chr10.2435_chr10_127747127_127748619_-_2.R 

fitVsDatCorrelation=0.935497951334527
cont.fitVsDatCorrelation=0.262106110287126

fstatistic=8306.61625119572,64,968
cont.fstatistic=1100.42913222516,64,968

residuals=-1.00471192884873,-0.112956598913450,0.00838514470841976,0.107300576922533,1.00501418334435
cont.residuals=-1.13702309886514,-0.421564621367503,-0.0912396676535031,0.340162819089527,1.67532236405604

predictedValues:
Include	Exclude	Both
chr10.2435_chr10_127747127_127748619_-_2.R.tl.Lung	127.342596523898	73.5110645433738	163.382498606319
chr10.2435_chr10_127747127_127748619_-_2.R.tl.cerebhem	91.3959741235178	61.5635657980371	139.577349563895
chr10.2435_chr10_127747127_127748619_-_2.R.tl.cortex	111.074408915700	78.6731891130725	163.063487481926
chr10.2435_chr10_127747127_127748619_-_2.R.tl.heart	106.392699751788	58.7244952770952	120.833146760042
chr10.2435_chr10_127747127_127748619_-_2.R.tl.kidney	124.287989703871	64.4409965961226	138.098242052422
chr10.2435_chr10_127747127_127748619_-_2.R.tl.liver	129.481820046822	62.7471024658923	139.284189666303
chr10.2435_chr10_127747127_127748619_-_2.R.tl.stomach	152.323324337648	63.3187405007102	171.406839334642
chr10.2435_chr10_127747127_127748619_-_2.R.tl.testicle	120.064976435197	61.5763964842478	157.366367608637


diffExp=53.8315319805238,29.8324083254807,32.4012198026274,47.6682044746925,59.8469931077484,66.73471758093,89.0045838369377,58.4885799509491
diffExpScore=0.99772110021876
diffExp1.5=1,0,0,1,1,1,1,1
diffExp1.5Score=0.857142857142857
diffExp1.4=1,1,1,1,1,1,1,1
diffExp1.4Score=0.888888888888889
diffExp1.3=1,1,1,1,1,1,1,1
diffExp1.3Score=0.888888888888889
diffExp1.2=1,1,1,1,1,1,1,1
diffExp1.2Score=0.888888888888889

cont.predictedValues:
Include	Exclude	Both
Lung	110.080140503059	156.21120813054	109.879959547501
cerebhem	141.242558136284	115.305623858892	112.14340125006
cortex	120.163952391715	113.378128286605	106.268156759273
heart	113.297357526078	115.176204569099	101.678317357876
kidney	114.617579948035	136.576883760162	123.518861131247
liver	111.195180287400	115.470554962862	114.981021269326
stomach	124.391025333894	132.354796344978	100.276377347317
testicle	116.293164291304	104.729956813147	111.231505879381
cont.diffExp=-46.1310676274809,25.9369342773919,6.78582410510992,-1.87884704302117,-21.9593038121269,-4.27537467546253,-7.96377101108337,11.5632074781570
cont.diffExpScore=3.24991099025255

cont.diffExp1.5=0,0,0,0,0,0,0,0
cont.diffExp1.5Score=0
cont.diffExp1.4=-1,0,0,0,0,0,0,0
cont.diffExp1.4Score=0.5
cont.diffExp1.3=-1,0,0,0,0,0,0,0
cont.diffExp1.3Score=0.5
cont.diffExp1.2=-1,1,0,0,0,0,0,0
cont.diffExp1.2Score=2

tran.correlation=0.0513240474496174
cont.tran.correlation=-0.27948936102687

tran.covariance=0.00150732921239917
cont.tran.covariance=-0.0028606222512253

tran.mean=92.932458788562
cont.tran.mean=121.280269696503

weightedLogRatios:
wLogRatio
Lung	2.51215022979569
cerebhem	1.70603262473760
cortex	1.56506092924957
heart	2.59700190678694
kidney	2.95200535894553
liver	3.2608872927874
stomach	4.02666088728983
testicle	2.97428235690374

cont.weightedLogRatios:
wLogRatio
Lung	-1.70667474142793
cerebhem	0.983832222270007
cortex	0.276679994931016
heart	-0.0779313623064432
kidney	-0.846501414686417
liver	-0.178461626546366
stomach	-0.301248901384241
testicle	0.492619400913513

varWeightedLogRatios=0.647901539948839
cont.varWeightedLogRatios=0.687816221244392

