chr3.15395_chr3_95872309_95891393_+_2.R 

fitVsDatCorrelation=0.806992028747653
cont.fitVsDatCorrelation=0.268827101040893

fstatistic=14380.3729883000,63,945
cont.fstatistic=5396.67706271722,63,945

residuals=-0.48150962361967,-0.0808340927994169,-0.00373019405352143,0.0742003674631739,1.04803861084175
cont.residuals=-0.512663607068316,-0.147551650500679,-0.0260480027891359,0.111825150915229,0.91501256906056

predictedValues:
Include	Exclude	Both
chr3.15395_chr3_95872309_95891393_+_2.R.tl.Lung	54.7383974222672	76.0980306105145	66.8705091316855
chr3.15395_chr3_95872309_95891393_+_2.R.tl.cerebhem	53.4115452484045	65.1975068332145	64.5178565441692
chr3.15395_chr3_95872309_95891393_+_2.R.tl.cortex	54.5762294772237	72.2570825754008	64.3722238411672
chr3.15395_chr3_95872309_95891393_+_2.R.tl.heart	55.1908748595445	76.4152541031861	66.6721590494581
chr3.15395_chr3_95872309_95891393_+_2.R.tl.kidney	55.8642853815155	82.9418352041166	70.6455945052744
chr3.15395_chr3_95872309_95891393_+_2.R.tl.liver	54.879210639673	94.4865878677646	65.1130423595695
chr3.15395_chr3_95872309_95891393_+_2.R.tl.stomach	55.4408072199006	77.284166991604	70.5543052423671
chr3.15395_chr3_95872309_95891393_+_2.R.tl.testicle	53.5443529423866	78.7597560866457	62.5825455746914


diffExp=-21.3596331882474,-11.78596158481,-17.6808530981771,-21.2243792436416,-27.0775498226010,-39.6073772280916,-21.8433597717034,-25.2154031442591
diffExpScore=0.994646523807958
diffExp1.5=0,0,0,0,0,-1,0,0
diffExp1.5Score=0.5
diffExp1.4=0,0,0,0,-1,-1,0,-1
diffExp1.4Score=0.75
diffExp1.3=-1,0,-1,-1,-1,-1,-1,-1
diffExp1.3Score=0.875
diffExp1.2=-1,-1,-1,-1,-1,-1,-1,-1
diffExp1.2Score=0.888888888888889

cont.predictedValues:
Include	Exclude	Both
Lung	56.7713131872156	58.1989486272512	60.4105410811102
cerebhem	57.8047022039197	52.8465695194501	64.61456468008
cortex	56.4152028707274	57.963323777039	61.01747236706
heart	58.0687705973206	57.9256743081599	56.766124641868
kidney	57.3574066986713	58.9683202972443	53.3324065822048
liver	57.6105430164957	60.1386650320143	53.0907045720664
stomach	58.2101184820781	58.9045514235386	60.5820769081029
testicle	57.5388993087841	58.018131972777	59.8976809309757
cont.diffExp=-1.42763544003558,4.95813268446962,-1.54812090631162,0.143096289160681,-1.61091359857292,-2.52812201551863,-0.694432941460491,-0.479232663992882
cont.diffExpScore=3.19774434198962

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=0.464309132202653
cont.tran.correlation=-0.107693297959229

tran.covariance=0.00083371131219838
cont.tran.covariance=-4.71066288279237e-05

tran.mean=66.3178702164602
cont.tran.mean=57.6713213326679

weightedLogRatios:
wLogRatio
Lung	-1.37294397576586
cerebhem	-0.813074953116695
cortex	-1.16179212622972
heart	-1.35798982026046
kidney	-1.66801480040252
liver	-2.32368303909053
stomach	-1.38894982838323
testicle	-1.61050262454726

cont.weightedLogRatios:
wLogRatio
Lung	-0.100622364566793
cerebhem	0.35980618456943
cortex	-0.109539876126891
heart	0.0100181980374975
kidney	-0.112542669701640
liver	-0.175018314904575
stomach	-0.0482666672986854
testicle	-0.0336469425813159

varWeightedLogRatios=0.191758082672833
cont.varWeightedLogRatios=0.0275630131821718

