chr12.5661_chr12_100057851_100063732_+_2.R 

fitVsDatCorrelation=0.910795717198601
cont.fitVsDatCorrelation=0.234476172716273

fstatistic=11692.3478113144,64,968
cont.fstatistic=2096.52384048259,64,968

residuals=-0.557249237725135,-0.091314719585089,-0.00328967836332906,0.0868292949684972,0.792139167010327
cont.residuals=-0.663588754687283,-0.252145755160682,-0.0946506422383216,0.152339443930952,1.09047180778306

predictedValues:
Include	Exclude	Both
chr12.5661_chr12_100057851_100063732_+_2.R.tl.Lung	71.1276362934414	46.3438258136551	105.277184036553
chr12.5661_chr12_100057851_100063732_+_2.R.tl.cerebhem	61.1903808552246	51.5337140442652	91.217808051055
chr12.5661_chr12_100057851_100063732_+_2.R.tl.cortex	65.443838930107	60.5920533752934	103.654073600030
chr12.5661_chr12_100057851_100063732_+_2.R.tl.heart	69.6742028753731	56.0452737960346	109.469186819700
chr12.5661_chr12_100057851_100063732_+_2.R.tl.kidney	68.6438201605708	47.411364387234	95.6196023334187
chr12.5661_chr12_100057851_100063732_+_2.R.tl.liver	60.3228430573928	49.9398686635149	87.4357319418396
chr12.5661_chr12_100057851_100063732_+_2.R.tl.stomach	69.5423286524418	50.454348218558	108.752511314905
chr12.5661_chr12_100057851_100063732_+_2.R.tl.testicle	67.1955044273627	51.3371258186933	108.646152332331


diffExp=24.7838104797863,9.6566668109594,4.85178555481361,13.6289290793385,21.2324557733368,10.3829743938778,19.0879804338838,15.8583786086694
diffExpScore=0.991700072569733
diffExp1.5=1,0,0,0,0,0,0,0
diffExp1.5Score=0.5
diffExp1.4=1,0,0,0,1,0,0,0
diffExp1.4Score=0.666666666666667
diffExp1.3=1,0,0,0,1,0,1,1
diffExp1.3Score=0.8
diffExp1.2=1,0,0,1,1,1,1,1
diffExp1.2Score=0.857142857142857

cont.predictedValues:
Include	Exclude	Both
Lung	71.256562271625	64.497948295103	77.0763863727084
cerebhem	69.3578324647273	80.2090433611249	72.6681573377738
cortex	68.6640008484372	66.426735525827	70.5399110064131
heart	69.6102467859344	79.6894142373638	71.6115444359737
kidney	69.4749552545904	67.8897095210967	72.3735506551278
liver	70.847179851537	60.4043531049204	67.7036679612782
stomach	70.9155970936541	75.7389619552761	72.2887322978777
testicle	76.4668928247326	66.4215432241085	69.0591089177858
cont.diffExp=6.7586139765221,-10.8512108963976,2.23726532261027,-10.0791674514294,1.58524573349372,10.4428267466165,-4.82336486162201,10.0453496006241
cont.diffExpScore=8.99731156867116

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=-0.168006981919818
cont.tran.correlation=-0.302416073318799

tran.covariance=-0.000912768376353934
cont.tran.covariance=-0.00105072508699493

tran.mean=59.1748830855727
cont.tran.mean=70.4919360387536

weightedLogRatios:
wLogRatio
Lung	1.73509173512717
cerebhem	0.69184359986609
cortex	0.319104698442346
heart	0.900065630350201
kidney	1.49652136710321
liver	0.756559362544952
stomach	1.30961808665222
testicle	1.09642241682455

cont.weightedLogRatios:
wLogRatio
Lung	0.420185025948481
cerebhem	-0.626774038772145
cortex	0.139546284529956
heart	-0.582890542614931
kidney	0.097623013196692
liver	0.666685948240606
stomach	-0.282580929351894
testicle	0.600870103555496

varWeightedLogRatios=0.215462505370665
cont.varWeightedLogRatios=0.257161872932378

