chr2.13696_chr2_32646298_32651222_+_2.R 

fitVsDatCorrelation=0.818382758693905
cont.fitVsDatCorrelation=0.253780337105436

fstatistic=8565.78867419877,52,692
cont.fstatistic=3014.97059445520,52,692

residuals=-0.484499533368278,-0.104769441421073,-0.00391156284091769,0.104223057951601,0.7497834249153
cont.residuals=-0.670160894256795,-0.202852037095016,-0.0286509964034456,0.164155477291812,1.06083303473205

predictedValues:
Include	Exclude	Both
chr2.13696_chr2_32646298_32651222_+_2.R.tl.Lung	66.5414353941569	55.8801994894513	66.9560643367104
chr2.13696_chr2_32646298_32651222_+_2.R.tl.cerebhem	68.8913125565408	58.8462652636357	59.6220869028327
chr2.13696_chr2_32646298_32651222_+_2.R.tl.cortex	76.3572663641222	49.8850843205046	80.0789135752527
chr2.13696_chr2_32646298_32651222_+_2.R.tl.heart	64.5385966173957	51.5044071419504	69.9401135277388
chr2.13696_chr2_32646298_32651222_+_2.R.tl.kidney	65.0154490532175	50.5113074158561	64.4350643313957
chr2.13696_chr2_32646298_32651222_+_2.R.tl.liver	65.2808816115285	53.0748937761054	59.3076687638646
chr2.13696_chr2_32646298_32651222_+_2.R.tl.stomach	78.8947334840337	50.5934545383278	70.3915155913442
chr2.13696_chr2_32646298_32651222_+_2.R.tl.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	66.5090241859555	64.8019354891704	72.9128052016285
cerebhem	65.639725818743	75.3951619718559	67.092166073227
cortex	65.4153553518351	60.7166868842982	71.759433400234
heart	67.2500982029136	67.6940620547697	64.5138539429972
kidney	74.2807247417876	68.272977376	64.107761042631
liver	72.3697837888687	70.7956313569255	65.8833192543658
stomach	65.6109108075202	66.7585093580182	68.0007730766121
testicle	69.869060558265	59.3202668393915	65.6388104793021
cont.diffExp=1.70708869678502,-9.75543615311288,4.69866846753687,-0.443963851856026,6.00774736578764,1.57415243194315,-1.14759855049806,10.5487937188735
cont.diffExpScore=2.5288819412562

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=0.0868541612151819

tran.covariance=0.00132844234822467
cont.tran.covariance=0.000347342725261649

tran.mean=63.3590627542841
cont.tran.mean=67.5437446741449

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	0.108801825978952
cerebhem	-0.589368295949615
cortex	0.308849176258026
heart	-0.0277129897172867
kidney	0.359756811712567
liver	0.0939214299680238
stomach	-0.072695435949522
testicle	0.681658643921997

varWeightedLogRatios=0.486262887783991
cont.varWeightedLogRatios=0.138628041602428

