chr14.7739_chr14_114228960_114234077_-_1.R 

fitVsDatCorrelation=0.791914240174085
cont.fitVsDatCorrelation=0.253964121542036

fstatistic=9317.35187232972,52,692
cont.fstatistic=3705.70020746076,52,692

residuals=-0.619779940186056,-0.0804000682927461,-0.00504287564939536,0.0707858060844884,1.06924275850848
cont.residuals=-0.49971602829762,-0.152681215915523,-0.0444892879019784,0.089877967890388,1.78097983659484

predictedValues:
Include	Exclude	Both
chr14.7739_chr14_114228960_114234077_-_1.R.tl.Lung	53.8166435999712	52.0392220370126	59.4890367459669
chr14.7739_chr14_114228960_114234077_-_1.R.tl.cerebhem	65.2820302297466	55.7846124698956	80.4900141176805
chr14.7739_chr14_114228960_114234077_-_1.R.tl.cortex	53.7324355283986	51.4851688260618	63.4023942159489
chr14.7739_chr14_114228960_114234077_-_1.R.tl.heart	54.3211064287489	54.1134113762108	59.597911556143
chr14.7739_chr14_114228960_114234077_-_1.R.tl.kidney	57.0597098726094	59.5378254434865	62.6265664142618
chr14.7739_chr14_114228960_114234077_-_1.R.tl.liver	56.7675827838409	58.2451350811898	54.6407142768633
chr14.7739_chr14_114228960_114234077_-_1.R.tl.stomach	54.527776003305	52.9651985683153	63.0587203381363
chr14.7739_chr14_114228960_114234077_-_1.R.tl.testicle	56.5489709772339	59.4442433969684	63.1135270074795


diffExp=1.77742156295857,9.49741775985092,2.24726670233687,0.207695052538071,-2.47811557087716,-1.47755229734883,1.56257743498978,-2.89527241973450
diffExpScore=2.34533322928204
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=0,0,0,0,0,0,0,0
diffExp1.2Score=0

cont.predictedValues:
Include	Exclude	Both
Lung	57.2566088244337	59.2702903622424	63.6190508746426
cerebhem	57.7919787698476	55.7224622215026	58.4322527679489
cortex	57.7936944032605	56.1834787313188	63.3418597255498
heart	56.6584009442601	52.1275225105074	58.772929540486
kidney	56.2903144478622	61.211660040925	55.963078592425
liver	57.4097241399899	53.6215823032494	61.2883145279432
stomach	55.3671809649089	54.4022936758185	58.860634766077
testicle	53.2575456184314	59.2653239338061	58.6084635406645
cont.diffExp=-2.01368153780876,2.06951654834494,1.61021567194173,4.53087843375268,-4.92134559306273,3.78814183674051,0.96488728909042,-6.00777831537464
cont.diffExpScore=25.3777173952837

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=0.389538612834468
cont.tran.correlation=-0.299463715857376

tran.covariance=0.00160653756835548
cont.tran.covariance=-0.000461486360947289

tran.mean=55.9794420389372
cont.tran.mean=56.4768788682728

weightedLogRatios:
wLogRatio
Lung	0.133292120774692
cerebhem	0.644613731032565
cortex	0.169296722823265
heart	0.0152963622070868
kidney	-0.172832760968401
liver	-0.104111877572288
stomach	0.115840441941058
testicle	-0.202726673718282

cont.weightedLogRatios:
wLogRatio
Lung	-0.140501108287979
cerebhem	0.147274833760738
cortex	0.114235759858842
heart	0.333002623579426
kidney	-0.341331580146964
liver	0.274146064722326
stomach	0.0704141639476564
testicle	-0.430594505260032

varWeightedLogRatios=0.0732308767942242
cont.varWeightedLogRatios=0.0781879051770409

