chr5.18445_chr5_139324859_139328027_+_2.R 

fitVsDatCorrelation=0.933868222772756
cont.fitVsDatCorrelation=0.309446204857448

fstatistic=9540.6891635029,52,692
cont.fstatistic=1337.94629221492,52,692

residuals=-0.556393726411352,-0.0938362620762037,0.00353173272615369,0.110643602981062,0.664211989592611
cont.residuals=-0.9615885753003,-0.372609034899314,-0.0391411985384182,0.335276266372704,1.26979249032099

predictedValues:
Include	Exclude	Both
chr5.18445_chr5_139324859_139328027_+_2.R.tl.Lung	94.1742194132774	49.802097136356	92.325319988109
chr5.18445_chr5_139324859_139328027_+_2.R.tl.cerebhem	80.6489860296592	60.2863117360698	68.3592009710277
chr5.18445_chr5_139324859_139328027_+_2.R.tl.cortex	94.3080354496348	47.3109872908765	90.1196119355576
chr5.18445_chr5_139324859_139328027_+_2.R.tl.heart	104.207547784096	47.7448124161552	95.119965025023
chr5.18445_chr5_139324859_139328027_+_2.R.tl.kidney	84.331606750528	48.3242147038408	76.4617428547027
chr5.18445_chr5_139324859_139328027_+_2.R.tl.liver	85.0338059202984	51.1087815781069	73.3984680120717
chr5.18445_chr5_139324859_139328027_+_2.R.tl.stomach	98.3402590486824	49.6604282691098	90.8894432001423
chr5.18445_chr5_139324859_139328027_+_2.R.tl.testicle	127.875455775869	51.3004691830714	114.313049602572


diffExp=44.3721222769214,20.3626742935894,46.9970481587583,56.4627353679407,36.0073920466871,33.9250243421915,48.6798307795727,76.5749865927978
diffExpScore=0.997255625934206
diffExp1.5=1,0,1,1,1,1,1,1
diffExp1.5Score=0.875
diffExp1.4=1,0,1,1,1,1,1,1
diffExp1.4Score=0.875
diffExp1.3=1,1,1,1,1,1,1,1
diffExp1.3Score=0.888888888888889
diffExp1.2=1,1,1,1,1,1,1,1
diffExp1.2Score=0.888888888888889

cont.predictedValues:
Include	Exclude	Both
Lung	79.5539794714511	78.3116975970401	93.865113546138
cerebhem	77.5173589514413	69.7540093195802	89.3677728474355
cortex	78.5536305459583	76.8200637525824	86.1767135620496
heart	83.4075223274523	69.7394727085026	110.613163176734
kidney	77.0825479731574	72.4685626301632	66.182007227086
liver	95.2565413854817	87.1434027054813	89.2200000132846
stomach	96.7224809146603	79.5485283125912	82.7287340795599
testicle	85.295891350092	72.2335383094251	139.272939890942
cont.diffExp=1.24228187441103,7.76334963186105,1.73356679337581,13.6680496189497,4.61398534299423,8.11313868000033,17.1739526020691,13.0623530406670
cont.diffExpScore=0.985373846869272

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=0,0,0,0,0,0,1,0
cont.diffExp1.2Score=0.5

tran.correlation=-0.284137808423524
cont.tran.correlation=0.674887340625565

tran.covariance=-0.00365773269267555
cont.tran.covariance=0.0045751194894972

tran.mean=73.403626155352
cont.tran.mean=79.9630767659413

weightedLogRatios:
wLogRatio
Lung	2.69272306730216
cerebhem	1.23518515523353
cortex	2.89840142449876
heart	3.32196769598189
kidney	2.31435230941064
liver	2.13233552171041
stomach	2.90153463489942
testicle	4.01363573923291

cont.weightedLogRatios:
wLogRatio
Lung	0.068755957099321
cerebhem	0.453527428923773
cortex	0.0971319773120495
heart	0.77570992636047
kidney	0.266278471037763
liver	0.401657739620616
stomach	0.874592094674876
testicle	0.72522839384175

varWeightedLogRatios=0.686879305206858
cont.varWeightedLogRatios=0.0953696689259845

