chr9.25228_chr9_61226137_61231191_-_2.R 

fitVsDatCorrelation=0.885714443221636
cont.fitVsDatCorrelation=0.306197961898242

fstatistic=8229.25021042212,55,761
cont.fstatistic=1946.41860773951,55,761

residuals=-0.671931423196985,-0.105314380079332,-0.00228034242402671,0.0900575199355879,0.834319853202069
cont.residuals=-0.720642124109323,-0.292852879282367,-0.0425417358316764,0.249782151746065,1.23221300002103

predictedValues:
Include	Exclude	Both
chr9.25228_chr9_61226137_61231191_-_2.R.tl.Lung	64.8477776967942	80.2572864778455	56.5819344869118
chr9.25228_chr9_61226137_61231191_-_2.R.tl.cerebhem	65.2234006832396	80.4845553297341	63.9958561807489
chr9.25228_chr9_61226137_61231191_-_2.R.tl.cortex	63.9689166779711	120.877353221684	80.92567954103
chr9.25228_chr9_61226137_61231191_-_2.R.tl.heart	65.2726123575892	90.6029924965687	55.3275301450301
chr9.25228_chr9_61226137_61231191_-_2.R.tl.kidney	63.963920391678	82.3041983446948	55.3760278806098
chr9.25228_chr9_61226137_61231191_-_2.R.tl.liver	67.40536810593	94.0560371424896	58.2232540422823
chr9.25228_chr9_61226137_61231191_-_2.R.tl.stomach	72.7299072494716	138.787004685568	57.6793099415232
chr9.25228_chr9_61226137_61231191_-_2.R.tl.testicle	66.9874903527997	91.4672971922963	56.5804197871961


diffExp=-15.4095087810513,-15.2611546464945,-56.9084365437134,-25.3303801389795,-18.3402779530168,-26.6506690365595,-66.0570974360965,-24.4798068394966
diffExpScore=0.995990976994157
diffExp1.5=0,0,-1,0,0,0,-1,0
diffExp1.5Score=0.666666666666667
diffExp1.4=0,0,-1,0,0,0,-1,0
diffExp1.4Score=0.666666666666667
diffExp1.3=0,0,-1,-1,0,-1,-1,-1
diffExp1.3Score=0.833333333333333
diffExp1.2=-1,-1,-1,-1,-1,-1,-1,-1
diffExp1.2Score=0.888888888888889

cont.predictedValues:
Include	Exclude	Both
Lung	80.464795091955	69.8971183903708	86.0313071885715
cerebhem	74.5413700737508	73.5486023541428	82.7226320084603
cortex	67.0831977125772	80.587918526501	72.2091906254731
heart	70.7081049998048	77.2169620513107	68.0489328915434
kidney	73.8113986188896	73.5822616106283	67.6824041300059
liver	64.5290802961713	72.485881322225	66.5606389197374
stomach	65.6521952050384	75.777343917526	73.650883071271
testicle	70.7093397597243	87.1326319935185	67.6707924240259
cont.diffExp=10.5676767015842,0.99276771960804,-13.5047208139237,-6.50885705150591,0.229137008261389,-7.95680102605368,-10.1251487124877,-16.4232922337942
cont.diffExpScore=1.51634018063794

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

tran.correlation=0.673029734950751
cont.tran.correlation=-0.350887698012122

tran.covariance=0.00546878221851036
cont.tran.covariance=-0.00178365203097500

tran.mean=81.8272574003972
cont.tran.mean=73.6080126202584

weightedLogRatios:
wLogRatio
Lung	-0.912184304858871
cerebhem	-0.900475649473842
cortex	-2.84880646317418
heart	-1.42397941426238
kidney	-1.08010243069099
liver	-1.45837046665127
stomach	-2.97882631632524
testicle	-1.35810956424780

cont.weightedLogRatios:
wLogRatio
Lung	0.607872898754346
cerebhem	0.0577160204896771
cortex	-0.788252449672327
heart	-0.378881498859030
kidney	0.0133693733731547
liver	-0.491294873611774
stomach	-0.610443136196654
testicle	-0.911230061935811

varWeightedLogRatios=0.685673814519693
cont.varWeightedLogRatios=0.257185594662769

