chr11.4823_chr11_105090407_105094316_+_2.R 

fitVsDatCorrelation=0.855280006129099
cont.fitVsDatCorrelation=0.255491611187739

fstatistic=3568.19234274252,51,669
cont.fstatistic=1015.60074178934,51,669

residuals=-1.06263829356743,-0.106510623349236,-0.00488149361372747,0.102244359480170,2.9772724118365
cont.residuals=-0.800489826726039,-0.306483402661013,-0.125985120251269,0.135989712861184,2.60556738667261

predictedValues:
Include	Exclude	Both
chr11.4823_chr11_105090407_105094316_+_2.R.tl.Lung	55.5019691175754	68.888744630926	109.674586701314
chr11.4823_chr11_105090407_105094316_+_2.R.tl.cerebhem	50.9431157557211	64.3734743126603	140.232288110526
chr11.4823_chr11_105090407_105094316_+_2.R.tl.cortex	53.2148599631615	101.779689298317	170.728343527686
chr11.4823_chr11_105090407_105094316_+_2.R.tl.heart	58.5878985122248	67.0102592075443	93.544800950509
chr11.4823_chr11_105090407_105094316_+_2.R.tl.kidney	59.1453816533758	69.2234032607193	100.361387374957
chr11.4823_chr11_105090407_105094316_+_2.R.tl.liver	54.6937128604163	62.2744242105921	75.3422374717685
chr11.4823_chr11_105090407_105094316_+_2.R.tl.stomach	54.3582087087687	61.1782927159539	93.3964687106852
chr11.4823_chr11_105090407_105094316_+_2.R.tl.testicle	50.9763820342479	57.9476584081831	75.653941247409


diffExp=-13.3867755133506,-13.4303585569392,-48.5648293351554,-8.42236069531958,-10.0780216073435,-7.58071135017575,-6.82008400718517,-6.97127637393518
diffExpScore=0.991398176327181
diffExp1.5=0,0,-1,0,0,0,0,0
diffExp1.5Score=0.5
diffExp1.4=0,0,-1,0,0,0,0,0
diffExp1.4Score=0.5
diffExp1.3=0,0,-1,0,0,0,0,0
diffExp1.3Score=0.5
diffExp1.2=-1,-1,-1,0,0,0,0,0
diffExp1.2Score=0.75

cont.predictedValues:
Include	Exclude	Both
Lung	69.6758118881615	87.1814412850472	80.0472816368319
cerebhem	81.8952730937826	69.9245737254321	73.1497825798213
cortex	69.7389957910141	65.932187937057	71.1482666249314
heart	76.387664240144	85.7182433808693	67.767308351121
kidney	79.8498088219778	106.866018380362	68.308102137077
liver	70.6928785686188	69.2231293606974	59.4135040535213
stomach	73.617878729684	81.9220695654002	71.6907317121473
testicle	71.0179633310874	81.665422194345	69.1246787117752
cont.diffExp=-17.5056293968857,11.9706993683505,3.8068078539571,-9.3305791407253,-27.0162095583839,1.46974920792141,-8.30419083571621,-10.6474588632577
cont.diffExpScore=1.59222774502701

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,-1,0,0,0
cont.diffExp1.3Score=0.5
cont.diffExp1.2=-1,0,0,0,-1,0,0,0
cont.diffExp1.2Score=0.666666666666667

tran.correlation=0.0194535252320561
cont.tran.correlation=0.338814918393078

tran.covariance=0.00076343552433355
cont.tran.covariance=0.00321700428015199

tran.mean=61.8810921656492
cont.tran.mean=77.581835018355

weightedLogRatios:
wLogRatio
Lung	-0.891188896659918
cerebhem	-0.94713084396798
cortex	-2.78750877883541
heart	-0.555764035311378
kidney	-0.65432699658201
liver	-0.527859483136777
stomach	-0.479251843839938
testicle	-0.512127892374829

cont.weightedLogRatios:
wLogRatio
Lung	-0.976328826965183
cerebhem	0.683680214711429
cortex	0.236695148091444
heart	-0.50632000711589
kidney	-1.31896465027499
liver	0.0892462433213252
stomach	-0.465179056406627
testicle	-0.605280226328885

varWeightedLogRatios=0.600970718961486
cont.varWeightedLogRatios=0.434172310499657

