chr2.14493_chr2_107611051_107716756_+_2.R 

fitVsDatCorrelation=0.891129415546112
cont.fitVsDatCorrelation=0.248627154144955

fstatistic=12052.8590545950,70,1106
cont.fstatistic=2632.71569837925,70,1106

residuals=-0.689379992499336,-0.0866204894161968,-0.00876860554115634,0.0756816270133537,0.705588710748786
cont.residuals=-0.496107076437961,-0.192604614538415,-0.0642092943310953,0.110218859068772,2.04629827082080

predictedValues:
Include	Exclude	Both
chr2.14493_chr2_107611051_107716756_+_2.R.tl.Lung	54.7939306874799	44.3756718210872	52.8316457470115
chr2.14493_chr2_107611051_107716756_+_2.R.tl.cerebhem	57.7586803420364	47.3787237884331	61.6459371694192
chr2.14493_chr2_107611051_107716756_+_2.R.tl.cortex	60.0803753095879	43.6095853920377	67.773840413516
chr2.14493_chr2_107611051_107716756_+_2.R.tl.heart	57.3949009922016	44.489825286681	59.4359952290786
chr2.14493_chr2_107611051_107716756_+_2.R.tl.kidney	59.1343453698689	45.2088119989533	55.6452190757601
chr2.14493_chr2_107611051_107716756_+_2.R.tl.liver	56.2212990185834	46.7702101209555	52.0881196971949
chr2.14493_chr2_107611051_107716756_+_2.R.tl.stomach	52.3490551629975	44.8526671815902	53.9994435062943
chr2.14493_chr2_107611051_107716756_+_2.R.tl.testicle	53.7806858823688	45.5914195899847	53.873254632471


diffExp=10.4182588663926,10.3799565536032,16.4707899175502,12.9050757055206,13.9255333709156,9.4510888976279,7.49638798140732,8.18926629238413
diffExpScore=0.98891799240618
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=0,0,1,0,1,0,0,0
diffExp1.3Score=0.666666666666667
diffExp1.2=1,1,1,1,1,1,0,0
diffExp1.2Score=0.857142857142857

cont.predictedValues:
Include	Exclude	Both
Lung	58.1669890321217	62.3657826290246	59.8976761073264
cerebhem	60.5320134369981	66.7741924610125	54.3297172799933
cortex	62.6065636195851	54.9560662827966	55.5262191877871
heart	58.6158623417531	57.8031419404501	58.9658065170132
kidney	59.5652748992335	56.6998300872148	65.0608269031263
liver	59.5147628424446	56.3979490398779	53.1398745851748
stomach	58.8711970189667	54.9949651411565	56.7399609187289
testicle	56.1614535009997	62.4761585980642	55.0137971163598
cont.diffExp=-4.19879359690297,-6.24217902401435,7.65049733678843,0.812720401303032,2.86544481201874,3.11681380256670,3.87623187781026,-6.31470509706444
cont.diffExpScore=13.6699021221772

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=-0.0924846958205796
cont.tran.correlation=-0.304859291257206

tran.covariance=-0.000117082666600041
cont.tran.covariance=-0.000727934143036856

tran.mean=50.8618867465529
cont.tran.mean=59.1563876794812

weightedLogRatios:
wLogRatio
Lung	0.822070162250537
cerebhem	0.783927487378058
cortex	1.2609525420444
heart	0.999068441349137
kidney	1.05945910706994
liver	0.724651617087678
stomach	0.599758999553062
testicle	0.644642753275416

cont.weightedLogRatios:
wLogRatio
Lung	-0.285637244701572
cerebhem	-0.407519101380441
cortex	0.530689574590173
heart	0.0567427816242666
kidney	0.200283718482037
liver	0.218357794153278
stomach	0.275254009735957
testicle	-0.434902539376495

varWeightedLogRatios=0.0513585028793196
cont.varWeightedLogRatios=0.126039125878904

