344856	59.6717906816983
heart	59.8874802901099	59.1860200586179	61.3456669656551
kidney	59.4969476042414	56.3235099097145	60.4783902999005
liver	60.847484605743	56.5101925185271	58.6753918320096
stomach	61.2222463253183	60.0192248197021	61.960765407563
testicle	58.3170325976693	59.2337917985381	58.5869620029933
cont.diffExp=-4.33110054118181,-1.86609353181235,-18.5084423922992,0.701460231491957,3.17343769452697,4.33729208721587,1.20302150561618,-0.916759200868768
cont.diffExpScore=2.03621969086024

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,-1,0,0,0,0,0
cont.diffExp1.3Score=0.5
cont.diffExp1.2=0,0,-1,0,0,0,0,0
cont.diffExp1.2Score=0.5

tran.correlation=0.992196657628171
cont.tran.correlation=-0.718677043381698

tran.covariance=0.040927305428308
cont.tran.covariance=-0.00226123177787332

tran.mean=63.8776318562633
cont.tran.mean=59.8866130656394

weightedLogRatios:
wLogRatio
Lung	-0.413049890863943
cerebhem	-0.431743561698306
cortex	-0.110942729112449
heart	-0.0172773674545629
kidney	-0.212606279219852
liver	-0.0552625421180271
stomach	-0.289868260313503
testicle	-0.0368703474522611

cont.weightedLogRatios:
wLogRatio
Lung	-0.301818031108034
cerebhem	-0.126246186282540
cortex	-1.20092894500691
heart	0.0481484796179216
kidney	0.222459493357523
liver	0.301077519316485
stomach	0.081458421517911
testicle	-0.0635413908083586

varWeightedLogRatios=0.0280112114198186
cont.varWeightedLogRatios=0.223906871349207

