fitVsDatCorrelation=0.91154622662734
cont.fitVsDatCorrelation=0.230522756614898

fstatistic=8943.16497723733,69,1083
cont.fstatistic=1584.11476187993,69,1083

residuals=-0.789066811591408,-0.109438766288028,-0.00905247900946601,0.0928062318907798,0.957242702645658
cont.residuals=-0.803520307085475,-0.298277226657105,-0.0804393248694833,0.2045657359627,2.03177054680877

predictedValues:
Include	Exclude	Both
Lung	67.126370974412	77.118765739927	66.006084953491
cerebhem	54.4688930395609	73.04828841053	67.9651375949987
cortex	57.9076719800321	66.2080383726195	65.5584064059424
heart	62.5031436714218	67.3862547928466	66.5252755217735
kidney	67.1474125340602	76.3354964922516	65.5169515384417
liver	58.3261085749643	70.9382822674392	62.151684758047
stomach	59.2410472094207	79.5812944049365	63.7589784752444
testicle	56.0513850987722	69.9663055397382	62.6501207299107


diffExp=-9.99239476551509,-18.5793953709691,-8.30036639258738,-4.88311112142488,-9.18808395819133,-12.612173692475,-20.3402471955157,-13.9149204409660
diffExpScore=0.989879637817832
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,-1,0,0,0,0,-1,0
diffExp1.3Score=0.666666666666667
diffExp1.2=0,-1,0,0,0,-1,-1,-1
diffExp1.2Score=0.8

cont.predictedValues:
Include	Exclude	Both
Lung	71.3500739784392	71.2831046577219	77.772849056446
cerebhem	70.8876467553251	69.2678875180433	72.4599546022487
cortex	72.7834456410193	76.3334626247891	70.1389138502607
heart	73.3002901130085	67.5260354616249	68.0631976216557
kidney	67.0030100193635	69.6490634365996	77.8617104480677
liver	72.1648117243545	65.6182167027583	74.8308824924292
stomach	71.5048038386933	69.67547133927	70.3021238893702
testicle	69.2225585463749	73.2598858746465	77.3413069505226
cont.diffExp=0.0669693207173054,1.61975923728177,-3.55001698376982,5.77425465138364,-2.64605341723616,6.54659502159619,1.82933249942339,-4.03732732827154
cont.diffExpScore=3.94794535200268

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=0.401681915600726
cont.tran.correlation=-0.0753047664570877

tran.covariance=0.00201585326096443
cont.tran.covariance=-0.000120058221211678

tran.mean=66.4596724439333
cont.tran.mean=70.676860514502

weightedLogRatios:
wLogRatio
Lung	-0.593373804971497
cerebhem	-1.21633657913152
cortex	-0.552662633973809
heart	-0.313897753822956
kidney	-0.547747593413706
liver	-0.81513265231363
stomach	-1.24830837170776
testicle	-0.917390052897197

cont.weightedLogRatios:
wLogRatio
Lung	0.00400701638788544
cerebhem	0.0982271050372571
cortex	-0.205316496851596
heart	0.349008535539889
kidney	-0.163606745233912
liver	0.402403051848139
stomach	0.110320656170829
testicle	-0.241805473666904

varWeightedLogRatios=0.112480332128354
cont.varWeightedLogRatios=0.0596236078564399