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.42215840885087	0.0683172917971579	64.7297088704977	0	***
df.mm.trans1	-0.290855766896678	0.0611566930843803	-4.75591063263301	2.28347113145669e-06	***
df.mm.trans2	0.0254831892650036	0.0549459241586037	0.463786707662707	0.642907374947452	   
df.mm.exp2	-0.143323638719537	0.0738358389134897	-1.94111207820721	0.0525417085683805	.  
df.mm.exp3	-0.0166832147328725	0.0738358389134897	-0.225950093861864	0.821289094311671	   
df.mm.exp4	0.0153627433684875	0.0738358389134897	0.208066212757294	0.835222100887075	   
df.mm.exp5	0.0515593421394326	0.0738358389134897	0.698296963888261	0.485163291576876	   
df.mm.exp6	0.245637861629877	0.0738358389134897	3.32681073641867	0.000912326346186454	***
df.mm.exp7	-0.0254074390413851	0.0738358389134897	-0.344107135711614	0.73084227062621	   
df.mm.exp8	0.0785963169297594	0.0738358389134897	1.06447381226138	0.287386021807383	   
df.mm.trans1:exp2	0.118785137460233	0.0712136066699224	1.66801181705073	0.095644668642034	.  
df.mm.trans2:exp2	-0.0112775173978572	0.0585079481616253	-0.192751886747142	0.8471947010954	   
df.mm.trans1:exp3	0.0137162181175498	0.0712136066699224	0.192606704799055	0.847308376826723	   
df.mm.trans2:exp3	-0.0351088199183184	0.0585079481616253	-0.600069238820887	0.548603954125736	   
df.mm.trans1:exp4	-0.00713054127678623	0.0712136066699224	-0.100128916512213	0.920263216221168	   
df.mm.trans2:exp4	-0.0112027916536592	0.0585079481616253	-0.191474697125116	0.84819483426677	   
df.mm.trans1:exp5	-0.0311994951144473	0.0712136066699224	-0.438111430854191	0.66140563310118	   
df.mm.trans2:exp5	0.0345578537578159	0.0585079481616253	0.590652293297853	0.55489464559985	   
df.mm.trans1:exp6	-0.243068688714133	0.0712136066699225	-3.41323379169328	0.000669229091478683	***
df.mm.trans2:exp6	-0.0292023500925606	0.0585079481616253	-0.499117658542573	0.617812666591858	   
df.mm.trans1:exp7	0.0381579270499909	0.0712136066699224	0.535823543200869	0.592206595264492	   
df.mm.trans2:exp7	0.0408741626262863	0.0585079481616253	0.6986087174579	0.484968477854163	   
df.mm.trans1:exp8	-0.100651406530906	0.0712136066699224	-1.41337324757933	0.157875309827567	   
df.mm.trans2:exp8	-0.0442165456473093	0.0585079481616253	-0.755735708337665	0.449995999989578	   
df.mm.trans1:probe2	-0.240879826673248	0.0390052987744169	-6.1755667625148	9.77915750806737e-10	***
df.mm.trans1:probe3	-0.23071672136234	0.0390052987744169	-5.91500971949136	4.63462526843971e-09	***
df.mm.trans1:probe4	-0.293263086153406	0.0390052987744169	-7.51854479693804	1.28547375386397e-13	***
df.mm.trans1:probe5	-0.190114572205115	0.0390052987744169	-4.87407040014289	1.28170848923380e-06	***
df.mm.trans1:probe6	-0.135101262286704	0.0390052987744169	-3.46366433617258	0.000556790239600218	***
df.mm.trans1:probe7	-0.0756734096582972	0.0390052987744169	-1.94008024642874	0.0526671490967173	.  
df.mm.trans1:probe8	-0.236399880717939	0.0390052987744169	-6.06071195826838	1.95572195790149e-09	***
df.mm.trans1:probe9	-0.244411842985171	0.0390052987744169	-6.26611898036474	5.61695235268728e-10	***