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.09744822496517	0.0748013765260404	54.7777115243158	8.06282648291619e-254	***
df.mm.trans1	-0.0753055285732587	0.063345012848635	-1.18881542818854	0.234920096234003	   
df.mm.trans2	-0.145100454166659	0.0575255448238787	-2.5223655788207	0.0118800221734264	*  
df.mm.exp2	-0.0397066937485738	0.0745260893976562	-0.532789175837562	0.594350571512828	   
df.mm.exp3	-0.075979454339253	0.0745260893976562	-1.01950142498209	0.308321266088341	   
df.mm.exp4	0.0465859292619608	0.0745260893976562	0.625095582479683	0.53211439420195	   
df.mm.exp5	0.141732076859355	0.0745260893976562	1.90177799485897	0.0576152017503888	.  
df.mm.exp6	0.251058275687825	0.0745260893976562	3.3687300342331	0.000797013339479738	***
df.mm.exp7	-0.0275095156986251	0.0745260893976562	-0.369125978848023	0.712146740204738	   
df.mm.exp8	0.123422227532896	0.0745260893976562	1.6560942420357	0.0981560782250692	.  
df.mm.trans1:exp2	0.232840724334072	0.0659416818757694	3.53100979093515	0.000441428728208299	***
df.mm.trans2:exp2	0.109207058712842	0.0523500584418853	2.08609239346073	0.0373359046676827	*  
df.mm.trans1:exp3	0.0744135071556444	0.0659416818757693	1.12847451018668	0.259510648827849	   
df.mm.trans2:exp3	0.0652755323682706	0.0523500584418853	1.24690467042619	0.212854322283234	   
df.mm.trans1:exp4	-0.0372558574210723	0.0659416818757694	-0.56498191070195	0.572269174261046	   
df.mm.trans2:exp4	-0.00750157859183597	0.0523500584418853	-0.143296470244892	0.886097758982678	   
df.mm.trans1:exp5	-0.0832165956088118	0.0659416818757693	-1.26197259823593	0.207383818453837	   
df.mm.trans2:exp5	-0.0071179487593298	0.0523500584418853	-0.135968305885113	0.89188587980461	   
df.mm.trans1:exp6	-0.197675618443068	0.0659416818757693	-2.99773394945367	0.00281737630839758	** 
df.mm.trans2:exp6	-0.138395408854684	0.0523500584418853	-2.64365337831129	0.00838741580283419	** 
df.mm.trans1:exp7	0.0406369590565538	0.0659416818757693	0.616256029579465	0.537928213781403	   
df.mm.trans2:exp7	0.0451468785824653	0.0523500584418853	0.862403594689081	0.388764224719092	   
df.mm.trans1:exp8	-0.0738980017452587	0.0659416818757693	-1.12065691446085	0.262822691943425	   
df.mm.trans2:exp8	0.00961885570873436	0.0523500584418853	0.183741069160647	0.854270414418119	   
df.mm.trans1:probe2	-0.212122047697689	0.045936989850544	-4.61767408765416	4.62684755509282e-06	***
df.mm.trans1:probe3	-0.133910402374811	0.045936989850544	-2.91508875114561	0.00367077348493093	** 
df.mm.trans1:probe4	0.161700510985645	0.045936989850544	3.52005021469055	0.00045973864550768	***
df.mm.trans1:probe5	-0.0606573582048304	0.045936989850544	-1.32044695140407	0.187122388469100	   
df.mm.trans1:probe6	-0.135571280520851	0.045936989850544	-2.95124432318993	0.00327197741342072	** 
df.mm.trans1:probe7	-0.221949356959652	0.045936989850544	-4.83160428408053	1.6689916370987e-06	***
df.mm.trans1:probe8	0.258256675576393	0.045936989850544	5.62197645985579	2.73945289071566e-08	***
df.mm.trans1:probe9	-0.107635623613248	0.0459369898505440	-2.34311442616158	0.0194057895574199	*  
df.mm.trans1:probe10	-0.104037177194081	0.045936989850544	-2.26478002874299	0.0238346589585404	*  
df.mm.trans1:probe11	0.0849977509535018	0.045936989850544	1.85031172547531	0.0646946927323622	.  
df.mm.trans1:probe12	-0.190189340686827	0.045936989850544	-4.14022210217971	3.89809834243434e-05	***
