chr1.622_chr1_83639674_83647036_-_2.R 

fitVsDatCorrelation=0.750357741989331
cont.fitVsDatCorrelation=0.249862984994012

fstatistic=9794.81452574061,55,761
cont.fstatistic=4557.58440284157,55,761

residuals=-0.458114461328716,-0.0858883867807453,-0.00914550241080902,0.0673737695906776,1.27712618627594
cont.residuals=-0.402553284880312,-0.129728971924804,-0.0429231744953267,0.065759796818604,1.73215555851348

predictedValues:
Include	Exclude	Both
chr1.622_chr1_83639674_83647036_-_2.R.tl.Lung	47.9419707071887	45.9510640367026	56.7786159472317
chr1.622_chr1_83639674_83647036_-_2.R.tl.cerebhem	59.3085427469711	62.2735831336011	56.9429345286711
chr1.622_chr1_83639674_83647036_-_2.R.tl.cortex	48.7400354854202	46.9388162050415	51.3654789645865
chr1.622_chr1_83639674_83647036_-_2.R.tl.heart	49.5869076222763	48.0167418512701	56.2168385131773
chr1.622_chr1_83639674_83647036_-_2.R.tl.kidney	48.4946135520731	46.7825964844381	56.0758229585915
chr1.622_chr1_83639674_83647036_-_2.R.tl.liver	51.7694867044772	49.915274171512	59.6708457262347
chr1.622_chr1_83639674_83647036_-_2.R.tl.stomach	51.919370141718	47.6828192486165	66.0715879452363
chr1.622_chr1_83639674_83647036_-_2.R.tl.testicle	50.3325764863671	52.0558579690898	54.3899985224421


diffExp=1.99090667048615,-2.96504038663,1.80121928037869,1.57016577100618,1.71201706763506,1.85421253296518,4.23655089310144,-1.72328148272269
diffExpScore=1.88391520644485
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=0,0,0,0,0,0,0,0
diffExp1.2Score=0

cont.predictedValues:
Include	Exclude	Both
Lung	51.905875946988	52.4295153390518	50.6354102165198
cerebhem	50.7845967892684	53.5753874883831	47.5405805534073
cortex	54.5432978576573	50.6598320711463	52.0518605499801
heart	49.8896854505904	47.9405080466901	50.9387577579834
kidney	49.9157341245519	49.7743555491208	54.638850965922
liver	50.5318496769622	51.6817129052941	48.7226137208267
stomach	54.3551871955443	48.5958437665601	52.1449617297433
testicle	50.7655428294904	55.9627916595176	49.3868593079143
cont.diffExp=-0.523639392063778,-2.79079069911466,3.88346578651102,1.94917740390029,0.141378575431105,-1.14986322833187,5.75934342898427,-5.19724883002716
cont.diffExpScore=6.96488926247698

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=0.938857663362691
cont.tran.correlation=-0.195201315145196

tran.covariance=0.00631672078499048
cont.tran.covariance=-0.00033840517718265

tran.mean=50.4818910341727
cont.tran.mean=51.4569822935511

weightedLogRatios:
wLogRatio
Lung	0.163243605789494
cerebhem	-0.200362787641261
cortex	0.145640337342854
heart	0.125092929865432
kidney	0.138859211570045
liver	0.143289910034430
stomach	0.332578129448146
testicle	-0.132487401072138

cont.weightedLogRatios:
wLogRatio
Lung	-0.0396935996603322
cerebhem	-0.211544131121456
cortex	0.292644046466292
heart	0.155025440959634
kidney	0.0110871129226749
liver	-0.0885124180960083
stomach	0.441236413995743
testicle	-0.387532909251915

varWeightedLogRatios=0.0296531737085163
cont.varWeightedLogRatios=0.0721571480628716

