fitVsDatCorrelation=0.893933632592463
cont.fitVsDatCorrelation=0.236179319006083

fstatistic=12821.6230690865,51,669
cont.fstatistic=2717.47341606535,51,669

residuals=-0.392420982722150,-0.0817584256967822,0.00208490010476045,0.0756103227888181,0.665744954352818
cont.residuals=-0.530589233526594,-0.169968998352657,-0.0503777962890498,0.0860684314326186,1.30515326494701

predictedValues:
Include	Exclude	Both
Lung	52.4423622794607	57.6444173938248	81.1166210568086
cerebhem	50.5803832574763	58.6895188236317	48.0719665910742
cortex	48.2570847411249	47.8219106412311	58.7317457412345
heart	52.2454734876413	63.6272206277331	125.369709034371
kidney	49.8779488677956	51.5061943680198	65.8841915279475
liver	51.2901707582295	49.8975983366514	71.1379245195282
stomach	49.7736209566176	53.9580038483204	92.2984269507712
testicle	52.5273631951239	65.877891762897	124.041617330226


diffExp=-5.20205511436412,-8.10913556615542,0.435174099893871,-11.3817471400918,-1.62824550022419,1.39257242157812,-4.18438289170287,-13.350528567773
diffExpScore=1.06171496583996
diffExp1.5=0,0,0,0,0,0,0,0
diffExp1.5Score=0
diffExp1.4=0,0,0,0,0,0,0,0
diffExp1.4Score=0
diffExp1.3=0,0,0,0,0,0,0,0
diffExp1.3Score=0
diffExp1.2=0,0,0,-1,0,0,0,-1
diffExp1.2Score=0.666666666666667

cont.predictedValues:
Include	Exclude	Both
Lung	58.6661484762227	56.2262486121327	54.6298897348961
cerebhem	55.2156866582252	53.3790694841137	57.4657393882376
cortex	64.4697706719055	52.0751261824779	54.9762605711562
heart	58.5172101128956	50.7050371368857	54.4054327001807
kidney	58.9442374485405	51.9565835086849	57.5601843620923
liver	55.908629360789	57.6122733642695	58.1921968942184
stomach	54.3581347266661	56.4839579256488	60.3171193941777
testicle	57.1588461139986	61.2013045642161	53.771360596901
cont.diffExp=2.43989986409,1.83661717411152,12.3946444894275,7.81217297600995,6.98765393985559,-1.70364400348051,-2.12582319898266,-4.04245845021754
cont.diffExpScore=1.59936638361959

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=0,0,1,0,0,0,0,0
cont.diffExp1.2Score=0.5

tran.correlation=0.780374564488033
cont.tran.correlation=-0.445466826496459

tran.covariance=0.00272165918674506
cont.tran.covariance=-0.00153549136975706

tran.mean=53.5010727091112
cont.tran.mean=56.4298915217295

weightedLogRatios:
wLogRatio
Lung	-0.37897723717465
cerebhem	-0.59447894224841
cortex	0.0350755419831847
heart	-0.79909325671905
kidney	-0.126103668212822
liver	0.108005771819987
stomach	-0.318673623316733
testicle	-0.922762109081025

cont.weightedLogRatios:
wLogRatio
Lung	0.172067109322726
cerebhem	0.135121809641598
cortex	0.866727515930831
heart	0.572849136235628
kidney	0.506437222429071
liver	-0.121230007775002
stomach	-0.154016390286522
testicle	-0.27880419762811

varWeightedLogRatios=0.142094670587461
cont.varWeightedLogRatios=0.162710877643840