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.56472392544214	0.094672603576119	37.6531730489065	8.1996367162034e-192	***
df.mm.trans1	1.52048952028576	0.0810975783935062	18.7488893060155	3.77661760364616e-67	***
df.mm.trans2	0.700776452627065	0.0709996242537621	9.870143116848	5.85632021240168e-22	***
df.mm.exp2	-0.351570191556587	0.0898570588990006	-3.91254950767699	9.77108083454107e-05	***
df.mm.exp3	-0.0668597947730484	0.0898570588990006	-0.744068363601784	0.457015692372061	   
df.mm.exp4	-0.102639671289273	0.0898570588990006	-1.14225496078878	0.253630511384274	   
df.mm.exp5	0.0121631345187807	0.0898570588990006	0.135360923980965	0.892354659413422	   
df.mm.exp6	0.0179135820125294	0.0898570588990006	0.199356424882149	0.842025811297517	   
df.mm.exp7	-0.0180760975792244	0.0898570588990006	-0.201165025883408	0.840611798818556	   
df.mm.exp8	-0.198487877546151	0.0898570588990006	-2.20892915902413	0.0274131645755812	*  
df.mm.trans1:exp2	0.0198905573955923	0.0822060249574693	0.241959849121558	0.808862523717161	   
df.mm.trans2:exp2	0.174204489900293	0.0569848156984559	3.05703348804572	0.00229675968843441	** 
df.mm.trans1:exp3	-0.0698209427696896	0.0822060249574693	-0.849340943146353	0.395901604285617	   
df.mm.trans2:exp3	0.134726287253643	0.0569848156984559	2.3642488898546	0.0182632576728649	*  
df.mm.trans1:exp4	-0.0771044303747943	0.0822060249574692	-0.937941354233898	0.34850850734111	   
df.mm.trans2:exp4	-0.121939325782334	0.0569848156984559	-2.13985645628118	0.0326158375040670	*  
df.mm.trans1:exp5	-0.0364428289932382	0.0822060249574693	-0.443310925349967	0.657639913417536	   
df.mm.trans2:exp5	-0.143849043734587	0.0569848156984559	-2.52433989601346	0.0117505014445966	*  
df.mm.trans1:exp6	-0.00125416132912686	0.0822060249574693	-0.0152563188619778	0.987830834677788	   
df.mm.trans2:exp6	-0.176237113803296	0.0569848156984559	-3.09270305858112	0.00204026316685441	** 
df.mm.trans1:exp7	0.197200428217451	0.0822060249574693	2.39885614612161	0.0166346856963878	*  
df.mm.trans2:exp7	-0.131178491518435	0.0569848156984559	-2.30199027426159	0.0215472373696431	*  
df.mm.trans1:exp8	0.139639879216997	0.0822060249574693	1.69865748026671	0.089705081646955	.  
df.mm.trans2:exp8	0.0213305678045427	0.0569848156984559	0.374320203427817	0.708248082769863	   
df.mm.trans1:probe2	0.134670358694038	0.0601686493146024	2.23821475516078	0.0254337625671692	*  
df.mm.trans1:probe3	-0.144498169892131	0.0601686493146024	-2.40155249516400	0.0165133348246529	*  
df.mm.trans1:probe4	-0.482812868679643	0.0601686493146024	-8.02432619278474	2.93922191873705e-15	***
df.mm.trans1:probe5	-0.408470990953512	0.0601686493146024	-6.78876783186123	1.9696368407051e-11	***
df.mm.trans1:probe6	-0.873331536064936	0.0601686493146024	-14.5147272876040	2.45061886801509e-43	***
df.mm.trans1:probe7	-0.696271023588225	0.0601686493146024	-11.5719902560493	4.24759302733527e-29	***
df.mm.trans1:probe8	-0.601730805373965	0.0601686493146024	-10.0007364670546	1.78321613210826e-22	***
df.mm.trans1:probe9	-0.48690627103383	0.0601686493146024	-8.09235833910705	1.74492898519026e-15	***
df.mm.trans1:probe10	-0.377036807820977	0.0601686493146024	-6.26633325022096	5.55564747037552e-10	***
