fitVsDatCorrelation=0.80294658751684
cont.fitVsDatCorrelation=0.252408700448204

fstatistic=11570.4994374756,62,922
cont.fstatistic=4381.21756603359,62,922

residuals=-0.576168548498508,-0.084064672175723,-0.000686930495639511,0.0724245505577004,1.00008844046924
cont.residuals=-0.541247699792697,-0.185156035841871,-0.0124975934401514,0.149295253224063,1.20384887474328

predictedValues:
Include	Exclude	Both
Lung	66.2139752723648	46.4757078113499	67.1154356388273
cerebhem	75.4217091970271	51.9848350267495	61.6869940253067
cortex	63.3134748849501	48.5907530778263	60.6921429622723
heart	61.5891528580038	50.0251937536843	66.4481266754677
kidney	65.3126851373913	46.6745406777672	60.3609073762447
liver	63.8998090216419	53.7373597840066	61.7846481165733
stomach	64.4391916960133	49.945313237404	69.2395761823131
testicle	61.4278516371184	49.8766704555237	66.9410187713849


diffExp=19.7382674610149,23.4368741702776,14.7227218071238,11.5639591043195,18.6381444596241,10.1624492376352,14.4938784586092,11.5511811815947
diffExpScore=0.992019630169903
diffExp1.5=0,0,0,0,0,0,0,0
diffExp1.5Score=0
diffExp1.4=1,1,0,0,0,0,0,0
diffExp1.4Score=0.666666666666667
diffExp1.3=1,1,1,0,1,0,0,0
diffExp1.3Score=0.8
diffExp1.2=1,1,1,1,1,0,1,1
diffExp1.2Score=0.875

cont.predictedValues:
Include	Exclude	Both
Lung	59.6276176705992	68.6397557464305	62.400386045566
cerebhem	63.4886120327965	60.7328879183219	63.2545143104026
cortex	60.4419812518626	59.325100327913	60.7431915799489
heart	65.3136937325956	60.8095698232944	62.8257629194082
kidney	62.5442672303414	64.8521112377076	60.4215593387239
liver	68.0455250985518	66.1500920755497	57.7542954640729
stomach	62.955576790269	60.6005781144911	59.7084225635129
testicle	62.4960114463124	61.0736362068868	64.1875558526641
cont.diffExp=-9.01213807583128,2.75572411447465,1.11688092394965,4.50412390930119,-2.30784400736616,1.89543302300207,2.35499867577785,1.42237523942561
cont.diffExpScore=6.80229306533503

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=0.189739406613747
cont.tran.correlation=0.00872384931918112

tran.covariance=0.000546277517167429
cont.tran.covariance=1.1526666777534e-05

tran.mean=57.4330139705514
cont.tran.mean=62.9435635439952

weightedLogRatios:
wLogRatio
Lung	1.42147890026217
cerebhem	1.53956483620412
cortex	1.06283236332088
heart	0.835268671241606
kidney	1.34771027345145
liver	0.705079550752042
stomach	1.02893841473296
testicle	0.836095485177509

cont.weightedLogRatios:
wLogRatio
Lung	-0.585320891539064
cerebhem	0.183210645117308
cortex	0.0763283074911966
heart	0.296070078196462
kidney	-0.150519420043565
liver	0.118823841376769
stomach	0.157203010072541
testicle	0.0949351825581381

varWeightedLogRatios=0.0942830877940556
cont.varWeightedLogRatios=0.0765147981583801

