fitVsDatCorrelation=0.794216581120543
cont.fitVsDatCorrelation=0.244288874953104

fstatistic=5911.03293152139,59,853
cont.fstatistic=2312.20021563435,59,853

residuals=-0.699264549436594,-0.101586052596364,-0.00759637445167264,0.0912990423231894,1.05943421886581
cont.residuals=-0.59842415428944,-0.214013913658989,-0.078613816736326,0.125521516812689,1.7819149769393

predictedValues:
Include	Exclude	Both
Lung	60.3939870344059	43.9066718820761	62.4104749215726
cerebhem	64.5323710907016	43.7045505658094	66.9079925582761
cortex	91.4543640536217	45.2194684925272	100.070119312410
heart	61.5450915806077	46.9186814813535	62.8340157671433
kidney	55.9557730459107	42.5332670426284	60.466319506309
liver	66.0961524891404	51.0845810507041	71.8154497910841
stomach	62.2130910721512	45.6165532168853	60.2004905441557
testicle	56.4448382747982	45.2583384291814	61.5922197970784


diffExp=16.4873151523298,20.8278205248922,46.2348955610945,14.6264100992542,13.4225060032823,15.0115714384363,16.5965378552659,11.1864998456169
diffExpScore=0.993564726732234
diffExp1.5=0,0,1,0,0,0,0,0
diffExp1.5Score=0.5
diffExp1.4=0,1,1,0,0,0,0,0
diffExp1.4Score=0.666666666666667
diffExp1.3=1,1,1,1,1,0,1,0
diffExp1.3Score=0.857142857142857
diffExp1.2=1,1,1,1,1,1,1,1
diffExp1.2Score=0.888888888888889

cont.predictedValues:
Include	Exclude	Both
Lung	55.5997340795562	59.3286183584607	52.3799811453731
cerebhem	54.7693231159078	54.1146840038468	54.8425651325988
cortex	52.7564911990272	59.8763135543279	57.1490236266746
heart	56.6242706743579	53.1759252003529	59.9692903391373
kidney	57.235097343359	50.2258123966862	60.086449048883
liver	53.6049245382785	55.7911335447448	53.2976953816288
stomach	52.8888514830173	57.8616456192502	57.0582682237704
testicle	50.7076849857667	57.3935963350639	59.9488684634738
cont.diffExp=-3.72888427890457,0.654639112060984,-7.1198223553007,3.44834547400497,7.0092849466728,-2.1862090064663,-4.97279413623287,-6.68591134929714
cont.diffExpScore=2.45559476633106

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=0.147585260100878
cont.tran.correlation=-0.66123049475115

tran.covariance=0.00167403046410855
cont.tran.covariance=-0.00161030969563042

tran.mean=55.1798613001564
cont.tran.mean=55.1221316520003

weightedLogRatios:
wLogRatio
Lung	1.25663483871214
cerebhem	1.54806763016984
cortex	2.93253412551415
heart	1.08110029485258
kidney	1.06622357819689
liver	1.04656065095409
stomach	1.23355149067661
testicle	0.866461588074328

cont.weightedLogRatios:
wLogRatio
Lung	-0.262940391926726
cerebhem	0.0480640144560499
cortex	-0.510045886072866
heart	0.251643299986533
kidney	0.520181538013459
liver	-0.159961156266259
stomach	-0.360628003735838
testicle	-0.493935402314046

varWeightedLogRatios=0.433995059917724
cont.varWeightedLogRatios=0.135440508476887