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.19780768894653	0.0724002493896568	44.1684623451501	3.14133460987663e-234	***
df.mm.trans1	1.14988265847181	0.0620188383840768	18.5408609453582	6.60728150597799e-66	***
df.mm.trans2	0.63752324860962	0.0542964945335142	11.7415176446817	7.41503510543086e-30	***
df.mm.exp2	0.0990087821960951	0.068717593347668	1.44080689344245	0.149962618074645	   
df.mm.exp3	0.200329653596176	0.068717593347668	2.91526003512134	0.00363584483908915	** 
df.mm.exp4	0.130379533905900	0.068717593347668	1.89732392469366	0.0580825766970452	.  
df.mm.exp5	0.0834479064676285	0.068717593347668	1.21436014275754	0.224906389737972	   
df.mm.exp6	0.095659166281472	0.068717593347668	1.39206223066481	0.164223286083189	   
df.mm.exp7	0.029962469256831	0.068717593347668	0.436023262707116	0.662917071447646	   
df.mm.exp8	0.0139569320456664	0.068717593347668	0.203105658474578	0.839095133267447	   
df.mm.trans1:exp2	-0.249494737996132	0.0628665155856603	-3.96864269749732	7.76236476604095e-05	***
df.mm.trans2:exp2	0.0071393776904273	0.0435787620932456	0.163826996167334	0.869901537456321	   
df.mm.trans1:exp3	-0.28361325706165	0.0628665155856603	-4.51135639409195	7.23168964699523e-06	***
df.mm.trans2:exp3	0.0677460232245639	0.0435787620932456	1.55456511315323	0.120376542996229	   
df.mm.trans1:exp4	-0.151025358611847	0.0628665155856603	-2.40231794628516	0.0164790274423979	*  
df.mm.trans2:exp4	0.0596922156094302	0.0435787620932456	1.36975473240168	0.171081121492164	   
df.mm.trans1:exp5	-0.118992755213352	0.0628665155856604	-1.89278432413222	0.0586846642675273	.  
df.mm.trans2:exp5	-0.060674026733319	0.0435787620932456	-1.39228431049727	0.164156071996644	   
df.mm.trans1:exp6	-0.260424268174874	0.0628665155856603	-4.14249566321242	3.73568190951067e-05	***
df.mm.trans2:exp6	-0.0209275865538879	0.0435787620932456	-0.480224438434234	0.631176310263916	   
df.mm.trans1:exp7	-0.0525028141167813	0.0628665155856604	-0.835147512593446	0.403840729760846	   
df.mm.trans2:exp7	0.0550183872668378	0.0435787620932456	1.26250459223956	0.207071334929334	   
df.mm.trans1:exp8	-0.07082654255342	0.0628665155856603	-1.12661791247065	0.260183271603197	   
df.mm.trans2:exp8	0.088369183320805	0.0435787620932456	2.02780389061353	0.0428537665439961	*  
df.mm.trans1:probe2	-0.191244584999429	0.0460135778595495	-4.1562641701799	3.5213437017544e-05	***
df.mm.trans1:probe3	-0.0183933842856493	0.0460135778595495	-0.399738189057863	0.689437546819009	   
df.mm.trans1:probe4	-0.249511277814372	0.0460135778595495	-5.4225576323574	7.42148266015025e-08	***
df.mm.trans1:probe5	0.0184958955937548	0.0460135778595495	0.401966038159674	0.687797755040235	   
df.mm.trans1:probe6	-0.0589825931631385	0.0460135778595495	-1.28185192082162	0.200201527263984	   
df.mm.trans1:probe7	-0.128693313625558	0.0460135778595495	-2.79685518084201	0.00526252141651306	** 
df.mm.trans1:probe8	-0.00234991666609106	0.0460135778595495	-0.0510700705183994	0.95928022122422	   
df.mm.trans1:probe9	-0.204718595440082	0.0460135778595495	-4.44909100667980	9.62397737225305e-06	***
df.mm.trans1:probe10	-0.163987909994719	0.0460135778595495	-3.56390260490655	0.000383219741050222	***
