chr8.23544_chr8_105016055_105039343_+_2.R 

fitVsDatCorrelation=0.828604136007792
cont.fitVsDatCorrelation=0.209550302870988

fstatistic=9043.24080586283,60,876
cont.fstatistic=2954.64850845708,60,876

residuals=-0.826056175812065,-0.0980126232776226,-0.00382166336141778,0.0872082989095215,1.27790345867193
cont.residuals=-0.638069568518111,-0.199536681702640,-0.0363359985314443,0.146066341041958,1.47570416163969

predictedValues:
Include	Exclude	Both
chr8.23544_chr8_105016055_105039343_+_2.R.tl.Lung	85.7444836878568	51.3720258886816	70.1687036968073
chr8.23544_chr8_105016055_105039343_+_2.R.tl.cerebhem	81.3773599530485	54.8599956779146	71.880490245706
chr8.23544_chr8_105016055_105039343_+_2.R.tl.cortex	82.3474048305486	51.0082497612215	65.0549265595147
chr8.23544_chr8_105016055_105039343_+_2.R.tl.heart	91.496726370986	49.690685107015	73.8515137303323
chr8.23544_chr8_105016055_105039343_+_2.R.tl.kidney	77.8074024731877	55.1077673276847	59.9537235296302
chr8.23544_chr8_105016055_105039343_+_2.R.tl.liver	76.8518476151487	53.88910620413	58.5816173631824
chr8.23544_chr8_105016055_105039343_+_2.R.tl.stomach	91.7959360893144	50.3970385944941	70.5941543350971
chr8.23544_chr8_105016055_105039343_+_2.R.tl.testicle	93.5932230715234	53.0416682901546	74.3564949240988


diffExp=34.3724577991751,26.5173642751339,31.3391550693271,41.806041263971,22.6996351455030,22.9627414110186,41.3988974948202,40.5515547813688
diffExpScore=0.996192620611564
diffExp1.5=1,0,1,1,0,0,1,1
diffExp1.5Score=0.833333333333333
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	64.7598161770521	60.0916759498	63.4756641846764
cerebhem	70.505614552164	65.2280577692556	63.9195771607832
cortex	66.0261978127309	67.0149005382596	65.4830361963654
heart	64.4693647181485	69.2589197769136	63.5067543292985
kidney	66.5432823870914	63.4438112128627	63.8622802902423
liver	70.2612084663739	67.7383802012013	68.653833455409
stomach	65.5958495704497	63.518928028942	63.5533335912477
testicle	66.431262125981	59.3314943682537	66.579035675582
cont.diffExp=4.66814022725205,5.27755678290839,-0.98870272552874,-4.78955505876512,3.09947117422866,2.52282826517261,2.07692154150773,7.09976775772729
cont.diffExpScore=1.52871327747535

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=-0.662441365578475
cont.tran.correlation=0.229896109586684

tran.covariance=-0.00204775545327749
cont.tran.covariance=0.000443570927082103

tran.mean=68.7738075589319
cont.tran.mean=65.6386727284675

weightedLogRatios:
wLogRatio
Lung	2.14912533794401
cerebhem	1.65687858013349
cortex	1.99796408665419
heart	2.57079212644014
kidney	1.44248221719283
liver	1.47816002727762
stomach	2.53031250717034
testicle	2.41634031989811

cont.weightedLogRatios:
wLogRatio
Lung	0.309226924980485
cerebhem	0.328077455055223
cortex	-0.0623888351550363
heart	-0.301124378423720
kidney	0.199091405857376
liver	0.154821977321260
stomach	0.134084757068243
testicle	0.467894717103174

varWeightedLogRatios=0.213738326300527
cont.varWeightedLogRatios=0.0584743481142484

