chr17.10255_chr17_56068760_56072067_-_2.R 

fitVsDatCorrelation=0.873209752833157
cont.fitVsDatCorrelation=0.215881451452821

fstatistic=8442.13896619708,62,922
cont.fstatistic=2091.89424629073,62,922

residuals=-0.874149365060678,-0.101995733431721,-0.00150139134128613,0.0962544063168815,0.752440402672927
cont.residuals=-0.901769454587062,-0.266705950954176,0.0146976740063719,0.21655612089119,1.55447647215732

predictedValues:
Include	Exclude	Both
chr17.10255_chr17_56068760_56072067_-_2.R.tl.Lung	69.3950784737002	89.8534611894575	80.6226193385842
chr17.10255_chr17_56068760_56072067_-_2.R.tl.cerebhem	64.2346680470826	67.760693629994	90.9262958048198
chr17.10255_chr17_56068760_56072067_-_2.R.tl.cortex	94.8026112676117	75.5659963936802	111.531090701568
chr17.10255_chr17_56068760_56072067_-_2.R.tl.heart	75.4840982203803	84.9633727741117	82.9966298123833
chr17.10255_chr17_56068760_56072067_-_2.R.tl.kidney	67.6141745419707	96.6205488967802	83.0820238901653
chr17.10255_chr17_56068760_56072067_-_2.R.tl.liver	68.4416510410939	91.2377168999635	74.8158759655391
chr17.10255_chr17_56068760_56072067_-_2.R.tl.stomach	75.3273629745182	81.01179241667	76.4623627771988
chr17.10255_chr17_56068760_56072067_-_2.R.tl.testicle	79.1976835438034	81.431766035529	89.9385261188437


diffExp=-20.4583827157573,-3.52602558291142,19.2366148739315,-9.47927455373144,-29.0063743548095,-22.7960658588696,-5.68442944215191,-2.23408249172552
diffExpScore=1.49998958858222
diffExp1.5=0,0,0,0,0,0,0,0
diffExp1.5Score=0
diffExp1.4=0,0,0,0,-1,0,0,0
diffExp1.4Score=0.5
diffExp1.3=0,0,0,0,-1,-1,0,0
diffExp1.3Score=0.666666666666667
diffExp1.2=-1,0,1,0,-1,-1,0,0
diffExp1.2Score=1.33333333333333

cont.predictedValues:
Include	Exclude	Both
Lung	78.7282112489394	85.9776949513956	89.9439088139514
cerebhem	76.5511812276787	87.6636412544997	76.2781612772832
cortex	83.1205160774792	85.4693932167428	85.8825193562184
heart	76.0762107563304	92.0531981594852	76.1301811689589
kidney	78.8501217524136	77.8106447152145	83.1886437725106
liver	76.355424362972	85.8630183017351	79.0090401692836
stomach	78.5626927618964	86.5701553926656	85.2599618133618
testicle	82.50949180205	75.7030794374665	81.674176379437
cont.diffExp=-7.24948370245622,-11.1124600268210,-2.34887713926361,-15.9769874031548,1.03947703719905,-9.50759393876305,-8.00746263076923,6.80641236458348
cont.diffExpScore=1.31023473664088

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

tran.correlation=-0.288086285066151
cont.tran.correlation=-0.601477035602162

tran.covariance=-0.00313911519453706
cont.tran.covariance=-0.00130992007493527

tran.mean=78.9339172716467
cont.tran.mean=81.7415422136853

weightedLogRatios:
wLogRatio
Lung	-1.12879261715292
cerebhem	-0.223870934739248
cortex	1.00658757971225
heart	-0.518509603707254
kidney	-1.56793762748902
liver	-1.25623825573746
stomach	-0.317065993742478
testicle	-0.122007471879348

cont.weightedLogRatios:
wLogRatio
Lung	-0.388464720224636
cerebhem	-0.597186938861828
cortex	-0.123567580750795
heart	-0.843933360509547
kidney	0.0578720010781699
liver	-0.51566303060417
stomach	-0.428262085626238
testicle	0.376221434957109

varWeightedLogRatios=0.661209101921545
cont.varWeightedLogRatios=0.152993086967445