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	2.90631276653669	0.127952055334780	22.7140764478732	4.2043620817401e-85	***
df.mm.trans1	1.07135094587292	0.108608376816519	9.86434911630107	1.60496008123639e-21	***
df.mm.trans2	1.29632330637028	0.0988029575770603	13.1202884828546	3.69258401182906e-35	***
df.mm.exp2	-0.399282584552958	0.128461939355424	-3.10817808415796	0.00196224285056559	** 
df.mm.exp3	-0.0943190382090501	0.128461939355424	-0.734217766618729	0.46307327516621	   
df.mm.exp4	0.185539791606481	0.128461939355424	1.44431722374310	0.149117921819752	   
df.mm.exp5	0.15716633858861	0.128461939355424	1.22344672186341	0.221591761701819	   
df.mm.exp6	0.259865051595843	0.128461939355424	2.02289528633736	0.0434812076513049	*  
df.mm.exp7	0.0211409047930851	0.128461939355424	0.164569403974146	0.869332625015634	   
df.mm.exp8	0.113339424932028	0.128461939355424	0.882280195213655	0.377942198435597	   
df.mm.trans1:exp2	0.313573717723300	0.114087828307694	2.7485291145835	0.00614777068804739	** 
df.mm.trans2:exp2	0.331491436624977	0.0914737640418172	3.623896317128	0.000312209102499143	***
df.mm.trans1:exp3	0.0522382184502104	0.114087828307694	0.457877226914377	0.64718932490074	   
df.mm.trans2:exp3	0.484636800052412	0.0914737640418171	5.29809618232022	1.59019445561820e-07	***
df.mm.trans1:exp4	-0.131430126098898	0.114087828307694	-1.15200830840983	0.249729163099704	   
df.mm.trans2:exp4	-0.213186868070429	0.0914737640418171	-2.33057937763412	0.0200718974396798	*  
df.mm.trans1:exp5	-0.0935863294880822	0.114087828307694	-0.820300735637466	0.412336859336715	   
df.mm.trans2:exp5	-0.152320142365123	0.0914737640418171	-1.66517846904704	0.0963453450782694	.  
df.mm.trans1:exp6	-0.274534787059235	0.114087828307694	-2.40634598038642	0.0163831276469557	*  
df.mm.trans2:exp6	-0.360807043039689	0.0914737640418171	-3.94437735037061	8.84509324947356e-05	***
df.mm.trans1:exp7	-0.0419637681493638	0.114087828307694	-0.367819852229880	0.713123904873351	   
df.mm.trans2:exp7	-0.139841279010947	0.0914737640418171	-1.52875833279386	0.126797024723451	   
df.mm.trans1:exp8	-0.19839549655705	0.114087828307694	-1.73897162826151	0.0824997491662765	.  
df.mm.trans2:exp8	-0.286292069702597	0.0914737640418171	-3.12977248396293	0.00182551491623672	** 
df.mm.trans1:probe2	-0.00509099710205107	0.0781105963761255	-0.0651767793134804	0.948052720035439	   
df.mm.trans1:probe3	0.195157854295609	0.0781105963761255	2.49848117092675	0.0127114922619913	*  
df.mm.trans1:probe4	0.263771791495497	0.0781105963761255	3.37690151826979	0.000775517964111272	***
df.mm.trans1:probe5	0.172533286582307	0.0781105963761255	2.20883330286596	0.0275244378179369	*  
df.mm.trans1:probe6	0.119232155753472	0.0781105963761255	1.52645302027057	0.127369692893759	   
df.mm.trans1:probe7	0.0631464828970383	0.0781105963761255	0.80842402729803	0.419133904492887	   
df.mm.trans1:probe8	0.000627081466502297	0.0781105963761255	0.00802812288722923	0.993596946821016	   
df.mm.trans1:probe9	0.06582945967789	0.0781105963761255	0.842772462789835	0.39965691031946	   
df.mm.trans1:probe10	-0.0227072738051012	0.0781105963761255	-0.290706701249073	0.771365741847461	   
df.mm.trans1:probe11	0.00481689088924351	0.0781105963761255	0.0616675728098242	0.950845982352282	   