df.mm.trans1:probe11	-0.18029575241046	0.0460135778595495	-3.91831630569545	9.54386190067206e-05	***
df.mm.trans1:probe12	-0.127294233519438	0.0460135778595495	-2.76644937083543	0.00577497012581494	** 
df.mm.trans1:probe13	-0.211971638628732	0.0460135778595495	-4.60671933131886	4.63677017413203e-06	***
df.mm.trans1:probe14	-0.155642536607507	0.0460135778595495	-3.38253497875313	0.000746967687357652	***
df.mm.trans1:probe15	-0.155631573841294	0.0460135778595495	-3.38229672807316	0.000747608236662531	***
df.mm.trans1:probe16	-0.225939503211223	0.0460135778595495	-4.91027895941659	1.06705682078511e-06	***
df.mm.trans1:probe17	-0.183178985630158	0.0460135778595495	-3.98097679318240	7.37651185220068e-05	***
df.mm.trans1:probe18	-0.177683274245180	0.0460135778595495	-3.86154006079543	0.000120159956953791	***
df.mm.trans1:probe19	-0.134157093299638	0.0460135778595495	-2.91559795043835	0.00363194450840444	** 
df.mm.trans1:probe20	-0.177053244228812	0.0460135778595495	-3.84784779764885	0.000126968209152756	***
df.mm.trans1:probe21	-0.196754590636364	0.0460135778595495	-4.27601155547895	2.09118027448824e-05	***
df.mm.trans1:probe22	-0.153944324146042	0.0460135778595495	-3.34562821034994	0.000852515295265947	***
df.mm.trans2:probe2	-0.00399004920690739	0.0460135778595495	-0.086714604525788	0.930916300751921	   
df.mm.trans2:probe3	0.0263742190553215	0.0460135778595495	0.573183401121846	0.566653646163167	   
df.mm.trans2:probe4	0.0555121148946804	0.0460135778595495	1.20642900371981	0.227946878754256	   
df.mm.trans2:probe5	-0.0272052202661354	0.0460135778595495	-0.591243314075159	0.554495437555901	   
df.mm.trans2:probe6	-0.0347911732207681	0.0460135778595495	-0.75610667196895	0.449769202267464	   
df.mm.trans3:probe2	-0.765404096356496	0.0460135778595495	-16.6343095225673	7.69459531215847e-55	***
df.mm.trans3:probe3	0.104633130509560	0.0460135778595495	2.27396206460925	0.0231864848578649	*  
df.mm.trans3:probe4	-0.219116186504504	0.0460135778595495	-4.76198975818241	2.20975799220342e-06	***
df.mm.trans3:probe5	-0.0612524478706556	0.0460135778595495	-1.33118202756631	0.183442547436979	   
df.mm.trans3:probe6	-0.839513485538428	0.0460135778595495	-18.2449077987575	3.77541009846722e-64	***
df.mm.trans3:probe7	-0.193716149802976	0.0460135778595495	-4.20997798506931	2.79181716229762e-05	***
df.mm.trans3:probe8	0.0323827488636771	0.0460135778595495	0.703765070443364	0.481748336756826	   
df.mm.trans3:probe9	-0.598976290438926	0.0460135778595495	-13.0173813535479	8.16271717748786e-36	***
df.mm.trans3:probe10	-0.354160382054079	0.0460135778595495	-7.69686684950054	3.43149974141008e-14	***
df.mm.trans3:probe11	-0.446887379346929	0.0460135778595495	-9.71207630736724	2.42902077139918e-21	***
df.mm.trans3:probe12	-0.78779170456082	0.0460135778595495	-17.1208530439744	1.32204084520248e-57	***
df.mm.trans3:probe13	0.0685865085340664	0.0460135778595495	1.49057108194059	0.136399907897045	   
df.mm.trans3:probe14	-0.883087661986786	0.0460135778595495	-19.1918929817259	8.11314431107994e-70	***
df.mm.trans3:probe15	0.015050253825748	0.0460135778595495	0.327082885657945	0.743675910773727	   