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.14851043329435	0.0877132216910881	47.2962952826511	8.84331916677117e-249	***
df.mm.trans1	-0.0411686054001777	0.0753306459974092	-0.546505407660961	0.584850795658973	   
df.mm.trans2	0.345775587875214	0.0661451618412336	5.22752652272845	2.12566580455765e-07	***
df.mm.exp2	-0.479740726476031	0.084161265954737	-5.70025558710157	1.60968806817598e-08	***
df.mm.exp3	-0.185716876543708	0.084161265954737	-2.20667874273171	0.0275823276745150	*  
df.mm.exp4	-0.00087456963391485	0.084161265954737	-0.0103915931395947	0.991711105488792	   
df.mm.exp5	0.0165638873991076	0.084161265954737	0.196811290933003	0.84401858930075	   
df.mm.exp6	0.0762030018630784	0.084161265954737	0.9054402996274	0.365468715074564	   
df.mm.exp7	0.0314227175661229	0.084161265954737	0.373363176155435	0.708963988693164	   
df.mm.exp8	-0.0756307374871577	0.084161265954737	-0.89864068261322	0.369078698786642	   
df.mm.trans1:exp2	0.402467842523796	0.0772608642951396	5.20920709592831	2.33995133035348e-07	***
df.mm.trans2:exp2	0.197542878986231	0.0547622229008282	3.60728379021377	0.000326041409704306	***
df.mm.trans1:exp3	0.497697880808734	0.0772608642951396	6.44178505313515	1.89895639460157e-10	***
df.mm.trans2:exp3	0.0125431410426686	0.0547622229008282	0.229047331869337	0.818882924101967	   
df.mm.trans1:exp4	0.0849806344742638	0.0772608642951396	1.09991824773839	0.271654843289775	   
df.mm.trans2:exp4	-0.0550853096347609	0.0547622229008282	-1.00589981043169	0.314727680475753	   
df.mm.trans1:exp5	-0.0425621933905715	0.0772608642951396	-0.550889428676104	0.581842931687826	   
df.mm.trans2:exp5	0.0560474183447498	0.0547622229008282	1.02346865002630	0.306354767049818	   
df.mm.trans1:exp6	-0.090037378843524	0.0772608642951396	-1.16536851697099	0.244171253906144	   
df.mm.trans2:exp6	-0.0609147621279573	0.0547622229008282	-1.11235006362454	0.266277609746849	   
df.mm.trans1:exp7	0.0506047877131645	0.0772608642951396	0.654986042090497	0.512640110908093	   
df.mm.trans2:exp7	-0.135008122450586	0.0547622229008282	-2.46535139187244	0.0138691026588228	*  
df.mm.trans1:exp8	0.207761838061906	0.0772608642951396	2.68909544252893	0.00729392630503033	** 
df.mm.trans2:exp8	-0.0227839538452121	0.0547622229008282	-0.41605239229373	0.677468488114184	   
df.mm.trans1:probe2	-0.0852575224581956	0.0553458509966916	-1.54045011365517	0.123793790132956	   
df.mm.trans1:probe3	-0.258248179556096	0.0553458509966916	-4.66608020123374	3.52434822717062e-06	***
df.mm.trans1:probe4	-0.364164969435945	0.0553458509966916	-6.57980612598609	7.88842337412335e-11	***
df.mm.trans1:probe5	-0.252272350965365	0.0553458509966916	-4.55810772483098	5.85630718797794e-06	***
df.mm.trans1:probe6	-0.0670290667556353	0.0553458509966916	-1.21109469903430	0.226169443616533	   
df.mm.trans1:probe7	-0.0355355172087700	0.0553458509966916	-0.642062893041326	0.520991948628646	   
df.mm.trans1:probe8	-0.305127313769759	0.0553458509966916	-5.51310185451839	4.5776419774093e-08	***
