fitVsDatCorrelation=0.818382758693905
cont.fitVsDatCorrelation=0.282629096182784

fstatistic=8565.78867419877,52,692
cont.fstatistic=3065.90068879566,52,692

residuals=-0.484499533368278,-0.104769441421073,-0.00391156284091769,0.104223057951601,0.7497834249153
cont.residuals=-0.627413893258611,-0.200020414693681,-0.0192463445735925,0.170734703902567,1.05797672268894

predictedValues:
Include	Exclude	Both
Lung	66.5414353941569	55.8801994894513	66.9560643367104
cerebhem	68.8913125565408	58.8462652636357	59.6220869028327
cortex	76.3572663641222	49.8850843205046	80.0789135752527
heart	64.5385966173957	51.5044071419504	69.9401135277388
kidney	65.0154490532175	50.5113074158561	64.4350643313957
liver	65.2808816115285	53.0748937761054	59.3076687638646
stomach	78.8947334840337	50.5934545383278	70.3915155913442
testicle	102.089787816288	55.8399292254309	76.9564855045392


diffExp=10.6612359047056,10.0450472929051,26.4721820436176,13.0341894754453,14.5041416373615,12.2059878354231,28.3012789457059,46.2498585908569
diffExpScore=0.993845166108034
diffExp1.5=0,0,1,0,0,0,1,1
diffExp1.5Score=0.75
diffExp1.4=0,0,1,0,0,0,1,1
diffExp1.4Score=0.75
diffExp1.3=0,0,1,0,0,0,1,1
diffExp1.3Score=0.75
diffExp1.2=0,0,1,1,1,1,1,1
diffExp1.2Score=0.857142857142857

cont.predictedValues:
Include	Exclude	Both
Lung	66.9530107144555	62.4006433566885	63.9194893189518
cerebhem	64.997873976498	72.1049742474908	75.0341944769275
cortex	65.9059343016716	72.9262552390199	65.6775576463191
heart	69.0859866626799	68.8574674507994	69.253179314594
kidney	70.1625603257145	66.3379285448003	71.775584878117
liver	71.9923291339479	73.644596546671	65.4083401594913
stomach	62.7443869448994	59.9939587046217	66.0964311161023
testicle	69.4602658367514	61.3137038394588	71.9570780202493
cont.diffExp=4.55236735776692,-7.10710027099279,-7.02032093734829,0.228519211880410,3.8246317809142,-1.65226741272306,2.75042824027771,8.1465619972926
cont.diffExpScore=7.47057847964124

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=0.159336122081176
cont.tran.correlation=0.283243749415367

tran.covariance=0.00132844234822467
cont.tran.covariance=0.00109332264705413

tran.mean=63.3590627542841
cont.tran.mean=67.4301172391355

weightedLogRatios:
wLogRatio
Lung	0.717756931285843
cerebhem	0.65463483980187
cortex	1.75498391570047
heart	0.91467143190192
kidney	1.02193109080091
liver	0.84354721099573
stomach	1.84202166767175
testicle	2.60904694954826

cont.weightedLogRatios:
wLogRatio
Lung	0.293546774609415
cerebhem	-0.438550340396627
cortex	-0.429056264564988
heart	0.0140272201180199
kidney	0.236700088496799
liver	-0.0972977140421774
stomach	0.184530173213336
testicle	0.521259075208826

varWeightedLogRatios=0.486262887783991
cont.varWeightedLogRatios=0.117696268366687