df.mm.trans1:probe13	-0.0623385169641471	0.045936989850544	-1.35704401108922	0.17520968883702	   
df.mm.trans1:probe14	-0.153981828628199	0.045936989850544	-3.35202261030118	0.00084587465455276	***
df.mm.trans2:probe2	0.0258687349491733	0.045936989850544	0.563135177845506	0.573525248330556	   
df.mm.trans2:probe3	0.26071093338174	0.045936989850544	5.67540307342652	2.03550581485649e-08	***
df.mm.trans2:probe4	-0.103511634230461	0.045936989850544	-2.25333951064788	0.0245500288432144	*  
df.mm.trans2:probe5	-0.12472255378455	0.045936989850544	-2.7150789416184	0.0067912172885075	** 
df.mm.trans2:probe6	-0.064296612399354	0.045936989850544	-1.39966969121275	0.162060376171478	   
df.mm.trans3:probe2	0.481867681277499	0.0459369898505440	10.4897530910331	5.4330760913693e-24	***
df.mm.trans3:probe3	0.190879767839081	0.045936989850544	4.15525197580661	3.6563242222467e-05	***
df.mm.trans3:probe4	0.322721504662037	0.045936989850544	7.02530805157264	5.12097557015834e-12	***
df.mm.trans3:probe5	0.734813044344532	0.045936989850544	15.9961078585089	3.0561875247297e-49	***
df.mm.trans3:probe6	0.0494599960330876	0.045936989850544	1.07669214273738	0.281993035436793	   
df.mm.trans3:probe7	-0.0834666415891943	0.0459369898505440	-1.81698108345264	0.0696524390668661	.  
df.mm.trans3:probe8	0.163334112812884	0.045936989850544	3.55561200993561	0.000402781586848467	***
df.mm.trans3:probe9	0.158060652606731	0.045936989850544	3.44081432242255	0.000614807597224024	***
df.mm.trans3:probe10	0.0522776618172515	0.045936989850544	1.13802976615004	0.25550190603335	   
df.mm.trans3:probe11	0.226299986962229	0.045936989850544	4.92631292774071	1.04928014709102e-06	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.87534082671652	0.118481955016380	32.7082788782541	1.45440928280936e-142	***
df.mm.trans1	0.164322909207536	0.100335599575915	1.63773286751736	0.101932033512691	   
df.mm.trans2	0.207786497641546	0.0911178287172673	2.28041537607633	0.0228863315881791	*  
df.mm.exp2	0.0326270357816758	0.118045912811322	0.276392761126978	0.78232894373813	   
df.mm.exp3	-0.0397822653590718	0.118045912811322	-0.33700671553667	0.736214021769509	   
df.mm.exp4	-0.0596863034130393	0.118045912811322	-0.505619398347477	0.613284871903493	   
df.mm.exp5	0.143429737173535	0.118045912811322	1.21503348788353	0.224767721365989	   
df.mm.exp6	-0.0601621371110141	0.118045912811322	-0.509650318915945	0.610458954936302	   
df.mm.exp7	-0.041517475063398	0.118045912811322	-0.351706163090604	0.725165705148335	   
df.mm.exp8	0.0095464785302113	0.118045912811322	0.0808708942381586	0.935568010051593	   
df.mm.trans1:exp2	-0.0233201190950157	0.104448604404887	-0.223268843350143	0.823392165149132	   
df.mm.trans2:exp2	-0.0943518734219793	0.0829200952907205	-1.13786499027984	0.255570667376755	   
df.mm.trans1:exp3	0.0491188679638213	0.104448604404887	0.470268303187815	0.638311514337734	   
df.mm.trans2:exp3	-0.0137031684747274	0.0829200952907204	-0.165257509976124	0.868789565380025	   
df.mm.trans1:exp4	0.0491835017129573	0.104448604404887	0.470887112309337	0.637869748365065	   
df.mm.trans2:exp4	-0.0687287989146166	0.0829200952907204	-0.828855763776576	0.407471822602676	   
df.mm.trans1:exp5	-0.160450325254675	0.104448604404886	-1.53616533383924	0.124954912447286	   
df.mm.trans2:exp5	-0.111200217020683	0.0829200952907204	-1.34105269212260	0.180343342684812	   