df.mm.trans1:probe9	-0.0220655780568039	0.0553458509966916	-0.398685315329652	0.690217402056659	   
df.mm.trans1:probe10	-0.291750138536366	0.0553458509966916	-5.27140035399953	1.68684124318124e-07	***
df.mm.trans1:probe11	0.694317030603508	0.0553458509966916	12.5450601644017	1.91735717195151e-33	***
df.mm.trans1:probe12	0.203735209724036	0.0553458509966916	3.68112886612249	0.000245684561575761	***
df.mm.trans1:probe13	0.432394827924968	0.0553458509966916	7.8125969722792	1.52205026556143e-14	***
df.mm.trans1:probe14	0.342294931457247	0.0553458509966916	6.1846538682314	9.34238254605529e-10	***
df.mm.trans1:probe15	0.443339459583513	0.0553458509966916	8.0103467848026	3.43821844807042e-15	***
df.mm.trans1:probe16	0.166384610229851	0.0553458509966916	3.00627070021558	0.00271641664453378	** 
df.mm.trans1:probe17	0.52504921292819	0.0553458509966916	9.48669509046985	1.96561624760329e-20	***
df.mm.trans1:probe18	0.716766639066187	0.0553458509966916	12.9506842185701	2.30149507022455e-35	***
df.mm.trans1:probe19	0.705928866660555	0.0553458509966916	12.7548651605836	1.97013269732923e-34	***
df.mm.trans1:probe20	0.85024246738596	0.0553458509966916	15.3623524089779	1.36151251065927e-47	***
df.mm.trans1:probe21	0.727703952025193	0.0553458509966916	13.1483017953540	2.57739522426016e-36	***
df.mm.trans1:probe22	0.50988769003369	0.0553458509966916	9.21275363647709	2.09337665853301e-19	***
df.mm.trans2:probe2	0.128242809736883	0.0553458509966916	2.31711695506406	0.0207156420010698	*  
df.mm.trans2:probe3	-0.109676221773254	0.0553458509966916	-1.98165209854321	0.0478148332075005	*  
df.mm.trans2:probe4	-0.16856742131779	0.0553458509966916	-3.04571017126227	0.00238733216270748	** 
df.mm.trans2:probe5	0.0517120728334989	0.0553458509966916	0.93434416315308	0.350371021621055	   
df.mm.trans2:probe6	0.172276910974963	0.0553458509966916	3.11273397865471	0.00191077316982138	** 
df.mm.trans3:probe2	-0.0976958953930148	0.0553458509966916	-1.76518914487113	0.0778629737285902	.  
df.mm.trans3:probe3	0.0772018873794564	0.0553458509966916	1.39489927409502	0.163382154631665	   
df.mm.trans3:probe4	-0.0338889412795627	0.0553458509966916	-0.612312227010268	0.54048217683422	   
df.mm.trans3:probe5	-0.122082151711628	0.0553458509966916	-2.20580494315511	0.0276436906183653	*  
df.mm.trans3:probe6	-0.0311407352066497	0.0553458509966916	-0.56265708532535	0.573805190768483	   
df.mm.trans3:probe7	0.130368273014441	0.0553458509966916	2.35552025430476	0.0187053985783371	*  
df.mm.trans3:probe8	0.0153497957621280	0.0553458509966916	0.27734320614287	0.781578757039589	   
df.mm.trans3:probe9	-0.139405449913327	0.0553458509966916	-2.51880578946486	0.0119435605961425	*  
df.mm.trans3:probe10	0.254748447712087	0.0553458509966916	4.60284634032125	4.75105891686553e-06	***
df.mm.trans3:probe11	0.0460714794759201	0.0553458509966916	0.832428784565515	0.405382518784244	   
df.mm.trans3:probe12	-0.353595701526215	0.0553458509966916	-6.38883846139346	2.64851879868573e-10	***