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.42737005001201	0.0883186748133326	50.129489141108	2.50919751547247e-243	***
df.mm.trans1	-0.466313528849071	0.077335900117829	-6.02971618793594	2.56042062318472e-09	***
df.mm.trans2	-0.0144861120998048	0.0693524880394768	-0.208876602834479	0.834600465912648	   
df.mm.exp2	-0.114525157952694	0.0914593527892917	-1.25219733641176	0.210882667715030	   
df.mm.exp3	0.0380520719848781	0.0914593527892917	0.416054463806935	0.67748745320113	   
df.mm.exp4	0.150198719335401	0.0914593527892917	1.64224559604566	0.100952244665202	   
df.mm.exp5	0.0330040678708249	0.0914593527892917	0.360860501023456	0.718303880230845	   
df.mm.exp6	0.168740134420148	0.0914593527892917	1.84497407070985	0.0654296971702322	.  
df.mm.exp7	0.64320412570915	0.0914593527892917	7.03267742546784	4.51629919773062e-12	***
df.mm.exp8	0.163233964915105	0.0914593527892918	1.78477061051560	0.0746967792647072	.  
df.mm.trans1:exp2	0.120300826864182	0.085796477941229	1.40216509757649	0.161273718763290	   
df.mm.trans2:exp2	0.117352909591883	0.0684419125907234	1.71463515775258	0.0868192760090426	.  
df.mm.trans1:exp3	-0.0516974253254961	0.085796477941229	-0.602558829523388	0.546981720822769	   
df.mm.trans2:exp3	0.371486794703949	0.0684419125907234	5.42776758629477	7.67743816500146e-08	***
df.mm.trans1:exp4	-0.143668825439780	0.085796477941229	-1.67453057383304	0.0944372417369367	.  
df.mm.trans2:exp4	-0.0289490321885121	0.0684419125907234	-0.422972285441886	0.67243497813331	   
df.mm.trans1:exp5	-0.0467275291688681	0.085796477941229	-0.54463225402885	0.586166021527894	   
df.mm.trans2:exp5	-0.0078195039151888	0.0684419125907235	-0.114250225033142	0.90906957419168	   
df.mm.trans1:exp6	-0.130058115999201	0.085796477941229	-1.51589108457682	0.129962142857996	   
df.mm.trans2:exp6	-0.0100869450772026	0.0684419125907234	-0.147379649331567	0.882871404552542	   
df.mm.trans1:exp7	-0.528494088629814	0.085796477941229	-6.15985762249862	1.17887937743563e-09	***
df.mm.trans2:exp7	-0.0955012633243446	0.0684419125907234	-1.39536228181456	0.16331365841141	   
df.mm.trans1:exp8	-0.130770715923437	0.085796477941229	-1.52419678594517	0.127875004806553	   
df.mm.trans2:exp8	-0.032490019373489	0.0684419125907235	-0.474709401646567	0.635130311325906	   
df.mm.trans1:probe2	0.505966021811561	0.0525393981709909	9.63022111834781	8.62612687529476e-21	***
df.mm.trans1:probe3	-0.16174611792626	0.0525393981709909	-3.07856815184393	0.00215467743199756	** 
df.mm.trans1:probe4	-0.160837518770394	0.0525393981709909	-3.06127447914314	0.00228148317458059	** 
df.mm.trans1:probe5	0.170933869680312	0.0525393981709909	3.25344171480616	0.00119048827325716	** 
df.mm.trans1:probe6	0.0212314357342606	0.0525393981709909	0.404105042565626	0.686249014648922	   
df.mm.trans1:probe7	0.149508160491056	0.0525393981709909	2.84563900036459	0.00455138087829234	** 
df.mm.trans1:probe8	0.648186670501469	0.0525393981709909	12.3371544605808	5.22318941417848e-32	***
df.mm.trans1:probe9	0.304004890867471	0.0525393981709909	5.78622712574816	1.05162586173213e-08	***
df.mm.trans1:probe10	-0.0787028046668163	0.0525393981709909	-1.49797689746418	0.134553914782683	   
df.mm.trans1:probe11	0.183808294149989	0.0525393981709909	3.49848495697989	0.000495094740284024	***
df.mm.trans1:probe12	-0.0443705469256359	0.0525393981709909	-0.84451951240916	0.398644575345895	   
df.mm.trans1:probe13	-0.0210044498845333	0.0525393981709909	-0.399784744700990	0.689427250936829	   
df.mm.trans1:probe14	0.0336028166808695	0.0525393981709909	0.639573688520532	0.522642273445287	   
