fitVsDatCorrelation=0.861640523791348
cont.fitVsDatCorrelation=0.232125977628638

fstatistic=8031.59722778807,54,738
cont.fstatistic=2176.6148473979,54,738

residuals=-0.620006266846733,-0.107078146350640,-0.00337683046605039,0.0901784013643728,1.19593292169364
cont.residuals=-0.776232305634957,-0.242960133300280,-0.084896581089829,0.211311363781285,1.16426267043937

predictedValues:
Include	Exclude	Both
Lung	86.186321363192	60.0641772849787	56.1628132223245
cerebhem	84.6302980450498	62.2266096653631	64.076785896408
cortex	76.1385426862035	61.2792063909598	60.7253821720059
heart	76.9420340175866	55.8446426261968	57.1612677432162
kidney	88.2754191657079	52.3034835827886	51.8187388947558
liver	82.1411074033245	52.6586270632384	51.2059893669322
stomach	78.637112747397	52.8560480806074	55.2630476739377
testicle	79.0195030438051	60.9831159116324	60.704896017709


diffExp=26.1221440782133,22.4036883796867,14.8593362952437,21.0973913913898,35.9719355829192,29.4824803400861,25.7810646667896,18.0363871321727
diffExpScore=0.994865328552707
diffExp1.5=0,0,0,0,1,1,0,0
diffExp1.5Score=0.666666666666667
diffExp1.4=1,0,0,0,1,1,1,0
diffExp1.4Score=0.8
diffExp1.3=1,1,0,1,1,1,1,0
diffExp1.3Score=0.857142857142857
diffExp1.2=1,1,1,1,1,1,1,1
diffExp1.2Score=0.888888888888889

cont.predictedValues:
Include	Exclude	Both
Lung	69.7151622772966	81.7129456718203	62.9645751810175
cerebhem	71.4905214918525	71.1387003497716	68.0770296357202
cortex	66.8265047932157	71.6319671582968	71.0844207480201
heart	66.6992157567092	73.2831758326894	78.1560180816008
kidney	66.0144027451087	71.2576069251828	78.2765333290786
liver	70.5076169147915	74.5578947257231	65.0476560786037
stomach	66.7742957276114	75.3628529422888	69.8657827827548
testicle	69.9276538802685	74.5160552751269	68.776256428027
cont.diffExp=-11.9977833945238,0.351821142080922,-4.80546236508113,-6.58396007598012,-5.24320418007409,-4.05027781093162,-8.58855721467738,-4.58840139485841
cont.diffExpScore=0.993627514102495

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=-0.144230041812863
cont.tran.correlation=0.241062718462778

tran.covariance=-0.000623409038236016
cont.tran.covariance=0.000345544223614583

tran.mean=69.386640567377
cont.tran.mean=71.3385357792346

weightedLogRatios:
wLogRatio
Lung	1.54404095987825
cerebhem	1.31753650231831
cortex	0.917089165594153
heart	1.34050261530051
kidney	2.20809477851663
liver	1.86119094287378
stomach	1.65511616572779
testicle	1.09861186553736

cont.weightedLogRatios:
wLogRatio
Lung	-0.686598550023402
cerebhem	0.0210512037094912
cortex	-0.294211868390549
heart	-0.399828178793027
kidney	-0.323146940786748
liver	-0.239263986238834
stomach	-0.51566390333665
testicle	-0.2719601933315

varWeightedLogRatios=0.173867101701913
cont.varWeightedLogRatios=0.0431528601535061

