fitVsDatCorrelation=0.901062278875184
cont.fitVsDatCorrelation=0.239771661342237

fstatistic=9320.64024223891,65,991
cont.fstatistic=1847.81901372204,65,991

residuals=-0.804716421641997,-0.106077455988801,-0.00158445792554234,0.107650145183209,1.20852750609416
cont.residuals=-0.841461490860726,-0.325623486431221,-0.0431695150649382,0.288265480867113,1.63203793230929

predictedValues:
Include	Exclude	Both
Lung	92.2424789963395	64.8190905858645	91.0024708763459
cerebhem	99.7750366027026	105.834477028986	88.2643327083242
cortex	93.8049581612247	56.0447234564584	120.158843877694
heart	91.0646626961824	55.0840893588492	111.622061195436
kidney	107.565918087989	63.6599884889407	115.170357064080
liver	107.955729621902	59.04650276369	111.265128475195
stomach	99.215879151531	61.6469036773799	112.304359977242
testicle	92.744951531808	68.0979223782512	110.189535400484


diffExp=27.4233884104751,-6.05944042628344,37.7602347047663,35.9805733373332,43.9059295990482,48.9092268582118,37.5689754741512,24.6470291535568
diffExpScore=1.04427435541863
diffExp1.5=0,0,1,1,1,1,1,0
diffExp1.5Score=0.833333333333333
diffExp1.4=1,0,1,1,1,1,1,0
diffExp1.4Score=0.857142857142857
diffExp1.3=1,0,1,1,1,1,1,1
diffExp1.3Score=0.875
diffExp1.2=1,0,1,1,1,1,1,1
diffExp1.2Score=0.875

cont.predictedValues:
Include	Exclude	Both
Lung	101.528402618541	99.492036315728	98.8546675781465
cerebhem	93.7321150234665	110.670956781682	99.038950856094
cortex	91.1260498347274	98.3150897439434	102.757141787745
heart	103.131674014548	96.2806908412461	90.7185072102255
kidney	91.2094991986285	101.309277386632	93.4617665458334
liver	89.1200802962841	103.016499557859	93.2508169960493
stomach	97.6378917892275	83.4484785949658	101.641300094384
testicle	100.594779380059	102.263638716287	101.166775794423
cont.diffExp=2.03636630281265,-16.9388417582151,-7.18903990921605,6.85098317330213,-10.0997781880034,-13.8964192615750,14.1894131942617,-1.66885933622848
cont.diffExpScore=2.62913980970902

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=0.111596340461793
cont.tran.correlation=-0.304746813765164

tran.covariance=0.00188223399568104
cont.tran.covariance=-0.00136078653203744

tran.mean=82.4127070367562
cont.tran.mean=97.679822505864

weightedLogRatios:
wLogRatio
Lung	1.53406758660526
cerebhem	-0.27311834767956
cortex	2.20638722861969
heart	2.1416482618863
kidney	2.31631309740121
liver	2.64288815681153
stomach	2.07451099911919
testicle	1.35158977946078

cont.weightedLogRatios:
wLogRatio
Lung	0.0934073531966371
cerebhem	-0.768058657404371
cortex	-0.345514834571278
heart	0.316310888793861
kidney	-0.479481672048437
liver	-0.661117586620589
stomach	0.70709452029593
testicle	-0.0760055355655034

varWeightedLogRatios=0.840472302352002
cont.varWeightedLogRatios=0.258111252361581