df.mm.trans1:probe11	-0.0788256477961093	0.0601686493146024	-1.31007839953255	0.190480108696862	   
df.mm.trans1:probe12	-0.089522487961931	0.0601686493146024	-1.48785935834868	0.137113658491896	   
df.mm.trans1:probe13	0.0065316054195596	0.0601686493146024	0.108554961661312	0.913577982062563	   
df.mm.trans1:probe14	0.101385904791183	0.0601686493146024	1.68502876408392	0.0923053450190402	.  
df.mm.trans1:probe15	-0.0331858816892751	0.0601686493146024	-0.551547725722691	0.581385552333217	   
df.mm.trans1:probe16	-0.208168489732676	0.0601686493146024	-3.45975008752864	0.000564260919969241	***
df.mm.trans1:probe17	-0.799771543332234	0.0601686493146024	-13.2921638169155	3.73672619279362e-37	***
df.mm.trans1:probe18	-0.821931735580443	0.0601686493146024	-13.6604651249994	5.59291956645132e-39	***
df.mm.trans1:probe19	-0.566055571326979	0.0601686493146024	-9.40781582726343	3.56462037090493e-20	***
df.mm.trans1:probe20	-0.766070397610606	0.0601686493146024	-12.7320524282517	1.91453546497284e-34	***
df.mm.trans1:probe21	-0.830650611458703	0.0601686493146024	-13.8053724143864	1.04840763498489e-39	***
df.mm.trans1:probe22	-0.7956451210332	0.0601686493146024	-13.2235828807296	8.10141398150012e-37	***
df.mm.trans2:probe2	0.0589854128517883	0.0601686493146024	0.980334668032394	0.327165891771470	   
df.mm.trans2:probe3	0.2137594398697	0.0601686493146024	3.55267140453862	0.000399735344637692	***
df.mm.trans2:probe4	0.0325219598621598	0.0601686493146024	0.540513377525113	0.588967378483699	   
df.mm.trans2:probe5	0.223116544440469	0.0601686493146024	3.7081860234865	0.000220611017690114	***
df.mm.trans2:probe6	0.142263294300193	0.0601686493146024	2.36440897245914	0.018255413467663	*  
df.mm.trans3:probe2	0.0953278546208706	0.0601686493146024	1.58434426743456	0.113442017361107	   
df.mm.trans3:probe3	-1.00048164660907	0.0601686493146024	-16.6279558874236	8.35614675889276e-55	***
df.mm.trans3:probe4	-1.47923565122305	0.0601686493146024	-24.584823958547	4.37665586300179e-104	***
df.mm.trans3:probe5	-0.518172308963683	0.0601686493146024	-8.61199835572721	2.87875412251432e-17	***
df.mm.trans3:probe6	-1.74411778739586	0.0601686493146024	-28.9871520677892	1.67800166466654e-133	***
df.mm.trans3:probe7	-0.0828620729914723	0.0601686493146024	-1.37716358827025	0.168780043256840	   
df.mm.trans3:probe8	-0.337624752625814	0.0601686493146024	-5.61130682625903	2.62108273749091e-08	***
df.mm.trans3:probe9	-0.00519453903336653	0.0601686493146024	-0.086332983913366	0.931219572813558	   
df.mm.trans3:probe10	-1.83565969427713	0.0601686493146024	-30.5085740695134	8.8297461023858e-144	***
df.mm.trans3:probe11	-1.05647791174299	0.0601686493146024	-17.5586110670195	3.96023597484105e-60	***
df.mm.trans3:probe12	-1.04227058687951	0.0601686493146024	-17.3224860247371	9.18362880698265e-59	***
df.mm.trans3:probe13	-1.14037805052897	0.0601686493146024	-18.9530272578715	2.24502929054106e-68	***
df.mm.trans3:probe14	-0.167132647575880	0.0601686493146024	-2.77773640392020	0.00557970410241953	** 
df.mm.trans3:probe15	-0.0341576448434191	0.0601686493146024	-0.567698381674149	0.570371431163296	   

