chr18.11504_chr18_89806066_89846618_+_2.R 

fitVsDatCorrelation=0.78256004976277
cont.fitVsDatCorrelation=0.296697772002945

fstatistic=10674.5455925441,52,692
cont.fstatistic=4529.17451256712,52,692

residuals=-0.595645927524806,-0.0835116224385947,-0.0089253934805169,0.0620767526937597,0.90197588977136
cont.residuals=-0.43277008032995,-0.137683160664881,-0.0455822643893277,0.0770022098346744,0.914119362051374

predictedValues:
Include	Exclude	Both
chr18.11504_chr18_89806066_89846618_+_2.R.tl.Lung	55.7837784232796	44.2861352278234	68.0057311944312
chr18.11504_chr18_89806066_89846618_+_2.R.tl.cerebhem	62.3219644534847	51.8458522640021	53.215743983315
chr18.11504_chr18_89806066_89846618_+_2.R.tl.cortex	53.4532400221481	42.8491406147748	53.3979387417047
chr18.11504_chr18_89806066_89846618_+_2.R.tl.heart	55.4406651105049	44.2456218033852	58.5553993835747
chr18.11504_chr18_89806066_89846618_+_2.R.tl.kidney	53.8468921448705	45.0473924254097	63.1527848068927
chr18.11504_chr18_89806066_89846618_+_2.R.tl.liver	56.4590910643336	48.6215080777945	60.712156569221
chr18.11504_chr18_89806066_89846618_+_2.R.tl.stomach	56.9260181362354	45.5520555479619	54.5402618203074
chr18.11504_chr18_89806066_89846618_+_2.R.tl.testicle	57.0077300207019	47.9395330991966	59.9035135738779


diffExp=11.4976431954562,10.4761121894826,10.6040994073733,11.1950433071197,8.79949971946085,7.83758298653908,11.3739625882735,9.06819692150527
diffExpScore=0.987782848485708
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=1,1,1,1,0,0,1,0
diffExp1.2Score=0.833333333333333

cont.predictedValues:
Include	Exclude	Both
Lung	50.595227315369	51.8919533843071	50.2760447747863
cerebhem	50.8038938420495	47.0607567015351	54.8692485752898
cortex	55.8618743548272	56.4721676929298	50.6898037679262
heart	50.9215700843669	55.1507772032609	50.1375836818562
kidney	50.035251563341	51.2513453701493	51.1512690461962
liver	51.6034307673512	51.9177208827521	49.0620525521784
stomach	49.3092404601745	53.5217434635021	48.3565423148314
testicle	52.1288524506941	55.135531404155	55.5090798981557
cont.diffExp=-1.29672606893803,3.74313714051437,-0.610293338102572,-4.22920711889399,-1.21609380680835,-0.314290115400965,-4.21250300332761,-3.00667895346086
cont.diffExpScore=1.53417264509153

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=0.887686092864185
cont.tran.correlation=0.517763551904109

tran.covariance=0.002607155897759
cont.tran.covariance=0.00108290595908490

tran.mean=51.3516636522442
cont.tran.mean=52.1038335587978

weightedLogRatios:
wLogRatio
Lung	0.901567441216423
cerebhem	0.743571692180107
cortex	0.8553530864854
heart	0.880243778761358
kidney	0.695327596192517
liver	0.591642675756804
stomach	0.876052005407981
testicle	0.685460348303185

cont.weightedLogRatios:
wLogRatio
Lung	-0.0996192531017281
cerebhem	0.297692985211195
cortex	-0.0437708455774601
heart	-0.316757654686908
kidney	-0.0942488981986393
liver	-0.0239639678643037
stomach	-0.322913588339467
testicle	-0.223280084427144

varWeightedLogRatios=0.0132328097840668
cont.varWeightedLogRatios=0.0398454661911189