df.mm.trans1:probe12	0.082796470072729	0.0781105963761255	1.05999024350089	0.289531575422347	   
df.mm.trans1:probe13	0.0439889047482636	0.0781105963761255	0.563161808884982	0.573513395630853	   
df.mm.trans1:probe14	-0.0539872115470962	0.0781105963761255	-0.691163735162537	0.489702406357101	   
df.mm.trans2:probe2	0.127546106133911	0.0781105963761255	1.63289120876429	0.102962409859407	   
df.mm.trans2:probe3	0.082536440481075	0.0781105963761255	1.05666125097340	0.291047627902047	   
df.mm.trans2:probe4	0.135394749719235	0.0781105963761255	1.73337237200533	0.0834902566695747	.  
df.mm.trans2:probe5	0.156261916413776	0.0781105963761255	2.00052135898859	0.0458476540979511	*  
df.mm.trans2:probe6	-0.0240314731230888	0.0781105963761255	-0.307659578059937	0.758437135417312	   
df.mm.trans3:probe2	-0.497691162417391	0.0781105963761255	-6.37162159178586	3.48116347643273e-10	***
df.mm.trans3:probe3	0.125588762142724	0.0781105963761255	1.60783258570934	0.108343656083164	   
df.mm.trans3:probe4	-1.04707735775305	0.0781105963761255	-13.4050616219990	1.82511574281714e-36	***
df.mm.trans3:probe5	-0.472735966058244	0.0781105963761255	-6.05213617601742	2.38062366634984e-09	***
df.mm.trans3:probe6	-1.34472138395140	0.0781105963761255	-17.2156076939442	2.93715619441390e-55	***
df.mm.trans3:probe7	-1.2114139826648	0.0781105963761255	-15.5089583086971	1.43615659419243e-46	***
df.mm.trans3:probe8	-0.157096758508657	0.0781105963761255	-2.01120930830165	0.044703980492249	*  
df.mm.trans3:probe9	-0.806170679546635	0.0781105963761255	-10.3208875229256	2.83277768506351e-23	***
df.mm.trans3:probe10	-0.353374872536465	0.0781105963761255	-4.5240324479775	7.17871241847931e-06	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.5111933018235	0.238737131841122	18.8960689400661	3.84258409167054e-64	***
df.mm.trans1	-0.272803507468777	0.202645063475176	-1.34621343737888	0.178689654761715	   
df.mm.trans2	0.0241637957462023	0.184349791393744	0.131075796525270	0.89575475345814	   
df.mm.exp2	0.0311220370849532	0.239688490131865	0.129843686143759	0.89672912836549	   
df.mm.exp3	-0.160606786040792	0.239688490131865	-0.670064657474516	0.503047790487038	   
df.mm.exp4	0.241579773016593	0.239688490131865	1.00789058700185	0.31387138680365	   
df.mm.exp5	0.498467756139237	0.239688490131865	2.07964827958575	0.0379380387956779	*  
df.mm.exp6	0.0819311207488561	0.239688490131865	0.341823342054438	0.732591188304162	   
df.mm.exp7	0.103067639336566	0.239688490131865	0.430006627685225	0.667329293443408	   
df.mm.exp8	0.100424496910865	0.239688490131865	0.418979221136637	0.675365802829043	   
df.mm.trans1:exp2	0.130466010722494	0.212868803372459	0.612893992240921	0.540154723102826	   
df.mm.trans2:exp2	-0.251696373147452	0.170674742261205	-1.47471365600335	0.140759967933704	   
df.mm.trans1:exp3	0.161513202055423	0.212868803372459	0.75874529051033	0.448272165563103	   
df.mm.trans2:exp3	-0.118757934589795	0.170674742261205	-0.695814348487751	0.486786743602101	   
df.mm.trans1:exp4	-0.149611778509321	0.212868803372459	-0.702835625225663	0.482402683675982	   
df.mm.trans2:exp4	-0.258505574108528	0.170674742261205	-1.51460943009894	0.130343639191302	   
df.mm.trans1:exp5	-0.362173501498849	0.212868803372459	-1.70139304473446	0.0893338593077838	.  