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.07638718032199	0.0835890519289314	48.7669986230707	1.67693632658785e-275	***
df.mm.trans1	-0.190823949205833	0.071289171514894	-2.67675924899709	0.0075461054571896	** 
df.mm.trans2	0.25611831637941	0.0620962931068187	4.12453471157824	3.99918117356965e-05	***
df.mm.exp2	-0.292421103485189	0.0778518684751023	-3.75612184027039	0.000181755707633825	***
df.mm.exp3	-0.293466373795719	0.0778518684751022	-3.76954824006020	0.000172372022294970	***
df.mm.exp4	-0.214100730509274	0.0778518684751022	-2.75010394359059	0.00605685561784163	** 
df.mm.exp5	-0.00245715740417933	0.0778518684751023	-0.0315619580147283	0.974827196795512	   
df.mm.exp6	-0.163894554307770	0.0778518684751023	-2.10521028612416	0.0355028467428954	*  
df.mm.exp7	-0.0588929645229045	0.0778518684751023	-0.756474644429876	0.449529191222821	   
df.mm.exp8	-0.225459659150500	0.0778518684751022	-2.89600832409827	0.0038553818431495	** 
df.mm.trans1:exp2	0.0834738954666546	0.070780562131361	1.17933360449633	0.238524370803231	   
df.mm.trans2:exp2	0.238195165259954	0.0469803516782246	5.07010179258317	4.67394804095614e-07	***
df.mm.trans1:exp3	0.145739276749451	0.070780562131361	2.05902965956917	0.0397300928257049	*  
df.mm.trans2:exp3	0.140921609170906	0.0469803516782246	2.99958608518087	0.00276527378980112	** 
df.mm.trans1:exp4	0.142740607909175	0.0707805621313609	2.01666394856069	0.0439770355808642	*  
df.mm.trans2:exp4	0.0791951470412095	0.0469803516782246	1.68570783768561	0.0921401431312496	.  
df.mm.trans1:exp5	0.00277057018321525	0.070780562131361	0.0391430938069321	0.968783517598475	   
df.mm.trans2:exp5	-0.00775143554933277	0.0469803516782246	-0.164993135905485	0.868980175470752	   
df.mm.trans1:exp6	0.0233674020718829	0.070780562131361	0.330138690174791	0.741359019077429	   
df.mm.trans2:exp6	0.0803581437167457	0.0469803516782246	1.71046279659911	0.0874667227252248	.  
df.mm.trans1:exp7	-0.0660693456498728	0.070780562131361	-0.933439120294854	0.350801348292703	   
df.mm.trans2:exp7	0.0903253890801942	0.0469803516782246	1.92262053930218	0.0547897582562619	.  
df.mm.trans1:exp8	0.0451515433855749	0.070780562131361	0.637908799053878	0.523667898555984	   
df.mm.trans2:exp8	0.128126790117161	0.0469803516782246	2.72724203928316	0.00648982007848317	** 
df.mm.trans1:probe2	0.782614166411036	0.0537616962710555	14.5570958636658	5.6143150910665e-44	***
df.mm.trans1:probe3	0.255204410560152	0.0537616962710555	4.74695607209757	2.34168840311269e-06	***
df.mm.trans1:probe4	0.62000881989854	0.0537616962710555	11.5325382735802	4.18556221245183e-29	***
df.mm.trans1:probe5	0.783130471298912	0.0537616962710555	14.5666994462103	4.9919467019672e-44	***
df.mm.trans1:probe6	0.521827918862889	0.0537616962710555	9.7063142545194	2.05024508661571e-21	***
df.mm.trans1:probe7	0.592974804569822	0.0537616962710555	11.0296892713385	6.97028611131437e-27	***
df.mm.trans1:probe8	0.202843074467950	0.0537616962710555	3.77300361665036	0.000170031963738317	***
df.mm.trans1:probe9	0.696575506901408	0.0537616962710555	12.9567248657746	8.46729841625565e-36	***
df.mm.trans1:probe10	0.640374715795803	0.0537616962710555	11.9113562296689	7.91015505454809e-31	***
df.mm.trans1:probe11	1.39189954634367	0.0537616962710555	25.8901716814514	1.97560905788685e-115	***
df.mm.trans1:probe12	1.14848786398517	0.0537616962710555	21.3625674717317	9.00115848711406e-85	***
