chr11.4622_chr11_62716482_62717945_+_2.R 

fitVsDatCorrelation=0.870572346588202
cont.fitVsDatCorrelation=0.241084194186948

fstatistic=8885.22701008113,53,715
cont.fstatistic=2273.86756820658,53,715

residuals=-0.7497047448649,-0.0950814920828894,-0.00160078046040846,0.0862779205592112,0.697322471635603
cont.residuals=-0.62965079466958,-0.231072304090771,-0.0733648565419705,0.175864196229255,1.51160110110805

predictedValues:
Include	Exclude	Both
chr11.4622_chr11_62716482_62717945_+_2.R.tl.Lung	60.9817484768876	50.2696737575303	72.9424794500039
chr11.4622_chr11_62716482_62717945_+_2.R.tl.cerebhem	70.955205642575	57.5890251963254	99.1917009536435
chr11.4622_chr11_62716482_62717945_+_2.R.tl.cortex	93.1394702241476	52.533619977517	173.888682527729
chr11.4622_chr11_62716482_62717945_+_2.R.tl.heart	71.0267830363745	51.2299194252889	106.390302214946
chr11.4622_chr11_62716482_62717945_+_2.R.tl.kidney	65.4435686565431	53.8941891975899	84.7868078021565
chr11.4622_chr11_62716482_62717945_+_2.R.tl.liver	62.0653791559405	57.8892794182037	80.4765551428148
chr11.4622_chr11_62716482_62717945_+_2.R.tl.stomach	59.034929160045	50.0928826129423	74.394366676977
chr11.4622_chr11_62716482_62717945_+_2.R.tl.testicle	60.9293687834163	52.6545684439925	79.478876093226


diffExp=10.7120747193573,13.3661804462495,40.6058502466306,19.7968636110856,11.5493794589532,4.17609973773678,8.94204654710278,8.27480033942384
diffExpScore=0.991555715460372
diffExp1.5=0,0,1,0,0,0,0,0
diffExp1.5Score=0.5
diffExp1.4=0,0,1,0,0,0,0,0
diffExp1.4Score=0.5
diffExp1.3=0,0,1,1,0,0,0,0
diffExp1.3Score=0.666666666666667
diffExp1.2=1,1,1,1,1,0,0,0
diffExp1.2Score=0.833333333333333

cont.predictedValues:
Include	Exclude	Both
Lung	63.3164696505673	69.0531026030201	69.6006919808347
cerebhem	68.4364189227658	64.8667839518271	64.9848512307845
cortex	63.0618538935667	69.3143703836618	59.0264521603027
heart	64.5544523056815	70.7283601262446	59.6577059644836
kidney	65.3153296828222	59.7938900634017	70.8692603811058
liver	60.0239953471336	64.8739447368043	59.1340096214664
stomach	64.6328539232226	68.9474652733641	57.8678835040574
testicle	64.819490185787	77.5152051856419	68.4479622203154
cont.diffExp=-5.7366329524528,3.56963497093871,-6.25251649009513,-6.17390782056307,5.52143961942052,-4.84994938967071,-4.31461135014153,-12.6957149998548
cont.diffExpScore=1.53808123931617

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=0.0546385754501205
cont.tran.correlation=-0.0534365477254933

tran.covariance=0.000765068816925634
cont.tran.covariance=-0.000150025111116438

tran.mean=60.6081006978325
cont.tran.mean=66.2033741397195

weightedLogRatios:
wLogRatio
Lung	0.775392661745428
cerebhem	0.867779654348229
cortex	2.43246625866802
heart	1.33950539350264
kidney	0.792992616895466
liver	0.285127599911366
stomach	0.65634508991343
testicle	0.589211000188641

cont.weightedLogRatios:
wLogRatio
Lung	-0.363531110640411
cerebhem	0.224944504071970
cortex	-0.396237517699918
heart	-0.384821285748653
kidney	0.365222529812537
liver	-0.321187255432798
stomach	-0.271479148828858
testicle	-0.762162807076876

varWeightedLogRatios=0.438195459888592
cont.varWeightedLogRatios=0.131904695593782