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.83942481667603	0.0685693351070619	55.9933213685283	0	***
df.mm.trans1	0.0234025113973154	0.059266781351234	0.394867257235122	0.693016991518784	   
df.mm.trans2	-0.0562640887893468	0.0512460259842767	-1.09792101355547	0.272477943890634	   
df.mm.exp2	-0.0361208749319403	0.0646369739256569	-0.558826825239146	0.576393036993224	   
df.mm.exp3	-0.174376515795138	0.0646369739256569	-2.69778278908447	0.00708649710665363	** 
df.mm.exp4	-0.0688444976584411	0.0646369739256569	-1.06509468926598	0.287065545807805	   
df.mm.exp5	0.0429472968576929	0.0646369739256569	0.664438544216029	0.506548170455076	   
df.mm.exp6	0.0924447828466143	0.0646369739256569	1.43021520396269	0.1529376981224	   
df.mm.exp7	-0.0568172555236318	0.0646369739256569	-0.879020970087197	0.379580838406339	   
df.mm.exp8	-0.0111606739254311	0.0646369739256569	-0.172667023958573	0.86294475688547	   
df.mm.trans1:exp2	0.0888150880493972	0.0604102973224084	1.47019783027044	0.141792594000360	   
df.mm.trans2:exp2	0.101602750328780	0.0402908870894995	2.52173028861555	0.0118174898607688	*  
df.mm.trans1:exp3	0.266480336119999	0.0604102973224084	4.41117405361869	1.12849460686681e-05	***
df.mm.trans2:exp3	0.156962103117120	0.0402908870894995	3.89572219565321	0.000103780107470190	***
df.mm.trans1:exp4	0.115220530207416	0.0604102973224085	1.90729950545494	0.0567406511068846	.  
df.mm.trans2:exp4	0.0714136281330923	0.0402908870894995	1.7724511246044	0.0765949342519499	.  
df.mm.trans1:exp5	0.0332851647458449	0.0604102973224084	0.550984951591989	0.581755270550921	   
df.mm.trans2:exp5	-0.0243466606029628	0.0402908870894995	-0.604272140965296	0.545786655773781	   
df.mm.trans1:exp6	-0.0667285455904699	0.0604102973224085	-1.10458892851232	0.269577994716514	   
df.mm.trans2:exp6	-0.0398897057861159	0.0402908870894995	-0.990042877375909	0.322369704115811	   
df.mm.trans1:exp7	0.0111717102727795	0.0604102973224085	0.184930562634981	0.853317403710908	   
df.mm.trans2:exp7	0.0675089238808947	0.0402908870894995	1.67553828564099	0.0941113233212666	.  
df.mm.trans1:exp8	-0.00750435577339082	0.0604102973224084	-0.124223122646463	0.90116118719442	   
df.mm.trans2:exp8	0.0381888185427484	0.0402908870894995	0.947827692597543	0.343424223983275	   
df.mm.trans1:probe2	-0.0825863323335554	0.043771418892417	-1.88676388436343	0.0594538308856333	.  
df.mm.trans1:probe3	0.241284672073407	0.043771418892417	5.51237949737122	4.40396141316304e-08	***
df.mm.trans1:probe4	0.503854828088956	0.043771418892417	11.5110462680533	4.85696891982966e-29	***
df.mm.trans1:probe5	0.342978050247995	0.043771418892417	7.83566214956338	1.08966698916957e-14	***
df.mm.trans1:probe6	-0.0549883086082408	0.043771418892417	-1.25626059194912	0.209286791185572	   
df.mm.trans1:probe7	0.135019941427361	0.043771418892417	3.08465991836403	0.00208842218581253	** 
df.mm.trans1:probe8	0.289478696459625	0.043771418892417	6.61341815697399	5.83422753564142e-11	***
df.mm.trans1:probe9	0.203550478006093	0.043771418892417	4.65030568249083	3.71417094861127e-06	***
df.mm.trans1:probe10	1.76030110400225	0.043771418892417	40.2157651852406	1.20488325751111e-218	***
df.mm.trans1:probe11	0.0465993720058939	0.043771418892417	1.06460729821045	0.287286040507451	   
df.mm.trans1:probe12	0.0676859097042163	0.043771418892417	1.54634945397994	0.122306267061708	   
