chr3.15126_chr3_88827468_88835641_+_2.R 

fitVsDatCorrelation=0.78323950482556
cont.fitVsDatCorrelation=0.280328630179749

fstatistic=8642.7505869199,52,692
cont.fstatistic=3617.92684161999,52,692

residuals=-0.553558077558089,-0.0988544086234024,-0.00706391903319109,0.0769301381433287,1.27182051997699
cont.residuals=-0.650133474803012,-0.157489070005355,-0.0391152833125245,0.0980370433885701,1.38979057917191

predictedValues:
Include	Exclude	Both
chr3.15126_chr3_88827468_88835641_+_2.R.tl.Lung	53.8041755097266	51.3662155665346	62.1567858368479
chr3.15126_chr3_88827468_88835641_+_2.R.tl.cerebhem	58.9212295119322	61.1996205007272	64.365542853991
chr3.15126_chr3_88827468_88835641_+_2.R.tl.cortex	49.6635506721263	55.8255652138938	68.0624970711229
chr3.15126_chr3_88827468_88835641_+_2.R.tl.heart	51.4748049847376	48.8071727877098	65.130004856998
chr3.15126_chr3_88827468_88835641_+_2.R.tl.kidney	53.7207747046909	50.190580332723	60.6843337969267
chr3.15126_chr3_88827468_88835641_+_2.R.tl.liver	54.4778484365224	51.1608663656463	63.048055221419
chr3.15126_chr3_88827468_88835641_+_2.R.tl.stomach	49.9427165231394	53.4912175556196	68.6976852678585
chr3.15126_chr3_88827468_88835641_+_2.R.tl.testicle	51.6107529853142	54.1649513917381	63.6485925801991


diffExp=2.43795994319201,-2.27839098879508,-6.16201454176749,2.66763219702782,3.53019437196799,3.31698207087608,-3.54850103248020,-2.55419840642393
diffExpScore=7.379774678739
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	52.9075065440598	55.4238250365714	57.2969673300301
cerebhem	53.9982724423948	57.4662370922833	53.0054828242575
cortex	56.8105252349004	54.8086752209571	57.0288036406869
heart	55.7596913838574	52.5810195441987	50.592197372523
kidney	54.6102562753765	63.4662400048318	52.5061400447775
liver	56.3182584614557	56.2344461611729	54.2464283142052
stomach	55.4666792916428	55.8757233759852	51.3716819767853
testicle	58.0791628066916	59.89076126187	56.7429601870368
cont.diffExp=-2.51631849251167,-3.46796464988851,2.00185001394334,3.17867183965869,-8.85598372945535,0.0838123002828723,-0.409044084342419,-1.81159845517834
cont.diffExpScore=1.74462644231249

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=0.441972186561142
cont.tran.correlation=0.0123913526213557

tran.covariance=0.00157329359984225
cont.tran.covariance=2.04074510267879e-05

tran.mean=53.1138776901739
cont.tran.mean=56.2310800086406

weightedLogRatios:
wLogRatio
Lung	0.183727233089504
cerebhem	-0.155368870160043
cortex	-0.463602944745865
heart	0.208310013116740
kidney	0.268478412805347
liver	0.249165331100604
stomach	-0.270801497507718
testicle	-0.191664590206545

cont.weightedLogRatios:
wLogRatio
Lung	-0.185475248799048
cerebhem	-0.250231728665408
cortex	0.144273992259716
heart	0.234297101083229
kidney	-0.612471929456395
liver	0.00600228328555561
stomach	-0.0295330826676427
testicle	-0.125231339216339

varWeightedLogRatios=0.0795373094096287
cont.varWeightedLogRatios=0.0686748447298319