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.86402465380696	0.0698752218736443	55.2989250008293	4.70815086020378e-295	***
df.mm.trans1	0.344520271342279	0.0598375650968527	5.7575917533516	1.16151505452761e-08	***
df.mm.trans2	-0.0332626989508344	0.0526001561284588	-0.632368825476507	0.527302681406147	   
df.mm.exp2	0.326566826892616	0.0668134914135745	4.88773779042877	1.20265838092366e-06	***
df.mm.exp3	0.100309923517379	0.0668134914135745	1.50134233962512	0.133609413186822	   
df.mm.exp4	0.0111836713181614	0.0668134914135745	0.167386422735113	0.867102712612475	   
df.mm.exp5	0.0966362047083041	0.0668134914135745	1.44635765417687	0.148416765061145	   
df.mm.exp6	0.192362669887806	0.0668134914135745	2.87909920313973	0.00408036354295236	** 
df.mm.exp7	0.0136708229578700	0.0668134914135745	0.204611713422486	0.837920661550926	   
df.mm.exp8	-0.00180245939549569	0.0668134914135745	-0.0269774765150124	0.978483536431888	   
df.mm.trans1:exp2	-0.196363220431207	0.0609920939881541	-3.21948645457794	0.00132909072829041	** 
df.mm.trans2:exp2	-0.214544548374786	0.0431279232577910	-4.97460884198797	7.7974956486319e-07	***
df.mm.trans1:exp3	-0.145103291137989	0.0609920939881541	-2.37905081872039	0.0175599756758504	*  
df.mm.trans2:exp3	-0.0558064400312611	0.043127923257791	-1.29397466457372	0.195998203805852	   
df.mm.trans1:exp4	-0.0835894537481734	0.0609920939881541	-1.37049653950901	0.170865520058338	   
df.mm.trans2:exp4	0.0624133188521194	0.0431279232577910	1.44716726745808	0.148189983841105	   
df.mm.trans1:exp5	-0.110341475410063	0.0609920939881541	-1.80911111908199	0.0707593440782393	.  
df.mm.trans2:exp5	-0.092367119620049	0.0431279232577911	-2.14170107537842	0.0324790344414450	*  
df.mm.trans1:exp6	-0.227937844711839	0.0609920939881541	-3.73717034139062	0.000197553550896939	***
df.mm.trans2:exp6	-0.0471839607743958	0.0431279232577910	-1.09404666884515	0.274220248030378	   
df.mm.trans1:exp7	-0.0408403557372629	0.0609920939881541	-0.669600813265977	0.50327994235937	   
df.mm.trans2:exp7	0.0583280852243358	0.0431279232577910	1.35244363322778	0.176565037874237	   
df.mm.trans1:exp8	-0.0732257460724992	0.0609920939881541	-1.20057766973407	0.230223425981311	   
df.mm.trans2:exp8	0.072426063458099	0.0431279232577910	1.67933111513815	0.093426334794931	.  
df.mm.trans1:probe2	0.0837857215327196	0.0441929706260669	1.89590607614188	0.0582849235835609	.  
df.mm.trans1:probe3	-0.338982355171352	0.0441929706260669	-7.6705039369181	4.34751170279738e-14	***
df.mm.trans1:probe4	-0.357369908951140	0.0441929706260669	-8.0865781115956	1.92150287598765e-15	***
df.mm.trans1:probe5	0.115795946924159	0.0441929706260669	2.62023451430661	0.00893143279131403	** 
df.mm.trans1:probe6	0.157884099855221	0.0441929706260669	3.57260662993527	0.000371745238578336	***
df.mm.trans1:probe7	-0.315246943715691	0.0441929706260669	-7.13341826199267	1.97933606668662e-12	***
df.mm.trans1:probe8	-0.290774528664413	0.0441929706260669	-6.57965564534605	7.89605209121406e-11	***