df.mm.trans1:probe13	0.583011037251336	0.0537616962710555	10.8443571853074	4.39038116017778e-26	***
df.mm.trans1:probe14	1.63542897867719	0.0537616962710555	30.4199661117777	2.05705505240865e-147	***
df.mm.trans1:probe15	1.38214823819375	0.0537616962710555	25.7087914641912	3.59975784939696e-114	***
df.mm.trans1:probe16	1.56791518550847	0.0537616962710555	29.1641688090227	1.78794712465762e-138	***
df.mm.trans1:probe17	0.0520869708240201	0.0537616962710555	0.96884909585829	0.332836800261752	   
df.mm.trans1:probe18	0.127482374261009	0.0537616962710555	2.37124910676681	0.0179022580815498	*  
df.mm.trans1:probe19	0.118741226674411	0.0537616962710555	2.20865848569475	0.0274072268878549	*  
df.mm.trans1:probe20	0.234621143784676	0.0537616962710555	4.3640948864739	1.39855679221802e-05	***
df.mm.trans1:probe21	0.0232049405234577	0.0537616962710555	0.431625899719814	0.666099240773142	   
df.mm.trans1:probe22	0.121995924778754	0.0537616962710555	2.26919783489857	0.0234523304789586	*  
df.mm.trans2:probe2	-0.0429473614115327	0.0537616962710555	-0.798846844322042	0.424554375074440	   
df.mm.trans2:probe3	-0.180946137864318	0.0537616962710555	-3.36570737932123	0.00079026669271859	***
df.mm.trans2:probe4	0.192599260792638	0.0537616962710555	3.58246249935254	0.00035549462708383	***
df.mm.trans2:probe5	-0.00496102263421674	0.0537616962710555	-0.0922780153588212	0.92649422107552	   
df.mm.trans2:probe6	0.370125136759558	0.0537616962710555	6.88455094298854	9.8010498572276e-12	***
df.mm.trans3:probe2	0.524822517979129	0.0537616962710555	9.76201560555458	1.23890564001696e-21	***
df.mm.trans3:probe3	0.0420276038090364	0.0537616962710555	0.781738797770476	0.434538961330811	   
df.mm.trans3:probe4	-0.136124032333681	0.0537616962710555	-2.53198916283019	0.0114821976230781	*  
df.mm.trans3:probe5	-0.0323885071178464	0.0537616962710555	-0.602445781371	0.547003558357967	   
df.mm.trans3:probe6	0.00466643783112032	0.0537616962710555	0.0867985602164243	0.930847677001942	   
df.mm.trans3:probe7	-0.0812959260482447	0.0537616962710555	-1.51215329290146	0.130786604322649	   
df.mm.trans3:probe8	0.476538785692238	0.0537616962710555	8.86390904203667	3.11550537696115e-18	***
df.mm.trans3:probe9	-0.176826228358023	0.0537616962710555	-3.28907457581885	0.00103752465224877	** 
df.mm.trans3:probe10	-0.054317061898495	0.0537616962710555	-1.01033013587666	0.31256281739996	   
df.mm.trans3:probe11	-0.161194425285469	0.0537616962710555	-2.99831360366235	0.00277675011883393	** 
df.mm.trans3:probe12	0.453570972059803	0.0537616962710555	8.43669384561437	1.03331232283191e-16	***
df.mm.trans3:probe13	0.043259455827494	0.0537616962710555	0.804651988832135	0.421197134376185	   
df.mm.trans3:probe14	-0.296986555322546	0.0537616962710555	-5.52412918344691	4.14558239976452e-08	***
df.mm.trans3:probe15	-0.00233876131839805	0.0537616962710555	-0.0435023721462674	0.965309092697023	   
df.mm.trans3:probe16	-0.0429103052377764	0.0537616962710555	-0.798157577123896	0.424954030778621	   
df.mm.trans3:probe17	-0.133244897673493	0.0537616962710555	-2.47843552036936	0.0133474766865594	*  
df.mm.trans3:probe18	0.330019753070884	0.0537616962710555	6.13856659966591	1.16671209749990e-09	***
df.mm.trans3:probe19	0.371696843805426	0.0537616962710555	6.91378564268893	8.0477162614186e-12	***
df.mm.trans3:probe20	-0.167667938559968	0.0537616962710555	-3.11872485783597	0.00186430514430450	** 