df.mm.trans1:probe10	-0.172557150708108	0.0390052987744169	-4.42394126259807	1.08187464061675e-05	***
df.mm.trans1:probe11	-0.137656544995742	0.0390052987744169	-3.52917550489395	0.00043694778075096	***
df.mm.trans1:probe12	-0.315944278594305	0.0390052987744169	-8.10003482915323	1.68737132372235e-15	***
df.mm.trans1:probe13	-0.192416424438971	0.0390052987744169	-4.93308423431883	9.56090793118275e-07	***
df.mm.trans1:probe14	-0.141108504390986	0.0390052987744169	-3.61767526015048	0.000313007104987552	***
df.mm.trans1:probe15	-0.150186111743251	0.0390052987744169	-3.85040280326622	0.000125863560696354	***
df.mm.trans1:probe16	-0.007695820627999	0.0390052987744169	-0.197301927425476	0.843633699148258	   
df.mm.trans1:probe17	-0.0780999988238706	0.0390052987744169	-2.00229202897673	0.0455386846709159	*  
df.mm.trans1:probe18	0.0390867706332432	0.0390052987744169	1.00208873823266	0.316557256490887	   
df.mm.trans1:probe19	0.093919646759661	0.0390052987744169	2.40786892321568	0.0162366446031813	*  
df.mm.trans1:probe20	-0.0668986293619846	0.0390052987744169	-1.71511644479090	0.0866517047438781	.  
df.mm.trans1:probe21	0.118527946776579	0.0390052987744169	3.03876525756343	0.00244084717288974	** 
df.mm.trans1:probe22	-0.0501304372338888	0.0390052987744169	-1.2852212086315	0.199029886501452	   
df.mm.trans1:probe23	-0.0341815952424226	0.0390052987744169	-0.876332096316153	0.381072223614127	   
df.mm.trans1:probe24	-0.191564291337912	0.0390052987744169	-4.91123763583518	1.06605231782272e-06	***
df.mm.trans1:probe25	-0.121580032459410	0.0390052987744169	-3.11701323357516	0.00188215248307290	** 
df.mm.trans1:probe26	-0.179299601703519	0.0390052987744169	-4.59680113567339	4.87249207723314e-06	***
df.mm.trans1:probe27	-0.150234498932299	0.0390052987744169	-3.85164333187561	0.000125237403478193	***
df.mm.trans1:probe28	-0.262788173388985	0.0390052987744169	-6.73724292970534	2.80183904565001e-11	***
df.mm.trans1:probe29	-0.282288976268013	0.0390052987744169	-7.23719558977365	9.4741915941948e-13	***
df.mm.trans1:probe30	-0.202215831051215	0.0390052987744169	-5.18431693654521	2.65174761492365e-07	***
df.mm.trans1:probe31	-0.164807352039470	0.0390052987744169	-4.22525547086863	2.61786371016477e-05	***
df.mm.trans1:probe32	-0.337859246755567	0.0390052987744169	-8.661880753934	1.98374850493055e-17	***
df.mm.trans2:probe2	-0.331664815225645	0.0390052987744169	-8.50307075312495	7.14278842061627e-17	***
df.mm.trans2:probe3	-0.193028038177545	0.0390052987744169	-4.94876450745585	8.8399682463515e-07	***
df.mm.trans2:probe4	-0.264470471498834	0.0390052987744169	-6.78037291877629	2.10934185235598e-11	***
df.mm.trans2:probe5	-0.337236909870295	0.0390052987744169	-8.64592556566917	2.25824123003480e-17	***
df.mm.trans2:probe6	-0.0297918907413796	0.0390052987744169	-0.763790861177039	0.445182532995716	   
df.mm.trans3:probe2	-0.311823819371855	0.0390052987744169	-7.99439638125209	3.7835247670006e-15	***
df.mm.trans3:probe3	0.132435983934849	0.0390052987744169	3.39533314949794	0.000713986202822918	***