df.mm.trans1:probe13	0.468954001901744	0.043771418892417	10.7137034569146	1.49647230714174e-25	***
df.mm.trans1:probe14	0.202729245334272	0.043771418892417	4.63154383531746	4.06015445028804e-06	***
df.mm.trans1:probe15	0.0847095678161785	0.043771418892417	1.93527123313916	0.0532116926610589	.  
df.mm.trans1:probe16	0.0912747184113579	0.043771418892417	2.08525838825779	0.0372747029745066	*  
df.mm.trans1:probe17	0.00825042587454027	0.043771418892417	0.188488883461111	0.850528007645334	   
df.mm.trans1:probe18	0.163847615007693	0.043771418892417	3.74325573978774	0.000190995444621594	***
df.mm.trans1:probe19	0.0782523298272848	0.043771418892417	1.78774944489728	0.0740901487622101	.  
df.mm.trans1:probe20	0.0235329828676021	0.043771418892417	0.537633539489371	0.59093822673212	   
df.mm.trans1:probe21	0.0688909078923854	0.043771418892417	1.57387879204254	0.115801468789052	   
df.mm.trans1:probe22	0.00473518026322767	0.043771418892417	0.108179729673968	0.913872745495913	   
df.mm.trans1:probe23	-0.00587997373174795	0.043771418892417	-0.134333633236792	0.893163199200012	   
df.mm.trans1:probe24	0.491908859192126	0.043771418892417	11.2381291637166	7.98946071791917e-28	***
df.mm.trans1:probe25	0.306118985046957	0.043771418892417	6.99358149205415	4.62926344994491e-12	***
df.mm.trans1:probe26	-0.0125047529323861	0.043771418892417	-0.285683060974575	0.775174373905625	   
df.mm.trans1:probe27	0.0894587538184063	0.043771418892417	2.04377093733884	0.0412126864053832	*  
df.mm.trans1:probe28	0.329443705035194	0.043771418892417	7.52645706653725	1.07783120545597e-13	***
df.mm.trans1:probe29	0.205439589661958	0.043771418892417	4.69346424814088	3.02244151539019e-06	***
df.mm.trans2:probe2	0.0331152172936396	0.043771418892417	0.75654886525455	0.449481306149063	   
df.mm.trans2:probe3	0.0752014663972607	0.043771418892417	1.71804954694509	0.0860674666892284	.  
df.mm.trans2:probe4	0.0885836306559369	0.043771418892417	2.02377791027659	0.0432330755591836	*  
df.mm.trans2:probe5	-0.0179371647809919	0.043771418892417	-0.409791714202332	0.68203810145667	   
df.mm.trans2:probe6	0.0116500389227519	0.043771418892417	0.26615630056192	0.790168444557725	   
df.mm.trans3:probe2	0.0664664153988147	0.043771418892417	1.51848893823109	0.129176971487180	   
df.mm.trans3:probe3	0.138145293504744	0.043771418892417	3.15606158082932	0.00164234904486245	** 
df.mm.trans3:probe4	0.153602399678833	0.043771418892417	3.50919398012576	0.000467542436657838	***
df.mm.trans3:probe5	0.135027913869687	0.043771418892417	3.08484205644701	0.00208715478167683	** 
df.mm.trans3:probe6	0.497180050478379	0.043771418892417	11.3585545787393	2.3371044492273e-28	***
df.mm.trans3:probe7	0.154978815995792	0.043771418892417	3.54063952956848	0.000415763197674429	***
df.mm.trans3:probe8	-0.000537734123052612	0.043771418892417	-0.0122850512197074	0.990200409424847	   
df.mm.trans3:probe9	0.495627616497845	0.043771418892417	11.3230877371377	3.36016322943515e-28	***
df.mm.trans3:probe10	0.117214041598031	0.043771418892417	2.67786707774139	0.00751902126501946	** 
df.mm.trans3:probe11	0.0399264854564233	0.043771418892417	0.912158811999128	0.361883898089599	   
df.mm.trans3:probe12	0.16305380449078	0.043771418892417	3.72512037801515	0.000205079108358808	***
df.mm.trans3:probe13	0.251857682252961	0.043771418892417	5.7539300444426	1.12799520652789e-08	***
df.mm.trans3:probe14	0.0351169391146929	0.043771418892417	0.802280118015014	0.422563290404844	   