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.45494188161821	0.0803227936964888	55.4629847469182	7.74371985750603e-289	***
df.mm.trans1	0.41182163009596	0.0692177241553542	5.94965574383312	3.88172640367509e-09	***
df.mm.trans2	-0.489282259630369	0.0610095414379305	-8.01976622178272	3.38279905796278e-15	***
df.mm.exp2	-0.0106865137953044	0.0781547618796063	-0.136735286990785	0.891271454500326	   
df.mm.exp3	0.0281392454790822	0.0781547618796062	0.360045182178783	0.718900132109134	   
df.mm.exp4	-0.0194989580741284	0.0781547618796062	-0.249491619002892	0.803039012223293	   
df.mm.exp5	0.130391118695344	0.0781547618796062	1.66837075002807	0.0955995073089894	.  
df.mm.exp6	0.118823767922865	0.0781547618796062	1.52036504321909	0.12878009845792	   
df.mm.exp7	0.0429899621265394	0.0781547618796062	0.550061967980447	0.582417089852274	   
df.mm.exp8	0.0616016120720265	0.0781547618796062	0.788200367968889	0.430792748317277	   
df.mm.trans1:exp2	-0.0415881389433686	0.0720551301698169	-0.577171102811906	0.563972172708649	   
df.mm.trans2:exp2	0.0763771396247932	0.0524278080984326	1.45680588975599	0.145528307739628	   
df.mm.trans1:exp3	-0.0685640570554202	0.0720551301698169	-0.951549971443129	0.341587727713678	   
df.mm.trans2:exp3	-0.0352456467086017	0.0524278080984326	-0.672270079314176	0.501589046214301	   
df.mm.trans1:exp4	0.0844303985432965	0.0720551301698169	1.17174722111131	0.241617147880032	   
df.mm.trans2:exp4	-0.0137773296791794	0.0524278080984326	-0.262786680940630	0.792776792066873	   
df.mm.trans1:exp5	-0.227526298372962	0.0720551301698169	-3.15766965983874	0.00164480779847012	** 
df.mm.trans2:exp5	-0.060194225532992	0.0524278080984326	-1.14813545933444	0.251226079891769	   
df.mm.trans1:exp6	-0.228316010196591	0.0720551301698169	-3.16862948770621	0.00158471573497415	** 
df.mm.trans2:exp6	-0.0709892025718267	0.0524278080984326	-1.35403720175647	0.176073705980623	   
df.mm.trans1:exp7	0.0252063115344048	0.0720551301698169	0.349819804294288	0.726558019893074	   
df.mm.trans2:exp7	-0.0621513277173436	0.0524278080984326	-1.18546492732741	0.236155145378277	   
df.mm.trans1:exp8	0.0259846119022668	0.0720551301698169	0.360621260984849	0.71846953261679	   
df.mm.trans2:exp8	-0.0296175941508346	0.0524278080984326	-0.564921464868947	0.572271817755462	   
df.mm.trans1:probe2	-1.06824678349610	0.0501958046736867	-21.2815949548089	2.45259902556850e-81	***
df.mm.trans1:probe3	-0.844735817832039	0.0501958046736867	-16.8288131512883	2.81214058883914e-55	***
df.mm.trans1:probe4	-0.807189217337664	0.0501958046736867	-16.0808103901321	3.52323103916581e-51	***
df.mm.trans1:probe5	-0.141979347186193	0.0501958046736867	-2.82851023325901	0.00478325589231171	** 
df.mm.trans1:probe6	-0.630932385583279	0.0501958046736867	-12.5694246697478	1.96955499369382e-33	***
df.mm.trans1:probe7	-0.145759755870084	0.0501958046736867	-2.90382347324922	0.00377897217040519	** 
df.mm.trans1:probe8	-0.814639851275783	0.0501958046736867	-16.2292417976283	5.51497075512984e-52	***