df.mm.trans1:probe15	-0.131517403417827	0.0525393981709909	-2.50321488247353	0.0125155424415729	*  
df.mm.trans1:probe16	0.087405681657333	0.0525393981709909	1.66362167630602	0.0965997666041302	.  
df.mm.trans1:probe17	0.863821274226314	0.0525393981709909	16.4414002500559	3.31887067739928e-52	***
df.mm.trans1:probe18	0.806982108401695	0.0525393981709909	15.3595613291068	1.41089697225731e-46	***
df.mm.trans1:probe19	0.741150918875222	0.0525393981709909	14.1065742029082	2.59484436337049e-40	***
df.mm.trans1:probe20	0.72095852452913	0.0525393981709909	13.7222455838331	1.88340686118354e-38	***
df.mm.trans1:probe21	0.497699767044579	0.0525393981709909	9.47288671683679	3.34839988692927e-20	***
df.mm.trans1:probe22	0.77052978518002	0.0525393981709909	14.6657520261711	4.51325700751391e-43	***
df.mm.trans2:probe2	-0.0340899098711746	0.0525393981709909	-0.648844696702236	0.516634516895946	   
df.mm.trans2:probe3	-0.0235670928053318	0.0525393981709909	-0.448560387552063	0.65387650506319	   
df.mm.trans2:probe4	-0.0491493615901211	0.0525393981709909	-0.93547629590585	0.349839644073703	   
df.mm.trans2:probe5	-0.124986072944158	0.0525393981709909	-2.37890187735663	0.0176100189376031	*  
df.mm.trans2:probe6	-0.0999641562953303	0.0525393981709909	-1.90265133928627	0.0574632489523998	.  
df.mm.trans3:probe2	0.0847620496181365	0.0525393981709909	1.61330454038084	0.107092928771773	   
df.mm.trans3:probe3	-0.0902967739872726	0.0525393981709909	-1.71864880700383	0.0860850471541422	.  
df.mm.trans3:probe4	-0.0162168086239082	0.0525393981709909	-0.308659961637362	0.757664679389577	   
df.mm.trans3:probe5	0.0625525256398015	0.0525393981709909	1.19058321597485	0.234188490243057	   
df.mm.trans3:probe6	0.493915086480331	0.0525393981709909	9.40085162134654	6.19487616367606e-20	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.20489270524387	0.181109483739224	23.2174076057685	1.43477591726450e-90	***
df.mm.trans1	0.175010348796557	0.158587806876081	1.10355488384620	0.270135104846620	   
df.mm.trans2	0.0721090482660758	0.142216731981176	0.507036318874353	0.612276223094648	   
df.mm.exp2	0.0136745726515483	0.187549872117099	0.0729116607608798	0.94189557352655	   
df.mm.exp3	0.135582100958985	0.187549872117099	0.722912254903235	0.469955907455558	   
df.mm.exp4	0.204819389224524	0.187549872117099	1.09207959948190	0.2751437272279	   
df.mm.exp5	0.204958004510056	0.187549872117099	1.09281868441952	0.274819237355107	   
df.mm.exp6	0.0722614691130252	0.187549872117099	0.385292020182812	0.700128739725797	   
df.mm.exp7	0.0327013430353294	0.187549872117099	0.174360785567009	0.861628304958047	   
df.mm.exp8	0.331221554145380	0.187549872117099	1.76604521456873	0.0777891678291425	.  
df.mm.trans1:exp2	-0.0901400591047948	0.175937375186182	-0.512341729603537	0.608560525214283	   
df.mm.trans2:exp2	0.0372474479067686	0.14034947287909	0.265390721765353	0.79078035678274	   
df.mm.trans1:exp3	-0.317468255724801	0.175937375186182	-1.80443896806376	0.0715577611023987	.  
df.mm.trans2:exp3	0.00674221946052628	0.140349472879090	0.0480387943197666	0.961697929104051	   
df.mm.trans1:exp4	-0.334078944292735	0.175937375186182	-1.89885147450451	0.05796198692828	.  
df.mm.trans2:exp4	-0.105224664282862	0.140349472879090	-0.749733234648567	0.453647093516923	   
df.mm.trans1:exp5	-0.291264592718226	0.175937375186182	-1.65550152382347	0.0982350908948715	.  
df.mm.trans2:exp5	-0.153578442193802	0.140349472879090	-1.09425734948152	0.274188352994345	   
df.mm.trans1:exp6	-0.292965250330401	0.175937375186182	-1.66516779064356	0.0962908801600078	.  