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.04874895709043	0.197806942414561	20.4681843198664	5.67089294522073e-79	***
df.mm.trans1	0.178228061437002	0.168700239076975	1.05647782369579	0.290985573037306	   
df.mm.trans2	0.220499716607069	0.146946012561327	1.50054916607584	0.133763672846378	   
df.mm.exp2	0.0355782075178705	0.18423034726382	0.193118061417548	0.846902717784694	   
df.mm.exp3	0.191656851470375	0.18423034726382	1.04031097111227	0.298427635383013	   
df.mm.exp4	0.106175818108050	0.18423034726382	0.576321000774127	0.564517980812065	   
df.mm.exp5	-0.0871928450780842	0.18423034726382	-0.473281662728579	0.636107621390438	   
df.mm.exp6	-0.0328900937787234	0.18423034726382	-0.178527013964884	0.858342497318441	   
df.mm.exp7	0.0803456296575747	0.18423034726382	0.436115063836464	0.662840166711127	   
df.mm.exp8	0.00264648243558521	0.18423034726382	0.0143650732623080	0.988541369795842	   
df.mm.trans1:exp2	-0.0420804046841001	0.167496654819008	-0.251231314019799	0.801682935033007	   
df.mm.trans2:exp2	-0.064256130313586	0.111175321463527	-0.577971167231268	0.563403735926674	   
df.mm.trans1:exp3	-0.17176669792783	0.167496654819008	-1.02549330381216	0.305359476777516	   
df.mm.trans2:exp3	-0.123204780829357	0.111175321463527	-1.10820260474602	0.268020374584555	   
df.mm.trans1:exp4	-0.0792096320987357	0.167496654819008	-0.472902770412504	0.636377839638641	   
df.mm.trans2:exp4	-0.160321922072370	0.111175321463527	-1.44206393974733	0.149573447500969	   
df.mm.trans1:exp5	0.0243320083610851	0.167496654819008	0.145268622751765	0.884525818161716	   
df.mm.trans2:exp5	0.0640027604845831	0.111175321463527	0.575692155795389	0.564942876215055	   
df.mm.trans1:exp6	0.0442442678732163	0.167496654819008	0.264150158228685	0.791714471622416	   
df.mm.trans2:exp6	-0.0499158931995721	0.111175321463527	-0.448983574254363	0.653533332202026	   
df.mm.trans1:exp7	-0.078179376420355	0.167496654819008	-0.466751867401969	0.640771294808762	   
df.mm.trans2:exp7	-0.103156629504386	0.111175321463527	-0.927873453806275	0.353679957262467	   
df.mm.trans1:exp8	-0.032918063141765	0.167496654819008	-0.196529675039393	0.844232480439389	   
df.mm.trans2:exp8	0.0247073790830300	0.111175321463527	0.222237981935009	0.824170529535707	   
df.mm.trans1:probe2	0.0446607652004643	0.127222842142525	0.351043605443367	0.725623910944554	   
df.mm.trans1:probe3	0.00673455101363093	0.127222842142525	0.0529350775396635	0.957793398018646	   
df.mm.trans1:probe4	0.107964377728351	0.127222842142525	0.84862416143322	0.396277986343395	   
df.mm.trans1:probe5	0.126550543959197	0.127222842142525	0.994715585880602	0.320096803333156	   
df.mm.trans1:probe6	0.286962076219855	0.127222842142525	2.25558611478257	0.0242954917601707	*  
df.mm.trans1:probe7	-0.0289122566477315	0.127222842142525	-0.227256805152503	0.820266950354039	   
df.mm.trans1:probe8	-0.00478369324080353	0.127222842142525	-0.0376008990228693	0.970012820682173	   
df.mm.trans1:probe9	0.108844270271526	0.127222842142525	0.855540313661521	0.392441329723169	   
df.mm.trans1:probe10	0.0894680552761807	0.127222842142525	0.703238929184992	0.482058009023161	   
df.mm.trans1:probe11	-0.0498844314112156	0.127222842142525	-0.392102790435472	0.695059412826103	   
df.mm.trans1:probe12	0.0427369557581312	0.127222842142525	0.335922032855184	0.7369947294732	   