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.65542280561176	0.0747104713685558	48.9278509243921	3.32577677906383e-237	***
df.mm.trans1	0.205999306273802	0.0654199303117526	3.14887688342271	0.00170301268183423	** 
df.mm.trans2	0.145006435289048	0.0586666079734845	2.47170307433806	0.0136648836071399	*  
df.mm.exp2	0.513832480988458	0.0773672314761625	6.6414743190956	5.90971446471268e-11	***
df.mm.exp3	0.137970849840403	0.0773672314761625	1.78332411807851	0.074932016989737	.  
df.mm.exp4	0.0876516827836543	0.0773672314761625	1.13293032607300	0.257600324503467	   
df.mm.exp5	0.0418506725900367	0.0773672314761625	0.540935377827643	0.588710423809442	   
df.mm.exp6	0.109875846815695	0.0773672314761625	1.42018584249779	0.155963129102051	   
df.mm.exp7	-0.0348842953370344	0.0773672314761625	-0.450892382620445	0.652195557329808	   
df.mm.exp8	0.216380992990231	0.0773672314761626	2.79680413608828	0.00529132864095756	** 
df.mm.trans1:exp2	-0.301070461492213	0.07257689636194	-4.14829617390607	3.72712140746222e-05	***
df.mm.trans2:exp2	-0.209872176804049	0.0578963346294066	-3.6249648297675	0.000308236879467485	***
df.mm.trans1:exp3	-0.121461409534041	0.07257689636194	-1.67355474844660	0.0946290872162925	.  
df.mm.trans2:exp3	-0.116702883993453	0.0578963346294066	-2.01572145698801	0.0441801360126074	*  
df.mm.trans1:exp4	-0.0539161790702736	0.07257689636194	-0.74288350388248	0.457781470186876	   
df.mm.trans2:exp4	-0.0436789490564864	0.0578963346294066	-0.754433753640444	0.450822197873387	   
df.mm.trans1:exp5	-0.0303892775421341	0.07257689636194	-0.418718339657061	0.675540137516102	   
df.mm.trans2:exp5	-0.023916413754125	0.0578963346294066	-0.41309029159123	0.679656828619647	   
df.mm.trans1:exp6	-0.0330662668190810	0.07257689636194	-0.455603208136375	0.648805303885328	   
df.mm.trans2:exp6	-0.0271258002107045	0.0578963346294066	-0.468523618711552	0.639544356502135	   
df.mm.trans1:exp7	0.114584900390607	0.07257689636194	1.57880683984024	0.114795919834662	   
df.mm.trans2:exp7	0.0718784399462669	0.0578963346294066	1.24150242681786	0.214802723436172	   
df.mm.trans1:exp8	-0.167719817596995	0.07257689636194	-2.31092573538249	0.0211033964307501	*  
df.mm.trans2:exp8	-0.091640664162883	0.0578963346294066	-1.58284051571612	0.113873296893512	   
df.mm.trans1:probe2	0.0308343715552769	0.0444440908004024	0.693778880386	0.488032599129889	   
df.mm.trans1:probe3	0.475721705492019	0.0444440908004024	10.7038235437974	5.29478005374105e-25	***
df.mm.trans1:probe4	0.0125394611894411	0.0444440908004024	0.282140121748820	0.777912781579805	   
df.mm.trans1:probe5	-0.194363023255861	0.0444440908004024	-4.37320282079211	1.39485174534932e-05	***
df.mm.trans1:probe6	-0.0892722253739773	0.0444440908004024	-2.00864105365317	0.0449280209807244	*  
df.mm.trans1:probe7	0.00926811989018855	0.0444440908004025	0.208534356835231	0.834867560516858	   
df.mm.trans1:probe8	-0.0422663909783538	0.0444440908004024	-0.951001364122203	0.341905615319043	   
df.mm.trans1:probe9	-0.00339319814175563	0.0444440908004025	-0.0763475656863904	0.939162651059264	   
df.mm.trans1:probe10	-0.115295804124413	0.0444440908004025	-2.59417623463610	0.00966418952798265	** 
df.mm.trans1:probe11	-0.0307478277756030	0.0444440908004024	-0.691831629849083	0.489254086505176	   
df.mm.trans1:probe12	-0.0925327218054866	0.0444440908004025	-2.08200280710093	0.037675797005636	*  
df.mm.trans1:probe13	-0.0412538591805041	0.0444440908004024	-0.92821921739325	0.353588176399129	   
df.mm.trans1:probe14	-0.105370822369152	0.0444440908004024	-2.37086236823632	0.0179948059845607	*  