df.mm.trans2:exp6	-0.0358940902455343	0.140349472879090	-0.255747951946045	0.798214576501787	   
df.mm.trans1:exp7	-0.236150065558876	0.175937375186182	-1.34223933549637	0.179918694259230	   
df.mm.trans2:exp7	0.0480735883813176	0.140349472879090	0.342527744459237	0.7320483518041	   
df.mm.trans1:exp8	-0.46046364658836	0.175937375186182	-2.61720197940366	0.00904141772084033	** 
df.mm.trans2:exp8	-0.110814514342912	0.140349472879090	-0.789561314835704	0.430029942244509	   
df.mm.trans1:probe2	-0.0558078722537574	0.107739198972687	-0.517990413757441	0.604615511104198	   
df.mm.trans1:probe3	-0.127237076222545	0.107739198972687	-1.18097291826720	0.237982440660473	   
df.mm.trans1:probe4	0.0468720852159974	0.107739198972687	0.435051361648605	0.663648495034951	   
df.mm.trans1:probe5	-0.0795376130928731	0.107739198972687	-0.738242105485087	0.460594936677615	   
df.mm.trans1:probe6	0.122612634447617	0.107739198972687	1.13805036251198	0.255457613322290	   
df.mm.trans1:probe7	-0.00863045596329348	0.107739198972687	-0.0801050689589904	0.936174751733555	   
df.mm.trans1:probe8	-0.0827037505960884	0.107739198972687	-0.767629158047245	0.442945639735931	   
df.mm.trans1:probe9	0.019269988276669	0.107739198972687	0.178857727367679	0.858097021845292	   
df.mm.trans1:probe10	-0.0345873707647901	0.107739198972687	-0.321028660827136	0.748276813051767	   
df.mm.trans1:probe11	0.0934085521966625	0.107739198972687	0.866987624628084	0.386222071263375	   
df.mm.trans1:probe12	-0.00827031798541636	0.107739198972687	-0.0767623860607407	0.93883275040968	   
df.mm.trans1:probe13	-0.0723582737623948	0.107739198972687	-0.671605826406212	0.502038486454904	   
df.mm.trans1:probe14	0.0929153370259646	0.107739198972687	0.862409762759788	0.388733753050642	   
df.mm.trans1:probe15	0.0820183897761555	0.107739198972687	0.761267863119606	0.446732923038497	   
df.mm.trans1:probe16	0.0088660257454673	0.107739198972687	0.082291550614878	0.934436512378654	   
df.mm.trans1:probe17	0.143509696279791	0.107739198972687	1.33201005435517	0.183255659618804	   
df.mm.trans1:probe18	-0.127252192725471	0.107739198972687	-1.18111322470228	0.237926739533386	   
df.mm.trans1:probe19	0.0731388061211184	0.107739198972687	0.678850472423318	0.497439003369235	   
df.mm.trans1:probe20	0.0775083374411951	0.107739198972687	0.719407032725798	0.472111025301986	   
df.mm.trans1:probe21	0.00928011503537364	0.107739198972687	0.08613499194222	0.931381764431479	   
df.mm.trans1:probe22	0.048652742789891	0.107739198972687	0.451578842740654	0.651701079995399	   
df.mm.trans2:probe2	-0.09545570225502	0.107739198972687	-0.885988601782895	0.375903548206976	   
df.mm.trans2:probe3	-0.0840141258901491	0.107739198972687	-0.77979163286194	0.435755981344233	   
df.mm.trans2:probe4	-0.0992927753303421	0.107739198972687	-0.921603058841321	0.357027734391266	   
df.mm.trans2:probe5	0.0494142091374856	0.107739198972687	0.458646524279548	0.64661897664061	   
df.mm.trans2:probe6	-0.130379564033290	0.107739198972687	-1.21014046212040	0.226600747370525	   
df.mm.trans3:probe2	0.159106280677983	0.107739198972687	1.47677244860822	0.140150325407628	   
df.mm.trans3:probe3	0.206849665089490	0.107739198972687	1.9199109243603	0.0552426915736122	.  
df.mm.trans3:probe4	-0.173156524324270	0.107739198972687	-1.60718221385855	0.108429203696792	   
df.mm.trans3:probe5	-0.105125371544327	0.107739198972687	-0.975739308874738	0.329503726624286	   
df.mm.trans3:probe6	-0.0714790612308549	0.107739198972687	-0.6634452633064	0.507246346936131