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.10425290361292	0.0771790660332877	53.1783178335267	1.27319890858575e-246	***
df.mm.trans1	0.406840939836423	0.0693180878889121	5.86918872442672	6.7928410818379e-09	***
df.mm.trans2	-0.327913842236046	0.063747396073488	-5.14395665444949	3.50694338489412e-07	***
df.mm.exp2	0.513671780079792	0.0873181455324513	5.88276098796543	6.2828671783089e-09	***
df.mm.exp3	0.166157910256876	0.0873181455324513	1.90290241786141	0.0574680402511684	.  
df.mm.exp4	0.142533684396539	0.0873181455324513	1.63234896397986	0.103060919278044	   
df.mm.exp5	0.0557400557372744	0.0873181455324514	0.638355927022738	0.523453209791717	   
df.mm.exp6	0.218875571518297	0.0873181455324514	2.50664475503495	0.0124165306094001	*  
df.mm.exp7	0.26910628192299	0.0873181455324513	3.08190560257579	0.00213843799780827	** 
df.mm.exp8	0.227829414504289	0.0873181455324513	2.60918750753379	0.00927183868576333	** 
df.mm.trans1:exp2	-0.402840974837795	0.0836007595317819	-4.81862816909755	1.77749009827681e-06	***
df.mm.trans2:exp2	-0.356068497035472	0.0727651212770428	-4.89339522543764	1.23404540088223e-06	***
df.mm.trans1:exp3	-0.208833774672111	0.0836007595317819	-2.49798896375721	0.0127210176143804	*  
df.mm.trans2:exp3	-0.199143974601840	0.0727651212770428	-2.73680536920468	0.00636312359713016	** 
df.mm.trans1:exp4	-0.148703450507038	0.0836007595317819	-1.77873324763882	0.0757224318918546	.  
df.mm.trans2:exp4	-0.143448913657195	0.0727651212770428	-1.97139661337241	0.0490765165439818	*  
df.mm.trans1:exp5	-0.0910784848504378	0.083600759531782	-1.08944566246211	0.276336642279704	   
df.mm.trans2:exp5	-0.038696608670154	0.0727651212770428	-0.531801610318523	0.595034084262656	   
df.mm.trans1:exp6	-0.20684236543973	0.0836007595317819	-2.47416849557565	0.0135934175543365	*  
df.mm.trans2:exp6	-0.125481238991628	0.0727651212770428	-1.72446959187873	0.0850696611784595	.  
df.mm.trans1:exp7	-0.248836902423179	0.0836007595317819	-2.9764909292311	0.00301740666407653	** 
df.mm.trans2:exp7	-0.240922185470473	0.0727651212770428	-3.31095697007356	0.000978050027464628	***
df.mm.trans1:exp8	-0.206125659329175	0.0836007595317819	-2.46559553386370	0.0139201201135314	*  
df.mm.trans2:exp8	-0.148560578342744	0.0727651212770428	-2.04164544407369	0.0415654411671671	*  
df.mm.trans1:probe2	-0.447199717047625	0.0418003797658910	-10.6984606252917	7.97244711917856e-25	***
df.mm.trans1:probe3	-0.6293882017216	0.0418003797658910	-15.0569972150153	1.58541006579826e-44	***
df.mm.trans1:probe4	-0.66730667188888	0.0418003797658910	-15.9641294080635	4.44547860477785e-49	***