df.mm.trans3:probe4	0.363276709721187	0.0390052987744169	9.31352203766367	8.43648557323385e-20	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.06933772891784	0.111423338473036	36.5214127011873	8.02071956527709e-183	***
df.mm.trans1	0.0083443037819473	0.0997446288366482	0.0836566728381212	0.933347123807867	   
df.mm.trans2	0.0310274080186478	0.0896150614900776	0.346229835730049	0.729247064623858	   
df.mm.exp2	-0.145711746439459	0.120423913979573	-1.20999012259455	0.226585263230002	   
df.mm.exp3	-0.0203459443741142	0.120423913979573	-0.168952691386242	0.865870009075813	   
df.mm.exp4	0.0801141413063543	0.120423913979573	0.665267708537887	0.506041609328084	   
df.mm.exp5	0.148023362599781	0.120423913979573	1.22918577970227	0.219308132978902	   
df.mm.exp6	0.176621883839367	0.120423913979573	1.46666785692854	0.142799118462057	   
df.mm.exp7	0.034243641933635	0.120423913979573	0.284359150952721	0.776197456922846	   
df.mm.exp8	0.0188441945555371	0.120423913979573	0.156482163158503	0.875686400647635	   
df.mm.trans1:exp2	0.163750723192019	0.116147136268633	1.40985588153707	0.158911266126133	   
df.mm.trans2:exp2	0.0492372571436511	0.0954246097859348	0.515980701981434	0.605988588862752	   
df.mm.trans1:exp3	0.0140534723745895	0.116147136268633	0.120997149185716	0.903719014844682	   
df.mm.trans2:exp3	0.0162891164816878	0.0954246097859348	0.170701420925158	0.8644950858887	   
df.mm.trans1:exp4	-0.0575172818189488	0.116147136268633	-0.49521050339045	0.620566661306334	   
df.mm.trans2:exp4	-0.084820719750182	0.0954246097859348	-0.88887677864714	0.374295507243134	   
df.mm.trans1:exp5	-0.137752526927808	0.116147136268633	-1.18601742025912	0.235913531746254	   
df.mm.trans2:exp5	-0.134890296765037	0.0954246097859348	-1.41357975754510	0.157814646862139	   
df.mm.trans1:exp6	-0.161947443031978	0.116147136268633	-1.39433005612308	0.163545672740548	   
df.mm.trans2:exp6	-0.143836193947575	0.0954246097859348	-1.50732808098709	0.132060792025055	   
df.mm.trans1:exp7	-0.00921559385303951	0.116147136268633	-0.079344133218447	0.936775701936306	   
df.mm.trans2:exp7	-0.0221925702676256	0.0954246097859348	-0.232566528879814	0.816148378610725	   
df.mm.trans1:exp8	-0.00541411438958167	0.116147136268633	-0.0466142736146292	0.962830504431794	   
df.mm.trans2:exp8	-0.0219559023947036	0.0954246097859348	-0.230086373357534	0.818074448509344	   
df.mm.trans1:probe2	-0.0634066698616441	0.0636164065239566	-0.996703104218352	0.319163783216775	   
df.mm.trans1:probe3	0.0126286249860654	0.0636164065239566	0.198512076932697	0.842687134452265	   
df.mm.trans1:probe4	-0.0537730846379877	0.0636164065239566	-0.845270702577926	0.398173672556189	   
df.mm.trans1:probe5	-0.087723425905594	0.0636164065239565	-1.37894343140176	0.168238482553586	   
df.mm.trans1:probe6	-0.018025483939097	0.0636164065239566	-0.2833464655428	0.776973321383501	   
df.mm.trans1:probe7	-0.053093213285015	0.0636164065239566	-0.834583658305522	0.404163092276519	   
df.mm.trans1:probe8	-0.0141859411109158	0.0636164065239565	-0.222991864615517	0.823590028051812	   
df.mm.trans1:probe9	-0.102580148999036	0.0636164065239566	-1.61247946251738	0.107191530532613	   