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.43056604684252	0.0658700545890646	52.0808137816853	1.44058137453483e-237	***
df.mm.trans1	0.460454268066406	0.0573213898928177	8.03285246445324	4.30366392725449e-15	***
df.mm.trans2	0.587002619739658	0.0519229546727085	11.3052622571226	3.05626629856548e-27	***
df.mm.exp2	0.50500555667386	0.0686876126998329	7.35220714222157	5.71210443108913e-13	***
df.mm.exp3	0.0529259474854743	0.0686876126998329	0.770531183210027	0.441256883555682	   
df.mm.exp4	-0.340392638586091	0.0686876126998329	-4.95566267637831	9.1431877637802e-07	***
df.mm.exp5	0.0452623254287021	0.068687612699833	0.658959070633302	0.510148788219591	   
df.mm.exp6	-0.0352688144301115	0.0686876126998329	-0.513466883530184	0.607794297884474	   
df.mm.exp7	-0.247456142737345	0.0686876126998329	-3.6026312898475	0.000338369628684589	***
df.mm.exp8	-0.289600217430703	0.068687612699833	-4.21619279004884	2.82641246328798e-05	***
df.mm.trans1:exp2	-0.541156443370122	0.0635923724428977	-8.50976968748963	1.1420063693596e-16	***
df.mm.trans2:exp2	-0.487037806585565	0.0519229546727085	-9.3800094708701	1.00470772237847e-19	***
df.mm.trans1:exp3	-0.136098001629345	0.0635923724428977	-2.14016235597364	0.0327026559330451	*  
df.mm.trans2:exp3	-0.239735437236346	0.0519229546727085	-4.6171378101939	4.66600335116022e-06	***
df.mm.trans1:exp4	0.336631188700021	0.0635923724428977	5.29357807813031	1.62838301699133e-07	***
df.mm.trans2:exp4	0.439140608819619	0.0519229546727085	8.45754274940054	1.71296051294856e-16	***
df.mm.trans1:exp5	-0.0953980319604232	0.0635923724428977	-1.50014896906205	0.134047581541032	   
df.mm.trans2:exp5	-0.157853651840509	0.0519229546727085	-3.04015156370673	0.00245690977180641	** 
df.mm.trans1:exp6	0.0130532399324255	0.0635923724428977	0.205264238948572	0.837428121572742	   
df.mm.trans2:exp6	-0.109051719335938	0.0519229546727085	-2.10026028031985	0.0360797465952507	*  
df.mm.trans1:exp7	0.195226581598449	0.0635923724428977	3.06996852136239	0.00222747945451918	** 
df.mm.trans2:exp7	0.181368774379205	0.0519229546727085	3.49303647148831	0.000509012879864174	***
df.mm.trans1:exp8	0.291219749802688	0.0635923724428977	4.57947610091425	5.55935026848481e-06	***
df.mm.trans2:exp8	0.423109715218503	0.0519229546727085	8.1487988864566	1.80820142451742e-15	***
df.mm.trans1:probe2	0.0532257061656719	0.0389422160045314	1.36678678376902	0.172151274147414	   
df.mm.trans1:probe3	0.124061624080650	0.0389422160045314	3.18578747717424	0.00151070768064943	** 
df.mm.trans1:probe4	0.0353346946631152	0.0389422160045314	0.907362196825256	0.364542004884662	   
df.mm.trans1:probe5	0.0135046724787362	0.0389422160045314	0.346787467800106	0.728860044630614	   
df.mm.trans1:probe6	0.0818004331908083	0.0389422160045314	2.10055927945369	0.0360533728226746	*  
df.mm.trans1:probe7	0.307954275074156	0.0389422160045314	7.90798025049016	1.08295945688448e-14	***
df.mm.trans1:probe8	0.131983826352593	0.0389422160045314	3.38922228609783	0.000742059522933329	***
df.mm.trans1:probe9	0.0123760245967234	0.0389422160045314	0.317804836665775	0.750732152018395	   
df.mm.trans1:probe10	0.0940418205776738	0.0389422160045314	2.41490675740515	0.0160064242159926	*  
df.mm.trans1:probe11	0.0612329845623542	0.0389422160045314	1.57240626869383	0.116329150981594	   