df.mm.trans3:probe13	-0.569327201060629	0.0553458509966916	-10.2867187116639	1.43545105651544e-23	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.36785339955455	0.175737892452157	24.8543631575851	8.80781286967426e-105	***
df.mm.trans1	-0.0722973235868002	0.150928773443848	-0.479016173902043	0.632040705938463	   
df.mm.trans2	0.0956426507920626	0.132525189632458	0.721694125149459	0.470665531460794	   
df.mm.exp2	0.156176891949309	0.168621368818034	0.926198696191617	0.354585096531187	   
df.mm.exp3	0.0945663672094402	0.168621368818034	0.56082077777159	0.575055980051342	   
df.mm.exp4	0.200754258889807	0.168621368818034	1.19056238421625	0.234131860074874	   
df.mm.exp5	-0.0201869623397623	0.168621368818034	-0.119717699371465	0.904732859634866	   
df.mm.exp6	0.0976867948326714	0.168621368818034	0.579326306727405	0.562510531636819	   
df.mm.exp7	0.0582438889131478	0.168621368818034	0.345412264895092	0.72986324671038	   
df.mm.exp8	0.0160910868457260	0.168621368818034	0.0954273290420892	0.92399614555189	   
df.mm.trans1:exp2	-0.184218896034383	0.154796063791595	-1.19007480889437	0.234323329685963	   
df.mm.trans2:exp2	-0.136757561023434	0.109718893606260	-1.24643583733359	0.212921012898018	   
df.mm.trans1:exp3	-0.0402763684823148	0.154796063791595	-0.260189874960514	0.794775407232078	   
df.mm.trans2:exp3	-0.100495931011258	0.109718893606260	-0.91594006928196	0.359937826018264	   
df.mm.trans1:exp4	-0.235020204803871	0.154796063791595	-1.51825698307345	0.129292429560204	   
df.mm.trans2:exp4	-0.132475509676961	0.109718893606260	-1.20740836261406	0.227584561824935	   
df.mm.trans1:exp5	0.0217342629803353	0.154796063791595	0.140405785831845	0.888370053746479	   
df.mm.trans2:exp5	-0.0796226958861194	0.109718893606260	-0.725697218310051	0.468208565529559	   
df.mm.trans1:exp6	-0.128289276549169	0.154796063791595	-0.828763170114506	0.407452922175298	   
df.mm.trans2:exp6	-0.0990214804786183	0.109718893606260	-0.902501631432496	0.367026165539796	   
df.mm.trans1:exp7	-0.0603485058225573	0.154796063791595	-0.389858142024889	0.696731487048026	   
df.mm.trans2:exp7	-0.0513766602760062	0.109718893606260	-0.468257185133289	0.639711356858382	   
df.mm.trans1:exp8	0.0308206951540662	0.154796063791595	0.199105160681352	0.842224382625428	   
df.mm.trans2:exp8	-0.143360149322076	0.109718893606260	-1.30661315120932	0.191669947493837	   
df.mm.trans1:probe2	0.0680118905818818	0.110888222124418	0.613337370542126	0.539804559396353	   
df.mm.trans1:probe3	0.139389539425010	0.110888222124418	1.25702745300230	0.209062033464695	   
df.mm.trans1:probe4	0.129411449179230	0.110888222124418	1.1670441341735	0.243494288719874	   
df.mm.trans1:probe5	0.245899831429674	0.110888222124418	2.21754688386807	0.0268288877752027	*  
df.mm.trans1:probe6	-0.0148006009606313	0.110888222124418	-0.133473155913933	0.893848318972093	   
df.mm.trans1:probe7	0.105806061527889	0.110888222124418	0.95416861683627	0.34024838636023	   
df.mm.trans1:probe8	0.202632700643180	0.110888222124418	1.82735999154016	0.067968764607337	.  