df.mm.trans1:probe15	-0.0540236982950387	0.0444440908004025	-1.21554288370209	0.224536057735741	   
df.mm.trans1:probe16	-0.0763482831031006	0.0444440908004025	-1.71785003873697	0.0862307662261458	.  
df.mm.trans1:probe17	0.0635699590619155	0.0444440908004024	1.43033546005940	0.153031210763324	   
df.mm.trans1:probe18	0.160660724284132	0.0444440908004024	3.61489506008024	0.000320256144491089	***
df.mm.trans1:probe19	0.097014472757659	0.0444440908004025	2.18284300590936	0.0293523091937241	*  
df.mm.trans1:probe20	0.0574414835061663	0.0444440908004025	1.29244366285126	0.196595836125960	   
df.mm.trans1:probe21	0.0637211100217831	0.0444440908004024	1.43373638371754	0.152058240961104	   
df.mm.trans1:probe22	0.114034720332171	0.0444440908004025	2.56580162353414	0.0104841554549653	*  
df.mm.trans2:probe2	0.071906330708597	0.0444440908004024	1.61790531460137	0.106097381273733	   
df.mm.trans2:probe3	0.00865150792533726	0.0444440908004025	0.194660477231743	0.84571070914954	   
df.mm.trans2:probe4	0.0660516170550069	0.0444440908004024	1.48617320920442	0.137647466284833	   
df.mm.trans2:probe5	0.0615185224799	0.0444440908004025	1.38417776968773	0.166709802367724	   
df.mm.trans2:probe6	0.117645190256546	0.0444440908004025	2.64703784322843	0.00828795656881293	** 
df.mm.trans3:probe2	0.000662090364090652	0.0444440908004024	0.0148971517285411	0.988118136852754	   
df.mm.trans3:probe3	-0.202446752165104	0.0444440908004024	-4.55508816851015	6.09672744968036e-06	***
df.mm.trans3:probe4	0.0238637175359692	0.0444440908004024	0.53693791696945	0.591467441605578	   
df.mm.trans3:probe5	-0.163783117832588	0.0444440908004025	-3.68514947393423	0.000244777991352667	***
df.mm.trans3:probe6	0.53809142928116	0.0444440908004025	12.1071534953368	5.56127433882997e-31	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.90095164940699	0.109436077111353	35.6459382716880	1.96981450474191e-164	***
df.mm.trans1	-0.0186882809361173	0.0958272703554315	-0.195020487036737	0.845428963272705	   
df.mm.trans2	0.0494630717267199	0.0859349876455196	0.575587116283233	0.565064447185708	   
df.mm.exp2	0.0628486311593766	0.113327705669928	0.554574283383326	0.579348826613332	   
df.mm.exp3	-0.0123629326065209	0.113327705669928	-0.109090116432151	0.91315977229817	   
df.mm.exp4	-0.135099544234346	0.113327705669928	-1.19211399750587	0.233588155501237	   
df.mm.exp5	-0.167159581961213	0.113327705669928	-1.47501073080993	0.140623244140103	   
df.mm.exp6	-0.00268600708502809	0.113327705669928	-0.0237012394202281	0.981097130592892	   
df.mm.exp7	-0.0592000728707003	0.113327705669928	-0.522379523354363	0.601558138197457	   
df.mm.exp8	0.0679699211828115	0.113327705669928	0.599764380484126	0.548841806876282	   
df.mm.trans1:exp2	-0.0846875358312027	0.106310811339772	-0.796603231260641	0.425929943029567	   
df.mm.trans2:exp2	-0.0412285597709072	0.0848066894092055	-0.486147496832154	0.627002549965771	   
df.mm.trans1:exp3	0.061925774498029	0.106310811339772	0.582497431047843	0.560404213025283	   
df.mm.trans2:exp3	-0.0219734402261991	0.0848066894092055	-0.259100318374342	0.795627908659947	   
df.mm.trans1:exp4	0.095481820760247	0.106310811339772	0.898138388344012	0.369395841202119	   
df.mm.trans2:exp4	0.045590668158091	0.0848066894092055	0.537583396730757	0.591021856965056	   
df.mm.trans1:exp5	0.128063847666938	0.106310811339772	1.20461734844298	0.228725554277167	   
df.mm.trans2:exp5	0.115189781967986	0.0848066894092056	1.35826292442778	0.174782791502133	   
df.mm.trans1:exp6	-0.0241421692286835	0.106310811339772	-0.227090442866856	0.820414405609844	   