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.42564339357453	0.0917327351329468	37.3437398177412	5.2833491512435e-168	***
df.mm.trans1	1.08981100686693	0.0823894110547197	13.2275615630159	9.00309148668263e-36	***
df.mm.trans2	0.522352625475276	0.0757682529729263	6.89408300943568	1.2228842672054e-11	***
df.mm.exp2	0.336550005070411	0.103783742510874	3.24280081762469	0.00124043223380336	** 
df.mm.exp3	-0.0257139864903022	0.103783742510874	-0.247765072526730	0.804389678544689	   
df.mm.exp4	0.0292309655242955	0.103783742510874	0.281652644403654	0.778294073051592	   
df.mm.exp5	0.0480138462923792	0.103783742510874	0.462633598777271	0.643772456699826	   
df.mm.exp6	0.153217031231554	0.103783742510874	1.47631052344735	0.140315440179348	   
df.mm.exp7	0.0561129096806224	0.103783742510874	0.540671480166976	0.588908020065305	   
df.mm.exp8	0.121930819907775	0.103783742510874	1.17485472153791	0.240456947233497	   
df.mm.trans1:exp2	-0.491590237916847	0.0993653684243144	-4.94729950396436	9.45736483841009e-07	***
df.mm.trans2:exp2	-0.145502024140628	0.0864864520923948	-1.68236782317289	0.0929486583075293	.  
df.mm.trans1:exp3	0.0271339191936559	0.0993653684243144	0.273072194306043	0.784879192749491	   
df.mm.trans2:exp3	-0.0256005498548855	0.0864864520923949	-0.296006475413467	0.7673139421293	   
df.mm.trans1:exp4	0.0720071318588466	0.0993653684243144	0.724670305164658	0.468899262188369	   
df.mm.trans2:exp4	-0.0714176393144959	0.0864864520923949	-0.825766782966184	0.409220992474484	   
df.mm.trans1:exp5	-0.158403584617189	0.0993653684243145	-1.59415284348131	0.111358411603574	   
df.mm.trans2:exp5	-0.0781381659543055	0.0864864520923948	-0.903472903141284	0.366589356158304	   
df.mm.trans1:exp6	-0.255314601960913	0.0993653684243144	-2.56945257698494	0.0103942431487720	*  
df.mm.trans2:exp6	-0.127317792258745	0.0864864520923949	-1.47211255842394	0.141445118880047	   
df.mm.trans1:exp7	-0.0128258783462505	0.0993653684243144	-0.129077952908913	0.897333473436603	   
df.mm.trans2:exp7	-0.0589615999279843	0.0864864520923948	-0.68174377028433	0.495629084889335	   
df.mm.trans1:exp8	0.183979503726593	0.0993653684243144	1.85154552983647	0.064516922155383	.  
df.mm.trans2:exp8	-0.0922880162197996	0.0864864520923948	-1.06708061189985	0.286307564452652	   
df.mm.trans1:probe2	0.024053332358731	0.0496826842121572	0.484139147072195	0.628440300976744	   
df.mm.trans1:probe3	0.409795972400491	0.0496826842121572	8.24826554560865	8.0936127429287e-16	***
df.mm.trans1:probe4	-0.0814151822659096	0.0496826842121572	-1.63870337436373	0.101729592897674	   
df.mm.trans1:probe5	0.0925076357607278	0.0496826842121572	1.86196936070719	0.0630310917424763	.  
df.mm.trans1:probe6	0.00766095854529125	0.0496826842121572	0.154197758570714	0.877498768221662	   
df.mm.trans1:probe7	0.184584042012784	0.0496826842121572	3.71525904728829	0.000219371065666962	***
df.mm.trans1:probe8	0.147661188422966	0.0496826842121572	2.97208556189148	0.00306048067418328	** 
df.mm.trans1:probe9	0.077398964802631	0.0496826842121572	1.55786600563123	0.119722212341810	   
df.mm.trans1:probe10	0.61293883982193	0.0496826842121572	12.3370717492745	9.48342798627949e-32	***
df.mm.trans1:probe11	0.185624380099114	0.0496826842121572	3.73619869865429	0.000202226003217228	***
df.mm.trans1:probe12	0.350678931491784	0.0496826842121572	7.05837329549867	4.10360075616992e-12	***