df.mm.trans1:probe9	-0.309514729126899	0.0441929706260669	-7.00370952081539	4.8028959571547e-12	***
df.mm.trans1:probe10	0.0971550926010047	0.0441929706260669	2.19842864656169	0.0281664101841818	*  
df.mm.trans1:probe11	0.091261448143863	0.0441929706260669	2.06506706498778	0.0391957274383555	*  
df.mm.trans1:probe12	0.126475644557734	0.0441929706260669	2.86189506534628	0.00430644731835444	** 
df.mm.trans1:probe13	-0.0792612095247398	0.0441929706260669	-1.79352526888039	0.0732165909193586	.  
df.mm.trans1:probe14	0.0959147922803033	0.0441929706260669	2.17036308991025	0.0302339972709030	*  
df.mm.trans1:probe15	0.0565807103923395	0.0441929706260669	1.28031018487284	0.200758073383433	   
df.mm.trans1:probe16	0.114338308711818	0.0441929706260669	2.58725102865967	0.00982655408147943	** 
df.mm.trans1:probe17	0.0132554854458438	0.0441929706260669	0.299945562791046	0.764286288528119	   
df.mm.trans1:probe18	0.0151137198200631	0.0441929706260669	0.341993751629550	0.73243353136406	   
df.mm.trans1:probe19	0.0569690877319616	0.0441929706260669	1.28909840015052	0.197687199469657	   
df.mm.trans1:probe20	0.171152158192787	0.0441929706260669	3.87283669253577	0.000115173136954039	***
df.mm.trans1:probe21	-0.0524007588865604	0.0441929706260669	-1.18572610404362	0.236035971079658	   
df.mm.trans2:probe2	0.0547588919100237	0.0441929706260669	1.23908601603995	0.215628975577721	   
df.mm.trans2:probe3	0.00133624642427739	0.0441929706260669	0.0302366282543862	0.97588488043192	   
df.mm.trans2:probe4	0.0183557543324800	0.0441929706260669	0.41535461573278	0.677978965382463	   
df.mm.trans2:probe5	0.00308728349381801	0.0441929706260669	0.0698591529395175	0.944320913332732	   
df.mm.trans2:probe6	0.0858179952995266	0.0441929706260669	1.94189243410823	0.0524544150818406	.  
df.mm.trans3:probe2	0.368349464842913	0.0441929706260669	8.33502386521274	2.7938382408518e-16	***
df.mm.trans3:probe3	-0.253397143175862	0.0441929706260669	-5.7338789311076	1.32980039385480e-08	***
df.mm.trans3:probe4	0.179710874023535	0.0441929706260669	4.06650359723802	5.18009638599862e-05	***
df.mm.trans3:probe5	-0.236658084244648	0.0441929706260669	-5.35510695234995	1.08002810071872e-07	***
df.mm.trans3:probe6	0.151504316140651	0.0441929706260669	3.42824467317631	0.000634389169437671	***
df.mm.trans3:probe7	-0.0953080616354584	0.0441929706260669	-2.15663396882494	0.0312921357280906	*  
df.mm.trans3:probe8	-0.19573472274332	0.0441929706260669	-4.42909177569221	1.05975194665363e-05	***
df.mm.trans3:probe9	-0.114017737551369	0.0441929706260669	-2.57999713384546	0.0100338409591508	*  
df.mm.trans3:probe10	0.162556290366057	0.0441929706260669	3.67832911124047	0.000248356938271729	***
df.mm.trans3:probe11	0.00169335691134603	0.0441929706260669	0.0383173361590501	0.969442963439242	   
df.mm.trans3:probe12	0.153342092595698	0.0441929706260669	3.46982993954362	0.000544918462603418	***
df.mm.trans3:probe13	0.371868809436698	0.0441929706260669	8.41465971100287	1.49039972157295e-16	***
df.mm.trans3:probe14	-0.0580638258518656	0.0441929706260669	-1.31387017051117	0.189216707330709	   