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.05860392752584	0.146372055538898	27.7279970728244	6.60859995330289e-129	***
df.mm.trans1	0.0610909387784622	0.126514288026997	0.48287778187888	0.629278082118427	   
df.mm.trans2	0.0875436576736794	0.109392721247866	0.80026949393936	0.423726531342668	   
df.mm.exp2	0.205720624937309	0.137977810672079	1.49096890242902	0.136254798509350	   
df.mm.exp3	0.0228518103700402	0.137977810672079	0.165619459090783	0.868486700529154	   
df.mm.exp4	-0.0526063004948879	0.137977810672079	-0.381266380721992	0.703078825175187	   
df.mm.exp5	-0.15417570499777	0.137977810672079	-1.11739492202980	0.264068172141955	   
df.mm.exp6	0.0420325532670922	0.137977810672079	0.304632701898624	0.760703221634559	   
df.mm.exp7	-0.0595822029197783	0.137977810672079	-0.431824527650918	0.665953118128951	   
df.mm.exp8	0.0517345953483363	0.137977810672079	0.37494866092121	0.707770534029357	   
df.mm.trans1:exp2	-0.165866247189351	0.12895530313938	-1.28623052446379	0.198631810756461	   
df.mm.trans2:exp2	-0.13742072911693	0.0860072502317191	-1.59778075390963	0.110377417797821	   
df.mm.trans1:exp3	0.0507003173342074	0.12895530313938	0.393161941385292	0.694275647366361	   
df.mm.trans2:exp3	-0.149334508871958	0.0860072502317191	-1.73630139865679	0.0827890184669919	.  
df.mm.trans1:exp4	0.060293653815646	0.12895530313938	0.467554666987818	0.640195196808847	   
df.mm.trans2:exp4	-0.0233673361423876	0.0860072502317191	-0.271690306101308	0.785910860303979	   
df.mm.trans1:exp5	0.177930478293382	0.12895530313938	1.37978411094166	0.167931995523835	   
df.mm.trans2:exp5	0.058930149359299	0.0860072502317192	0.685176531054423	0.49337610378021	   
df.mm.trans1:exp6	-0.0191261515504027	0.12895530313938	-0.148316130355108	0.882120321601107	   
df.mm.trans2:exp6	-0.142616529634992	0.0860072502317191	-1.65819194603661	0.0975622896107572	.  
df.mm.trans1:exp7	0.0716161639538626	0.12895530313938	0.555356485622441	0.578763157144793	   
df.mm.trans2:exp7	-0.066192928595656	0.0860072502317191	-0.769620333370968	0.441689475140305	   
df.mm.trans1:exp8	-0.086821949596169	0.12895530313938	-0.673271649032754	0.500915144093965	   
df.mm.trans2:exp8	-0.0499663434817898	0.0860072502317191	-0.580955016550017	0.561389132545894	   
df.mm.trans1:probe2	-0.0828406646110454	0.093436994060591	-0.88659385336525	0.3754903412246	   
df.mm.trans1:probe3	-0.0502183324290951	0.093436994060591	-0.537456635179531	0.591060346524343	   
df.mm.trans1:probe4	-0.126452088473602	0.093436994060591	-1.35334071632914	0.176223337113464	   
df.mm.trans1:probe5	-0.136752083570571	0.093436994060591	-1.46357537445920	0.143594141886007	   
df.mm.trans1:probe6	-0.126916329622141	0.093436994060591	-1.35830921037378	0.174642582669166	   
df.mm.trans1:probe7	-0.162031444132486	0.093436994060591	-1.73412517987697	0.083174514448098	.  
df.mm.trans1:probe8	-0.067135246695673	0.093436994060591	-0.718508202994393	0.472595749140278	   
df.mm.trans1:probe9	-0.169001966464678	0.093436994060591	-1.80872649172645	0.0707649758928006	.  
df.mm.trans1:probe10	-0.0815128196272088	0.093436994060591	-0.87238272642151	0.383188966285849	   
df.mm.trans1:probe11	-0.0214969109426949	0.093436994060591	-0.230068520063421	0.818081070024437	   
df.mm.trans1:probe12	-0.038638467392477	0.093436994060591	-0.413524298174887	0.679302690720923	   
df.mm.trans1:probe13	-0.172130342675359	0.093436994060591	-1.84220762242991	0.0657123449462585	.  