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	5.26796029428152	0.258574742083573	20.3730660304744	4.62976370716724e-77	***
df.mm.trans1	-0.447490911035971	0.221497926798252	-2.02029390299242	0.0436279203135073	*  
df.mm.trans2	-0.184176628812871	0.193917868907939	-0.949766155383586	0.342468146095613	   
df.mm.exp2	-0.074742683093491	0.245422275838397	-0.304547265883504	0.760776449246	   
df.mm.exp3	-0.199409247005802	0.245422275838397	-0.812514863716392	0.416696035363523	   
df.mm.exp4	-0.198364211323853	0.245422275838397	-0.808256751129103	0.41914137931189	   
df.mm.exp5	-0.210934111174753	0.245422275838397	-0.859474187720621	0.390291746967545	   
df.mm.exp6	-0.33749362940095	0.245422275838397	-1.37515483567262	0.169401620674231	   
df.mm.exp7	0.0479569073858761	0.245422275838397	0.195405682805477	0.845116375965655	   
df.mm.exp8	-0.357143336546625	0.245422275838397	-1.45521972415329	0.145932646334049	   
df.mm.trans1:exp2	0.324012715414788	0.224525373742391	1.44310066169423	0.149315629495680	   
df.mm.trans2:exp2	-0.228880104770001	0.155639894386775	-1.47057478850005	0.141731112582853	   
df.mm.trans1:exp3	0.287057676664821	0.224525373742391	1.27850884681825	0.201376514866333	   
df.mm.trans2:exp3	-0.121071242383328	0.155639894386775	-0.777893372777925	0.436821956977183	   
df.mm.trans1:exp4	0.227171405723329	0.224525373742391	1.01178500201039	0.311893694739584	   
df.mm.trans2:exp4	-0.106381609138583	0.155639894386775	-0.683511188167593	0.494447453002879	   
df.mm.trans1:exp5	0.251326655821831	0.224525373742391	1.11936861136323	0.263260589802734	   
df.mm.trans2:exp5	0.0766128283952904	0.155639894386775	0.492244155633405	0.622658369466386	   
df.mm.trans1:exp6	0.347572016982413	0.224525373742391	1.54803001188275	0.121941925797661	   
df.mm.trans2:exp6	0.0353002016464016	0.155639894386775	0.226806897971020	0.82062178664213	   
df.mm.trans1:exp7	0.0742644761420282	0.224525373742391	0.330762064457069	0.740895797702827	   
df.mm.trans2:exp7	-0.213679729341515	0.155639894386775	-1.37291104047210	0.170097959553107	   
df.mm.trans1:exp8	0.412048967272338	0.224525373742391	1.83520000614764	0.0667827427325005	.  
df.mm.trans2:exp8	-0.0426804558605053	0.155639894386775	-0.274225679917526	0.783969702257344	   
df.mm.trans1:probe2	-0.213361976048346	0.164335746460497	-1.29832967351162	0.194483307234593	   
df.mm.trans1:probe3	-0.0823448521143637	0.164335746460497	-0.501076934799196	0.616430899736992	   
df.mm.trans1:probe4	-0.286569219022973	0.164335746460497	-1.74380331239654	0.0815108380957473	.  
df.mm.trans1:probe5	-0.297208919530514	0.164335746460497	-1.80854698951306	0.0708316092928107	.  
df.mm.trans1:probe6	-0.247231811377658	0.164335746460497	-1.50443112166766	0.132796589479027	   
df.mm.trans1:probe7	-0.314863713448584	0.164335746460497	-1.91597823498657	0.0556621668810586	.  
df.mm.trans1:probe8	-0.163396874191866	0.164335746460497	-0.99428686522042	0.32033170209035	   
df.mm.trans1:probe9	-0.141033558002608	0.164335746460497	-0.858203775138534	0.390992394359322	   