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.68523180197024	0.0815700053933022	45.1787612885562	1.37304816907912e-208	***
df.mm.trans1	0.153683871193545	0.0716624407819333	2.14455256500683	0.0323362494382239	*  
df.mm.trans2	0.202141516239568	0.0649392956074971	3.11277654536541	0.00192970576247396	** 
df.mm.exp2	0.231092255620861	0.0865857274766628	2.66894166458493	0.00778741133912291	** 
df.mm.exp3	-0.0875949087738914	0.0865857274766628	-1.01165528461374	0.312056493720537	   
df.mm.exp4	-0.142087348052983	0.0865857274766628	-1.64100195486929	0.101251405404221	   
df.mm.exp5	-0.000730164116580255	0.0865857274766628	-0.0084328461267136	0.993274072457161	   
df.mm.exp6	-0.00579988827012417	0.0865857274766628	-0.0669843453320571	0.94661351392693	   
df.mm.exp7	-0.133993094324716	0.0865857274766628	-1.54751941491547	0.122195230698931	   
df.mm.exp8	-0.0122848756458057	0.0865857274766628	-0.141881069823162	0.88721524266892	   
df.mm.trans1:exp2	-0.140241872625037	0.0812246121464631	-1.72659331844091	0.0846870218424513	.  
df.mm.trans2:exp2	-0.0559319387473771	0.0670690161072738	-0.833946015518053	0.404599175560385	   
df.mm.trans1:exp3	0.0075151100769523	0.0812246121464631	0.0925225726335407	0.926309636235504	   
df.mm.trans2:exp3	0.170846159536872	0.0670690161072738	2.54731870918773	0.0110706725376825	*  
df.mm.trans1:exp4	0.0978287365553754	0.0812246121464631	1.20442232926852	0.228838161983043	   
df.mm.trans2:exp4	0.0909839618087378	0.0670690161072738	1.35657218622699	0.175359576326397	   
df.mm.trans1:exp5	-0.000821119033879237	0.0812246121464631	-0.0101092392093988	0.991937045137297	   
df.mm.trans2:exp5	-0.0224231412243479	0.0670690161072738	-0.334329359901197	0.738232337527548	   
df.mm.trans1:exp6	0.0182429807487727	0.0812246121464631	0.224599173411591	0.822357408952352	   
df.mm.trans2:exp6	0.00179412761437553	0.0670690161072738	0.0267504686740283	0.978666471602325	   
df.mm.trans1:exp7	0.0595186975735604	0.0812246121464631	0.732766780914104	0.463948815093551	   
df.mm.trans2:exp7	0.174529905255357	0.0670690161072738	2.60224341111855	0.00945981091203527	** 
df.mm.trans1:exp8	-0.0293361582499064	0.0812246121464631	-0.361173263554744	0.718080164360468	   
df.mm.trans2:exp8	0.0653382499749646	0.0670690161072738	0.974194251939809	0.330300510107693	   
df.mm.trans1:probe2	0.143364311166736	0.0474249560969631	3.02297193219577	0.00259553617952878	** 
df.mm.trans1:probe3	0.599793335098557	0.0474249560969631	12.6472090743162	3.93884922019332e-33	***
df.mm.trans1:probe4	0.0501056145115462	0.0474249560969631	1.05652421499563	0.29109747902019	   
df.mm.trans1:probe5	0.563819223236225	0.0474249560969631	11.8886609422150	8.63311624842428e-30	***
df.mm.trans1:probe6	-0.00853199574228381	0.0474249560969631	-0.179905190103701	0.857279668075025	   
df.mm.trans1:probe7	0.820210555400495	0.0474249560969631	17.2949143848131	5.73280671514481e-56	***
df.mm.trans1:probe8	0.230381921667726	0.0474249560969631	4.85782045209904	1.46891960103623e-06	***
df.mm.trans1:probe9	0.0344792426102876	0.0474249560969631	0.727027401771186	0.467455042314584	   
df.mm.trans1:probe10	0.104959686142141	0.0474249560969631	2.21317413404758	0.0272111646111569	*  
df.mm.trans1:probe11	0.212908878818236	0.0474249560969631	4.48938483744574	8.36604222997592e-06	***
df.mm.trans1:probe12	-0.023459701667566	0.0474249560969631	-0.494669971219399	0.620990109525635	   
df.mm.trans1:probe13	0.135152920739036	0.0474249560969631	2.84982700801468	0.00450438937754012	** 
df.mm.trans1:probe14	0.0655899530605031	0.0474249560969631	1.38302612081286	0.167102927887005	   
df.mm.trans1:probe15	0.173225615018420	0.0474249560969631	3.65262573283673	0.000279179135456578	***
df.mm.trans1:probe16	0.0361636547791824	0.0474249560969631	0.762544823557532	0.445994743173618	   
df.mm.trans1:probe17	0.0329576609604836	0.0474249560969631	0.694943415300158	0.487324033884819	   
df.mm.trans1:probe18	0.244633420334932	0.0474249560969631	5.15832676438888	3.25745992577983e-07	***
df.mm.trans1:probe19	0.00301644225359153	0.0474249560969631	0.0636045344443596	0.949303488934923	   
df.mm.trans1:probe20	0.242114329043690	0.0474249560969631	5.1052093448157	4.27526051800254e-07	***
df.mm.trans2:probe2	0.119925807257764	0.0474249560969631	2.52874893574111	0.0116681237718175	*  
df.mm.trans2:probe3	0.224399353370145	0.0474249560969631	4.73167234802175	2.70063344071899e-06	***
df.mm.trans2:probe4	0.057826239832676	0.0474249560969631	1.21932089329607	0.223137865070021	   
df.mm.trans2:probe5	0.0636777489194548	0.0474249560969631	1.34270549010656	0.179807616826453	   
df.mm.trans2:probe6	0.101851738539758	0.0474249560969631	2.14764012288205	0.0320889913511039	*  
df.mm.trans3:probe2	0.071607821063202	0.0474249560969631	1.50991855251896	0.131520661382931	   
df.mm.trans3:probe3	0.0375385869598562	0.0474249560969631	0.79153656743733	0.428902169278643	   
df.mm.trans3:probe4	0.185429760576911	0.0474249560969631	3.90996167076653	0.000101399113312815	***
df.mm.trans3:probe5	0.14843786848865	0.0474249560969631	3.12995268114029	0.00182185434793610	** 