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.21506266557427	0.113434443969754	37.1585782771427	1.88160552898648e-185	***
df.mm.trans1	-0.0995488008658962	0.0971394543482034	-1.02480296532299	0.305724959955587	   
df.mm.trans2	0.00219327666914142	0.0853903472956915	0.0256852997862451	0.979513907054053	   
df.mm.exp2	-0.0732398688171312	0.108464074173272	-0.675245415363343	0.499689159192458	   
df.mm.exp3	-0.105357877556511	0.108464074173272	-0.971361977314276	0.331622809870638	   
df.mm.exp4	-0.0368356178804196	0.108464074173272	-0.339611232209244	0.734226662451199	   
df.mm.exp5	0.0232187785203649	0.108464074173272	0.214068839819467	0.83054070622721	   
df.mm.exp6	0.172486238233955	0.108464074173272	1.59026147181608	0.112118681946674	   
df.mm.exp7	-0.0261585968773157	0.108464074173272	-0.241172914411524	0.80947472406373	   
df.mm.exp8	-0.098045618098839	0.108464074173272	-0.903945558436344	0.366260388577213	   
df.mm.trans1:exp2	0.135981570089485	0.0990137001726906	1.37336115964072	0.169973958882336	   
df.mm.trans2:exp2	-0.0491466656990411	0.0700132588224812	-0.701962264371276	0.48287985513377	   
df.mm.trans1:exp3	0.118922944420913	0.0990137001726906	1.20107565128360	0.230030310175616	   
df.mm.trans2:exp3	-0.0404815256663133	0.0700132588224812	-0.578197992025401	0.563271646155827	   
df.mm.trans1:exp4	0.127918486640904	0.0990137001726906	1.29192714157536	0.196706111526175	   
df.mm.trans2:exp4	-0.0842891038244671	0.0700132588224812	-1.20390202144686	0.228936437831141	   
df.mm.trans1:exp5	0.0245369527215032	0.0990137001726906	0.247813713442767	0.80433372143939	   
df.mm.trans2:exp5	-0.0799812092797877	0.0700132588224812	-1.14237232525600	0.253595857671687	   
df.mm.trans1:exp6	-0.0404281207978172	0.0990137001726906	-0.408308352554305	0.683142113299934	   
df.mm.trans2:exp6	-0.209431852453954	0.0700132588224812	-2.99131701589507	0.00285168689287995	** 
df.mm.trans1:exp7	0.0804690937572482	0.0990137001726906	0.812706661976084	0.416596055822027	   
df.mm.trans2:exp7	-0.0984088669504578	0.0700132588224812	-1.40557472406725	0.160187318844760	   
df.mm.trans1:exp8	0.145029505468709	0.0990137001726906	1.46474180053630	0.143332263312903	   
df.mm.trans2:exp8	-0.0187459915167634	0.0700132588224812	-0.267749163973269	0.788952288413375	   
df.mm.trans1:probe2	-0.0294850362646724	0.0717422416118351	-0.410985712214049	0.681178513050488	   
df.mm.trans1:probe3	-0.0443747165838288	0.0717422416118351	-0.618529831057138	0.536378911242362	   
df.mm.trans1:probe4	-0.0760003970287046	0.0717422416118351	-1.05935353177154	0.289716245353175	   
df.mm.trans1:probe5	-0.0408876466179679	0.0717422416118351	-0.56992429702981	0.568867883402014	   
df.mm.trans1:probe6	-0.0311091800962594	0.0717422416118351	-0.433624311107773	0.664662713531577	   
df.mm.trans1:probe7	-0.0880609047119194	0.0717422416118351	-1.22746240894419	0.219962141166449	   
df.mm.trans1:probe8	-0.0327955357533577	0.0717422416118351	-0.457130067538168	0.6476852451797	   
df.mm.trans1:probe9	-0.173333070828567	0.0717422416118351	-2.41605317779717	0.0158831967373681	*  
df.mm.trans1:probe10	-0.0820885274787912	0.0717422416118351	-1.14421470021714	0.252831571138782	   
df.mm.trans1:probe11	-0.030296486503754	0.0717422416118351	-0.422296346240122	0.672907180669394	   
df.mm.trans1:probe12	0.0276447941227668	0.0717422416118351	0.385334964473794	0.700078139671293	   
df.mm.trans1:probe13	-0.0934804387014731	0.0717422416118351	-1.30300415210405	0.192898661511519	   
df.mm.trans1:probe14	-0.100894812515565	0.0717422416118351	-1.40635154755076	0.159956698768593	   
df.mm.trans1:probe15	0.0247259409005612	0.0717422416118351	0.344649683994293	0.730436347832653	   
df.mm.trans1:probe16	-0.092435816755989	0.0717422416118351	-1.28844338675835	0.197914887224191	   
df.mm.trans1:probe17	0.0461228633426444	0.0717422416118351	0.642896880643825	0.520450857233649	   
df.mm.trans1:probe18	-0.0644159366374521	0.0717422416118351	-0.897880177566484	0.369483835021511	   
df.mm.trans1:probe19	-0.0283628721260742	0.0717422416118351	-0.395344102565584	0.692680406383233	   
df.mm.trans1:probe20	-0.0287585881381517	0.0717422416118351	-0.400859904737176	0.688616145940413	   
df.mm.trans1:probe21	-0.0205391297179583	0.0717422416118351	-0.286290604482171	0.77471986797533	   
df.mm.trans2:probe2	0.0822336813636641	0.0717422416118351	1.14623796965522	0.251994096384341	   
df.mm.trans2:probe3	0.0354897809754766	0.0717422416118351	0.494684584397235	0.62094063640331	   
df.mm.trans2:probe4	0.0646815146265859	0.0717422416118351	0.901582013237729	0.367514399490064	   
df.mm.trans2:probe5	0.0338767449591811	0.0717422416118351	0.47220081500203	0.636895196784658	   
df.mm.trans2:probe6	0.0160373672193966	0.0717422416118351	0.223541484892088	0.823163618650447	   
df.mm.trans3:probe2	-0.0619821944583476	0.0717422416118351	-0.86395675777327	0.387836344744955	   
df.mm.trans3:probe3	0.0122410497918580	0.0717422416118351	0.170625415610635	0.86455574687281	   
df.mm.trans3:probe4	0.0850055079866454	0.0717422416118351	1.18487387732560	0.236372638451894	   
df.mm.trans3:probe5	-0.0458147834038958	0.0717422416118351	-0.63860261924598	0.52324007055246	   
df.mm.trans3:probe6	-0.0196113388304225	0.0717422416118351	-0.273358322653627	0.784639006338177	   
df.mm.trans3:probe7	0.070025331392874	0.0717422416118351	0.976068349965274	0.329286613807251	   
df.mm.trans3:probe8	-0.0112970800663712	0.0717422416118351	-0.157467620366459	0.87491080202784	   
df.mm.trans3:probe9	-0.0320903701026695	0.0717422416118351	-0.447300912010751	0.654762869785804	   
df.mm.trans3:probe10	0.0243172162071053	0.0717422416118351	0.338952556552035	0.734722651471556	   
df.mm.trans3:probe11	0.0585689480575826	0.0717422416118351	0.816380234875748	0.414493568350161	   
df.mm.trans3:probe12	0.0101473383169043	0.0717422416118351	0.141441612206752	0.88755198051003	   
df.mm.trans3:probe13	0.117369008527653	0.0717422416118351	1.63598189700684	0.102184667799362	   
df.mm.trans3:probe14	0.0152218550705705	0.0717422416118351	0.21217423276135	0.832018003643459