df.mm.trans1:probe9	0.116261603037647	0.110888222124418	1.04845763427607	0.294702565427731	   
df.mm.trans1:probe10	0.0497277284459246	0.110888222124418	0.448449145393723	0.653934447174534	   
df.mm.trans1:probe11	0.0882500668337599	0.110888222124418	0.795847071429661	0.426325775594117	   
df.mm.trans1:probe12	-0.0129055726935831	0.110888222124418	-0.116383619886185	0.907373861082803	   
df.mm.trans1:probe13	0.0788644459927621	0.110888222124418	0.711206695191444	0.477136043206614	   
df.mm.trans1:probe14	0.111642686597743	0.110888222124418	1.00680382874638	0.314293202993889	   
df.mm.trans1:probe15	0.138202742496526	0.110888222124418	1.24632481113694	0.212961735513689	   
df.mm.trans1:probe16	0.177997060857031	0.110888222124418	1.60519356742248	0.108793649256910	   
df.mm.trans1:probe17	0.0670908066840844	0.110888222124418	0.605030952780608	0.545307307283158	   
df.mm.trans1:probe18	0.217921095554549	0.110888222124418	1.96523211734821	0.0496872413741175	*  
df.mm.trans1:probe19	0.0895347446687538	0.110888222124418	0.807432412148286	0.419625648554484	   
df.mm.trans1:probe20	0.152021546124901	0.110888222124418	1.37094403005516	0.170726015971507	   
df.mm.trans1:probe21	0.0056437400307427	0.110888222124418	0.0508957572104488	0.959419610220878	   
df.mm.trans1:probe22	0.308988265083532	0.110888222124418	2.78648407525953	0.00543771726720375	** 
df.mm.trans2:probe2	0.021934892966826	0.110888222124418	0.19781084543149	0.843236662910654	   
df.mm.trans2:probe3	-0.0430125220287482	0.110888222124418	-0.387890807560135	0.698186375807425	   
df.mm.trans2:probe4	-0.0147206021099744	0.110888222124418	-0.132751719055047	0.894418704988222	   
df.mm.trans2:probe5	-0.0673589923999057	0.110888222124418	-0.60744947578227	0.54370223608615	   
df.mm.trans2:probe6	-0.0755976029305434	0.110888222124418	-0.681746009469986	0.495570779681071	   
df.mm.trans3:probe2	0.0996948925760703	0.110888222124418	0.899057543408092	0.368856746171206	   
df.mm.trans3:probe3	0.211186436200005	0.110888222124418	1.90449835116890	0.0571563026464527	.  
df.mm.trans3:probe4	0.135952759189695	0.110888222124418	1.22603425850902	0.220498828276433	   
df.mm.trans3:probe5	0.197991239087157	0.110888222124418	1.78550287211754	0.0745083965178338	.  
df.mm.trans3:probe6	0.130569463958287	0.110888222124418	1.17748721601638	0.239304952075177	   
df.mm.trans3:probe7	0.113233160911023	0.110888222124418	1.02114686971871	0.307452716929701	   
df.mm.trans3:probe8	0.0761329521352213	0.110888222124418	0.686573836938238	0.492523949504848	   
df.mm.trans3:probe9	0.0778258791128294	0.110888222124418	0.70184080528866	0.482955570470693	   
df.mm.trans3:probe10	0.107841873545972	0.110888222124418	0.972527753443206	0.331043132778588	   
df.mm.trans3:probe11	0.106225279806144	0.110888222124418	0.957949165123754	0.338339513114314	   
df.mm.trans3:probe12	0.0790000402509238	0.110888222124418	0.71242949645531	0.476379099053262	   
df.mm.trans3:probe13	0.0681837036406332	0.110888222124418	0.614886796220162	0.538781201762076