fitVsDatCorrelation=0.899292732871709
cont.fitVsDatCorrelation=0.261501618806188

fstatistic=12725.0054774289,55,761
cont.fstatistic=2601.60689856295,55,761

residuals=-0.962011986212243,-0.0882333644542333,-0.000900417171380294,0.077467645976713,1.00273558975177
cont.residuals=-0.585916618115216,-0.201541342130876,-0.0405495747305914,0.145615627923636,1.53299003889025

predictedValues:
Include	Exclude	Both
Lung	69.8438812913458	49.1230522788884	69.6722380050703
cerebhem	65.094314609436	52.7300560964531	71.2331835740427
cortex	63.7859797461102	48.1185013103818	66.778787367592
heart	77.090563979512	50.1992750215398	75.9832135838327
kidney	78.6009107855922	47.6677744867668	78.452947015875
liver	75.2952774756812	50.4609174009204	81.1327859044599
stomach	78.9232852690179	48.7204596106899	77.3650511639298
testicle	71.352893297885	48.9345646058289	74.9514367124717


diffExp=20.7208290124574,12.3642585129829,15.6674784357283,26.8912889579722,30.9331362988254,24.8343600747608,30.2028256583279,22.4183286920560
diffExpScore=0.994595544190874
diffExp1.5=0,0,0,1,1,0,1,0
diffExp1.5Score=0.75
diffExp1.4=1,0,0,1,1,1,1,1
diffExp1.4Score=0.857142857142857
diffExp1.3=1,0,1,1,1,1,1,1
diffExp1.3Score=0.875
diffExp1.2=1,1,1,1,1,1,1,1
diffExp1.2Score=0.888888888888889

cont.predictedValues:
Include	Exclude	Both
Lung	68.0619729569421	61.0014554474002	63.0895179055864
cerebhem	70.9515446341823	65.9598999078023	63.0204406546903
cortex	62.5520968605911	60.2557651490387	65.9478279564129
heart	63.9793172207053	60.534601962589	58.8032515359509
kidney	63.6095161719824	67.2329229259763	55.7538529796174
liver	67.0543724664427	59.2930657245732	70.7267632055744
stomach	64.5278901779743	61.3173939436583	74.6934628554782
testicle	62.3314297155132	62.8454798411248	69.7263455067457
cont.diffExp=7.06051750954192,4.99164472637997,2.29633171155234,3.44471525811635,-3.62340675399385,7.76130674186955,3.21049623431604,-0.514050125611618
cont.diffExpScore=1.28387076619028

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=-0.30654530198867
cont.tran.correlation=0.174742590786535

tran.covariance=-0.000791152023668271
cont.tran.covariance=0.000337592435584466

tran.mean=60.9963567041281
cont.tran.mean=63.844295319156

weightedLogRatios:
wLogRatio
Lung	1.43247561581634
cerebhem	0.857459721290227
cortex	1.13158194461019
heart	1.77189916112439
kidney	2.05768513511643
liver	1.64942296047682
stomach	1.99090968663537
testicle	1.53843376058188

cont.weightedLogRatios:
wLogRatio
Lung	0.456226773510666
cerebhem	0.308252654813602
cortex	0.153993176881540
heart	0.228622844570311
kidney	-0.231597518326438
liver	0.509759238779558
stomach	0.211361348595899
testicle	-0.0339745968011505

varWeightedLogRatios=0.168541380625362
cont.varWeightedLogRatios=0.0596294591395972