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)	4.06894127523229	0.141731907033191	28.7087174681099	5.61664949804432e-120	***
df.mm.trans1	0.116366789813148	0.123559011966596	0.941791197267	0.346628207754499	   
df.mm.trans2	0.0929891315035771	0.111599815197805	0.833237325158275	0.404998390925893	   
df.mm.exp2	0.221448076018927	0.147501787023180	1.50132469909755	0.133727734983246	   
df.mm.exp3	-0.0657525661121378	0.147501787023180	-0.445774708490851	0.655899424703751	   
df.mm.exp4	0.177128120449472	0.147501787023180	1.20085406437575	0.230218693373428	   
df.mm.exp5	0.291391267738117	0.147501787023180	1.97551008444613	0.0486072054778319	*  
df.mm.exp6	0.274292034573334	0.147501787023180	1.85958448442545	0.0633685110460885	.  
df.mm.exp7	0.0858958675551333	0.147501787023180	0.582337809518433	0.56052884366731	   
df.mm.exp8	0.0659979641615996	0.147501787023180	0.447438403924068	0.654698574100082	   
df.mm.trans1:exp2	-0.234604627350677	0.137076416366532	-1.71148789536021	0.0874391780647047	.  
df.mm.trans2:exp2	-0.0700404394653725	0.111183655332455	-0.629952660361287	0.528933500896579	   
df.mm.trans1:exp3	0.0491719478290254	0.137076416366532	0.35871923947547	0.719914547245384	   
df.mm.trans2:exp3	0.00063566230956407	0.111183655332455	0.00571722801938242	0.995439984620353	   
df.mm.trans1:exp4	-0.166047282201777	0.137076416366532	-1.21134828735075	0.226175447455916	   
df.mm.trans2:exp4	-0.133465125628607	0.111183655332455	-1.2004023903471	0.230393864479765	   
df.mm.trans1:exp5	-0.180877414910794	0.137076416366532	-1.31953708526448	0.187426013353319	   
df.mm.trans2:exp5	-0.239212697054332	0.111183655332455	-2.15150955721911	0.0317814132951391	*  
df.mm.trans1:exp6	-0.189840813778863	0.137076416366532	-1.38492688101243	0.166521142196721	   
df.mm.trans2:exp6	-0.185830211347865	0.111183655332455	-1.67138066105316	0.0950986232945276	.  
df.mm.trans1:exp7	-0.099491502649467	0.137076416366532	-0.72581050254067	0.46820034155551	   
df.mm.trans2:exp7	-0.0561495689529963	0.111183655332455	-0.505016396385791	0.613708109540443	   
df.mm.trans1:exp8	-0.0167126777537477	0.137076416366533	-0.121922342272643	0.902995917387415	   
df.mm.trans2:exp8	-0.154382420170223	0.111183655332455	-1.38853520968165	0.16542090361073	   
df.mm.trans1:probe2	-0.0446358874558561	0.0839418189668244	-0.531747917846493	0.595071256143414	   
df.mm.trans1:probe3	0.0158863020571815	0.0839418189668244	0.189253726601518	0.84994945433013	   
df.mm.trans1:probe4	-0.0298688936904346	0.0839418189668243	-0.355828525734467	0.722077429835318	   
df.mm.trans1:probe5	-0.0214668901743152	0.0839418189668244	-0.255735346678625	0.798231196290323	   
df.mm.trans1:probe6	0.0540785051339333	0.0839418189668244	0.644237947182278	0.519634624815154	   
df.mm.trans1:probe7	-0.0516627755127639	0.0839418189668244	-0.615459328242364	0.538453774050853	   
df.mm.trans1:probe8	0.0521675361757108	0.0839418189668244	0.621472548698624	0.534493420177395	   
df.mm.trans1:probe9	-0.0527399031790101	0.0839418189668244	-0.628291164382012	0.530020519998356	   
df.mm.trans1:probe10	0.084478467210463	0.0839418189668244	1.00639309762695	0.314578281859981	   
df.mm.trans1:probe11	-0.00966865542947778	0.0839418189668244	-0.115182820058963	0.908333629408112	   
df.mm.trans1:probe12	0.104574484664526	0.0839418189668244	1.24579721945096	0.213260470281171	   
df.mm.trans1:probe13	-0.0116721731841201	0.0839418189668243	-0.139050753578894	0.889450499308716	   
df.mm.trans1:probe14	-0.00802292726872641	0.0839418189668244	-0.095577238705028	0.92388401367909	   
df.mm.trans1:probe15	0.0908034483625018	0.0839418189668244	1.08174268177807	0.279743697081657	   
df.mm.trans1:probe16	0.114056742717284	0.0839418189668244	1.35875948509481	0.174665531070398	   
df.mm.trans1:probe17	-0.077417566577679	0.0839418189668244	-0.922276494964639	0.356705806938498	   
df.mm.trans1:probe18	0.0325834004088999	0.0839418189668244	0.388166480187635	0.698012218572586	   
df.mm.trans1:probe19	0.0592661720949961	0.0839418189668244	0.706038692328306	0.480401700109763	   
df.mm.trans2:probe2	-0.0519733916852232	0.0839418189668244	-0.619159702814687	0.536014933523133	   
df.mm.trans2:probe3	0.041471043470991	0.0839418189668244	0.494045089580216	0.621431116176078	   
df.mm.trans2:probe4	0.0710023574023794	0.0839418189668244	0.84585202317859	0.397927681714963	   
df.mm.trans2:probe5	0.0154322336350211	0.0839418189668244	0.183844403480466	0.854189377644168	   
df.mm.trans2:probe6	0.0369285352129271	0.0839418189668244	0.439930128599216	0.660125078635263	   
df.mm.trans3:probe2	0.0243769834707249	0.0839418189668244	0.290403326622684	0.77159470358123	   
df.mm.trans3:probe3	-0.0057715845836335	0.0839418189668244	-0.0687569635096251	0.945202937602112	   
df.mm.trans3:probe4	0.103128003222081	0.0839418189668243	1.22856526688847	0.219652494839342	   
df.mm.trans3:probe5	-0.0300718951607818	0.0839418189668244	-0.358246884936659	0.720267818179067	   
df.mm.trans3:probe6	-0.0258590186473451	0.0839418189668244	-0.308058831290815	0.758130268928077