fitVsDatCorrelation=0.955470259373845
cont.fitVsDatCorrelation=0.261229624817121

fstatistic=7347.6346094521,55,761
cont.fstatistic=674.122308441386,55,761

residuals=-0.838423496475869,-0.0873873311207468,0.00524212951502305,0.0957590783687966,1.02195636081118
cont.residuals=-1.04161853230974,-0.433700173342735,-0.220950436031773,0.352301829537994,2.22257703981541

predictedValues:
Include	Exclude	Both
Lung	51.520758425604	153.569693627067	141.520661080546
cerebhem	61.4721493608876	103.584632090534	84.6635156364477
cortex	49.569071846713	129.749032113062	113.989617216662
heart	50.7208255852289	147.083233244393	105.734884349244
kidney	50.8060239873033	110.329526053404	118.841483620564
liver	53.3942067536559	115.624069361086	109.056359250453
stomach	53.2440277061638	345.665930365049	191.807026412011
testicle	54.9975498714811	310.279993979375	181.622645944132


diffExp=-102.048935201463,-42.1124827296461,-80.1799602663494,-96.3624076591645,-59.523502066101,-62.22986260743,-292.421902658886,-255.282444107894
diffExpScore=0.998991082681554
diffExp1.5=-1,-1,-1,-1,-1,-1,-1,-1
diffExp1.5Score=0.888888888888889
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	79.8252275089654	89.2613189498858	58.0876742930054
cerebhem	70.9144168115346	83.587301645366	73.1740322556047
cortex	72.8987636254343	80.3716385002995	99.5158660326128
heart	83.389690071291	88.352855638148	88.9679635718788
kidney	88.4085667562577	67.3627772192027	80.5022249834528
liver	81.1501532287015	75.7787802949918	66.8773204176317
stomach	84.2753387247802	64.8375523886421	79.0569018892797
testicle	68.3894086439561	83.2155459471594	94.0866505296655
cont.diffExp=-9.4360914409204,-12.6728848338314,-7.47287487486521,-4.96316556685714,21.0457895370551,5.37137293370968,19.4377863361381,-14.8261373032033
cont.diffExpScore=21.0854242312153

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=0,0,0,0,1,0,1,-1
cont.diffExp1.2Score=1.5

tran.correlation=0.0288215445284804
cont.tran.correlation=-0.510225102477974

tran.covariance=-0.000596211900694654
cont.tran.covariance=-0.00568390474546655

tran.mean=115.100670273188
cont.tran.mean=78.8762084971635

weightedLogRatios:
wLogRatio
Lung	-4.90173364310084
cerebhem	-2.28523696904958
cortex	-4.21890440383953
heart	-4.74697420927218
kidney	-3.34667180236265
liver	-3.37182715355438
stomach	-9.18491813054114
testicle	-8.43012913195923

cont.weightedLogRatios:
wLogRatio
Lung	-0.495595196120779
cerebhem	-0.714179017507258
cortex	-0.423331005387038
heart	-0.257412162165005
kidney	1.18158264922198
liver	0.298726379493596
stomach	1.12826186491759
testicle	-0.84830666750691

varWeightedLogRatios=6.09827442493242
cont.varWeightedLogRatios=0.639569962568525