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.87382913173087	0.0695631908471317	55.6879160451938	1.22250598832095e-270	***
df.mm.trans1	0.542772396923012	0.0609127343747138	8.91065558778016	3.69921027409146e-18	***
df.mm.trans2	0.0198608172228257	0.0546246914529707	0.363586808356161	0.716267559941866	   
df.mm.exp2	-0.0217249153862944	0.0720369098187171	-0.3015803348723	0.763054391417548	   
df.mm.exp3	-0.0689742906507581	0.0720369098187172	-0.957485417188685	0.338626381524066	   
df.mm.exp4	0.0336800553954119	0.0720369098187172	0.467538869728985	0.640248239469864	   
df.mm.exp5	-0.0306491484357188	0.0720369098187172	-0.425464508581062	0.670618377725404	   
df.mm.exp6	-0.0502595777777075	0.0720369098187172	-0.697692029047152	0.485582919548569	   
df.mm.exp7	0.00925124822200368	0.0720369098187172	0.128423723967126	0.897847588176454	   
df.mm.exp8	-0.0555074836988229	0.0720369098187172	-0.770542265603965	0.441217439793663	   
df.mm.trans1:exp2	-0.0487003564341154	0.067576611420538	-0.7206688144075	0.47133461542878	   
df.mm.trans2:exp2	0.0925821116348198	0.0539074871487143	1.71742584438065	0.0863082330090538	.  
df.mm.trans1:exp3	-0.021754780376316	0.067576611420538	-0.321927659866417	0.747595907521496	   
df.mm.trans2:exp3	0.0483126152974337	0.0539074871487143	0.896213454805526	0.370422175318244	   
df.mm.trans1:exp4	0.0650383468744594	0.067576611420538	0.962438712259739	0.336134985645955	   
df.mm.trans2:exp4	-0.0120078917405180	0.0539074871487143	-0.222749981044226	0.82378987094482	   
df.mm.trans1:exp5	0.148769951373917	0.067576611420538	2.20150061162586	0.0280000352684287	*  
df.mm.trans2:exp5	0.000576309602062212	0.0539074871487143	0.0106907153819348	0.991473007653258	   
df.mm.trans1:exp6	0.125414510457624	0.067576611420538	1.85588634619681	0.0638561539183277	.  
df.mm.trans2:exp6	0.0771302804443	0.0539074871487143	1.43078975711706	0.152900967056857	   
df.mm.trans1:exp7	0.112962575871759	0.067576611420538	1.67162237787832	0.0950099111036513	.  
df.mm.trans2:exp7	-0.0174806122844156	0.0539074871487143	-0.324270582974715	0.745822296235455	   
df.mm.trans1:exp8	0.07688289354297	0.067576611420538	1.1377145424549	0.255597770921431	   
df.mm.trans2:exp8	0.0516630520025162	0.0539074871487143	0.958365057157897	0.338183077285559	   
df.mm.trans1:probe2	-0.176384486095496	0.0413820541316631	-4.26234245246267	2.27700726682579e-05	***
df.mm.trans1:probe3	-0.174639690640724	0.0413820541316631	-4.2201793580638	2.73570088409702e-05	***
df.mm.trans1:probe4	-0.147883205370803	0.0413820541316631	-3.57360717040026	0.00037428416274406	***
df.mm.trans1:probe5	0.0613797213371599	0.0413820541316631	1.48324491437451	0.138423365501164	   
df.mm.trans1:probe6	-0.460652874154721	0.0413820541316631	-11.131706335531	9.02416057805132e-27	***
df.mm.trans1:probe7	-0.290424642386392	0.0413820541316631	-7.01813016488652	4.98053240706088e-12	***
df.mm.trans1:probe8	-0.356721142031248	0.0413820541316631	-8.6201893433392	3.84721976222677e-17	***
df.mm.trans1:probe9	0.902864635430377	0.0413820541316631	21.8177819921114	2.4304267792346e-82	***
df.mm.trans1:probe10	-0.299954376875323	0.0413820541316631	-7.2484168118135	1.03739259874058e-12	***
df.mm.trans1:probe11	-0.327177554180603	0.0413820541316631	-7.90626664253154	9.31834348420106e-15	***
df.mm.trans1:probe12	-0.345859317218691	0.0413820541316631	-8.35771264805483	3.02703566943948e-16	***
df.mm.trans1:probe13	-0.232153694162737	0.0413820541316631	-5.61000895277227	2.83305015713009e-08	***
df.mm.trans1:probe14	-0.380243497676919	0.0413820541316631	-9.18860858059676	3.71636243601382e-19	***