df.mm.trans1:probe12	0.168597658367636	0.0389422160045314	4.32943154411187	1.72381020207503e-05	***
df.mm.trans1:probe13	0.00934120305426465	0.0389422160045314	0.239873433324331	0.810501854104484	   
df.mm.trans1:probe14	0.144027195869738	0.0389422160045314	3.69848484875587	0.000234689273012247	***
df.mm.trans1:probe15	0.0512962768490949	0.0389422160045314	1.31724082787497	0.188208884756421	   
df.mm.trans1:probe16	0.0210262897281948	0.0389422160045314	0.539935624766401	0.589421105134532	   
df.mm.trans1:probe17	0.20071063566156	0.0389422160045314	5.15406302605391	3.35957886586387e-07	***
df.mm.trans1:probe18	0.138150044819024	0.0389422160045314	3.54756505903383	0.000415981545953108	***
df.mm.trans2:probe2	0.105541103248848	0.0389422160045314	2.71019767433283	0.00689665507853387	** 
df.mm.trans2:probe3	0.144976547319450	0.0389422160045314	3.72286331375133	0.000213558123659227	***
df.mm.trans2:probe4	0.0571738620007203	0.0389422160045314	1.46817176490592	0.142527570155258	   
df.mm.trans2:probe5	0.099390515754682	0.0389422160045314	2.55225629027164	0.0109238453386970	*  
df.mm.trans2:probe6	0.0336148427609586	0.0389422160045314	0.863197994614564	0.388337961240085	   
df.mm.trans3:probe2	0.210929269839195	0.0389422160045314	5.41646807707735	8.4868075214503e-08	***
df.mm.trans3:probe3	-0.233192146341716	0.0389422160045314	-5.98815810365238	3.46353595331772e-09	***
df.mm.trans3:probe4	0.262070679792633	0.0389422160045314	6.72973206666355	3.65640867338646e-11	***
df.mm.trans3:probe5	-0.313620073731773	0.0389422160045314	-8.05347270672219	3.69128450668128e-15	***
df.mm.trans3:probe6	-0.418998025431521	0.0389422160045314	-10.7594808005473	5.19215716855884e-25	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.97938590736812	0.142808231470328	27.8652418449347	5.07315271495854e-114	***
df.mm.trans1	0.0547842940492962	0.124274472931338	0.440833042837082	0.659476286344862	   
df.mm.trans2	0.0115439472116054	0.112570505304462	0.102548595481411	0.918351972435612	   
df.mm.exp2	-0.163188544638215	0.148916780998242	-1.09583717526194	0.273544364936079	   
df.mm.exp3	0.0113169717966141	0.148916780998242	0.0759952754871032	0.939445576812855	   
df.mm.exp4	-0.101783270826874	0.148916780998242	-0.683490941347134	0.494533319172422	   
df.mm.exp5	-0.126496193213399	0.148916780998243	-0.849442167400137	0.395939163351009	   
df.mm.exp6	-0.0869623224330606	0.148916780998242	-0.583965902634485	0.55944009375193	   
df.mm.exp7	-0.170730439039697	0.148916780998242	-1.14648220230943	0.252005576966165	   
df.mm.exp8	0.0745962625421418	0.148916780998243	0.500925832818144	0.616588088268876	   
df.mm.trans1:exp2	0.102572762601840	0.1378701490416	0.743980936517955	0.457149228856342	   
df.mm.trans2:exp2	0.111223551524929	0.112570505304462	0.988034576411555	0.323492919174060	   
df.mm.trans1:exp3	0.0830165968514017	0.1378701490416	0.602136121767395	0.547287548240068	   
df.mm.trans2:exp3	-0.0880132665790304	0.112570505304462	-0.781850151076309	0.434579259516054	   
df.mm.trans1:exp4	0.0992412982239557	0.1378701490416	0.719817153414487	0.471888972828286	   
df.mm.trans2:exp4	-0.00157517671650002	0.112570505304462	-0.0139928013313944	0.988839895946283	   
df.mm.trans1:exp5	0.131225188673827	0.1378701490416	0.951802762135493	0.341540761565325	   