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.34630959907421	0.0958930480838901	45.3245536138546	2.24468609789089e-218	***
df.mm.trans1	-0.459327353306269	0.0839683702714524	-5.47024256659214	6.1011735776829e-08	***
df.mm.trans2	0.545581702737139	0.0753002859742073	7.24541342278591	1.05912587084226e-12	***
df.mm.exp2	0.296594859799403	0.0993030764250219	2.98676406086316	0.00290999566233774	** 
df.mm.exp3	0.00916820726850034	0.099303076425022	0.0923255109364383	0.926463718692493	   
df.mm.exp4	0.232706780597108	0.099303076425022	2.34339950960947	0.0193654464440724	*  
df.mm.exp5	-0.169997601360024	0.099303076425022	-1.7119066949389	0.0873212880498926	.  
df.mm.exp6	0.0124880177237863	0.099303076425022	0.125756604662850	0.899957808449132	   
df.mm.exp7	0.540175060495365	0.099303076425022	5.43966088405348	7.20010406540083e-08	***
df.mm.exp8	0.519138996255293	0.099303076425022	5.22782389976876	2.21789376115771e-07	***
df.mm.trans1:exp2	-0.119995446159281	0.0931545429325746	-1.28813305697967	0.198091176198133	   
df.mm.trans2:exp2	-0.6903603740023	0.0743116178870121	-9.29007325680846	1.58439621983861e-19	***
df.mm.trans1:exp3	-0.0477859221858675	0.0931545429325747	-0.512974683590633	0.608117902092304	   
df.mm.trans2:exp3	-0.177720639010417	0.0743116178870121	-2.3915592751679	0.0170188496331271	*  
df.mm.trans1:exp4	-0.248354995970928	0.0931545429325747	-2.66605350799357	0.0078376103429888	** 
df.mm.trans2:exp4	-0.275862635639834	0.0743116178870121	-3.71224101269431	0.000220418880336276	***
df.mm.trans1:exp5	0.156027728659737	0.0931545429325747	1.67493418729637	0.09435798344431	.  
df.mm.trans2:exp5	-0.160685313725764	0.0743116178870121	-2.16231752577476	0.0309046388255638	*  
df.mm.trans1:exp6	0.023229431916276	0.0931545429325747	0.249364455935225	0.803146167059248	   
df.mm.trans2:exp6	-0.29629836482309	0.0743116178870121	-3.98724147378409	7.32886994665333e-05	***
df.mm.trans1:exp7	-0.507274220630162	0.0931545429325747	-5.4455124211959	6.97595262067734e-08	***
df.mm.trans2:exp7	0.271143234841801	0.0743116178870121	3.64873276281057	0.000281524788889353	***
df.mm.trans1:exp8	-0.453835162487971	0.0931545429325747	-4.8718521738275	1.34551855830668e-06	***
df.mm.trans2:exp8	0.184181605833018	0.0743116178870121	2.47850351089191	0.0134092313524354	*  
df.mm.trans1:probe2	-0.138374503979901	0.0570452743517492	-2.42569617820864	0.0155106000760018	*  
df.mm.trans1:probe3	-0.141574231451118	0.0570452743517492	-2.48178719552038	0.0132873093694096	*  
df.mm.trans1:probe4	0.039574478095126	0.0570452743517492	0.693738062352092	0.48805818687817	   
df.mm.trans1:probe5	0.0692236706421473	0.0570452743517492	1.21348650574111	0.225320367714827	   
df.mm.trans1:probe6	-0.0571668166593737	0.0570452743517492	-1.00213062885586	0.316599042740438	   
df.mm.trans1:probe7	-0.0125000793717963	0.0570452743517492	-0.219125589522439	0.826610974072992	   
df.mm.trans1:probe8	0.0213002221920331	0.0570452743517492	0.373391528642547	0.708961052973947	   
df.mm.trans1:probe9	-0.0896076390133682	0.0570452743517492	-1.57081616368141	0.116641019288433	   
df.mm.trans1:probe10	0.0528840966648647	0.0570452743517492	0.92705482208349	0.354191986714584	   
df.mm.trans1:probe11	0.148880620478564	0.0570452743517492	2.60986772647538	0.00923577343894104	** 
df.mm.trans1:probe12	-0.0259456090535239	0.0570452743517492	-0.454824862328467	0.649364958080718	   
df.mm.trans1:probe13	0.0103925259905613	0.0570452743517492	0.182180313946419	0.855489738966164	   
df.mm.trans1:probe14	0.157446634386263	0.0570452743517492	2.76002940077779	0.00591862452713299	** 