df.mm.trans2:exp5	-0.29488334992372	0.170674742261205	-1.72775037487634	0.0844944771567588	.  
df.mm.trans1:exp6	-0.067439506182441	0.212868803372459	-0.316812539526712	0.751484684121654	   
df.mm.trans2:exp6	-0.31258755361796	0.170674742261205	-1.83148103507646	0.0674732897982719	.  
df.mm.trans1:exp7	-0.0480329513818773	0.212868803372459	-0.225645799764437	0.821545830485522	   
df.mm.trans2:exp7	-0.165290694078612	0.170674742261205	-0.968454335355883	0.333167494462427	   
df.mm.trans1:exp8	-0.0813448730099673	0.212868803372459	-0.382136187742068	0.702481708457214	   
df.mm.trans2:exp8	-0.165785292550592	0.170674742261205	-0.971352236154953	0.331723962067287	   
df.mm.trans1:probe2	-0.00186986958152359	0.145741306745285	-0.0128300591183226	0.989767199807324	   
df.mm.trans1:probe3	0.141356556739261	0.145741306745285	0.969914157461982	0.332439805557297	   
df.mm.trans1:probe4	0.0954443468710625	0.145741306745285	0.65488878206556	0.51276446030195	   
df.mm.trans1:probe5	-0.0599243662251648	0.145741306745285	-0.41116940394871	0.681079997699883	   
df.mm.trans1:probe6	-0.106743704302337	0.145741306745285	-0.732419014801998	0.464169372737638	   
df.mm.trans1:probe7	0.0558103948133276	0.145741306745285	0.382941501347100	0.701884779942387	   
df.mm.trans1:probe8	0.112285118914280	0.145741306745285	0.77044127997647	0.441310156707133	   
df.mm.trans1:probe9	0.107592754961519	0.145741306745285	0.738244752735483	0.460624618842242	   
df.mm.trans1:probe10	-0.0888914237325858	0.145741306745285	-0.609926078733076	0.542117883621616	   
df.mm.trans1:probe11	-0.148209516667573	0.145741306745285	-1.01693555504207	0.309551711449617	   
df.mm.trans1:probe12	0.069990266749569	0.145741306745285	0.480236305770831	0.63121635820818	   
df.mm.trans1:probe13	0.0115108160879823	0.145741306745285	0.0789811505402516	0.937071233845072	   
df.mm.trans1:probe14	-0.0572290198610472	0.145741306745285	-0.392675358407948	0.694684353892021	   
df.mm.trans2:probe2	-0.318110968949601	0.145741306745285	-2.18270973448571	0.0294041695503949	*  
df.mm.trans2:probe3	-0.148087400581405	0.145741306745285	-1.01609765884850	0.309950209687789	   
df.mm.trans2:probe4	-0.149437701570710	0.145741306745285	-1.02536271224661	0.305562677907498	   
df.mm.trans2:probe5	-0.262328369180143	0.145741306745285	-1.7999589480738	0.0723175234155414	.  
df.mm.trans2:probe6	-0.199885457415023	0.145741306745285	-1.37150861261569	0.170676269861936	   
df.mm.trans3:probe2	0.25326368910527	0.145741306745285	1.73776189305002	0.0827129386202543	.  
df.mm.trans3:probe3	0.00830683335616883	0.145741306745285	0.0569971104395741	0.954564515717976	   
df.mm.trans3:probe4	0.075863812410645	0.145741306745285	0.520537479077458	0.602861237929582	   
df.mm.trans3:probe5	0.116318054106473	0.145741306745285	0.798113154767882	0.425088037333851	   
df.mm.trans3:probe6	0.169067211478993	0.145741306745285	1.16005005893405	0.246442247470143	   
df.mm.trans3:probe7	0.240760546742022	0.145741306745285	1.65197192284549	0.0990095157371481	.  
df.mm.trans3:probe8	0.0996896523079412	0.145741306745285	0.68401782949683	0.49420076740069	   
df.mm.trans3:probe9	0.112575743311678	0.145741306745285	0.772435391350161	0.440129391691463	   
df.mm.trans3:probe10	0.124793361676309	0.145741306745285	0.856266246428085	0.392157114411242