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.96462595480555	0.170475036448274	23.2563432007742	2.13209698004130e-95	***
df.mm.trans1	0.278913352384049	0.146030764025455	1.90996297420885	0.0564332871325656	.  
df.mm.trans2	0.176587712312308	0.127847582883281	1.38123622152110	0.167525092554241	   
df.mm.exp2	0.249888692419210	0.161803782850711	1.54439338819273	0.122819899980769	   
df.mm.exp3	0.0810226425218848	0.161803782850711	0.50074628104734	0.616663530523826	   
df.mm.exp4	0.261668871244987	0.161803782850711	1.61719872449717	0.106161191386504	   
df.mm.exp5	0.0888865210577322	0.161803782850711	0.549347607897052	0.582893624412593	   
df.mm.exp6	0.0583226064290814	0.161803782850711	0.360452675466142	0.718587277966055	   
df.mm.exp7	0.219991473101346	0.161803782850711	1.35961884960578	0.174267179825624	   
df.mm.exp8	0.209793164817421	0.161803782850711	1.29658998770744	0.195081295738571	   
df.mm.trans1:exp2	-0.276896527909298	0.148026721263925	-1.87058475351625	0.0617042249619377	.  
df.mm.trans2:exp2	-0.0318858377074584	0.102611401464012	-0.310743613794626	0.756062436182545	   
df.mm.trans1:exp3	-0.118084502715125	0.148026721263925	-0.797724233211821	0.42522617824498	   
df.mm.trans2:exp3	-0.0515564375349219	0.102611401464012	-0.502443556947262	0.615469824801512	   
df.mm.trans1:exp4	-0.285044007770676	0.148026721263925	-1.92562535559005	0.0544438320333561	.  
df.mm.trans2:exp4	-0.0501655282960981	0.102611401464012	-0.488888443003013	0.625031422203325	   
df.mm.trans1:exp5	-0.114207106445409	0.148026721263925	-0.771530339051303	0.440580941413138	   
df.mm.trans2:exp5	-0.0376354653408346	0.102611401464012	-0.366776642788902	0.713865789494501	   
df.mm.trans1:exp6	-0.0640843625450248	0.148026721263925	-0.432924285546831	0.665166216447752	   
df.mm.trans2:exp6	-0.123894846729354	0.102611401464012	-1.20741793759445	0.227566167452698	   
df.mm.trans1:exp7	-0.224787993505866	0.148026721263925	-1.51856361869340	0.129198895659928	   
df.mm.trans2:exp7	-0.059332170009168	0.102611401464012	-0.578222002259435	0.56324873596746	   
df.mm.trans1:exp8	-0.139222208156851	0.148026721263925	-0.940520785491312	0.347185129830351	   
df.mm.trans2:exp8	-0.180405128727638	0.102611401464012	-1.7581392141	0.0790398299241914	.  
df.mm.trans1:probe2	0.120315835440745	0.108344466059848	1.11049359340868	0.267062155205806	   
df.mm.trans1:probe3	-0.0242240207851074	0.108344466059848	-0.223583369470172	0.823128680763153	   
df.mm.trans1:probe4	-0.00650845919408358	0.108344466059848	-0.0600719116607987	0.952110760197342	   
df.mm.trans1:probe5	-0.0625518002496722	0.108344466059848	-0.577341903324528	0.563842763142438	   
df.mm.trans1:probe6	0.044909666258209	0.108344466059848	0.414508169096533	0.678593823801631	   
df.mm.trans1:probe7	0.0388454267796279	0.108344466059848	0.358536325779393	0.720020165749053	   
df.mm.trans1:probe8	0.0838631702548754	0.108344466059848	0.774042028215366	0.439094938017935	   
df.mm.trans1:probe9	0.0628937930338854	0.108344466059848	0.580498435417494	0.561713649568362	   