df.mm.trans1:probe15	0.158285586316758	0.0570452743517492	2.77473617430160	0.00566011520443027	** 
df.mm.trans1:probe16	0.0256214052493258	0.0570452743517492	0.449141590438159	0.653457397587367	   
df.mm.trans1:probe17	0.116409471235072	0.0570452743517492	2.04065056322237	0.0416301941854754	*  
df.mm.trans1:probe18	0.220874823450664	0.0570452743517492	3.87192148623422	0.000117257806839434	***
df.mm.trans1:probe19	0.196962038987137	0.0570452743517492	3.45273190856514	0.000585605521336636	***
df.mm.trans1:probe20	0.162733238734843	0.0570452743517492	2.85270323587905	0.00445249195737664	** 
df.mm.trans1:probe21	0.38293093494618	0.0570452743517492	6.71275472504477	3.73347115270742e-11	***
df.mm.trans1:probe22	0.241720725742331	0.0570452743517492	4.23734881616744	2.53918640068846e-05	***
df.mm.trans2:probe2	0.110542448033050	0.0570452743517492	1.93780202285345	0.053016995028085	.  
df.mm.trans2:probe3	0.069562777207249	0.0570452743517492	1.21943102207407	0.223058454147748	   
df.mm.trans2:probe4	0.754381565905192	0.0570452743517492	13.2242604576423	4.36819479499469e-36	***
df.mm.trans2:probe5	0.477382424765674	0.0570452743517492	8.36848328263034	2.78415560688268e-16	***
df.mm.trans2:probe6	0.295289091918813	0.0570452743517492	5.17639883889451	2.89740075749801e-07	***
df.mm.trans3:probe2	1.24825748215196	0.0570452743517492	21.8818735878985	1.02607802323049e-82	***
df.mm.trans3:probe3	0.489714136671759	0.0570452743517492	8.58465740127942	5.10174317977956e-17	***
df.mm.trans3:probe4	0.843869625825393	0.0570452743517492	14.7929804074913	1.04302173819857e-43	***
df.mm.trans3:probe5	-0.337227983222598	0.0570452743517492	-5.9115849131201	5.11332477761933e-09	***
df.mm.trans3:probe6	0.874677374309125	0.0570452743517492	15.3330382621309	1.92777563935988e-46	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.58736444692299	0.313681026629113	14.6242968413482	7.26325983917045e-43	***
df.mm.trans1	-0.163096056188366	0.274673556815926	-0.593781425773236	0.552834765573341	   
df.mm.trans2	-0.0878699639566744	0.246318909262238	-0.356732514851816	0.721390950761158	   
df.mm.exp2	-0.414929569465144	0.324835757991337	-1.27735189017033	0.201867602595255	   
df.mm.exp3	-0.734038380433661	0.324835757991337	-2.25972160507415	0.024120524623246	*  
df.mm.exp4	-0.392867472813780	0.324835757991337	-1.20943419296917	0.226871668017136	   
df.mm.exp5	-0.505677680395485	0.324835757991337	-1.5567180273576	0.119953201007529	   
df.mm.exp6	-0.288194739502423	0.324835757991337	-0.88720140074637	0.375250776422262	   
df.mm.exp7	-0.573647971551603	0.324835757991337	-1.76596312887112	0.0778029503377412	.  
df.mm.exp8	-0.70701835220211	0.324835757991337	-2.17654102052695	0.0298216130668109	*  
df.mm.trans1:exp2	0.296563733717059	0.304722951727351	0.973224143557152	0.330751154582472	   
df.mm.trans2:exp2	0.349252948238219	0.243084822675320	1.43675341139954	0.151199056306496	   
df.mm.trans1:exp3	0.643270470740428	0.304722951727351	2.11100104896592	0.0350977916952255	*  
df.mm.trans2:exp3	0.629131503828207	0.243084822675320	2.58811511514447	0.00983436855682914	** 
df.mm.trans1:exp4	0.436552565595121	0.304722951727351	1.43262121583058	0.152376757838058	   
df.mm.trans2:exp4	0.382637757351408	0.243084822675320	1.57409151727455	0.115881915470671	   
df.mm.trans1:exp5	0.60780696565771	0.304722951727351	1.99462154790868	0.0464404542344765	*  
df.mm.trans2:exp5	0.224202043394567	0.243084822675320	0.922320204639128	0.356653892978155	   