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.19791240597395	0.0871147292315372	48.1883195069867	9.61071979592754e-220	***
df.mm.trans1	-0.175116443209997	0.073047921151202	-2.39728168098752	0.0167904752247264	*  
df.mm.trans2	0.00969946534167705	0.0671264070924673	0.144495523621813	0.885152708727667	   
df.mm.exp2	0.901867823671072	0.0871147292315372	10.3526445140414	2.12890347237393e-23	***
df.mm.exp3	-0.188171337338325	0.0871147292315372	-2.16004043171844	0.0311240375148417	*  
df.mm.exp4	-0.201069270602866	0.0871147292315372	-2.30809729165840	0.0212978663452992	*  
df.mm.exp5	-0.234889968907706	0.0871147292315372	-2.69632897880455	0.00718722301131393	** 
df.mm.exp6	-0.251100754668195	0.0871147292315372	-2.88241445371205	0.00407299342602195	** 
df.mm.exp7	-0.0568821890464393	0.0871147292315372	-0.652957192752737	0.514008195593581	   
df.mm.exp8	-0.0246097192607425	0.0871147292315372	-0.282497798912211	0.777649211379893	   
df.mm.trans1:exp2	-0.414595884197361	0.075443568558314	-5.49544370872249	5.54536130871374e-08	***
df.mm.trans2:exp2	-0.421030284172506	0.0615994157808499	-6.83497203399445	1.84914186342455e-11	***
df.mm.trans1:exp3	0.122512771716300	0.075443568558314	1.62389947953752	0.104868306801548	   
df.mm.trans2:exp3	0.0500704831503398	0.06159941578085	0.812840227713747	0.416598831264999	   
df.mm.trans1:exp4	0.123567224986725	0.075443568558314	1.63787619472445	0.101917742700733	   
df.mm.trans2:exp4	0.0279063760343337	0.0615994157808499	0.45302988154328	0.650674113852457	   
df.mm.trans1:exp5	0.140741100907938	0.075443568558314	1.86551489540361	0.0625467562592485	.  
df.mm.trans2:exp5	0.093734587388608	0.0615994157808499	1.52167981141386	0.128561826098071	   
df.mm.trans1:exp6	0.182657917618391	0.075443568558314	2.42111979998939	0.0157378292795361	*  
df.mm.trans2:exp6	0.0964312437841063	0.0615994157808499	1.56545711613851	0.117948633523436	   
df.mm.trans1:exp7	0.0404495479817421	0.0754435685583141	0.536156345129362	0.592028720638833	   
df.mm.trans2:exp7	0.0111666810864902	0.0615994157808499	0.181279009629206	0.85620348756881	   
df.mm.trans1:exp8	0.110058047460307	0.075443568558314	1.45881285261895	0.145085953977693	   
df.mm.trans2:exp8	0.0189232886870154	0.0615994157808499	0.307199158419588	0.758787386256018	   
df.mm.trans1:probe2	0.0363456987986846	0.0533466589244961	0.681311623472547	0.495910094580619	   
df.mm.trans1:probe3	0.0483674126692159	0.0533466589244961	0.9066624535507	0.36491176372838	   
df.mm.trans1:probe4	0.0778663395796593	0.0533466589244961	1.45962917171377	0.144861408293703	   
df.mm.trans1:probe5	0.232007442474123	0.0533466589244961	4.34905291449449	1.58045677153265e-05	***
df.mm.trans1:probe6	0.196490797731589	0.0533466589244961	3.68328217161061	0.000248846290276401	***
df.mm.trans1:probe7	0.107848501583255	0.0533466589244961	2.02165428458973	0.0436097025319913	*  
df.mm.trans1:probe8	0.153267140017602	0.0533466589244961	2.87304103213901	0.00419427356688237	** 
df.mm.trans1:probe9	0.160415853014821	0.0533466589244961	3.00704591906804	0.00273689801587662	** 
df.mm.trans1:probe10	0.136530825336011	0.0533466589244961	2.55931351819519	0.0107066887166716	*  
df.mm.trans1:probe11	0.174298731347845	0.0533466589244961	3.26728486585331	0.00114133988826239	** 
df.mm.trans1:probe12	0.0880897225155511	0.0533466589244961	1.65126971944444	0.0991528058008717	.  
df.mm.trans2:probe2	-0.142943198851703	0.0533466589244961	-2.67951548857103	0.00755422869715706	** 
df.mm.trans2:probe3	-0.00155121342563744	0.0533466589244961	-0.0290779864552143	0.976811064658688	   
df.mm.trans2:probe4	-0.0994831815204672	0.0533466589244961	-1.86484371329178	0.0626409489249276	.  
df.mm.trans2:probe5	-0.0418056196237551	0.0533466589244961	-0.783659566814194	0.433517243717037	   
df.mm.trans2:probe6	-0.182734918271582	0.0533466589244961	-3.42542385888149	0.000651362777613395	***
df.mm.trans3:probe2	-0.0724841567883282	0.0533466589244961	-1.35873845241027	0.174687409657106	   
df.mm.trans3:probe3	0.0308353127776795	0.0533466589244961	0.578017694066317	0.563446676460998	   
df.mm.trans3:probe4	-0.0133671980009158	0.0533466589244961	-0.250572355802731	0.802221690087249	   
df.mm.trans3:probe5	-0.110241025553030	0.0533466589244961	-2.06650290337881	0.0391652916919598	*  
df.mm.trans3:probe6	-0.132939453296844	0.0533466589244961	-2.49199211303934	0.0129438734313597	*  
df.mm.trans3:probe7	-0.0829053453226307	0.0533466589244961	-1.55408692866727	0.120636599043240	   
df.mm.trans3:probe8	0.121901393330613	0.0533466589244961	2.28508018661760	0.0226202299782397	*  
df.mm.trans3:probe9	-0.0909619668329035	0.0533466589244961	-1.70511084792857	0.088637989098707	.  
df.mm.trans3:probe10	-0.00941005602033974	0.0533466589244961	-0.176394477368456	0.860037426482704	   
df.mm.trans3:probe11	0.131692737319357	0.0533466589244961	2.46862202759029	0.0138122982454525	*  
df.mm.trans3:probe12	0.00408313110825464	0.0533466589244961	0.0765395844945731	0.939012705985734	   