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.17417130199822	0.0842063317807399	49.5707533355937	6.79225985701084e-230	***
df.mm.trans1	-0.063420384958429	0.0734093781277005	-0.863927560428383	0.387927032618415	   
df.mm.trans2	-0.105526571397534	0.0663041319483197	-1.59155347180755	0.111941752835274	   
df.mm.exp2	0.202434062568959	0.0876343561327888	2.30998516451948	0.0211823535141851	*  
df.mm.exp3	-0.154865621091406	0.0876343561327888	-1.76717931100839	0.0776389140276863	.  
df.mm.exp4	-0.155706831747669	0.0876343561327888	-1.77677840767989	0.0760439399506952	.  
df.mm.exp5	-0.0858341207733545	0.0876343561327888	-0.979457424703315	0.327696307160029	   
df.mm.exp6	0.0506663042339859	0.0876343561327888	0.578155719626824	0.563347083628909	   
df.mm.exp7	0.0208656501352306	0.0876343561327888	0.238098972321012	0.81187481526305	   
df.mm.exp8	0.288103452506745	0.0876343561327888	3.28756283745833	0.00106167210484200	** 
df.mm.trans1:exp2	-0.167728822469178	0.081440392904415	-2.05952865018761	0.0398171633911169	*  
df.mm.trans2:exp2	-0.150715797180716	0.0660568813720094	-2.28160630732681	0.0228154635699224	*  
df.mm.trans1:exp3	0.292463977734105	0.081440392904415	3.59114153682147	0.000352526932603924	***
df.mm.trans2:exp3	0.0413775634092906	0.0660568813720094	0.626392929091922	0.531263814330792	   
df.mm.trans1:exp4	0.125145431493506	0.081440392904415	1.53665063527366	0.124835968938251	   
df.mm.trans2:exp4	0.0741641069838962	0.0660568813720094	1.12273097735616	0.261941146609739	   
df.mm.trans1:exp5	0.0626341979132981	0.081440392904415	0.769080252188993	0.442108080851259	   
df.mm.trans2:exp5	-0.0151787631283224	0.0660568813720094	-0.22978322338351	0.818328125083542	   
df.mm.trans1:exp6	-0.0697919308473873	0.081440392904415	-0.856969476182424	0.391758425601407	   
df.mm.trans2:exp6	-0.102172402382871	0.0660568813720094	-1.54673366742023	0.122384661012428	   
df.mm.trans1:exp7	0.149423983980356	0.081440392904415	1.83476501833349	0.066969623801836	.  
df.mm.trans2:exp7	-0.12025354350312	0.0660568813720094	-1.82045444782496	0.0691216226598007	.  
df.mm.trans1:exp8	0.139924404016806	0.081440392904415	1.71812044400415	0.086221971830954	.  
df.mm.trans2:exp8	-0.288824365850253	0.0660568813720094	-4.37235848637321	1.41810594312938e-05	***
df.mm.trans1:probe2	0.352157340844739	0.0498718517668991	7.06124453711327	4.02526905934103e-12	***
df.mm.trans1:probe3	0.0247400734967364	0.0498718517668991	0.496072887214443	0.620000506810473	   
df.mm.trans1:probe4	-0.110500037498751	0.0498718517668991	-2.21567945812856	0.0270381641114486	*  
df.mm.trans1:probe5	0.288915341568668	0.0498718517668991	5.79315448159128	1.04879665403286e-08	***
df.mm.trans1:probe6	0.059920253265047	0.0498718517668991	1.20148442743041	0.229974380629325	   
df.mm.trans1:probe7	0.0879943467327963	0.0498718517668991	1.76440905270736	0.0781042609999633	.  
df.mm.trans1:probe8	0.233290858503141	0.0498718517668991	4.67780622210585	3.48789036098941e-06	***
df.mm.trans1:probe9	0.161286884797534	0.0498718517668991	3.23402639130764	0.00127858616578447	** 
df.mm.trans1:probe10	0.0665484353103149	0.0498718517668991	1.33438869728283	0.182515418336411	   
df.mm.trans1:probe11	0.257178522133563	0.0498718517668991	5.15678710579295	3.28334614805449e-07	***
df.mm.trans1:probe12	0.0295870324764485	0.0498718517668991	0.593261156909476	0.553200232682836	   