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.96887452901579	0.086170759480723	46.0582516961993	3.8867958287286e-216	***
df.mm.trans1	0.361810671821205	0.0765213021062333	4.72823464659435	2.72869040098228e-06	***
df.mm.trans2	-0.0482023873070021	0.0696060751296997	-0.692502589999288	0.488846602544483	   
df.mm.exp2	-0.0199789889681327	0.0938393329392573	-0.212906340468823	0.831460703550621	   
df.mm.exp3	-0.401169368317953	0.0938393329392573	-4.27506628353413	2.17002805677837e-05	***
df.mm.exp4	-0.206039095410414	0.0938393329392573	-2.19565814202648	0.0284369854479153	*  
df.mm.exp5	-0.0102344974225006	0.0938393329392573	-0.109064047046513	0.913182291740984	   
df.mm.exp6	0.0604492168276225	0.0938393329392573	0.644177818982916	0.519666722175859	   
df.mm.exp7	-0.0556774236609322	0.0938393329392573	-0.593327146698416	0.553149867886074	   
df.mm.exp8	-0.0403283916200959	0.0938393329392573	-0.429759998893009	0.667499716225368	   
df.mm.trans1:exp2	0.171453146356072	0.0891048491216638	1.9241730169137	0.0547295000679983	.  
df.mm.trans2:exp2	0.155909015850928	0.0750329977522356	2.07787267630905	0.038077349254416	*  
df.mm.trans1:exp3	0.824692803798423	0.0891048491216638	9.25530778546504	2.43564687122934e-19	***
df.mm.trans2:exp3	0.445220725604577	0.0750329977522356	5.93366570631668	4.61577234922277e-09	***
df.mm.trans1:exp4	0.35852151307882	0.0891048491216638	4.02359149488368	6.34242728007478e-05	***
df.mm.trans2:exp4	0.224960832678051	0.0750329977522356	2.99815866908168	0.00281034418532641	** 
df.mm.trans1:exp5	0.08084810738469	0.0891048491216638	0.907336785614214	0.364534416875166	   
df.mm.trans2:exp5	0.0798551745520677	0.0750329977522356	1.06426741492797	0.287566856262447	   
df.mm.trans1:exp6	-0.0428354989701379	0.0891048491216638	-0.480731401179415	0.630854470692306	   
df.mm.trans2:exp6	0.0806810058755751	0.0750329977522356	1.07527365682482	0.282614935217928	   
df.mm.trans1:exp7	0.0232320979483757	0.0891048491216638	0.26072765037349	0.794377614194152	   
df.mm.trans2:exp7	0.0521543701668588	0.0750329977522356	0.695085785311101	0.487227365184334	   
df.mm.trans1:exp8	0.0394690820202577	0.0891048491216638	0.442950999965969	0.657935157511883	   
df.mm.trans2:exp8	0.0866794086231353	0.0750329977522356	1.15521718736811	0.248387407147565	   
df.mm.trans1:probe2	-0.432104228611554	0.0488047358470297	-8.85373562856509	6.60793599551889e-18	***
df.mm.trans1:probe3	0.0904835260434557	0.0488047358470297	1.85399069317907	0.0641521238619242	.  
df.mm.trans1:probe4	-0.180976021858171	0.0488047358470297	-3.70816517531025	0.000224931984407333	***