df.mm.trans1:exp6	0.0628327624393452	0.104448604404887	0.601566318643943	0.547659873399094	   
df.mm.trans2:exp6	-0.0399943957072426	0.0829200952907204	-0.482324526606259	0.629727950012858	   
df.mm.trans1:exp7	0.0079614181689168	0.104448604404887	0.0762233082411998	0.93926347048638	   
df.mm.trans2:exp7	-0.0441843835677648	0.0829200952907204	-0.532854954071784	0.594305057933913	   
df.mm.trans1:exp8	-0.081950055978115	0.104448604404887	-0.784596945502902	0.432958423880062	   
df.mm.trans2:exp8	-0.00963027492286087	0.0829200952907204	-0.116139216785712	0.907575903429526	   
df.mm.trans1:probe2	-0.0400198572180336	0.0727620883175233	-0.550009739184405	0.582490167462496	   
df.mm.trans1:probe3	0.045826059770777	0.0727620883175233	0.629806824273634	0.529028867332729	   
df.mm.trans1:probe4	0.0290501326914097	0.0727620883175233	0.399248198658607	0.689833513984878	   
df.mm.trans1:probe5	0.145475923563410	0.0727620883175233	1.99933683773031	0.045962475703189	*  
df.mm.trans1:probe6	0.00317331944200075	0.0727620883175233	0.043612264509958	0.965226056363622	   
df.mm.trans1:probe7	-0.0547877732628716	0.0727620883175233	-0.75297142412661	0.451723163254269	   
df.mm.trans1:probe8	0.00865599650786987	0.0727620883175233	0.118963002684809	0.9053391923859	   
df.mm.trans1:probe9	-0.0341799964933911	0.0727620883175233	-0.469750075674498	0.63868157444507	   
df.mm.trans1:probe10	0.0138932322576448	0.0727620883175233	0.190940537564242	0.848628184977235	   
df.mm.trans1:probe11	0.0480778708034538	0.0727620883175233	0.660754410918622	0.508989725449199	   
df.mm.trans1:probe12	0.00623967337760651	0.0727620883175233	0.0857544570515551	0.931686415923309	   
df.mm.trans1:probe13	0.0541397434693772	0.0727620883175233	0.744065277966172	0.457089558301254	   
df.mm.trans1:probe14	-0.0364402170080945	0.0727620883175233	-0.500813237369915	0.61666181936832	   
df.mm.trans2:probe2	-0.0123715398121200	0.0727620883175233	-0.170027277916109	0.86503837921573	   
df.mm.trans2:probe3	0.0415076815392515	0.0727620883175233	0.570457534947568	0.568552591376471	   
df.mm.trans2:probe4	-0.0316665295793419	0.0727620883175233	-0.435206442139947	0.663548286433522	   
df.mm.trans2:probe5	-0.0389065329222273	0.0727620883175233	-0.534708854870201	0.593022955617798	   
df.mm.trans2:probe6	0.0241113776522955	0.0727620883175233	0.331372809794531	0.740463225283979	   
df.mm.trans3:probe2	-0.113302292892196	0.0727620883175233	-1.55716109188293	0.119889442314916	   
df.mm.trans3:probe3	-0.0286758620603969	0.0727620883175233	-0.394104439873407	0.693625347774186	   
df.mm.trans3:probe4	-0.151929372229961	0.0727620883175233	-2.08802929854023	0.0371602585090215	*  
df.mm.trans3:probe5	-0.100703805717516	0.0727620883175233	-1.38401478085757	0.166800127553776	   
df.mm.trans3:probe6	-0.157105879927029	0.0727620883175233	-2.15917222223532	0.0311797845487225	*  
df.mm.trans3:probe7	-0.100778142711459	0.0727620883175233	-1.38503642544835	0.166487659348209	   
df.mm.trans3:probe8	-0.0981698125130737	0.0727620883175233	-1.34918904587613	0.177717526689731	   
df.mm.trans3:probe9	-0.0862343982003948	0.0727620883175233	-1.18515562423223	0.236362756871969	   
df.mm.trans3:probe10	-0.107379544022975	0.0727620883175233	-1.47576226172049	0.140462582159842	   
df.mm.trans3:probe11	-0.0956308159357753	0.0727620883175233	-1.31429454743597	0.189182566673446