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.943904236293	0.0877083316176898	44.9661299394454	1.59950068262027e-241	***
df.mm.trans1	0.449802589895609	0.0756143640869386	5.94863945927579	3.74553197816344e-09	***
df.mm.trans2	0.227837627668585	0.0659490798446492	3.45475066832295	0.00057413457653763	***
df.mm.exp2	0.59932400080469	0.0836773533573992	7.1623202307186	1.54524765146035e-12	***
df.mm.exp3	-0.406581108063309	0.0836773533573992	-4.85891453003702	1.37132903470412e-06	***
df.mm.exp4	-0.379822223270191	0.0836773533573993	-4.5391280678765	6.34184356245057e-06	***
df.mm.exp5	-0.0998865660490742	0.0836773533573992	-1.19371086729336	0.232876759341186	   
df.mm.exp6	-0.137003610269910	0.0836773533573992	-1.63728422055542	0.101888571717169	   
df.mm.exp7	-0.187625867897461	0.0836773533573992	-2.24225385207968	0.0251654778956080	*  
df.mm.exp8	-0.136536219007961	0.0836773533573992	-1.63169858426083	0.103060706888081	   
df.mm.trans1:exp2	-0.52082673411956	0.0775518521729545	-6.71585164668953	3.14541470252600e-11	***
df.mm.trans2:exp2	-0.109047832401768	0.0537294992738512	-2.02957097824359	0.0426669666059897	*  
df.mm.trans1:exp3	0.423378070438134	0.0775518521729545	5.45929025001138	6.04260502127712e-08	***
df.mm.trans2:exp3	0.261130945435317	0.0537294992738512	4.86010383428984	1.36330747791782e-06	***
df.mm.trans1:exp4	0.366971305422143	0.0775518521729545	4.73194766004726	2.5466856160429e-06	***
df.mm.trans2:exp4	0.217082970586096	0.0537294992738512	4.04029394503859	5.75130076453821e-05	***
df.mm.trans1:exp5	0.253569666105824	0.0775518521729545	3.26967904699837	0.00111376796506766	** 
df.mm.trans2:exp5	0.081842639305671	0.0537294992738512	1.52323472974374	0.128019016883628	   
df.mm.trans1:exp6	0.294304091347326	0.0775518521729545	3.7949331073483	0.000156639081577845	***
df.mm.trans2:exp6	0.0437287583819735	0.0537294992738512	0.813868712215137	0.41591567395482	   
df.mm.trans1:exp7	0.260503190368121	0.0775518521729545	3.35908405884559	0.0008118083984609	***
df.mm.trans2:exp7	0.137448704258922	0.0537294992738512	2.55816090074404	0.0106704843482580	*  
df.mm.trans1:exp8	0.141968736988143	0.0775518521729545	1.83062986905235	0.067455980516154	.  
df.mm.trans2:exp8	0.185882755525893	0.0537294992738512	3.4596033471013	0.000564009931223728	***
df.mm.trans1:probe2	0.106970046618496	0.0555543002791882	1.92550434585474	0.0544521802868463	.  
df.mm.trans1:probe3	-0.0262863552817970	0.0555543002791882	-0.473165086225458	0.636199606875162	   
df.mm.trans1:probe4	0.083099483636496	0.0555543002791882	1.49582450357361	0.135017771674324	   
df.mm.trans1:probe5	-0.268515260177135	0.0555543002791882	-4.8333838933748	1.55490324734690e-06	***
df.mm.trans1:probe6	-0.16038998652471	0.0555543002791882	-2.88708499105686	0.00397277393226207	** 
df.mm.trans1:probe7	-0.317353942898393	0.0555543002791882	-5.71250004596459	1.47137293701847e-08	***
df.mm.trans1:probe8	-0.362183471368044	0.0555543002791882	-6.51944979142733	1.12217339823905e-10	***
df.mm.trans1:probe9	-0.20537412587788	0.0555543002791882	-3.69681779530608	0.000230289433146461	***
df.mm.trans1:probe10	-0.267568264325505	0.0555543002791882	-4.81633758288451	1.69040934165548e-06	***