df.mm.trans1:probe5	-0.321329467256282	0.0488047358470297	-6.58398128131326	8.86842263916879e-11	***
df.mm.trans1:probe6	-0.391773257026300	0.0488047358470297	-8.02736148914416	4.09133060308518e-15	***
df.mm.trans1:probe7	-0.424576325012189	0.0488047358470297	-8.69949027780732	2.27779576167528e-17	***
df.mm.trans1:probe8	0.118539900502131	0.0488047358470297	2.42886061044721	0.0153918228072242	*  
df.mm.trans1:probe9	-0.0436479202474466	0.0488047358470297	-0.894337803287241	0.371442124005452	   
df.mm.trans1:probe10	-0.119952033081968	0.0488047358470297	-2.45779494551385	0.0142155287708831	*  
df.mm.trans1:probe11	-0.460182188965641	0.0488047358470297	-9.429047836833	5.64120639547845e-20	***
df.mm.trans1:probe12	-0.433012086171041	0.0488047358470297	-8.8723374618448	5.68528949241865e-18	***
df.mm.trans1:probe13	-0.518005007995344	0.0488047358470297	-10.6138266913060	1.53996156467802e-24	***
df.mm.trans1:probe14	-0.460723076998923	0.0488047358470297	-9.44013053247503	5.13506970018511e-20	***
df.mm.trans1:probe15	-0.394280967099975	0.0488047358470297	-8.07874400418401	2.78382869058757e-15	***
df.mm.trans1:probe16	-0.311945828194543	0.0488047358470297	-6.39171225457064	2.9585082242271e-10	***
df.mm.trans1:probe17	0.219538021247581	0.0488047358470297	4.49829340201095	7.9923568869893e-06	***
df.mm.trans1:probe18	-0.118329339551527	0.0488047358470297	-2.42454625556033	0.0155743828607652	*  
df.mm.trans1:probe19	-0.346675537785081	0.0488047358470297	-7.1033175729437	2.94874801409696e-12	***
df.mm.trans1:probe20	-0.362774792503533	0.0488047358470297	-7.43318832091603	3.03652166621499e-13	***
df.mm.trans1:probe21	-0.398624988810475	0.0488047358470297	-8.16775220462003	1.42213729641221e-15	***
df.mm.trans1:probe22	-0.432523635640692	0.0488047358470297	-8.86232920092767	6.16461603109032e-18	***
df.mm.trans2:probe2	0.0637308751125692	0.0488047358470297	1.30583382957595	0.192029047129492	   
df.mm.trans2:probe3	-0.0673585393395036	0.0488047358470297	-1.38016399782652	0.167967521751746	   
df.mm.trans2:probe4	0.0313646679121594	0.0488047358470297	0.642656237510777	0.520653201340262	   
df.mm.trans2:probe5	-0.0653758549624742	0.0488047358470297	-1.33953916208837	0.180820827860267	   
df.mm.trans2:probe6	0.00493731297913141	0.0488047358470297	0.101164628666501	0.919448136145412	   
df.mm.trans3:probe2	0.225659105873743	0.0488047358470297	4.62371329251804	4.47300815556449e-06	***
df.mm.trans3:probe3	-0.325525746093344	0.0488047358470297	-6.66996225763112	5.12425579421558e-11	***
df.mm.trans3:probe4	0.128620097342797	0.0488047358470297	2.63540197709369	0.00858589362688	** 