df.mm.trans1:probe10	-0.0308323270332726	0.108344466059848	-0.28457685154165	0.776029214555433	   
df.mm.trans1:probe11	0.154648486743152	0.108344466059848	1.42737781048943	0.153793520030091	   
df.mm.trans1:probe12	0.129012797768604	0.108344466059848	1.19076499668510	0.234037767557299	   
df.mm.trans1:probe13	-0.0527929976821971	0.108344466059848	-0.487269904980981	0.626177395786728	   
df.mm.trans1:probe14	-0.0535365544338626	0.108344466059848	-0.494132800509533	0.621324502705275	   
df.mm.trans1:probe15	0.0259537152505152	0.108344466059848	0.239548139322397	0.810731285002324	   
df.mm.trans1:probe16	0.069536040873874	0.108344466059848	0.641805192297162	0.521151624236265	   
df.mm.trans1:probe17	0.0154372625968621	0.108344466059848	0.142483166499107	0.886728025357714	   
df.mm.trans1:probe18	-0.0321086418890458	0.108344466059848	-0.296357008869373	0.767021022784727	   
df.mm.trans1:probe19	0.0646288209453735	0.108344466059848	0.596512432020972	0.550972422677465	   
df.mm.trans1:probe20	0.164839451905354	0.108344466059848	1.52143859211323	0.128476384901624	   
df.mm.trans1:probe21	0.127108197964591	0.108344466059848	1.17318588191093	0.241009868319425	   
df.mm.trans1:probe22	0.0022236971605268	0.108344466059848	0.0205243261736918	0.983629336375464	   
df.mm.trans2:probe2	0.195935057320821	0.108344466059848	1.80844545592748	0.0708474104106241	.  
df.mm.trans2:probe3	0.0795067167222566	0.108344466059848	0.73383274304327	0.46322837819359	   
df.mm.trans2:probe4	0.128232435156827	0.108344466059848	1.18356238966551	0.236876905735301	   
df.mm.trans2:probe5	0.0778700169879674	0.108344466059848	0.718726297889117	0.472483025723795	   
df.mm.trans2:probe6	0.0522704566055431	0.108344466059848	0.482446944513712	0.62959755326435	   
df.mm.trans3:probe2	-0.177424729718655	0.108344466059848	-1.63759845030431	0.101830490475051	   
df.mm.trans3:probe3	-0.237585501633642	0.108344466059848	-2.1928715907133	0.0285539698965431	*  
df.mm.trans3:probe4	0.0277644301653208	0.108344466059848	0.256260713398912	0.797803979800325	   
df.mm.trans3:probe5	-0.0104406391744215	0.108344466059848	-0.0963652280002398	0.923250454087289	   
df.mm.trans3:probe6	-0.0696227361069056	0.108344466059848	-0.642605373757134	0.520632363565236	   
df.mm.trans3:probe7	-0.127953695658007	0.108344466059848	-1.18098967405800	0.237896910010261	   
df.mm.trans3:probe8	0.0495628268843518	0.108344466059848	0.457456007554406	0.647445967769295	   
df.mm.trans3:probe9	-0.210403259517011	0.108344466059848	-1.94198436864128	0.0524288026914285	.  
df.mm.trans3:probe10	-0.0536446844960537	0.108344466059848	-0.495130821600258	0.620620146962515	   
df.mm.trans3:probe11	-0.102740671943592	0.108344466059848	-0.948277984838095	0.343224626116856	   
df.mm.trans3:probe12	-0.06910166124575	0.108344466059848	-0.637795946196083	0.523757353782782	   
df.mm.trans3:probe13	-0.0124923675464176	0.108344466059848	-0.115302313082766	0.90822941806405	   
df.mm.trans3:probe14	-0.0809795880745372	0.108344466059848	-0.747427081599215	0.454987322679252	   
df.mm.trans3:probe15	-0.0548886005029116	0.108344466059848	-0.506611943360282	0.61254249699739