df.mm.trans1:probe11	0.101880106321127	0.0555543002791882	1.83388335032802	0.0669710517561351	.  
df.mm.trans1:probe12	-0.136864585163484	0.0555543002791882	-2.46361819833336	0.0139232136133432	*  
df.mm.trans1:probe13	0.0157450368178973	0.0555543002791882	0.283417066523575	0.776916342465974	   
df.mm.trans1:probe14	1.18858755339448	0.0555543002791882	21.3950593819242	8.61965277416258e-84	***
df.mm.trans1:probe15	0.526561880800972	0.0555543002791882	9.47832801699841	1.84170905461955e-20	***
df.mm.trans1:probe16	0.552806852284785	0.0555543002791882	9.95074817802858	2.67064030893046e-22	***
df.mm.trans1:probe17	0.536046539691986	0.0555543002791882	9.64905573462511	4.06531567338941e-21	***
df.mm.trans1:probe18	0.94899338159846	0.0555543002791882	17.0822668421579	1.53089939249992e-57	***
df.mm.trans1:probe19	0.763924162445246	0.0555543002791882	13.7509456262818	1.65949884211164e-39	***
df.mm.trans1:probe20	0.0953986599121982	0.0555543002791882	1.71721467884164	0.0862523907915455	.  
df.mm.trans1:probe21	0.198879639396287	0.0555543002791882	3.57991439720809	0.000360373649498694	***
df.mm.trans1:probe22	0.447398338959194	0.0555543002791882	8.05335206655099	2.29852226271246e-15	***
df.mm.trans1:probe23	0.573627121857503	0.0555543002791882	10.3255214983312	8.26729479411593e-24	***
df.mm.trans1:probe24	0.281087950476061	0.0555543002791882	5.05969743230413	5.0028210033888e-07	***
df.mm.trans1:probe25	0.290658384116133	0.0555543002791882	5.23196913030007	2.04650953056462e-07	***
df.mm.trans2:probe2	0.0107719281724919	0.0555543002791882	0.19389908824983	0.846294634197176	   
df.mm.trans2:probe3	0.145370037826565	0.0555543002791882	2.61671980559575	0.00901306651082083	** 
df.mm.trans2:probe4	-0.222211962879466	0.0555543002791882	-3.99990570959835	6.80800783302159e-05	***
df.mm.trans2:probe5	0.0945376100707091	0.0555543002791882	1.70171543148974	0.0891223835429249	.  
df.mm.trans2:probe6	-0.0311598407084766	0.0555543002791882	-0.560889806043506	0.574999451471612	   
df.mm.trans3:probe2	0.0336781142041159	0.0555543002791882	0.606219753194019	0.54450766873661	   
df.mm.trans3:probe3	-0.0307051322208245	0.0555543002791882	-0.55270486832731	0.580590161310188	   
df.mm.trans3:probe4	-0.305506491276341	0.0555543002791882	-5.49924109818715	4.85356362889313e-08	***
df.mm.trans3:probe5	-0.54760044676183	0.0555543002791882	-9.85703076107274	6.26711901555069e-22	***
df.mm.trans3:probe6	-0.531187104562141	0.0555543002791882	-9.5615839258646	8.83984052275845e-21	***
df.mm.trans3:probe7	-0.211223432529701	0.0555543002791882	-3.80210769406144	0.000152234283088700	***
df.mm.trans3:probe8	-0.338559134365402	0.0555543002791882	-6.09420211691936	1.57281335580364e-09	***
df.mm.trans3:probe9	0.223116783937548	0.0555543002791882	4.01619285665151	6.3614550251906e-05	***
df.mm.trans3:probe10	0.325815222846727	0.0555543002791882	5.86480652639566	6.12203857021305e-09	***
df.mm.trans3:probe11	-0.0927206271010568	0.0555543002791882	-1.66900899903498	0.0954314488716013	.  
df.mm.trans3:probe12	-0.0220147202433723	0.0555543002791882	-0.396273918179823	0.691988273233362	   
df.mm.trans3:probe13	0.058355630190809	0.0555543002791882	1.05042507776253	0.293778803477227	   