df.mm.trans1:probe13	0.446148224790695	0.0496826842121572	8.9799541201424	2.53463040983337e-18	***
df.mm.trans1:probe14	0.290033960578175	0.0496826842121572	5.83772727213487	8.13508012699361e-09	***
df.mm.trans1:probe15	0.309788588808694	0.0496826842121572	6.23534323318404	7.84074789840014e-10	***
df.mm.trans1:probe16	0.555256858899188	0.0496826842121572	11.1760640091044	8.9451227122145e-27	***
df.mm.trans1:probe17	-0.468079499317231	0.0496826842121572	-9.42138104532391	6.52070412358297e-20	***
df.mm.trans1:probe18	-0.42936897221867	0.0496826842121572	-8.64222573774717	3.80570006613503e-17	***
df.mm.trans1:probe19	-0.445721392388677	0.0496826842121572	-8.97136294982247	2.71815434112448e-18	***
df.mm.trans1:probe20	-0.437518114017946	0.0496826842121572	-8.80624952044935	1.03132900194698e-17	***
df.mm.trans1:probe21	-0.505821188820025	0.0496826842121572	-10.1810358446022	8.8395920422293e-23	***
df.mm.trans1:probe22	-0.583905919282508	0.0496826842121572	-11.7527047610610	3.31856363650799e-29	***
df.mm.trans2:probe2	-0.0860910134227119	0.0496826842121572	-1.73281727402413	0.0835736606954485	.  
df.mm.trans2:probe3	-0.154202952094817	0.0496826842121572	-3.10375646042659	0.00198866835483514	** 
df.mm.trans2:probe4	-0.153709810030194	0.0496826842121572	-3.09383062665889	0.00205546374287368	** 
df.mm.trans2:probe5	-0.0395016576045809	0.0496826842121572	-0.795078974314254	0.426840175307244	   
df.mm.trans2:probe6	0.0740551105459462	0.0496826842121572	1.49056178667225	0.136532241118521	   
df.mm.trans3:probe2	-0.788801109127543	0.0496826842121572	-15.8767812495631	1.23503130290878e-48	***
df.mm.trans3:probe3	-0.7486646887608	0.0496826842121572	-15.0689259373310	1.38394573873133e-44	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.15016291966092	0.243920553010719	17.0144043559894	1.69658001905218e-54	***
df.mm.trans1	0.271748018175761	0.219076327306378	1.24042620906147	0.215238194579152	   
df.mm.trans2	0.11968173358322	0.201470436251872	0.594041169561957	0.55267871826118	   
df.mm.exp2	-0.092557311718323	0.275964603367444	-0.335395592727826	0.737428343842363	   
df.mm.exp3	0.0535734638168239	0.275964603367444	0.194131650085179	0.846129773267143	   
df.mm.exp4	-0.232808091901883	0.275964603367444	-0.84361577195428	0.399175665212922	   
df.mm.exp5	0.240347140993881	0.275964603367444	0.870934670827553	0.384091858156578	   
df.mm.exp6	0.337749551108603	0.275964603367444	1.22388721954639	0.221411315642365	   
df.mm.exp7	0.337372144360874	0.275964603367444	1.22251962840200	0.221927398835891	   
df.mm.exp8	-0.405678835589101	0.275964603367444	-1.47003938417763	0.142005590540850	   
df.mm.trans1:exp2	0.0666234311514705	0.264215992045217	0.252155180448233	0.800995992472133	   
df.mm.trans2:exp2	-0.0231647743845922	0.229970502806203	-0.100729328770104	0.919794505193703	   
df.mm.trans1:exp3	-0.0662276589445311	0.264215992045217	-0.250657268819660	0.802153503935519	   
df.mm.trans2:exp3	-0.0728045974404873	0.229970502806203	-0.316582329264375	0.751656021663076	   
df.mm.trans1:exp4	0.280110814656859	0.264215992045217	1.06015844267641	0.289442406239268	   
df.mm.trans2:exp4	0.116877585865378	0.229970502806203	0.508228596446871	0.611455006636286	   
df.mm.trans1:exp5	-0.271906020092834	0.264215992045217	-1.02910508174805	0.303789858064891	   