df.mm.trans2:exp5	0.0475209255014511	0.112570505304462	0.422143663412758	0.673055785590461	   
df.mm.trans1:exp6	0.0388181885322770	0.1378701490416	0.281556151219973	0.77837093295902	   
df.mm.trans2:exp6	0.111314241659751	0.112570505304462	0.988840206044086	0.323098828298318	   
df.mm.trans1:exp7	0.0944618408513949	0.1378701490416	0.685150785054222	0.493486095169001	   
df.mm.trans2:exp7	0.175303401338912	0.112570505304462	1.55727648965223	0.119877767880598	   
df.mm.trans1:exp8	-0.100624970172548	0.1378701490416	-0.729853205150204	0.465735391017163	   
df.mm.trans2:exp8	0.0101885381399199	0.112570505304462	0.0905080608136524	0.927910579563372	   
df.mm.trans1:probe2	0.127462966436093	0.0844278789783468	1.50972602863543	0.131585481523504	   
df.mm.trans1:probe3	-0.000413228721403998	0.0844278789783468	-0.00489445816245104	0.996096262080387	   
df.mm.trans1:probe4	0.0591926877496934	0.0844278789783468	0.701103574624615	0.483482167303665	   
df.mm.trans1:probe5	0.0391892355858767	0.0844278789783468	0.46417411002268	0.642674013576001	   
df.mm.trans1:probe6	0.047054988900704	0.0844278789783468	0.557339464997956	0.577481937658966	   
df.mm.trans1:probe7	-0.00517089743432847	0.0844278789783468	-0.0612463264137507	0.95118132793109	   
df.mm.trans1:probe8	0.00919870213992085	0.0844278789783468	0.108953372407709	0.913272144333276	   
df.mm.trans1:probe9	0.0333679561367017	0.0844278789783468	0.395224380151248	0.692803286920816	   
df.mm.trans1:probe10	0.0600690834209806	0.0844278789783468	0.711483980740372	0.477032377657486	   
df.mm.trans1:probe11	0.0412577213046014	0.0844278789783468	0.488674141810228	0.625232561084714	   
df.mm.trans1:probe12	0.0227345706863311	0.0844278789783468	0.269278003444357	0.787798786031984	   
df.mm.trans1:probe13	0.159395994758803	0.0844278789783468	1.88795450848271	0.059464558192047	.  
df.mm.trans1:probe14	0.125842697165709	0.0844278789783468	1.49053486465038	0.136554973357165	   
df.mm.trans1:probe15	-0.0147179713840137	0.0844278789783468	-0.174325963912802	0.86166203481703	   
df.mm.trans1:probe16	0.052372667571154	0.0844278789783468	0.620324331309875	0.535255525119052	   
df.mm.trans1:probe17	0.0818803744651915	0.0844278789783468	0.9698262642153	0.33248358925822	   
df.mm.trans1:probe18	0.0659065821450231	0.0844278789783468	0.780625818657912	0.435298720803041	   
df.mm.trans2:probe2	0.0498820343241715	0.0844278789783468	0.590824203187252	0.554837759855762	   
df.mm.trans2:probe3	0.0885566415973993	0.0844278789783468	1.04890283481019	0.294601612175602	   
df.mm.trans2:probe4	0.0895383679046023	0.0844278789783468	1.06053082214189	0.28928589511523	   
df.mm.trans2:probe5	0.109658993496712	0.0844278789783468	1.29884813907070	0.194443538495355	   
df.mm.trans2:probe6	0.123810167933887	0.0844278789783468	1.46646071691131	0.142992696897851	   
df.mm.trans3:probe2	-0.0338574938014929	0.0844278789783468	-0.401022674159282	0.688531478390179	   
df.mm.trans3:probe3	-0.05185562625999	0.0844278789783468	-0.614200272321058	0.539291800259879	   
df.mm.trans3:probe4	-0.109102715828555	0.0844278789783468	-1.29225934784571	0.196713526340445	   
df.mm.trans3:probe5	-0.0650975018447575	0.0844278789783468	-0.771042724660334	0.440953834316959	   
df.mm.trans3:probe6	-0.0108845699961137	0.0844278789783468	-0.128921514170754	0.897458500674382