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.86916793188054	0.118785576454853	32.5727082980576	3.75843803695154e-140	***
df.mm.trans1	0.145632260461616	0.099604733887331	1.46210179755462	0.144182888863054	   
df.mm.trans2	0.233340691437021	0.0915304338561296	2.54932355945994	0.0110152357511466	*  
df.mm.exp2	0.16766115513707	0.118785576454853	1.41146055052226	0.158573757192528	   
df.mm.exp3	0.296258136824903	0.118785576454853	2.49405816486064	0.0128694801159380	*  
df.mm.exp4	0.129196961449353	0.118785576454853	1.08764856226848	0.277141972349421	   
df.mm.exp5	0.0873195876763976	0.118785576454853	0.73510261331758	0.462534611095517	   
df.mm.exp6	0.143339744244566	0.118785576454853	1.20671001078187	0.227970361160544	   
df.mm.exp7	0.155242843919079	0.118785576454853	1.30691661860211	0.191690077775951	   
df.mm.exp8	0.149442873626317	0.118785576454853	1.25808939171260	0.208798346671761	   
df.mm.trans1:exp2	-0.106458175321551	0.102871326813081	-1.03486733008691	0.301104802100254	   
df.mm.trans2:exp2	-0.149934516894702	0.0839940866183796	-1.78506038854756	0.0747044925801595	.  
df.mm.trans1:exp3	-0.313585327681603	0.102871326813081	-3.04832587851610	0.00239194551750147	** 
df.mm.trans2:exp3	-0.098867007406683	0.0839940866183796	-1.17707104615444	0.239585755198958	   
df.mm.trans1:exp4	-0.065859305997257	0.102871326813081	-0.640210523549718	0.522254940824542	   
df.mm.trans2:exp4	-0.151866703991820	0.0839940866183796	-1.80806423530520	0.0710454508395441	.  
df.mm.trans1:exp5	-0.0305243948516236	0.102871326813081	-0.296724031829461	0.766769305655876	   
df.mm.trans2:exp5	-0.159562665815069	0.0839940866183796	-1.89968927860397	0.0579036907067423	.  
df.mm.trans1:exp6	-0.0640990809285983	0.102871326813081	-0.623099583862345	0.533431436196619	   
df.mm.trans2:exp6	-0.212273832690729	0.0839940866183796	-2.5272473484375	0.0117253134582819	*  
df.mm.trans1:exp7	-0.0698620360458076	0.102871326813081	-0.67912058889595	0.497296335887896	   
df.mm.trans2:exp7	-0.163933028243468	0.0839940866183796	-1.95172106565412	0.0513882199196887	.  
df.mm.trans1:exp8	-0.112678486246517	0.102871326813081	-1.09533423683020	0.273764400714159	   
df.mm.trans2:exp8	-0.171305796063897	0.0839940866183796	-2.03949829042384	0.0417927171748458	*  
df.mm.trans1:probe2	0.0969245412013888	0.0727410127791873	1.33246070542918	0.183162515842792	   
df.mm.trans1:probe3	-0.0206497243290379	0.0727410127791873	-0.28388007727803	0.77659011879257	   
df.mm.trans1:probe4	0.0349845369828366	0.0727410127791873	0.480946520349335	0.630711758543302	   
df.mm.trans1:probe5	0.0937724336001098	0.0727410127791873	1.28912741268486	0.197799344143834	   
df.mm.trans1:probe6	0.0142421965615093	0.0727410127791873	0.195793212348348	0.844831477026674	   
df.mm.trans1:probe7	0.00612364007948911	0.0727410127791873	0.0841841465429969	0.932935207010297	   
df.mm.trans1:probe8	0.0745829838333434	0.0727410127791873	1.02532231795765	0.305581716863725	   
df.mm.trans1:probe9	0.042228422185022	0.0727410127791873	0.580531127786337	0.561751992061654	   
df.mm.trans1:probe10	0.00246127931929692	0.0727410127791873	0.0338361981124511	0.973017863128844	   
df.mm.trans1:probe11	-0.00500633911677499	0.0727410127791873	-0.0688241602020615	0.945150152016185	   
df.mm.trans1:probe12	0.00424703687486206	0.0727410127791873	0.0583857264643039	0.953458818804154	   
df.mm.trans2:probe2	0.048727056013784	0.0727410127791873	0.669870464433866	0.503171507809623	   
df.mm.trans2:probe3	-0.0440667844982678	0.0727410127791873	-0.60580383492923	0.544850493505159	   
df.mm.trans2:probe4	0.0188668580338164	0.0727410127791873	0.259370296246612	0.795429337256925	   
df.mm.trans2:probe5	-0.0576003186539803	0.0727410127791873	-0.791854779762716	0.428726030550835	   
df.mm.trans2:probe6	0.0493099293490198	0.0727410127791873	0.677883458932653	0.498079965961683	   
df.mm.trans3:probe2	-0.0828215817393789	0.0727410127791873	-1.13858164156708	0.255285241625366	   
df.mm.trans3:probe3	-0.0079252829641065	0.0727410127791873	-0.108952056911340	0.913273187337123	   
df.mm.trans3:probe4	-0.00622099436089719	0.0727410127791873	-0.0855225150601304	0.931871584203602	   
df.mm.trans3:probe5	-0.0625422376578903	0.0727410127791873	-0.859793330727242	0.390210967884433	   
df.mm.trans3:probe6	-0.0951232382143239	0.0727410127791873	-1.30769746776939	0.191425137326666	   
df.mm.trans3:probe7	0.000714038944768152	0.0727410127791873	0.00981618096156689	0.992170872961383	   
df.mm.trans3:probe8	-0.112969383252535	0.0727410127791873	-1.55303561136088	0.120887542831581	   
df.mm.trans3:probe9	-0.0807706921260563	0.0727410127791873	-1.11038723603208	0.267231132389883	   
df.mm.trans3:probe10	-0.000322140128571506	0.0727410127791873	-0.00442859009331359	0.996467828098449	   
df.mm.trans3:probe11	-0.0628161383049982	0.0727410127791873	-0.863558753239839	0.388139819301507	   
df.mm.trans3:probe12	-0.00624988639803843	0.0727410127791873	-0.0859197055313293	0.931555953975231