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.71552141527379	0.0902722807376782	52.236648689276	4.0252077585657e-250	***
df.mm.trans1	-0.0877565557930809	0.0795218414868553	-1.10355286236155	0.27014684921283	   
df.mm.trans2	-0.624371643942446	0.071754985938201	-8.70143915128331	2.12015841421381e-17	***
df.mm.exp2	-0.114677332770462	0.095556282962989	-1.20010248635225	0.230484638537660	   
df.mm.exp3	-0.182036823442918	0.0955562829629889	-1.90502202260656	0.0571656523390127	.  
df.mm.exp4	-0.203920842440420	0.095556282962989	-2.13403908269855	0.0331686002032427	*  
df.mm.exp5	-0.0338974125517896	0.095556282962989	-0.354737663507891	0.722887476672649	   
df.mm.exp6	-0.0872580734240284	0.0955562829629889	-0.91315893333592	0.361457172899239	   
df.mm.exp7	-0.203358805520041	0.0955562829629889	-2.12815734574781	0.0336547824816799	*  
df.mm.exp8	-0.149402861329739	0.095556282962989	-1.56350641419995	0.118362208938616	   
df.mm.trans1:exp2	0.0964581879924554	0.090132720312964	1.07017948262881	0.284888405771145	   
df.mm.trans2:exp2	0.150046437155586	0.0737449529833342	2.03466720209986	0.042241344136828	*  
df.mm.trans1:exp3	0.0580799540942132	0.090132720312964	0.644382571529459	0.519527610543031	   
df.mm.trans2:exp3	0.202063786251215	0.0737449529833342	2.74003546109629	0.00629167600242043	** 
df.mm.trans1:exp4	0.0904616956316418	0.090132720312964	1.00364989892167	0.315876350313147	   
df.mm.trans2:exp4	0.131080827024073	0.0737449529833342	1.77748878697768	0.0758996037301621	.  
df.mm.trans1:exp5	0.0578476225667651	0.090132720312964	0.641804911311933	0.521199149691648	   
df.mm.trans2:exp5	-0.104453222895038	0.0737449529833342	-1.41641181761474	0.157076905318348	   
df.mm.trans1:exp6	0.0391851835661652	0.090132720312964	0.434749815939252	0.6638711271178	   
df.mm.trans2:exp6	-0.0443254565684381	0.0737449529833342	-0.6010642732182	0.547981739191008	   
df.mm.trans1:exp7	0.111691085679546	0.090132720312964	1.23918467446367	0.215671056778335	   
df.mm.trans2:exp7	0.0755173378989793	0.0737449529833342	1.02403398258381	0.306154764743662	   
df.mm.trans1:exp8	0.0625860771045054	0.090132720312964	0.694376879863278	0.487664415137932	   
df.mm.trans2:exp8	0.164586286855181	0.0737449529833342	2.23183119924663	0.025925574467968	*  
df.mm.trans1:probe2	-0.455752257465273	0.0526261706985362	-8.66018278388532	2.94657163141932e-17	***
df.mm.trans1:probe3	0.269219728807808	0.0526261706985362	5.11570051999424	3.98890370586575e-07	***
df.mm.trans1:probe4	-0.482100219712843	0.0526261706985362	-9.16084551305292	4.99700013591442e-19	***
df.mm.trans1:probe5	-0.663824554285725	0.0526261706985362	-12.6139627009607	3.57292309433426e-33	***