df.mm.trans1:probe9	-0.642939427754021	0.0501958046736867	-12.8086287675563	1.49290766343976e-34	***
df.mm.trans1:probe10	-0.785702849922125	0.0501958046736867	-15.6527593297852	7.03171122954882e-49	***
df.mm.trans1:probe11	-0.619887513140923	0.0501958046736867	-12.3493889015366	2.05197980936372e-32	***
df.mm.trans1:probe12	-0.628899493846816	0.0501958046736867	-12.5289254338121	3.03827827880101e-33	***
df.mm.trans1:probe13	-0.677161652362187	0.0501958046736867	-13.4904033666615	8.01914651218448e-38	***
df.mm.trans1:probe14	-0.630895541477955	0.0501958046736867	-12.5686906620839	1.98510650906927e-33	***
df.mm.trans1:probe15	-0.672112881439646	0.0501958046736867	-13.3898218348909	2.47464039025980e-37	***
df.mm.trans1:probe16	-0.621932204717054	0.0501958046736867	-12.3901232136852	1.33255005136709e-32	***
df.mm.trans1:probe17	-0.672563076092226	0.0501958046736867	-13.3987906053988	2.23856926416295e-37	***
df.mm.trans1:probe18	-0.426085614563465	0.0501958046736867	-8.48847064676752	8.87599644998624e-17	***
df.mm.trans1:probe19	-0.6611378334755	0.0501958046736867	-13.1711771087929	2.81251040544298e-36	***
df.mm.trans1:probe20	-0.645251678958881	0.0501958046736867	-12.8546933982539	9.0500197071388e-35	***
df.mm.trans1:probe21	-0.868909429967957	0.0501958046736867	-17.3103994570178	5.76748647037058e-58	***
df.mm.trans1:probe22	-0.700965654669536	0.0501958046736867	-13.9646263114294	3.67562833531035e-40	***
df.mm.trans2:probe2	0.115362115151786	0.0501958046736867	2.29824217186542	0.0217827687583473	*  
df.mm.trans2:probe3	-0.221026589301380	0.0501958046736867	-4.40328809824311	1.19793367879961e-05	***
df.mm.trans2:probe4	-0.118489597116494	0.0501958046736867	-2.36054781643151	0.0184662194854118	*  
df.mm.trans2:probe5	-0.0941530310889029	0.0501958046736867	-1.87571514593647	0.0610267845602344	.  
df.mm.trans2:probe6	-0.133312196205478	0.0501958046736867	-2.65584339313046	0.00805478723989747	** 
df.mm.trans3:probe2	-0.0413104800836505	0.0501958046736867	-0.822986708793734	0.410739526605873	   
df.mm.trans3:probe3	-0.308319487225917	0.0501958046736867	-6.14233578344333	1.23239394442421e-09	***
df.mm.trans3:probe4	0.0999635563957925	0.0501958046736867	1.99147233609734	0.0467391397747103	*  
df.mm.trans3:probe5	-0.093554050068153	0.0501958046736867	-1.86378225583453	0.0626866617547424	.  
df.mm.trans3:probe6	0.130158780910298	0.0501958046736867	2.59302110517871	0.00967234827316208	** 
df.mm.trans3:probe7	-0.218608316436014	0.0501958046736867	-4.35511130575841	1.48719849302379e-05	***
df.mm.trans3:probe8	-0.387654041237625	0.0501958046736867	-7.72283747133232	3.10498414456678e-14	***
df.mm.trans3:probe9	-0.320105611119526	0.0501958046736867	-6.37713875094685	2.91679883707742e-10	***
df.mm.trans3:probe10	-0.250888716483573	0.0501958046736867	-4.99820090771634	6.9881343042974e-07	***
df.mm.trans3:probe11	-0.00204905881871745	0.0501958046736867	-0.0408213162840598	0.967447647278376	   