df.mm.trans1:probe15	-0.350402822244224	0.0413820541316631	-8.46750673925867	1.28521391792337e-16	***
df.mm.trans1:probe16	-0.480500987560527	0.0413820541316631	-11.6113372727159	8.2311709294081e-29	***
df.mm.trans1:probe17	-0.336299623392951	0.0413820541316631	-8.12670203182674	1.78234971852881e-15	***
df.mm.trans1:probe18	-0.291939886189649	0.0413820541316631	-7.05474612886058	3.89204607837371e-12	***
df.mm.trans1:probe19	-0.176442895482473	0.0413820541316631	-4.26375391905618	2.26299895751680e-05	***
df.mm.trans1:probe20	-0.323954767713480	0.0413820541316631	-7.82838780024719	1.65677170508877e-14	***
df.mm.trans1:probe21	-0.273604127885064	0.0413820541316631	-6.61166134997919	7.15239049837255e-11	***
df.mm.trans1:probe22	-0.308498014896193	0.0413820541316631	-7.45487437415893	2.45195732776419e-13	***
df.mm.trans2:probe2	-0.0116862639746963	0.0413820541316631	-0.28239932066965	0.7777141230199	   
df.mm.trans2:probe3	-0.0353583807976302	0.0413820541316631	-0.854437546409181	0.393131492918242	   
df.mm.trans2:probe4	0.0413295227905714	0.0413820541316631	0.99873057676343	0.318242680317201	   
df.mm.trans2:probe5	-0.0244157898997287	0.0413820541316631	-0.590009133477188	0.555359668879015	   
df.mm.trans2:probe6	0.0377925780752991	0.0413820541316631	0.913260080204246	0.361395010480458	   
df.mm.trans3:probe2	0.0117888960297461	0.0413820541316631	0.284879430881754	0.775814023016386	   
df.mm.trans3:probe3	-0.338356067837697	0.0413820541316631	-8.1763961441152	1.22133286915870e-15	***
df.mm.trans3:probe4	-0.044350284551033	0.0413820541316631	-1.07172747901605	0.284182213940868	   
df.mm.trans3:probe5	-0.612623204881002	0.0413820541316631	-14.8040791530515	9.17598352250462e-44	***
df.mm.trans3:probe6	-0.172421825237386	0.0413820541316631	-4.16658449792755	3.44661859229157e-05	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.16932380787488	0.153522422966682	27.1577514691761	4.35649358979371e-114	***
df.mm.trans1	-0.0200632262257274	0.134431305649596	-0.149245193511870	0.881399701592976	   
df.mm.trans2	-0.0935489729669743	0.120553914844078	-0.775992825185054	0.437994315233614	   
df.mm.exp2	0.120823281878102	0.158981794879207	0.759981870690933	0.44750079342479	   
df.mm.exp3	-0.141027673554777	0.158981794879207	-0.887068067522628	0.375322506780572	   
df.mm.exp4	0.000816102056290838	0.158981794879207	0.00513330508635223	0.995905578434161	   
df.mm.exp5	0.153217873001979	0.158981794879207	0.963744767873533	0.335480043096621	   
df.mm.exp6	-0.157589607881112	0.158981794879207	-0.991243104286549	0.321881952302963	   
df.mm.exp7	-0.216993230172548	0.158981794879207	-1.36489357374169	0.172689898251224	   
df.mm.exp8	-0.158195173459522	0.158981794879207	-0.99505212895425	0.320027194941633	   
df.mm.trans1:exp2	-0.0792447649136985	0.149138143245289	-0.53135142485557	0.595330328533384	   
df.mm.trans2:exp2	-0.0426740253380994	0.118971081434473	-0.358692421919385	0.719924684431055	   
df.mm.trans1:exp3	0.0566087757097154	0.149138143245289	0.379572753675834	0.704368485077144	   
df.mm.trans2:exp3	0.128728204786679	0.118971081434473	1.08201256334364	0.279589684086563	   
df.mm.trans1:exp4	-0.0626748965004637	0.149138143245289	-0.420247262951246	0.674423462572614	   
df.mm.trans2:exp4	-0.00849869092717432	0.118971081434473	-0.0714349304444649	0.943070378946825	   
df.mm.trans1:exp5	-0.220873445650218	0.149138143245289	-1.48099903112609	0.139020733141566	   
df.mm.trans2:exp5	-0.0559525446351182	0.118971081434473	-0.470303740711443	0.638272778663797	   