df.mm.trans1:probe14	-0.0544647709065776	0.093436994060591	-0.582903714467301	0.560076944678859	   
df.mm.trans1:probe15	-0.0986194568861745	0.093436994060591	-1.05546478541704	0.291443306283106	   
df.mm.trans1:probe16	-0.0905430111374992	0.093436994060591	-0.969027439803819	0.332743354975867	   
df.mm.trans1:probe17	-0.0944982904287473	0.093436994060591	-1.01135841728243	0.312066116117711	   
df.mm.trans1:probe18	0.0166937236853579	0.093436994060591	0.178662893142009	0.858235128086569	   
df.mm.trans1:probe19	-0.175689750220265	0.093436994060591	-1.88030182249159	0.0603295806297604	.  
df.mm.trans1:probe20	-0.129996765826243	0.093436994060591	-1.39127726799456	0.164421150372341	   
df.mm.trans1:probe21	-0.0285464306962098	0.093436994060591	-0.305515293842805	0.760031213557486	   
df.mm.trans1:probe22	-0.113032590828304	0.093436994060591	-1.20971989697149	0.226644894888814	   
df.mm.trans1:probe23	-0.0504117504628719	0.093436994060591	-0.539526672167787	0.589632096043151	   
df.mm.trans1:probe24	-0.0734877979497908	0.093436994060591	-0.786495741741609	0.43174559375238	   
df.mm.trans1:probe25	-0.0389450198736781	0.093436994060591	-0.416805145170053	0.67690181925555	   
df.mm.trans1:probe26	-0.0851665208699383	0.093436994060591	-0.911486095268758	0.362237926545315	   
df.mm.trans1:probe27	-0.0105407031101013	0.093436994060591	-0.112810811350224	0.910201018581922	   
df.mm.trans1:probe28	0.0157750431183717	0.093436994060591	0.168830807079925	0.865960596048252	   
df.mm.trans1:probe29	-0.177604674538723	0.093436994060591	-1.90079610677064	0.0575884950430465	.  
df.mm.trans2:probe2	-0.100547383716835	0.093436994060591	-1.07609822777082	0.282118004622597	   
df.mm.trans2:probe3	0.00203186268975752	0.093436994060591	0.0217458053973774	0.982654647393858	   
df.mm.trans2:probe4	-0.0677255479197912	0.093436994060591	-0.724825842276918	0.468712191405622	   
df.mm.trans2:probe5	0.00893037110075622	0.093436994060591	0.0955763955223683	0.92387432677768	   
df.mm.trans2:probe6	-0.105305612753669	0.093436994060591	-1.12702269387414	0.259977340227653	   
df.mm.trans3:probe2	-0.199942551857258	0.093436994060591	-2.13986498460772	0.032583995144577	*  
df.mm.trans3:probe3	-0.147187290447058	0.093436994060591	-1.57525712301502	0.115483088364558	   
df.mm.trans3:probe4	-0.118047690416497	0.093436994060591	-1.26339349422935	0.206714050280786	   
df.mm.trans3:probe5	-0.180405075141125	0.093436994060591	-1.93076711162323	0.0537672178654156	.  
df.mm.trans3:probe6	-0.181289403780349	0.093436994060591	-1.94023154964498	0.0526054652610187	.  
df.mm.trans3:probe7	0.0911188337673311	0.093436994060591	0.975190123392062	0.329679200020764	   
df.mm.trans3:probe8	-0.0937954697735105	0.093436994060591	-1.00383655014294	0.315676971820624	   
df.mm.trans3:probe9	-0.21137954823027	0.093436994060591	-2.26226828415731	0.0238742284369459	*  
df.mm.trans3:probe10	-0.196577432570468	0.093436994060591	-2.10385013502246	0.035616839228386	*  
df.mm.trans3:probe11	-0.0746073099617632	0.093436994060591	-0.798477206077313	0.42476503417147	   
df.mm.trans3:probe12	-0.0842661983559958	0.093436994060591	-0.901850484416823	0.367332648592681	   
df.mm.trans3:probe13	-0.0283687672588426	0.093436994060591	-0.303613868832791	0.761479188951788	   
df.mm.trans3:probe14	-0.179663517371251	0.093436994060591	-1.9228306644234	0.0547578732504684	.