df.mm.trans1:probe13	0.195056851012779	0.0498718517668991	3.91116118816830	0.000100907660365805	***
df.mm.trans1:probe14	0.00476928479008865	0.0498718517668991	0.0956307941477744	0.923841493100186	   
df.mm.trans1:probe15	0.182950967900419	0.0498718517668991	3.66842139240250	0.000262796924420603	***
df.mm.trans1:probe16	0.118490570528367	0.0498718517668991	2.37590076025634	0.0177774600111602	*  
df.mm.trans1:probe17	0.164501595464744	0.0498718517668991	3.29848581186886	0.00102183559987171	** 
df.mm.trans1:probe18	0.0496863012055608	0.0498718517668991	0.996279453143116	0.319462623903393	   
df.mm.trans1:probe19	0.0102735078884574	0.0498718517668991	0.205998123680583	0.836852965516113	   
df.mm.trans2:probe2	-0.123017575362408	0.0498718517668991	-2.46667350427235	0.0138786620196556	*  
df.mm.trans2:probe3	-0.0938694689542438	0.0498718517668991	-1.88221342558101	0.0602264236644983	.  
df.mm.trans2:probe4	-0.0618516723597765	0.0498718517668991	-1.24021206689639	0.215317320073588	   
df.mm.trans2:probe5	-0.156130027893042	0.0498718517668991	-3.13062423715071	0.00181775255135175	** 
df.mm.trans2:probe6	-0.110346770511605	0.0498718517668991	-2.21260624184090	0.0272505121826232	*  
df.mm.trans3:probe2	0.177211171968622	0.0498718517668991	3.5533304998761	0.00040622767127441	***
df.mm.trans3:probe3	0.308495035262719	0.0498718517668991	6.18575457563966	1.05723532299074e-09	***
df.mm.trans3:probe4	0.282685796439534	0.0498718517668991	5.66824343641393	2.118453913744e-08	***
df.mm.trans3:probe5	-0.145108536889028	0.0498718517668991	-2.90962801155379	0.00373471501479443	** 
df.mm.trans3:probe6	0.569590348086661	0.0498718517668991	11.4210787830563	8.4873757949588e-28	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.24122463676749	0.140554897217355	30.174862069792	2.62125267931633e-128	***
df.mm.trans1	-0.0658056909409312	0.122532918598039	-0.537044997326818	0.591409160601878	   
df.mm.trans2	-0.109475294424451	0.110673036742037	-0.989177650195159	0.322921971546469	   
df.mm.exp2	-0.0454086560242815	0.146276861353205	-0.310429521143719	0.756327819769424	   
df.mm.exp3	0.112977622900876	0.146276861353205	0.772354710483412	0.44016806660817	   
df.mm.exp4	0.0496792223787664	0.146276861353205	0.339624612663854	0.734242290863694	   
df.mm.exp5	-0.00791004067607482	0.146276861353205	-0.0540758162493998	0.956890367317612	   
df.mm.exp6	0.215218257200536	0.146276861353205	1.47130759581219	0.141662533607904	   
df.mm.exp7	-0.137744075577610	0.146276861353205	-0.94166687952792	0.346691826286162	   
df.mm.exp8	-0.099253870726888	0.146276861353205	-0.678534320525422	0.497659814692799	   
df.mm.trans1:exp2	0.0157721766089935	0.135938067980765	0.116024722458357	0.90766660960774	   
df.mm.trans2:exp2	0.189956103359786	0.110260332868050	1.72279638940606	0.085372113775424	.  
df.mm.trans1:exp3	-0.128740176067571	0.135938067980765	-0.947050211761049	0.34394377675478	   
df.mm.trans2:exp3	0.0428955199185854	0.110260332868050	0.389038549066592	0.697367309754919	   
df.mm.trans1:exp4	-0.0183183508287206	0.135938067980765	-0.134755121216763	0.892844674470924	   
df.mm.trans2:exp4	0.0487838710955953	0.110260332868049	0.442442624891908	0.658307192581431	   
df.mm.trans1:exp5	0.0547338400218521	0.135938067980765	0.402638060367291	0.68733885610049	   
df.mm.trans2:exp5	0.0690962633522401	0.110260332868049	0.626664744744865	0.531085691321947	   