df.mm.trans1:exp6	0.142674770362875	0.149138143245289	0.956661838871202	0.339041771490831	   
df.mm.trans2:exp6	0.129184247860519	0.118971081434473	1.0858457896062	0.277891061822386	   
df.mm.trans1:exp7	0.163672109226673	0.149138143245289	1.09745304363539	0.272790524289201	   
df.mm.trans2:exp7	0.222159060302899	0.118971081434473	1.86733664705957	0.0622388303163765	.  
df.mm.trans1:exp8	0.0702423047091514	0.149138143245289	0.470988193768934	0.637784143088593	   
df.mm.trans2:exp8	0.187976462436138	0.118971081434473	1.58001810330414	0.114518250454737	   
df.mm.trans1:probe2	0.167935512911864	0.0913280880342661	1.83881559908333	0.0663317971159664	.  
df.mm.trans1:probe3	0.119154686567529	0.0913280880342661	1.30468828519460	0.192393416160022	   
df.mm.trans1:probe4	0.173007501310143	0.0913280880342661	1.89435150821542	0.0585572708085098	.  
df.mm.trans1:probe5	0.134402128275744	0.0913280880342661	1.47164066574257	0.141531335075456	   
df.mm.trans1:probe6	0.126843941354679	0.0913280880342661	1.38888204149295	0.165274938513598	   
df.mm.trans1:probe7	0.0557807501949333	0.0913280880342661	0.61077321769842	0.541532097313428	   
df.mm.trans1:probe8	0.0527773437236414	0.0913280880342661	0.577887316592453	0.563511148077538	   
df.mm.trans1:probe9	0.0735500958324475	0.0913280880342661	0.805339270924534	0.420875476669709	   
df.mm.trans1:probe10	0.0406673912301288	0.0913280880342661	0.445288980700772	0.656237565713637	   
df.mm.trans1:probe11	-0.0112312641299583	0.0913280880342662	-0.122977107828473	0.902157695658103	   
df.mm.trans1:probe12	0.136926639254868	0.0913280880342661	1.49928288440127	0.134214972751026	   
df.mm.trans1:probe13	0.125567186681569	0.0913280880342661	1.37490217286118	0.169566436575658	   
df.mm.trans1:probe14	-0.00811682884725375	0.0913280880342661	-0.0888754929831481	0.929204243611281	   
df.mm.trans1:probe15	0.0340725336958038	0.0913280880342662	0.373078364270802	0.709194013966079	   
df.mm.trans1:probe16	0.169345571561954	0.0913280880342661	1.85425508413596	0.0640893707881285	.  
df.mm.trans1:probe17	0.138112319856439	0.0913280880342661	1.51226553439529	0.13088147239695	   
df.mm.trans1:probe18	0.0388250289660271	0.0913280880342661	0.425115972552278	0.670872312581633	   
df.mm.trans1:probe19	0.0891092138545273	0.0913280880342662	0.9757043618508	0.32952103809415	   
df.mm.trans1:probe20	0.105684615478332	0.0913280880342661	1.1571972845712	0.247554858829481	   
df.mm.trans1:probe21	0.0645592541384825	0.0913280880342661	0.706893744608559	0.479848901194572	   
df.mm.trans1:probe22	0.165452492033414	0.0913280880342662	1.81162767769031	0.0704378360165881	.  
df.mm.trans2:probe2	0.0732733688637031	0.0913280880342661	0.802309239586961	0.422624572467618	   
df.mm.trans2:probe3	0.0484650923120752	0.0913280880342661	0.530670173385116	0.595802180955485	   
df.mm.trans2:probe4	0.0504977583176088	0.0913280880342661	0.552926918810149	0.580475834713909	   
df.mm.trans2:probe5	0.186603941876216	0.0913280880342661	2.04322619571541	0.0413740005292196	*  
df.mm.trans2:probe6	0.062634503920983	0.0913280880342661	0.685818626767733	0.493036362320468	   
df.mm.trans3:probe2	0.126986706562347	0.0913280880342661	1.39044525398037	0.16480020584702	   
df.mm.trans3:probe3	0.150925609433989	0.0913280880342661	1.65256508356293	0.0988318894801814	.  
df.mm.trans3:probe4	0.0300368931682698	0.0913280880342661	0.328889981327541	0.742329334073423	   
df.mm.trans3:probe5	0.0307425358360671	0.0913280880342661	0.33661643967114	0.736498865094025	   
df.mm.trans3:probe6	0.194366424771252	0.0913280880342661	2.12822176566672	0.0336394183237632	*