df.mm.trans1:probe6	0.521863893335394	0.0526261706985362	9.91643295357435	7.63229617501504e-22	***
df.mm.trans1:probe7	-0.200938350313169	0.0526261706985362	-3.81822100384663	0.000145704504761789	***
df.mm.trans1:probe8	-0.625484867666763	0.0526261706985362	-11.8854337939538	6.23244701253232e-30	***
df.mm.trans1:probe9	-0.326528099263393	0.0526261706985362	-6.20467145774061	9.13136513940085e-10	***
df.mm.trans1:probe10	-0.0946831278112842	0.0526261706985362	-1.79916430465114	0.0724011083967712	.  
df.mm.trans1:probe11	-0.456242736037717	0.0526261706985362	-8.66950283445964	2.73571594256229e-17	***
df.mm.trans1:probe12	-0.488664832729757	0.0526261706985362	-9.28558597829632	1.76018150976789e-19	***
df.mm.trans1:probe13	-0.485523040065328	0.0526261706985362	-9.22588578307547	2.90417930651448e-19	***
df.mm.trans1:probe14	-0.446016245493756	0.0526261706985362	-8.47517954609914	1.26970979533477e-16	***
df.mm.trans1:probe15	-0.328219860856592	0.0526261706985362	-6.23681823130866	7.51596250625733e-10	***
df.mm.trans1:probe16	-0.306590579386494	0.0526261706985362	-5.82581965050749	8.48580972795783e-09	***
df.mm.trans1:probe17	-0.0823701232001647	0.0526261706985362	-1.56519317493218	0.117966290132537	   
df.mm.trans1:probe18	-0.102638062199733	0.0526261706985362	-1.95032359066527	0.0515157853386713	.  
df.mm.trans1:probe19	-0.0138775659803114	0.0526261706985362	-0.263700850662453	0.792084052886853	   
df.mm.trans1:probe20	-0.102797954634989	0.0526261706985362	-1.95336185913767	0.0511542375314159	.  
df.mm.trans1:probe21	0.149375919151365	0.0526261706985362	2.83843413207945	0.00465807557021854	** 
df.mm.trans1:probe22	0.097951695124391	0.0526261706985362	1.86127346573433	0.0631029707904893	.  
df.mm.trans2:probe2	0.0156470281875803	0.0526261706985362	0.297324087614368	0.766302763093191	   
df.mm.trans2:probe3	-0.0202420973990168	0.0526261706985362	-0.384639374864868	0.700615430253523	   
df.mm.trans2:probe4	0.00073768765572093	0.0526261706985362	0.0140175058517311	0.988819803224779	   
df.mm.trans2:probe5	0.0651013649092618	0.0526261706985362	1.23705304879940	0.216460871560448	   
df.mm.trans2:probe6	-0.0143417359872107	0.0526261706985362	-0.272520987121899	0.785297687270162	   
df.mm.trans3:probe2	-0.066339456607156	0.0526261706985362	-1.26057920853058	0.207858893018165	   
df.mm.trans3:probe3	0.372860216991724	0.0526261706985362	7.08507216167439	3.24946224609075e-12	***
df.mm.trans3:probe4	-0.106973577865010	0.0526261706985362	-2.03270685374008	0.0424396449489539	*  
df.mm.trans3:probe5	-0.0752390029456336	0.0526261706985362	-1.42968796602422	0.153229774809433	   