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.08841034492402	0.169963845455379	24.0545883977269	3.90585497402104e-94	***
df.mm.trans1	0.0163915202115526	0.150931184123284	0.10860260791543	0.913548161916556	   
df.mm.trans2	0.151293140692323	0.137291539118283	1.10198444612073	0.270839235833978	   
df.mm.exp2	0.0838398463923199	0.185089396651909	0.452969472638102	0.650708143924114	   
df.mm.exp3	0.164535835615447	0.185089396651910	0.88895333061614	0.374327131242576	   
df.mm.exp4	0.19748560734539	0.185089396651909	1.06697418068088	0.286343624188013	   
df.mm.exp5	-0.130953308463983	0.185089396651909	-0.707513832952094	0.47947776251248	   
df.mm.exp6	0.0471374808098362	0.185089396651909	0.254674128623834	0.799048029553437	   
df.mm.exp7	0.203658374930068	0.185089396651909	1.10032437629629	0.27156112363672	   
df.mm.exp8	0.155759883349142	0.185089396651909	0.841538662758051	0.400327599572859	   
df.mm.trans1:exp2	-0.00608020242991833	0.175751065636451	-0.0345955366352970	0.972411916181754	   
df.mm.trans2:exp2	-0.14637996876125	0.147995641571056	-0.989082970331726	0.322957138868538	   
df.mm.trans1:exp3	-0.168565262408922	0.175751065636451	-0.959113743057504	0.337825654067006	   
df.mm.trans2:exp3	-0.160759397620191	0.147995641571057	-1.08624413471667	0.277737067572691	   
df.mm.trans1:exp4	-0.17812199726626	0.175751065636451	-1.01349028309571	0.311168868725927	   
df.mm.trans2:exp4	-0.173514793095054	0.147995641571056	-1.17243177740302	0.241414227203884	   
df.mm.trans1:exp5	0.162034595610505	0.175751065636451	0.92195512456056	0.356862967039590	   
df.mm.trans2:exp5	-0.0130190201993364	0.147995641571057	-0.0879689432819252	0.92992599179282	   
df.mm.trans1:exp6	-0.100538555444504	0.175751065636451	-0.572050900974177	0.567467294625056	   
df.mm.trans2:exp6	-0.109567217102524	0.147995641571057	-0.740340836658477	0.459336241670446	   
df.mm.trans1:exp7	-0.183080998120213	0.175751065636451	-1.04170633308662	0.297899949810600	   
df.mm.trans2:exp7	-0.205189344721206	0.147995641571057	-1.38645532086625	0.166040066227131	   
df.mm.trans1:exp8	-0.132299030103790	0.175751065636451	-0.752763743563617	0.45183966061102	   
df.mm.trans2:exp8	-0.0401615818370714	0.147995641571056	-0.271370030973438	0.786184778549081	   
df.mm.trans1:probe2	0.0725453348177383	0.0962628231546552	0.753617361722162	0.451327104153575	   
df.mm.trans1:probe3	0.0593295585171447	0.0962628231546552	0.616328885574301	0.537873647287972	   
df.mm.trans1:probe4	0.060358016012579	0.0962628231546552	0.627012734870743	0.530851043973524	   
df.mm.trans1:probe5	0.0137084385815916	0.0962628231546552	0.142406363457342	0.88679915453774	   
df.mm.trans1:probe6	0.0419085240432545	0.0962628231546552	0.435355235488207	0.663435983545438	   
df.mm.trans1:probe7	0.120471570392197	0.0962628231546552	1.25148594695429	0.211166527631892	   
df.mm.trans1:probe8	0.148216233089186	0.0962628231546552	1.53970378420195	0.124074993659662	   
df.mm.trans1:probe9	-0.0979263507335558	0.0962628231546552	-1.01728110109786	0.309363819900912	   
df.mm.trans1:probe10	0.000151103892280026	0.0962628231546552	0.00156970144161743	0.998747997815188	   
df.mm.trans1:probe11	0.129288999104657	0.0962628231546552	1.34308339260882	0.179671133839734	   
df.mm.trans1:probe12	0.0855108564837414	0.0962628231546552	0.888306136070415	0.374674831400529	   
df.mm.trans1:probe13	0.0332395242810321	0.0962628231546552	0.345299703371776	0.729970628047824	   
df.mm.trans1:probe14	0.1243901868173	0.0962628231546552	1.29219342151908	0.196707643404882	   
df.mm.trans1:probe15	-0.0585613946514912	0.0962628231546552	-0.608349025432247	0.543149212006369	   
df.mm.trans1:probe16	0.183989805961751	0.0962628231546552	1.91132775802922	0.0563620068811031	.  
df.mm.trans1:probe17	0.00300409058984854	0.0962628231546552	0.0312071731474381	0.975113028263668	   
df.mm.trans1:probe18	0.0387384128321915	0.0962628231546552	0.402423402541962	0.68749272884092	   
df.mm.trans1:probe19	-0.00902302230270513	0.0962628231546552	-0.0937331984146029	0.925347357080787	   
df.mm.trans1:probe20	0.0837913994143659	0.0962628231546552	0.870444026763553	0.384349934343012	   
df.mm.trans1:probe21	0.00495893339083899	0.0962628231546552	0.0515145227236064	0.958929917816372	   
df.mm.trans1:probe22	0.0888437493355978	0.0962628231546552	0.922928981553575	0.356355550590520	   
df.mm.trans2:probe2	0.00708866942179504	0.0962628231546552	0.07363870276697	0.941318469994495	   
df.mm.trans2:probe3	0.0693886574834888	0.0962628231546552	0.720825082929569	0.471252746073288	   
df.mm.trans2:probe4	-0.0623554598075758	0.0962628231546552	-0.647762633217145	0.517346423969564	   
df.mm.trans2:probe5	-0.0249773094744212	0.0962628231546552	-0.259469945466827	0.795347357470917	   
df.mm.trans2:probe6	-0.0374212983158395	0.0962628231546552	-0.388740918762778	0.6975835417068	   
df.mm.trans3:probe2	-0.00881416049287373	0.0962628231546552	-0.0915634946495695	0.927070502946445	   
df.mm.trans3:probe3	0.0123760635549611	0.0962628231546552	0.128565350042537	0.897737738971867	   
df.mm.trans3:probe4	-0.0568438881963859	0.0962628231546552	-0.590507179548026	0.555037265602714