df.mm.trans1:probe13	0.032616888606223	0.127222842142525	0.256376041101825	0.79770917736526	   
df.mm.trans1:probe14	-0.0757340354079124	0.127222842142525	-0.595286460611131	0.551776369018672	   
df.mm.trans1:probe15	0.0555493849184313	0.127222842142525	0.436630592297258	0.66246630717654	   
df.mm.trans1:probe16	0.173195619066059	0.127222842142525	1.36135631109413	0.173684275375827	   
df.mm.trans1:probe17	0.181960283025347	0.127222842142525	1.43024853053904	0.152934135148629	   
df.mm.trans1:probe18	0.104601913307523	0.127222842142525	0.822194438875522	0.411147219935051	   
df.mm.trans1:probe19	0.100653732504294	0.127222842142525	0.791160854522758	0.429023358558004	   
df.mm.trans1:probe20	0.217338859551726	0.127222842142525	1.7083320565048	0.0878612953696367	.  
df.mm.trans1:probe21	0.00939254654360003	0.127222842142525	0.0738275170199212	0.94116126451546	   
df.mm.trans1:probe22	0.176180807566056	0.127222842142525	1.38482056051448	0.166392550712485	   
df.mm.trans2:probe2	0.0521456299372222	0.127222842142525	0.409876316697944	0.681977739774499	   
df.mm.trans2:probe3	-0.0546405182574312	0.127222842142525	-0.42948669702111	0.66765450247922	   
df.mm.trans2:probe4	0.0628037074401845	0.127222842142525	0.493651190167775	0.621652635379037	   
df.mm.trans2:probe5	-0.0216155975752148	0.127222842142525	-0.169903432521963	0.865117813317146	   
df.mm.trans2:probe6	-0.106015949206377	0.127222842142525	-0.833309077371575	0.404854176390054	   
df.mm.trans3:probe2	-0.0804103216262631	0.127222842142525	-0.632043116409717	0.527492101222095	   
df.mm.trans3:probe3	-0.240469397738020	0.127222842142525	-1.89014326113409	0.0590056242013802	.  
df.mm.trans3:probe4	-0.206647273362898	0.127222842142525	-1.62429379726791	0.104604102960245	   
df.mm.trans3:probe5	-0.108047196099598	0.127222842142525	-0.849275132358344	0.395915903783147	   
df.mm.trans3:probe6	0.00774109212264155	0.127222842142525	0.0608467158277235	0.951492511740512	   
df.mm.trans3:probe7	-0.237725926284144	0.127222842142525	-1.86857896177028	0.0619512233749155	.  
df.mm.trans3:probe8	0.0285314455073226	0.127222842142525	0.224263544398414	0.822594541849754	   
df.mm.trans3:probe9	-0.128299167621121	0.127222842142525	-1.00846015904431	0.313458861080056	   
df.mm.trans3:probe10	-0.129904398782905	0.127222842142525	-1.02107763507889	0.307445689452052	   
df.mm.trans3:probe11	-0.212822038497172	0.127222842142525	-1.67282883256728	0.0946498100180122	.  
df.mm.trans3:probe12	-0.0739437029483733	0.127222842142525	-0.58121404696757	0.561217138955308	   
df.mm.trans3:probe13	-0.198123039884588	0.127222842142525	-1.55729141518970	0.119693472129790	   
df.mm.trans3:probe14	0.100131209676951	0.127222842142525	0.787053708207337	0.43142262471658	   
df.mm.trans3:probe15	0.0726392530257253	0.127222842142525	0.570960778759753	0.568144682305605	   
df.mm.trans3:probe16	-0.152808094965426	0.127222842142525	-1.20110581081217	0.229972773665687	   
df.mm.trans3:probe17	-0.168730498288025	0.127222842142525	-1.32625946289582	0.185033389140580	   
df.mm.trans3:probe18	-0.0134890751215146	0.127222842142525	-0.106027148068293	0.9155804475934	   
df.mm.trans3:probe19	-0.0889031819813296	0.127222842142525	-0.698798898720822	0.484827749899091	   
df.mm.trans3:probe20	-0.0424066613212098	0.127222842142525	-0.333325844691494	0.738952852640399