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.43341816032533	0.173011349332241	25.6250134886333	4.33311118486051e-104	***
df.mm.trans1	-0.286541842696907	0.152407593832765	-1.88010213592982	0.0604878218610983	.  
df.mm.trans2	-0.00715282754560116	0.137522025997761	-0.0520122321766669	0.958533021507878	   
df.mm.exp2	-0.191501484916228	0.183138404364030	-1.04566535665329	0.296057927041326	   
df.mm.exp3	-0.295284976031693	0.183138404364030	-1.61235966348568	0.107311221940919	   
df.mm.exp4	-0.369240811777123	0.183138404364030	-2.01618449750807	0.0441425389022996	*  
df.mm.exp5	-0.409131340989068	0.183138404364030	-2.23400079524459	0.0257818027409231	*  
df.mm.exp6	-0.112881496276013	0.183138404364030	-0.616372609928581	0.537838699114056	   
df.mm.exp7	-0.228001317538378	0.183138404364030	-1.24496726030861	0.213538959216266	   
df.mm.exp8	-0.177440778403372	0.183138404364030	-0.968888961436328	0.332918009397927	   
df.mm.trans1:exp2	0.216648528912419	0.172743874785283	1.25416041050201	0.210180797812147	   
df.mm.trans2:exp2	0.0529205392911287	0.141335897551595	0.374430984681792	0.708191254016864	   
df.mm.trans1:exp3	0.252966925851602	0.172743874785283	1.46440460575539	0.143509262338209	   
df.mm.trans2:exp3	0.163613975983160	0.141335897551595	1.15762505363106	0.247391595762986	   
df.mm.trans1:exp4	0.325016176490570	0.172743874785283	1.88149175705684	0.0602984348804053	.  
df.mm.trans2:exp4	0.260359426463166	0.141335897551595	1.84213233137123	0.0658568136159304	.  
df.mm.trans1:exp5	0.354586452250435	0.172743874785283	2.05267163707644	0.040456549122421	*  
df.mm.trans2:exp5	0.27222047523225	0.141335897551595	1.92605332366376	0.0544815283739819	.  
df.mm.trans1:exp6	0.124184411234218	0.172743874785283	0.718893282836084	0.472434227911354	   
df.mm.trans2:exp6	0.0212449872110239	0.141335897551595	0.150315578554757	0.880556715116434	   
df.mm.trans1:exp7	0.184901699163210	0.172743874785283	1.07038063950481	0.284797948840645	   
df.mm.trans2:exp7	0.147103361535417	0.141335897551595	1.04080678782767	0.298306075985538	   
df.mm.trans1:exp8	0.180484139642106	0.172743874785283	1.04480775290263	0.296453927998369	   
df.mm.trans2:exp8	0.0852429445503226	0.141335897551595	0.603123099134841	0.546612101546708	   
df.mm.trans1:probe2	-0.000821595117911979	0.100860693098035	-0.00814584049222625	0.99350283292112	   
df.mm.trans1:probe3	0.147890069325672	0.100860693098035	1.46628051804011	0.142997831581484	   
df.mm.trans1:probe4	0.0326966028078044	0.100860693098035	0.324175868750216	0.745896745221559	   
df.mm.trans1:probe5	0.302866431191103	0.100860693098035	3.00281925384670	0.00276526510086138	** 
df.mm.trans1:probe6	0.0424530216878432	0.100860693098035	0.420907495118832	0.673945175700031	   
df.mm.trans1:probe7	0.174769733279204	0.100860693098035	1.73278338578668	0.0835519213099486	.  
df.mm.trans1:probe8	0.232317353493204	0.100860693098035	2.30334877103606	0.0215363668872957	*  
df.mm.trans1:probe9	0.132417197427242	0.100860693098035	1.31287217408406	0.189634117516432	   
df.mm.trans1:probe10	0.0431547866277579	0.100860693098035	0.427865259520000	0.66887404638678	   
df.mm.trans1:probe11	0.117399136874048	0.100860693098035	1.16397313232755	0.244811058439863	   
df.mm.trans1:probe12	0.0931537011362992	0.100860693098035	0.923587755298839	0.356002855888443	   
df.mm.trans1:probe13	0.0575387447990455	0.100860693098035	0.570477388481941	0.568527596881071	   
df.mm.trans1:probe14	0.174109267366321	0.100860693098035	1.72623508741001	0.084723647297341	.  
df.mm.trans1:probe15	0.155139836215871	0.100860693098035	1.53815952925366	0.124438231566643	   
df.mm.trans1:probe16	0.0307149133436439	0.100860693098035	0.304528081259461	0.76081145417508	   
df.mm.trans1:probe17	0.127223893361958	0.100860693098035	1.26138230319613	0.207569702587776	   
df.mm.trans1:probe18	0.110945357801701	0.100860693098035	1.09998607380046	0.271696864092733	   
df.mm.trans1:probe19	0.114322138595888	0.100860693098035	1.13346572469781	0.257386800123963	   
df.mm.trans1:probe20	0.208577542665973	0.100860693098035	2.06797649569232	0.0389901392053289	*  
df.mm.trans1:probe21	0.167169123159826	0.100860693098035	1.65742588143173	0.0978583969285798	.  
df.mm.trans1:probe22	0.169583587940537	0.100860693098035	1.68136449127614	0.0931152312481932	.  
df.mm.trans2:probe2	-0.100172057417368	0.100860693098035	-0.993172407808088	0.320951459969781	   
df.mm.trans2:probe3	0.00414421614233774	0.100860693098035	0.0410885154071827	0.967236443379672	   
df.mm.trans2:probe4	-0.0622772114233885	0.100860693098035	-0.617457698440125	0.53712333918179	   
df.mm.trans2:probe5	-0.0898076188589306	0.100860693098035	-0.89041246991669	0.37353462287223	   
df.mm.trans2:probe6	-0.00546911526131111	0.100860693098035	-0.054224446544257	0.956771018926083	   
df.mm.trans3:probe2	-0.0372356786253381	0.100860693098035	-0.369179285622652	0.712099989251076	   
df.mm.trans3:probe3	0.077154656517362	0.100860693098035	0.764962585001958	0.444538365663572	   
df.mm.trans3:probe4	-0.0575593657404866	0.100860693098035	-0.570681838211639	0.568389047494617	   
df.mm.trans3:probe5	0.0318842850759447	0.100860693098035	0.31612201043427	0.751999285888129