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.40518142406562	0.196338109992171	22.4367109586686	1.75596333407078e-90	***
df.mm.trans1	0.132169137708908	0.169265348676352	0.780839898670727	0.435083142836851	   
df.mm.trans2	0.207947380486842	0.147629278240765	1.40857818289747	0.159273590850591	   
df.mm.exp2	0.0247236786922441	0.187314626835579	0.131990112624499	0.895018904617684	   
df.mm.exp3	-0.158712548622113	0.187314626835579	-0.84730461952353	0.397029930076109	   
df.mm.exp4	0.0687475388460693	0.187314626835579	0.367016393794033	0.713685143766283	   
df.mm.exp5	-0.0329808235627584	0.187314626835579	-0.176071800264206	0.860273507169312	   
df.mm.exp6	-0.0371844021105936	0.187314626835579	-0.198513072570853	0.84268437408514	   
df.mm.exp7	-0.242720302038468	0.187314626835579	-1.2957893686088	0.195349781761657	   
df.mm.exp8	-0.00488129175414317	0.187314626835579	-0.0260593197477732	0.979215270580221	   
df.mm.trans1:exp2	-0.104621393215422	0.173602482240799	-0.602649177966846	0.546879972671062	   
df.mm.trans2:exp2	0.0817601629707265	0.120275327824455	0.679775016619015	0.496805681525132	   
df.mm.trans1:exp3	0.0506176716134505	0.173602482240799	0.291572280304381	0.770674691575801	   
df.mm.trans2:exp3	0.146812467120121	0.120275327824455	1.22063659917350	0.222513995946237	   
df.mm.trans1:exp4	-0.0530795666596684	0.173602482240799	-0.305753500609762	0.759856549366418	   
df.mm.trans2:exp4	-0.101557354555575	0.120275327824455	-0.844373957590044	0.398664256526618	   
df.mm.trans1:exp5	-0.0741987149924861	0.173602482240799	-0.427405841407078	0.669176603337326	   
df.mm.trans2:exp5	0.0510812099739812	0.120275327824455	0.424702313416559	0.671145886120335	   
df.mm.trans1:exp6	-0.0931695088113915	0.173602482240799	-0.53668304513157	0.591607027390576	   
df.mm.trans2:exp6	0.0719959634597733	0.120275327824455	0.598592951372814	0.549581148262847	   
df.mm.trans1:exp7	0.203647367567786	0.173602482240799	1.17306714131721	0.241050759553933	   
df.mm.trans2:exp7	0.0668721167100258	0.120275327824455	0.555991972082814	0.578341847235914	   
df.mm.trans1:exp8	-0.00435693484840841	0.173602482240799	-0.0250971921148283	0.97998249242843	   
df.mm.trans2:exp8	0.0323578598267383	0.120275327824455	0.269031566257424	0.787961390847843	   
df.mm.trans1:probe2	0.134358960231857	0.124360207492003	1.08040154436456	0.280226266057478	   
df.mm.trans1:probe3	0.188336550927747	0.124360207492003	1.51444384603377	0.130232167890835	   
df.mm.trans1:probe4	0.220026827828090	0.124360207492003	1.76927034994082	0.0771562830921201	.  
df.mm.trans1:probe5	0.152558307360670	0.124360207492003	1.22674535880362	0.220209686038187	   
df.mm.trans1:probe6	0.147940389725153	0.124360207492003	1.18961195633793	0.234483876194970	   
df.mm.trans1:probe7	-0.0368436622396824	0.124360207492003	-0.296265686449997	0.76708926223433	   
df.mm.trans1:probe8	0.064572248823335	0.124360207492003	0.51923561503777	0.603712384007064	   
df.mm.trans1:probe9	0.195283096090826	0.124360207492003	1.57030210892325	0.116664055519494	   
df.mm.trans1:probe10	0.278327658012055	0.124360207492003	2.23807650071630	0.0254375393911927	*  