df.mm.trans1:exp6	-0.142649724388657	0.135938067980765	-1.0493728983175	0.294372868991602	   
df.mm.trans2:exp6	-0.049543069152914	0.110260332868050	-0.449328129747287	0.653335660737786	   
df.mm.trans1:exp7	0.0728221577890894	0.135938067980765	0.535700991420546	0.592337345825689	   
df.mm.trans2:exp7	0.0984123589484291	0.110260332868050	0.89254545482101	0.372410905428229	   
df.mm.trans1:exp8	0.136017704505294	0.135938067980765	1.00058582945684	0.317376830734246	   
df.mm.trans2:exp8	0.081681656820728	0.110260332868049	0.74080727579952	0.459061595527362	   
df.mm.trans1:probe2	-0.096213402422616	0.0832447257931616	-1.15578976933239	0.248166048987032	   
df.mm.trans1:probe3	0.0670164018349259	0.0832447257931615	0.805052827027646	0.421065709484765	   
df.mm.trans1:probe4	0.0395255665892111	0.0832447257931615	0.474811661791288	0.635071029678062	   
df.mm.trans1:probe5	0.0493954527135639	0.0832447257931615	0.593376364002892	0.553123190308623	   
df.mm.trans1:probe6	0.106559691054589	0.0832447257931616	1.28007738675671	0.200946864078245	   
df.mm.trans1:probe7	0.041383526030141	0.0832447257931615	0.497130906923362	0.619254646248244	   
df.mm.trans1:probe8	0.0864115178632898	0.0832447257931615	1.03804195448967	0.299613093953488	   
df.mm.trans1:probe9	0.0562719956515785	0.0832447257931615	0.675982713804568	0.499277466497601	   
df.mm.trans1:probe10	-0.0294400547206515	0.0832447257931615	-0.353656696447068	0.723703897600286	   
df.mm.trans1:probe11	0.0883783788420171	0.0832447257931615	1.06166940908198	0.288756166167974	   
df.mm.trans1:probe12	-0.113772963477512	0.0832447257931616	-1.36672879144565	0.172154194791926	   
df.mm.trans1:probe13	0.080906476143811	0.0832447257931615	0.97191113758774	0.331434352223974	   
df.mm.trans1:probe14	0.0440913841363043	0.0832447257931615	0.529659791851057	0.596517711750368	   
df.mm.trans1:probe15	0.0385264214899558	0.0832447257931615	0.462809158452664	0.643646664324711	   
df.mm.trans1:probe16	0.0457390736308041	0.0832447257931616	0.549453111833801	0.582871799048814	   
df.mm.trans1:probe17	0.0282325859258287	0.0832447257931616	0.339151647829057	0.734598385963887	   
df.mm.trans1:probe18	0.0790301411797805	0.0832447257931615	0.949371151466664	0.342763309510703	   
df.mm.trans1:probe19	0.102260179924293	0.0832447257931616	1.22842833524828	0.219703834286182	   
df.mm.trans2:probe2	0.157641728833845	0.0832447257931615	1.89371431441240	0.0586797796941736	.  
df.mm.trans2:probe3	-0.0646520023055451	0.0832447257931615	-0.776649832040845	0.437630776234986	   
df.mm.trans2:probe4	-0.0255588211233299	0.0832447257931616	-0.307032318021396	0.758911142497748	   
df.mm.trans2:probe5	-0.029453060478566	0.0832447257931615	-0.353812931665462	0.723586852287224	   
df.mm.trans2:probe6	-0.0160629267359467	0.0832447257931616	-0.192960293675041	0.847046682442021	   
df.mm.trans3:probe2	0.168518321038014	0.0832447257931615	2.02437234830627	0.0433154890936798	*  
df.mm.trans3:probe3	0.165322223347923	0.0832447257931615	1.9859783520544	0.0474298761617703	*  
df.mm.trans3:probe4	0.109963298352207	0.0832447257931615	1.32096414883308	0.186949960587071	   
df.mm.trans3:probe5	0.055388922329091	0.0832447257931615	0.66537455437977	0.506032659286761	   
df.mm.trans3:probe6	0.0508911988081583	0.0832447257931615	0.611344422403503	0.541172358423607