df.mm.trans1:exp6	0.304656332983469	0.304722951727351	0.999781379303712	0.317734109240828	   
df.mm.trans2:exp6	0.124444814035154	0.243084822675320	0.511939876235593	0.608841614844028	   
df.mm.trans1:exp7	0.627897663249716	0.304722951727351	2.06055257633341	0.039685115543381	*  
df.mm.trans2:exp7	0.253964683641599	0.243084822675320	1.04475746715298	0.296466823235333	   
df.mm.trans1:exp8	0.552396731760939	0.304722951727351	1.81278347636640	0.0702591263595005	.  
df.mm.trans2:exp8	0.636884297315055	0.243084822675320	2.62000848224786	0.00896801953767792	** 
df.mm.trans1:probe2	-0.159422327288823	0.18660393616169	-0.854335286639857	0.393188098293388	   
df.mm.trans1:probe3	-0.0769017764328816	0.18660393616169	-0.412112295242513	0.680373174985747	   
df.mm.trans1:probe4	-0.176790488266141	0.18660393616169	-0.947410284598467	0.343730491132518	   
df.mm.trans1:probe5	-0.0869708161893881	0.18660393616169	-0.46607171305341	0.641297541500643	   
df.mm.trans1:probe6	-0.0867576386549088	0.18660393616169	-0.464929306634424	0.642115081645346	   
df.mm.trans1:probe7	0.0605262926556527	0.18660393616169	0.324356998574818	0.745756904677641	   
df.mm.trans1:probe8	-0.164657751995816	0.18660393616169	-0.882391633224405	0.377843693009707	   
df.mm.trans1:probe9	-0.0915538083806014	0.18660393616169	-0.490631710476199	0.62382839185473	   
df.mm.trans1:probe10	0.0135420331319588	0.18660393616169	0.072570994002103	0.942166578117756	   
df.mm.trans1:probe11	0.0280414276940076	0.18660393616169	0.150272434069719	0.88058950001052	   
df.mm.trans1:probe12	0.0309438729847713	0.18660393616169	0.165826475160518	0.868337554370454	   
df.mm.trans1:probe13	0.209333329269477	0.18660393616169	1.12180553945064	0.262298967177687	   
df.mm.trans1:probe14	-0.000124660866158381	0.18660393616169	-0.00066805057129322	0.999467147875582	   
df.mm.trans1:probe15	-0.225902217341042	0.18660393616169	-1.21059727885536	0.22642563838306	   
df.mm.trans1:probe16	-0.247881666795604	0.18660393616169	-1.32838391244232	0.184449533670386	   
df.mm.trans1:probe17	-0.148837078404073	0.18660393616169	-0.797609533140327	0.425345918216189	   
df.mm.trans1:probe18	-0.0674051107105179	0.18660393616169	-0.361220197692465	0.718035102140728	   
df.mm.trans1:probe19	0.098988826425985	0.18660393616169	0.530475554064477	0.595937010709382	   
df.mm.trans1:probe20	0.121658714946658	0.18660393616169	0.651962211779083	0.514622405311022	   
df.mm.trans1:probe21	-0.216197292557137	0.18660393616169	-1.15858913270621	0.246987143984484	   
df.mm.trans1:probe22	-0.0576383203785779	0.18660393616169	-0.308880517550471	0.75749695833898	   
df.mm.trans2:probe2	-0.110595174764644	0.18660393616169	-0.592673322114783	0.553575865643268	   
df.mm.trans2:probe3	0.00729305464498402	0.18660393616169	0.0390830697090155	0.96883440953991	   
df.mm.trans2:probe4	-0.0179991266934664	0.18660393616169	-0.0964563077483552	0.923183568872579	   
df.mm.trans2:probe5	-0.0187364792781397	0.18660393616169	-0.100407738783735	0.920047072031409	   
df.mm.trans2:probe6	0.0449227575311491	0.18660393616169	0.240738531325642	0.809822614245257	   
df.mm.trans3:probe2	-0.563071455064945	0.18660393616169	-3.01746826271151	0.00263394563022727	** 
df.mm.trans3:probe3	-0.337971979126524	0.18660393616169	-1.81117282988969	0.0705082669853784	.  
df.mm.trans3:probe4	-0.337990196409481	0.18660393616169	-1.81127045528459	0.0704931452914824	.  
df.mm.trans3:probe5	-0.384841634867944	0.18660393616169	-2.06234467923808	0.0395138240679767	*  
df.mm.trans3:probe6	-0.0227943256082447	0.18660393616169	-0.122153509069036	0.902809694411402