df.mm.trans1:probe10	-0.100529466440299	0.0636164065239565	-1.58024434156686	0.114385552592266	   
df.mm.trans1:probe11	-0.0339523742202753	0.0636164065239566	-0.533704685244829	0.593671375503146	   
df.mm.trans1:probe12	-0.0375854107091672	0.0636164065239566	-0.590813168534021	0.554786882181332	   
df.mm.trans1:probe13	-0.0874662671822628	0.0636164065239566	-1.37490109802610	0.169487999294821	   
df.mm.trans1:probe14	0.0597475332897746	0.0636164065239566	0.939184348101696	0.347876089640596	   
df.mm.trans1:probe15	-0.099542016848055	0.0636164065239566	-1.56472240868509	0.117982800387043	   
df.mm.trans1:probe16	0.0203103645867393	0.0636164065239566	0.319262996709676	0.749597649227742	   
df.mm.trans1:probe17	0.0267637053541589	0.0636164065239566	0.420704450574087	0.674066563955529	   
df.mm.trans1:probe18	-0.118934675437245	0.0636164065239566	-1.86955978710393	0.0618540395622032	.  
df.mm.trans1:probe19	-0.0717658317476619	0.0636164065239566	-1.12810257084603	0.259562926086394	   
df.mm.trans1:probe20	0.0237887778426496	0.0636164065239565	0.373940924086797	0.708532149475042	   
df.mm.trans1:probe21	-0.0239112316085294	0.0636164065239566	-0.375865801214738	0.707101024687851	   
df.mm.trans1:probe22	-0.0657814420083464	0.0636164065239566	-1.03403265922564	0.301385436876428	   
df.mm.trans1:probe23	0.0234541526424086	0.0636164065239566	0.368680878470812	0.712448181860279	   
df.mm.trans1:probe24	-0.0742339775879592	0.0636164065239566	-1.16689988705986	0.243545172725549	   
df.mm.trans1:probe25	-0.0770801772238225	0.0636164065239566	-1.21163991233607	0.225953127770113	   
df.mm.trans1:probe26	0.0359290801945057	0.0636164065239566	0.564776952325586	0.57235952134861	   
df.mm.trans1:probe27	-0.0712712318734195	0.0636164065239566	-1.12032784886365	0.262858718031091	   
df.mm.trans1:probe28	-0.0693146543955465	0.0636164065239566	-1.08957198595372	0.276179492533467	   
df.mm.trans1:probe29	-0.098580031596283	0.0636164065239566	-1.54960075525737	0.121572184890239	   
df.mm.trans1:probe30	-0.150407234161605	0.0636164065239565	-2.3642837183041	0.0182663439720397	*  
df.mm.trans1:probe31	-0.014373740711961	0.0636164065239566	-0.225943927004871	0.82129388934685	   
df.mm.trans1:probe32	-0.00653633406848744	0.0636164065239566	-0.102746043444406	0.918186327965747	   
df.mm.trans2:probe2	0.0334038826600653	0.0636164065239566	0.52508282824001	0.599648777335631	   
df.mm.trans2:probe3	-0.096858784450307	0.0636164065239566	-1.52254410053532	0.128207345114496	   
df.mm.trans2:probe4	-0.115788453476446	0.0636164065239565	-1.82010364626368	0.0690594479089415	.  
df.mm.trans2:probe5	-0.123766290270157	0.0636164065239566	-1.94550898161073	0.0520099747793931	.  
df.mm.trans2:probe6	-0.0619688263244146	0.0636164065239566	-0.974101331880142	0.330255502826958	   
df.mm.trans3:probe2	-0.00614079268744439	0.0636164065239565	-0.0965284432582954	0.923121351758726	   
df.mm.trans3:probe3	-0.0936075200141638	0.0636164065239566	-1.47143677439425	0.141505948063804	   
df.mm.trans3:probe4	0.0695649935244689	0.0636164065239566	1.09350712065562	0.274449866419898