df.mm.trans2:exp6	-0.0116796921169303	0.0848066894092055	-0.137721354274001	0.890497072507567	   
df.mm.trans1:exp7	0.105308121995861	0.106310811339772	0.990568321967688	0.32221126078617	   
df.mm.trans2:exp7	-0.0167316216657350	0.0848066894092055	-0.197291296032113	0.8436522721427	   
df.mm.trans1:exp8	-0.0901840879679763	0.106310811339772	-0.84830589505846	0.396534377162481	   
df.mm.trans2:exp8	-0.00275258851298542	0.0848066894092055	-0.032457209828151	0.974115950312472	   
df.mm.trans1:probe2	0.167970145958898	0.0651018104809322	2.58011481889732	0.0100630887181608	*  
df.mm.trans1:probe3	0.102028717398381	0.0651018104809322	1.56721781843939	0.117479486051114	   
df.mm.trans1:probe4	0.0959878727591583	0.0651018104809322	1.47442708659035	0.140780189508172	   
df.mm.trans1:probe5	0.0356825608284116	0.0651018104809322	0.548103970762268	0.583781250532649	   
df.mm.trans1:probe6	0.112503271406610	0.0651018104809322	1.72811279095781	0.0843736621941163	.  
df.mm.trans1:probe7	0.136525867976061	0.0651018104809322	2.09711322876417	0.036312987039108	*  
df.mm.trans1:probe8	0.0424136365620452	0.0651018104809322	0.651497035930635	0.514922380608186	   
df.mm.trans1:probe9	0.0399764130204549	0.0651018104809322	0.614059927444931	0.539359247369788	   
df.mm.trans1:probe10	0.0523017557959422	0.0651018104809322	0.803384044307968	0.422003650070637	   
df.mm.trans1:probe11	0.163642226145370	0.0651018104809322	2.51363556460999	0.0121547975328829	*  
df.mm.trans1:probe12	0.0795604860603478	0.0651018104809322	1.22209329468111	0.222050743847927	   
df.mm.trans1:probe13	0.0876238462049478	0.0651018104809322	1.34595098903758	0.178719143527970	   
df.mm.trans1:probe14	0.16650504704026	0.0651018104809322	2.55761008503793	0.0107321377465148	*  
df.mm.trans1:probe15	0.0801149690502323	0.0651018104809322	1.23061046165064	0.218848823048710	   
df.mm.trans1:probe16	0.0401596413100315	0.0651018104809322	0.616874415832014	0.537502068110336	   
df.mm.trans1:probe17	0.118185605535563	0.0651018104809322	1.81539660206805	0.0698564585634918	.  
df.mm.trans1:probe18	0.0145926139633778	0.0651018104809322	0.224150662717005	0.822700236712699	   
df.mm.trans1:probe19	0.0692894991038148	0.0651018104809322	1.06432522524253	0.287518982876403	   
df.mm.trans1:probe20	0.0683064409828449	0.0651018104809322	1.04922490600859	0.294407711099751	   
df.mm.trans1:probe21	0.0889739646054646	0.0651018104809322	1.36668955821934	0.172126259971873	   
df.mm.trans1:probe22	0.118377114219524	0.0651018104809322	1.81833828191607	0.069405435441426	.  
df.mm.trans2:probe2	0.0120598905064908	0.0651018104809322	0.185246622442598	0.853084962322713	   
df.mm.trans2:probe3	0.0606220600139401	0.0651018104809322	0.931188542470656	0.352051352662187	   
df.mm.trans2:probe4	-0.0237339001397234	0.0651018104809322	-0.364565900155339	0.715536753677201	   
df.mm.trans2:probe5	0.0253403422700285	0.0651018104809322	0.38924174432062	0.697206219433362	   
df.mm.trans2:probe6	0.0343713846966955	0.0651018104809322	0.52796357647784	0.597678525623986	   
df.mm.trans3:probe2	-0.0105351622487876	0.0651018104809322	-0.161825948786375	0.871485872923562	   
df.mm.trans3:probe3	-0.00620559739246178	0.0651018104809322	-0.0953214257271593	0.92408461883364	   
df.mm.trans3:probe4	-0.024460838294466	0.0651018104809322	-0.37573207432734	0.70722080074814	   
df.mm.trans3:probe5	0.000761979966559322	0.0651018104809322	0.0117044358817409	0.990664492178284	   
df.mm.trans3:probe6	-0.0020121940552928	0.0651018104809322	-0.0309084192962983	0.975350679663982