df.mm.trans1:probe5	-0.553536333435935	0.0418003797658910	-13.2423757041466	7.6923547001951e-36	***
df.mm.trans1:probe6	-0.679464179755874	0.0418003797658910	-16.2549762361326	1.45356189289076e-50	***
df.mm.trans1:probe7	-0.648292957645242	0.041800379765891	-15.5092599941938	8.839226537269e-47	***
df.mm.trans1:probe8	-0.538202416742468	0.041800379765891	-12.8755389246880	3.67119355749786e-34	***
df.mm.trans1:probe9	-0.0278945500903412	0.041800379765891	-0.667327671340994	0.504785320394045	   
df.mm.trans1:probe10	-0.593895873837811	0.0418003797658910	-14.2079061760685	2.18476191263906e-40	***
df.mm.trans1:probe11	-0.672437255564457	0.041800379765891	-16.0868695291894	1.05315762748353e-49	***
df.mm.trans1:probe12	-0.644758551186415	0.0418003797658910	-15.4247055839559	2.34542101353565e-46	***
df.mm.trans1:probe13	-0.61177134765688	0.0418003797658910	-14.6355452051678	1.86317804158372e-42	***
df.mm.trans1:probe14	-0.684534259946592	0.0418003797658910	-16.3762689186181	3.46290062069465e-51	***
df.mm.trans1:probe15	-0.613014262457922	0.0418003797658910	-14.6652797388731	1.33410181928693e-42	***
df.mm.trans1:probe16	-0.716899764947724	0.0418003797658910	-17.1505562619963	3.28611285789961e-55	***
df.mm.trans1:probe17	-0.656435176670629	0.041800379765891	-15.7040481533203	9.24528737862912e-48	***
df.mm.trans1:probe18	-0.543380868271065	0.041800379765891	-12.9994241993577	1.00232563590726e-34	***
df.mm.trans1:probe19	-0.594811294855528	0.041800379765891	-14.2298060014491	1.71488430876664e-40	***
df.mm.trans1:probe20	-0.547044145714083	0.041800379765891	-13.0870616194848	3.98319300160909e-35	***
df.mm.trans1:probe21	-0.636978621279072	0.041800379765891	-15.2385845498668	1.99141032988579e-45	***
df.mm.trans1:probe22	-0.533021688944516	0.041800379765891	-12.7515991943083	1.33535351756493e-33	***
df.mm.trans2:probe2	0.0202600212117907	0.041800379765891	0.484685099160819	0.628053116445473	   
df.mm.trans2:probe3	-0.00278696535580023	0.041800379765891	-0.0666732065930748	0.946861122713587	   
df.mm.trans2:probe4	-0.0101328917540955	0.041800379765891	-0.242411475944627	0.808533169331731	   
df.mm.trans2:probe5	0.0118369294560209	0.041800379765891	0.283177557771371	0.777125421674543	   
df.mm.trans2:probe6	0.109816235758167	0.041800379765891	2.62715880509242	0.00880078281448478	** 
df.mm.trans3:probe2	0.107652281209983	0.041800379765891	2.57539002786351	0.0102191748610835	*  
df.mm.trans3:probe3	0.00158366683694409	0.041800379765891	0.0378864222242393	0.9697891655011	   