df.mm.trans1:probe10	-0.189961836939285	0.164335746460497	-1.15593740881536	0.247991977787405	   
df.mm.trans1:probe11	-0.161275407129889	0.164335746460497	-0.9813775189116	0.326651818035397	   
df.mm.trans1:probe12	-0.421807458146128	0.164335746460497	-2.56674197325365	0.0104152432009242	*  
df.mm.trans1:probe13	-0.0508661439627085	0.164335746460497	-0.30952574262311	0.756988247960045	   
df.mm.trans1:probe14	-0.207110869957220	0.164335746460497	-1.26029104694519	0.207868108812547	   
df.mm.trans1:probe15	-0.225892675857659	0.164335746460497	-1.37458027679911	0.169579724869499	   
df.mm.trans1:probe16	-0.137973970094093	0.164335746460497	-0.83958586653123	0.401347928476906	   
df.mm.trans1:probe17	0.00824596419769661	0.164335746460497	0.0501775442975747	0.959991257561118	   
df.mm.trans1:probe18	-0.323977547020026	0.164335746460497	-1.97143685411076	0.0489583865353529	*  
df.mm.trans1:probe19	-0.0430845037250253	0.164335746460497	-0.262173657606393	0.793243341511128	   
df.mm.trans1:probe20	-0.277764200824122	0.164335746460497	-1.69022386672817	0.0913070940633598	.  
df.mm.trans1:probe21	-0.204017071209119	0.164335746460497	-1.24146496184359	0.214734772388065	   
df.mm.trans1:probe22	-0.431150465090969	0.164335746460497	-2.62359513603821	0.00883760290472949	** 
df.mm.trans2:probe2	-0.286769827497998	0.164335746460497	-1.74502403569835	0.0812980116671211	.  
df.mm.trans2:probe3	-0.215329171876904	0.164335746460497	-1.31030026342238	0.190405101004672	   
df.mm.trans2:probe4	-0.0757239630583994	0.164335746460497	-0.460788140677609	0.645054113859064	   
df.mm.trans2:probe5	0.0098090204241141	0.164335746460497	0.0596889029647118	0.952415730190717	   
df.mm.trans2:probe6	-0.116054246436644	0.164335746460497	-0.706202082847149	0.480232406435746	   
df.mm.trans3:probe2	0.313619460266253	0.164335746460497	1.90840682578845	0.0566342197735814	.  
df.mm.trans3:probe3	-0.0585966658311837	0.164335746460497	-0.356566767080521	0.72149386595509	   
df.mm.trans3:probe4	0.205126235524061	0.164335746460497	1.24821434132329	0.212254399626917	   
df.mm.trans3:probe5	0.0230554390693728	0.164335746460497	0.140294729332762	0.888456314896765	   
df.mm.trans3:probe6	-0.102633558666776	0.164335746460497	-0.624535810846526	0.532422928409992	   
df.mm.trans3:probe7	0.103014294807031	0.164335746460497	0.62685262960602	0.530903649175225	   
df.mm.trans3:probe8	0.0246884749802162	0.164335746460497	0.150231921611472	0.880612938817457	   
df.mm.trans3:probe9	0.101637991068468	0.164335746460497	0.618477679126857	0.536406005376587	   
df.mm.trans3:probe10	-0.0443423722127238	0.164335746460497	-0.269827917344708	0.78735015431432	   
df.mm.trans3:probe11	0.323624641331737	0.164335746460497	1.96928938652754	0.0492047261341445	*  
df.mm.trans3:probe12	-0.121554250384174	0.164335746460497	-0.739670175249384	0.459679487197816	   
df.mm.trans3:probe13	0.220377669793462	0.164335746460497	1.34102089496661	0.180228241689162	   
df.mm.trans3:probe14	-0.102884184890622	0.164335746460497	-0.62606089731885	0.531422588380684	   
df.mm.trans3:probe15	0.0613029047855331	0.164335746460497	0.373034510785936	0.709204427771252