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.32967183545301	0.095377173817181	45.3952624319959	9.40772934701374e-230	***
df.mm.trans1	0.0369047726060788	0.0817852518226884	0.451239945877882	0.651931315246056	   
df.mm.trans2	-0.546597021900884	0.0723335983148233	-7.55661317334009	1.06669232879109e-13	***
df.mm.exp2	-0.00792193290551132	0.0925294498637958	-0.0856152599758506	0.931792358142757	   
df.mm.exp3	-0.0277260326527537	0.0925294498637958	-0.299645493338247	0.764520577751727	   
df.mm.exp4	0.078466738802887	0.0925294498637958	0.848019078448977	0.396665235736866	   
df.mm.exp5	-0.0764610788739192	0.0925294498637958	-0.826343169514901	0.408840523609603	   
df.mm.exp6	0.101270911089565	0.0925294498637958	1.09447220575326	0.274056881964931	   
df.mm.exp7	0.103932897354489	0.0925294498637958	1.12324127623670	0.261651067717110	   
df.mm.exp8	-0.0241074917709135	0.0925294498637958	-0.260538583190541	0.79451123842186	   
df.mm.trans1:exp2	0.0741993606095292	0.0844674448732405	0.878437375735462	0.379953776201662	   
df.mm.trans2:exp2	0.00330787390237526	0.0616862999091972	0.0536241257336634	0.957247190570592	   
df.mm.trans1:exp3	0.442676579428394	0.0844674448732405	5.24079519740078	2.01651749921277e-07	***
df.mm.trans2:exp3	0.0571874579953737	0.0616862999091972	0.9270690263406	0.354152925132949	   
df.mm.trans1:exp4	-0.0595861837895637	0.0844674448732405	-0.70543371921554	0.480732957180703	   
df.mm.trans2:exp4	-0.0121171044569382	0.0616862999091972	-0.196431046679323	0.844319586345477	   
df.mm.trans1:exp5	0.000133142966874296	0.0844674448732405	0.00157626369631641	0.998742692605758	   
df.mm.trans2:exp5	0.0446813149323476	0.0616862999091972	0.724331253424487	0.469061009689448	   
df.mm.trans1:exp6	-0.0110499209266578	0.0844674448732405	-0.130818695217315	0.895949578791918	   
df.mm.trans2:exp6	0.0501455122724455	0.0616862999091972	0.812911656984779	0.416495536882572	   
df.mm.trans1:exp7	-0.074256999931182	0.0844674448732405	-0.879119760786168	0.379583923071643	   
df.mm.trans2:exp7	-0.0657285251878503	0.0616862999091972	-1.06552873627699	0.286938233965014	   
df.mm.trans1:exp8	-0.043518208276348	0.0844674448732405	-0.515206874573457	0.606541951278948	   
df.mm.trans2:exp8	0.0544281319943529	0.0616862999091972	0.88233744080082	0.377842928738892	   
df.mm.trans1:probe2	0.0281287416470649	0.0597275030593692	0.470951240320643	0.637795966346142	   
df.mm.trans1:probe3	-0.466045102528472	0.0597275030593692	-7.80285594837646	1.76721232510460e-14	***
df.mm.trans1:probe4	-0.560559252661456	0.0597275030593692	-9.38527853917247	5.53705862375763e-20	***
df.mm.trans1:probe5	-0.5268333031071	0.0597275030593692	-8.82061489467301	6.33488932287824e-18	***
df.mm.trans1:probe6	-0.562948624549728	0.0597275030593692	-9.42528308926887	3.92277555011584e-20	***
df.mm.trans1:probe7	-0.541330873836613	0.0597275030593692	-9.0633434533255	8.49986386910999e-19	***
df.mm.trans1:probe8	-0.0782926120525576	0.0597275030593692	-1.31083015432161	0.190267944754272	   
df.mm.trans1:probe9	-0.341082835705239	0.0597275030593692	-5.71064950373368	1.55377671799253e-08	***
df.mm.trans1:probe10	-0.439275099164256	0.0597275030593692	-7.35465366311422	4.49381353002659e-13	***
df.mm.trans1:probe11	-0.595368461096468	0.0597275030593692	-9.96807886820032	3.26166547631332e-22	***
df.mm.trans1:probe12	-0.417446971352046	0.0597275030593692	-6.98919174533552	5.57500462687383e-12	***
df.mm.trans1:probe13	-0.373654788777019	0.0597275030593692	-6.2559921248609	6.24352806700004e-10	***
df.mm.trans1:probe14	-0.519234344660921	0.0597275030593692	-8.69338777053515	1.78369557248905e-17	***
df.mm.trans1:probe15	-0.585195180692467	0.0597275030593692	-9.7977506294007	1.49955003220824e-21	***
df.mm.trans1:probe16	-0.53913426477111	0.0597275030593692	-9.02656627442152	1.15559729377961e-18	***