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.85865359508914	0.118385399260374	32.5939990843170	6.32597138288363e-142	***
df.mm.trans1	-0.00929693646838703	0.106327401100645	-0.0874368824230635	0.930349548836458	   
df.mm.trans2	0.0885818383872168	0.0977824858973304	0.905906999339594	0.365300388430354	   
df.mm.exp2	-0.181032862343198	0.13393778977678	-1.35161900644252	0.176938873477219	   
df.mm.exp3	0.175412972021744	0.13393778977678	1.30966004675818	0.190745494269910	   
df.mm.exp4	0.0700942747825369	0.13393778977678	0.523334563750497	0.60090898901632	   
df.mm.exp5	-0.0408099160604266	0.13393778977678	-0.304693067792445	0.760691540986504	   
df.mm.exp6	0.0446701864330774	0.13393778977678	0.333514436123849	0.73884702499134	   
df.mm.exp7	0.0441056928853043	0.13393778977678	0.329299841059126	0.742028706692444	   
df.mm.exp8	-0.00852593318357241	0.13393778977678	-0.0636559196458422	0.949262587289898	   
df.mm.trans1:exp2	0.185148614410000	0.128235670685259	1.44381523035372	0.149243518241868	   
df.mm.trans2:exp2	0.08330858763179	0.111614824813983	0.746393570662604	0.455683191936812	   
df.mm.trans1:exp3	-0.0763881075736519	0.128235670685259	-0.59568532815755	0.551580230436708	   
df.mm.trans2:exp3	-0.0908287998319056	0.111614824813983	-0.813770034431182	0.416056622743707	   
df.mm.trans1:exp4	-0.0636649172473199	0.128235670685259	-0.496468080270573	0.619721865993455	   
df.mm.trans2:exp4	-0.00918717403511354	0.111614824813983	-0.0823114138325696	0.934422880454832	   
df.mm.trans1:exp5	0.029680454323419	0.128235670685259	0.231452404505035	0.817031777534296	   
df.mm.trans2:exp5	0.0283880474722699	0.111614824813983	0.25433939908593	0.799308922289514	   
df.mm.trans1:exp6	-0.0249392784305936	0.128235670685259	-0.194480040516998	0.845857101975933	   
df.mm.trans2:exp6	-0.0441737490989623	0.111614824813983	-0.395769550976602	0.69239702748866	   
df.mm.trans1:exp7	-0.0698514461450064	0.128235670685259	-0.544711512574762	0.586127435576826	   
df.mm.trans2:exp7	-0.0131814390579734	0.111614824813983	-0.118097565264664	0.90602462323562	   
df.mm.trans1:exp8	0.0383872684842824	0.128235670685259	0.299349379772029	0.764763364923425	   
df.mm.trans2:exp8	0.0691565571970195	0.111614824813983	0.619600105203547	0.535725044983341	   
df.mm.trans1:probe2	0.0861430677152402	0.0641178353426294	1.34351178973703	0.179546699318929	   
df.mm.trans1:probe3	0.134627797474182	0.0641178353426294	2.09969342780781	0.0361173492086076	*  
df.mm.trans1:probe4	0.0406285240861878	0.0641178353426294	0.633654019495189	0.526516013841224	   
df.mm.trans1:probe5	0.0661346385725994	0.0641178353426294	1.03145463690707	0.302688024820816	   
df.mm.trans1:probe6	0.130982874223067	0.0641178353426294	2.04284616788961	0.0414460508370119	*  
df.mm.trans1:probe7	0.0670104656435396	0.0641178353426294	1.04511428505739	0.296335083602808	   
df.mm.trans1:probe8	0.105678449075474	0.0641178353426294	1.64819115478174	0.0997673662348824	.  
df.mm.trans1:probe9	0.0584926201487709	0.0641178353426294	0.912267543596897	0.361945592397216	   
df.mm.trans1:probe10	0.157494799172295	0.0641178353426294	2.45633369140868	0.0142808637459666	*  
df.mm.trans1:probe11	0.095112088686684	0.0641178353426294	1.48339519228042	0.138424734941102	   
df.mm.trans1:probe12	0.114429761038584	0.0641178353426294	1.78467910569813	0.0747513572410307	.  
df.mm.trans1:probe13	0.0790308947173017	0.0641178353426294	1.23258831641743	0.218147991869445	   
df.mm.trans1:probe14	0.0420455932511702	0.0641178353426294	0.655755033314666	0.512199707652543	   
df.mm.trans1:probe15	0.200883178408010	0.0641178353426294	3.13303119692893	0.00180312085382165	** 
df.mm.trans1:probe16	0.131506941648619	0.0641178353426294	2.05101967254320	0.0406410521683442	*  
df.mm.trans1:probe17	-0.00952468101406474	0.0641178353426294	-0.148549634640148	0.881952294271826	   
df.mm.trans1:probe18	0.0407717598739431	0.0641178353426294	0.635887965588188	0.525059687158494	   
df.mm.trans1:probe19	0.0562059182023839	0.0641178353426294	0.87660348952883	0.381006244040389	   
df.mm.trans1:probe20	0.0972655257296252	0.0641178353426294	1.51698080900366	0.129728201915961	   
df.mm.trans1:probe21	0.0413032866991662	0.0641178353426294	0.644177809161085	0.519673593180898	   
df.mm.trans1:probe22	0.126291281482915	0.0641178353426294	1.96967475286785	0.0492740942675416	*  
df.mm.trans2:probe2	0.105037745816635	0.0641178353426294	1.63819856449208	0.101834852466844	   
df.mm.trans2:probe3	-0.0819133826913228	0.0641178353426294	-1.27754441885941	0.201838537815728	   
df.mm.trans2:probe4	0.0578915864137835	0.0641178353426294	0.902893650486259	0.366896515689095	   
df.mm.trans2:probe5	-0.0777750583266022	0.0641178353426294	-1.21300193481255	0.225542985397682	   
df.mm.trans2:probe6	0.0141138439831297	0.0641178353426294	0.220123525813199	0.825839874421956	   
df.mm.trans3:probe2	-0.008521912378744	0.0641178353426294	-0.132910169739903	0.894303062592606	   
df.mm.trans3:probe3	-0.0527074287194174	0.0641178353426294	-0.822040052315588	0.411337243727852