df.mm.trans1:probe11	0.259599295142919	0.124360207492003	2.08747878745387	0.0370998352527167	*  
df.mm.trans1:probe12	0.205529861913185	0.124360207492003	1.65269796551603	0.0987090986383531	.  
df.mm.trans1:probe13	0.121042340715711	0.124360207492003	0.973320511092701	0.330631542746997	   
df.mm.trans1:probe14	0.129648684977532	0.124360207492003	1.04252547975098	0.297422371190798	   
df.mm.trans1:probe15	-0.0109305295857590	0.124360207492003	-0.0878941086236274	0.929978596369774	   
df.mm.trans1:probe16	0.0559015198529496	0.124360207492003	0.449512918805191	0.6531599862839	   
df.mm.trans1:probe17	0.0523326329206636	0.124360207492003	0.420814937318505	0.673981463927658	   
df.mm.trans1:probe18	0.132781287345037	0.124360207492003	1.06771522839068	0.285909011124302	   
df.mm.trans1:probe19	0.122316544975761	0.124360207492003	0.983566588079442	0.3255687235523	   
df.mm.trans1:probe20	0.0529789392993581	0.124360207492003	0.426011988623972	0.670191619224636	   
df.mm.trans1:probe21	0.0720999548308881	0.124360207492003	0.579767083739582	0.56220347663272	   
df.mm.trans1:probe22	0.118815315971034	0.124360207492003	0.955412654636123	0.339602053264310	   
df.mm.trans1:probe23	0.00281728180001006	0.124360207492003	0.0226542063319669	0.981930664996035	   
df.mm.trans1:probe24	0.220037299796024	0.124360207492003	1.76935455668304	0.0771422293578758	.  
df.mm.trans1:probe25	0.274014193317120	0.124360207492003	2.20339125226001	0.0277966162785283	*  
df.mm.trans2:probe2	-0.0804509832824133	0.124360207492003	-0.646919017786188	0.517834163495875	   
df.mm.trans2:probe3	-0.0644937134022754	0.124360207492003	-0.518604099357285	0.604152625169653	   
df.mm.trans2:probe4	-0.0323213728706033	0.124360207492003	-0.259901245924519	0.794993935637728	   
df.mm.trans2:probe5	0.0480943090377052	0.124360207492003	0.386733907956834	0.699036236406266	   
df.mm.trans2:probe6	-0.118801051241694	0.124360207492003	-0.955297949702543	0.339660011186709	   
df.mm.trans3:probe2	-0.301455069671364	0.124360207492003	-2.42404765761386	0.0155266692952763	*  
df.mm.trans3:probe3	-0.238709491345342	0.124360207492003	-1.91950058752267	0.0552078994020888	.  
df.mm.trans3:probe4	-0.0974467007893959	0.124360207492003	-0.783584257011327	0.433471326871474	   
df.mm.trans3:probe5	-0.155709259645635	0.124360207492003	-1.25208266201749	0.210835167617081	   
df.mm.trans3:probe6	-0.153151579312014	0.124360207492003	-1.23151595193231	0.218422111026452	   
df.mm.trans3:probe7	0.0270392366030666	0.124360207492003	0.217426756905381	0.827920537582735	   
df.mm.trans3:probe8	-0.246507722398341	0.124360207492003	-1.98220739068960	0.0477319093251161	*  
df.mm.trans3:probe9	-0.0668581520616065	0.124360207492003	-0.537616922727518	0.590962245492002	   
df.mm.trans3:probe10	-0.114009524124017	0.124360207492003	-0.916768526068502	0.359486990431381	   
df.mm.trans3:probe11	-0.211983038418253	0.124360207492003	-1.70458897338109	0.0885845489300969	.  
df.mm.trans3:probe12	-0.204133748483104	0.124360207492003	-1.64147159770725	0.101016848300843	   
df.mm.trans3:probe13	-0.208488224858435	0.124360207492003	-1.67648662753993	0.093958310221915	.