df.mm.trans1:probe17	-0.369394536005245	0.0597275030593692	-6.18466396691766	9.6448048631151e-10	***
df.mm.trans1:probe18	-0.569372193981682	0.0597275030593692	-9.53283102117503	1.54415324921690e-20	***
df.mm.trans1:probe19	-0.500635457004183	0.0597275030593692	-8.38199206999413	2.13402889159120e-16	***
df.mm.trans1:probe20	-0.544310775204186	0.0597275030593692	-9.1132350646425	5.59442849424636e-19	***
df.mm.trans2:probe2	0.0390654703931611	0.0597275030593692	0.654061669953454	0.51324836543424	   
df.mm.trans2:probe3	-0.00556572447645297	0.0597275030593692	-0.0931852863649871	0.925778245131104	   
df.mm.trans2:probe4	-0.0495860167285768	0.0597275030593692	-0.830204080007969	0.406655698681971	   
df.mm.trans2:probe5	-0.0410185684453262	0.0597275030593692	-0.68676181565055	0.492419485961999	   
df.mm.trans2:probe6	0.0389513725516611	0.0597275030593692	0.652151363383521	0.514479219750232	   
df.mm.trans3:probe2	0.553451259660182	0.0597275030593692	9.26627150493887	1.53315016351557e-19	***
df.mm.trans3:probe3	0.0229413137719461	0.0597275030593692	0.384099662581614	0.701000307991022	   
df.mm.trans3:probe4	0.575758678157609	0.0597275030593692	9.6397580455574	6.0610357875436e-21	***
df.mm.trans3:probe5	0.186997894909107	0.0597275030593692	3.13085070245161	0.0018023940249014	** 
df.mm.trans3:probe6	0.102761895644936	0.0597275030593692	1.72051216577380	0.0857020472028295	.  
df.mm.trans3:probe7	0.415185231975475	0.0597275030593692	6.95132410880764	7.19188642469263e-12	***
df.mm.trans3:probe8	0.199281826490121	0.0597275030593692	3.33651695253415	0.00088503037759586	***
df.mm.trans3:probe9	0.383395631920573	0.0597275030593692	6.41908019391799	2.27281790207841e-10	***
df.mm.trans3:probe10	0.361130265835641	0.0597275030593692	6.04629772446166	2.21459318018444e-09	***
df.mm.trans3:probe11	0.565391927042069	0.0597275030593692	9.4661905835921	2.75428774453313e-20	***
df.mm.trans3:probe12	0.398746584135427	0.0597275030593692	6.6760966675448	4.4175764385822e-11	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.10442906841172	0.152209004051724	26.9657442014202	2.56479194474933e-116	***
df.mm.trans1	-0.156589782278595	0.130518144204105	-1.19975489410667	0.230567671833256	   
df.mm.trans2	-0.0303889119959148	0.115434590042271	-0.263256550612661	0.792416422646337	   
df.mm.exp2	-0.152976547987916	0.147664423735370	-1.03597429982231	0.300507727244708	   
df.mm.exp3	-0.130440223218847	0.147664423735370	-0.88335578685228	0.377292959233276	   
df.mm.exp4	-0.226534871600103	0.147664423735370	-1.53411949791018	0.125371056845126	   
df.mm.exp5	-0.274833688850954	0.147664423735370	-1.86120449258302	0.0630591293584935	.  
df.mm.exp6	-0.115382924313659	0.147664423735370	-0.781386073875431	0.434792273237415	   
df.mm.exp7	-0.160571417031243	0.147664423735370	-1.08740760278863	0.277163875217423	   
df.mm.exp8	-0.260227526870420	0.147664423735370	-1.76228992933853	0.0783784411894371	.  
df.mm.trans1:exp2	0.137928369538688	0.134798559701439	1.02321842194887	0.306494676146415	   
df.mm.trans2:exp2	0.0609903277752483	0.0984429491569135	0.619549986033357	0.535719548534776	   
df.mm.trans1:exp3	0.07794862540669	0.134798559701439	0.578260076215471	0.563241174135273	   
df.mm.trans2:exp3	0.139629424388403	0.0984429491569135	1.41837912805558	0.156445341597330	   
df.mm.trans1:exp4	0.244794156894010	0.134798559701439	1.8159997958153	0.0697213468282972	.  
df.mm.trans2:exp4	0.117048839097659	0.0984429491569135	1.18900175279276	0.23476986277321	   
df.mm.trans1:exp5	0.30382257048294	0.134798559701439	2.25390071789985	0.0244555143354987	*  
df.mm.trans2:exp5	0.108270981968949	0.0984429491569135	1.09983480682167	0.271714399578258	   
df.mm.trans1:exp6	0.0788454453809471	0.134798559701439	0.584913114469318	0.558760821366766	   
df.mm.trans2:exp6	0.0539060914301093	0.0984429491569135	0.547587124235637	0.584118678796693	   