df.mm.trans2:exp5	-0.317891278504120	0.229970502806203	-1.38231327333318	0.167321510859055	   
df.mm.trans1:exp6	-0.157611641811956	0.264215992045217	-0.596525746197009	0.551019149704661	   
df.mm.trans2:exp6	-0.230891468898162	0.229970502806203	-1.00400471399906	0.315727280403319	   
df.mm.trans1:exp7	-0.141962066216017	0.264215992045217	-0.537295510075418	0.591236227838285	   
df.mm.trans2:exp7	-0.321701876340904	0.229970502806203	-1.39888321508782	0.162296036653533	   
df.mm.trans1:exp8	0.47536934355516	0.264215992045217	1.79916945933313	0.0724274447648354	.  
df.mm.trans2:exp8	0.324886306716503	0.229970502806203	1.41273034042233	0.158184614787154	   
df.mm.trans1:probe2	-0.158446132157639	0.132107996022608	-1.19936822091013	0.230795300253995	   
df.mm.trans1:probe3	0.081465029503107	0.132107996022608	0.61665479725516	0.537665255507839	   
df.mm.trans1:probe4	-0.000835422156930722	0.132107996022608	-0.00632378192148	0.99495620822274	   
df.mm.trans1:probe5	-0.0840595737793374	0.132107996022608	-0.636294367563882	0.524794972893217	   
df.mm.trans1:probe6	0.114320666985194	0.132107996022608	0.865357665145641	0.38714240536514	   
df.mm.trans1:probe7	-0.00371284029958384	0.132107996022608	-0.0281045842141792	0.97758684080891	   
df.mm.trans1:probe8	0.0103364543323801	0.132107996022608	0.0782424580160248	0.937657807141972	   
df.mm.trans1:probe9	-0.0828358355471408	0.132107996022608	-0.627031202055057	0.530845596931075	   
df.mm.trans1:probe10	-0.203291581072328	0.132107996022608	-1.53882874006762	0.124303222162694	   
df.mm.trans1:probe11	0.0382727667566206	0.132107996022608	0.289708177467704	0.772126281289728	   
df.mm.trans1:probe12	-0.0892706556824688	0.132107996022608	-0.675739988268321	0.499431493792086	   
df.mm.trans1:probe13	-0.109657880439340	0.132107996022608	-0.830062401526198	0.406789765588615	   
df.mm.trans1:probe14	0.0113629682142233	0.132107996022608	0.0860127210791899	0.931481184857731	   
df.mm.trans1:probe15	-0.126475331247664	0.132107996022608	-0.957363180545252	0.338718318858023	   
df.mm.trans1:probe16	-0.0607171732262232	0.132107996022608	-0.459602560437238	0.645945872648165	   
df.mm.trans1:probe17	-0.144032565650833	0.132107996022608	-1.09026379921912	0.27597645041675	   
df.mm.trans1:probe18	-0.271103518458368	0.132107996022608	-2.05213557559357	0.0405321855190128	*  
df.mm.trans1:probe19	-0.107615561896644	0.132107996022608	-0.814602939539158	0.415579870391654	   
df.mm.trans1:probe20	0.0772309555013472	0.132107996022608	0.584604700900393	0.55900408937771	   
df.mm.trans1:probe21	-0.0383669441760816	0.132107996022608	-0.290421059520997	0.771581144710946	   
df.mm.trans1:probe22	0.0105531864590530	0.132107996022608	0.0798830258332509	0.936353385553947	   
df.mm.trans2:probe2	0.165076247274558	0.132107996022608	1.24955530508772	0.211884498806581	   
df.mm.trans2:probe3	0.125393381286350	0.132107996022608	0.949173290501587	0.342863843464415	   
df.mm.trans2:probe4	0.280250054912632	0.132107996022608	2.12137087345319	0.0342454694071943	*  
df.mm.trans2:probe5	0.114996740819714	0.132107996022608	0.870475249658878	0.384342596813682	   
df.mm.trans2:probe6	0.131954574439843	0.132107996022608	0.99883866542992	0.318221988390976	   
df.mm.trans3:probe2	-0.140237432603592	0.132107996022608	-1.06153629474170	0.288816578922395	   
df.mm.trans3:probe3	0.141035786695476	0.132107996022608	1.06757948755304	0.286082530020279