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.12462356380855	0.140290374156184	29.4006170317618	9.88421196175115e-133	***
df.mm.trans1	0.072842210352611	0.120894455647136	0.602527303363036	0.546979129225279	   
df.mm.trans2	-0.0256796310298923	0.106558188548149	-0.240991625137179	0.809617999654856	   
df.mm.exp2	0.160056341990213	0.136503728040236	1.17254190993989	0.241298315018173	   
df.mm.exp3	0.097275443213186	0.136503728040236	0.712621146761012	0.476269988281156	   
df.mm.exp4	0.136995792302562	0.136503728040236	1.00360476793851	0.315846292373322	   
df.mm.exp5	0.0753783283582736	0.136503728040236	0.552207104087701	0.580947321987607	   
df.mm.exp6	0.122895782845467	0.136503728040236	0.900310816487318	0.368202298378441	   
df.mm.exp7	0.067070885787455	0.136503728040236	0.491348381105645	0.623303145668574	   
df.mm.exp8	-0.0349817478028492	0.136503728040236	-0.25626954153617	0.797802883227423	   
df.mm.trans1:exp2	-0.0750492865845637	0.125850218925317	-0.596338148836276	0.551103414421726	   
df.mm.trans2:exp2	-0.0780379603579627	0.0915694845240333	-0.85222670809598	0.394321204487534	   
df.mm.trans1:exp3	-0.0779091336680483	0.125850218925317	-0.619062361061777	0.536036231760497	   
df.mm.trans2:exp3	0.0117682189272858	0.0915694845240333	0.128516819641996	0.897769487027596	   
df.mm.trans1:exp4	-0.141490937222172	0.125850218925317	-1.12428042184127	0.261202060185338	   
df.mm.trans2:exp4	0.00498482129539714	0.0915694845240333	0.0544375817042939	0.956598949717342	   
df.mm.trans1:exp5	-0.048211019731464	0.125850218925317	-0.383082525744939	0.70175151529399	   
df.mm.trans2:exp5	-0.0210950055889422	0.0915694845240332	-0.230371566451328	0.817856840243446	   
df.mm.trans1:exp6	-0.0413612265151205	0.125850218925317	-0.328654386685377	0.742495483370443	   
df.mm.trans2:exp6	-0.00311417635811380	0.0915694845240333	-0.0340088881607328	0.97287781049549	   
df.mm.trans1:exp7	-0.0542437508614491	0.125850218925317	-0.431018327378825	0.666561082394581	   
df.mm.trans2:exp7	-0.0116042737272254	0.0915694845240333	-0.126726428433478	0.89918600880708	   
df.mm.trans1:exp8	0.060464218357534	0.125850218925317	0.480445873466578	0.631030337028693	   
df.mm.trans2:exp8	0.022250686535454	0.0915694845240333	0.242992375146702	0.808068189088682	   
df.mm.trans1:probe2	-0.0485522741151608	0.087671106726584	-0.553800173489069	0.579856936345795	   
df.mm.trans1:probe3	-0.113019057568205	0.087671106726584	-1.28912548030987	0.197694763396201	   
df.mm.trans1:probe4	-0.0357064784278088	0.087671106726584	-0.407277605598901	0.683903609861552	   
df.mm.trans1:probe5	-0.0548775976926408	0.087671106726584	-0.625948499358918	0.531511774160682	   
df.mm.trans1:probe6	-0.0763649371421345	0.087671106726584	-0.87103881761514	0.383971598983576	   
df.mm.trans1:probe7	0.00348595933153914	0.087671106726584	0.0397617808385908	0.968292106471423	   
df.mm.trans1:probe8	-0.0867005046075368	0.087671106726584	-0.988929053649635	0.3229709246078	   
df.mm.trans1:probe9	-0.0921737915493472	0.087671106726584	-1.05135882265985	0.293383754592624	   
df.mm.trans1:probe10	-0.0503724111752917	0.087671106726584	-0.574561141704142	0.565735653583036	   
df.mm.trans1:probe11	-0.115415948969866	0.087671106726584	-1.31646506219898	0.188362327563639	   
df.mm.trans1:probe12	-0.0708546894617566	0.087671106726584	-0.808187464574025	0.419202087796107	   
df.mm.trans1:probe13	0.00247514862906412	0.087671106726584	0.0282322046735791	0.977483382145602	   
df.mm.trans1:probe14	-0.034993218242644	0.087671106726584	-0.399141969905499	0.689885882397034	   
df.mm.trans1:probe15	0.107086632690969	0.087671106726584	1.22145866168811	0.222241023166482	   
df.mm.trans1:probe16	0.0237923548377146	0.087671106726584	0.271381937859126	0.786161230729736	   
df.mm.trans1:probe17	-0.0818082341904693	0.087671106726584	-0.93312651391069	0.351011769288473	   
df.mm.trans1:probe18	-0.0200472942486085	0.087671106726584	-0.228664779048919	0.819182841003524	   
df.mm.trans1:probe19	-0.0993656257673741	0.087671106726584	-1.13339079974502	0.257360265396835	   
df.mm.trans1:probe20	0.00977831010494972	0.087671106726584	0.111534010120859	0.91121844752653	   
df.mm.trans1:probe21	-0.0205409824409345	0.087671106726584	-0.234295918095282	0.814810002212465	   
df.mm.trans1:probe22	-0.029581328094427	0.087671106726584	-0.337412509079885	0.73588678623341	   
df.mm.trans2:probe2	-0.061815905139737	0.087671106726584	-0.705088682552161	0.480942477673713	   
df.mm.trans2:probe3	-0.0196194895161093	0.087671106726584	-0.223785124297515	0.822976683890306	   
df.mm.trans2:probe4	0.0125866981828655	0.087671106726584	0.143567232727186	0.885875235217296	   
df.mm.trans2:probe5	0.0373867068747201	0.087671106726584	0.426442738898192	0.669890034827326	   
df.mm.trans2:probe6	-0.0207722811400994	0.087671106726584	-0.236934172678817	0.812763253561035	   
df.mm.trans3:probe2	-0.0490811082896115	0.087671106726584	-0.559832196970875	0.575737019014356	   
df.mm.trans3:probe3	0.123162740356683	0.087671106726584	1.40482702859889	0.160427210729146	   
df.mm.trans3:probe4	-0.111665055075200	0.087671106726584	-1.27368136715149	0.203114202653623	   
df.mm.trans3:probe5	-0.0244818106749847	0.087671106726584	-0.279246054818665	0.780121915632903	   
df.mm.trans3:probe6	0.0481147310781318	0.087671106726584	0.548809441041792	0.583276078308611	   
df.mm.trans3:probe7	-0.0325836530639855	0.087671106726584	-0.371657827539496	0.710237504711625	   
df.mm.trans3:probe8	-0.0399210506527489	0.087671106726584	-0.455350139211188	0.648970197967966	   
df.mm.trans3:probe9	-0.0631949971754764	0.087671106726584	-0.720818973719128	0.471213211123022	   
df.mm.trans3:probe10	-0.0183040976772937	0.087671106726584	-0.208781414547188	0.83466741711907	   
df.mm.trans3:probe11	-0.0644707694448848	0.087671106726584	-0.73537077210565	0.462310549245073