df.mm.trans1:exp7	0.110585568169016	0.134798559701439	0.820376481870046	0.412230706038924	   
df.mm.trans2:exp7	0.135534364974369	0.0984429491569135	1.37678082722139	0.168941254628763	   
df.mm.trans1:exp8	0.168126585106061	0.134798559701439	1.24724318626578	0.212650671241178	   
df.mm.trans2:exp8	0.227068469267768	0.0984429491569135	2.30659962153136	0.0213159793658914	*  
df.mm.trans1:probe2	0.148606434454014	0.0953169756590674	1.55907626554952	0.119349247197466	   
df.mm.trans1:probe3	0.0579312903615451	0.0953169756590674	0.607775162409218	0.543498375960874	   
df.mm.trans1:probe4	0.134219304502548	0.0953169756590674	1.40813641614719	0.159455026975273	   
df.mm.trans1:probe5	0.168158583723714	0.0953169756590674	1.76420393703204	0.0780555363656775	.  
df.mm.trans1:probe6	0.00643128890205179	0.0953169756590674	0.0674726496259744	0.94622124654491	   
df.mm.trans1:probe7	0.0809138962832083	0.0953169756590674	0.848892820231975	0.396179098141075	   
df.mm.trans1:probe8	0.0572154863432433	0.0953169756590674	0.600265440102646	0.548488783057224	   
df.mm.trans1:probe9	0.0441789381584816	0.0953169756590674	0.463494963546705	0.643127864277172	   
df.mm.trans1:probe10	0.126255289077352	0.0953169756590674	1.32458345645529	0.185664106363674	   
df.mm.trans1:probe11	0.171957846619415	0.0953169756590674	1.80406318423781	0.0715740641605405	.  
df.mm.trans1:probe12	0.080679753937238	0.0953169756590674	0.846436360148644	0.397546755632569	   
df.mm.trans1:probe13	0.0165294371809577	0.0953169756590674	0.173415460012922	0.86236601644951	   
df.mm.trans1:probe14	0.190023617923186	0.0953169756590674	1.99359680276542	0.0465141946253641	*  
df.mm.trans1:probe15	0.101559274090040	0.0953169756590674	1.06548989188767	0.286955792436740	   
df.mm.trans1:probe16	0.240503151243525	0.0953169756590674	2.52319326731226	0.0118101561582866	*  
df.mm.trans1:probe17	0.160926046702847	0.0953169756590674	1.68832514449946	0.0917143468675775	.  
df.mm.trans1:probe18	0.141417490780776	0.0953169756590674	1.48365482436835	0.138269905843876	   
df.mm.trans1:probe19	0.195855179041080	0.0953169756590674	2.05477752191404	0.0402046100919188	*  
df.mm.trans1:probe20	0.127489926520609	0.0953169756590674	1.33753642138855	0.181404116954578	   
df.mm.trans2:probe2	0.0341917267097047	0.0953169756590674	0.358716025905005	0.719896302754662	   
df.mm.trans2:probe3	0.00646880351689326	0.0953169756590674	0.0678662271034602	0.945908027977846	   
df.mm.trans2:probe4	-0.0233758949665436	0.0953169756590674	-0.245243775360175	0.806326671726185	   
df.mm.trans2:probe5	0.0496223143844091	0.0953169756590674	0.520603114411641	0.602778380616618	   
df.mm.trans2:probe6	0.0960225016307014	0.0953169756590674	1.00740189212630	0.314027360358571	   
df.mm.trans3:probe2	-0.0703633397557371	0.0953169756590674	-0.738203654377525	0.460593744773231	   
df.mm.trans3:probe3	-0.0461606414485527	0.0953169756590674	-0.484285628340343	0.628307490663346	   
df.mm.trans3:probe4	0.0565748376553061	0.0953169756590674	0.593544195712468	0.552974365618464	   
df.mm.trans3:probe5	0.156150115543927	0.0953169756590674	1.63821936716130	0.101744724789265	   
df.mm.trans3:probe6	0.0345510923342836	0.0953169756590674	0.362486242302389	0.717078493848768	   
df.mm.trans3:probe7	0.0390404565232292	0.0953169756590674	0.409585556542103	0.682212825714915	   
df.mm.trans3:probe8	0.0991783815257375	0.0953169756590674	1.04051120841771	0.298397287599065	   
df.mm.trans3:probe9	0.00532498765566139	0.0953169756590674	0.0558660995991728	0.95546155974725	   
df.mm.trans3:probe10	0.200130696193768	0.0953169756590674	2.09963330046896	0.0360543610494538	*  
df.mm.trans3:probe11	-0.0302601862565479	0.0953169756590674	-0.317469013754522	0.750965440628635	   
df.mm.trans3:probe12	0.0487228598343099	0.0953169756590674	0.511166657328526	0.60936664283046