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.7736488117991	0.125939924006930	29.9638803306842	4.11441254686627e-127	***
df.mm.trans1	0.0961278436914115	0.110643150048987	0.86880971527701	0.385252437906691	   
df.mm.trans2	0.239250160541463	0.100262957130358	2.38622685176141	0.0172900848871972	*  
df.mm.exp2	0.134447201453873	0.133683942840477	1.00570942625702	0.314906899244661	   
df.mm.exp3	0.0647065437224488	0.133683942840477	0.484026296259544	0.628520346585673	   
df.mm.exp4	0.124301989474697	0.133683942840477	0.929819893351175	0.352788582184174	   
df.mm.exp5	0.254492614625926	0.133683942840477	1.90368872445367	0.057365316742403	.  
df.mm.exp6	0.131704037181472	0.133683942840477	0.985189652422448	0.324875251544795	   
df.mm.exp7	0.164518290755225	0.133683942840477	1.23065109585780	0.218871526685889	   
df.mm.exp8	0.180490524608621	0.133683942840477	1.35012867494488	0.177416130606873	   
df.mm.trans1:exp2	-0.114040376650014	0.125406654466862	-0.909364635671298	0.36347429085217	   
df.mm.trans2:exp2	-0.0982591632305464	0.103551136855303	-0.948895069764894	0.343005240489701	   
df.mm.trans1:exp3	0.00646983882828063	0.125406654466862	0.0515908733534572	0.958869579946421	   
df.mm.trans2:exp3	-0.0758676114638614	0.103551136855303	-0.732658411755297	0.464014882356034	   
df.mm.trans1:exp4	-0.071795987201695	0.125406654466862	-0.572505402579468	0.567165577151639	   
df.mm.trans2:exp4	-0.176956336136573	0.103551136855303	-1.70887873866458	0.0879218029098233	.  
df.mm.trans1:exp5	-0.222816135085315	0.125406654466862	-1.77674889767667	0.0760488019310546	.  
df.mm.trans2:exp5	-0.118994059690242	0.103551136855303	-1.14913330074317	0.250898041046112	   
df.mm.trans1:exp6	-0.0692304774552135	0.125406654466862	-0.552047877758412	0.581093789427661	   
df.mm.trans2:exp6	-0.117184103141816	0.103551136855303	-1.13165443374672	0.258171748828017	   
df.mm.trans1:exp7	-0.117281052863662	0.125406654466862	-0.935205977404119	0.350008388284860	   
df.mm.trans2:exp7	-0.156397847696868	0.103551136855303	-1.51034409130062	0.131412114209674	   
df.mm.trans1:exp8	-0.0872287981224676	0.125406654466862	-0.69556753980322	0.48693321572655	   
df.mm.trans2:exp8	-0.102977823529629	0.103551136855303	-0.994463476277672	0.320344880510391	   
df.mm.trans1:probe2	0.148372560805667	0.0732217110699606	2.02634653899173	0.0431123641458694	*  
df.mm.trans1:probe3	0.0996436636510438	0.0732217110699606	1.36084860890287	0.174004558201011	   
df.mm.trans1:probe4	0.134787520640561	0.0732217110699606	1.84081358754068	0.0660768202284362	.  
df.mm.trans1:probe5	0.0942150036520415	0.0732217110699606	1.28670857694137	0.198626152244492	   
df.mm.trans1:probe6	0.192022858172365	0.0732217110699606	2.62248526245029	0.00892117592175098	** 
df.mm.trans1:probe7	0.123637747690718	0.0732217110699606	1.68853944935248	0.0917582518907922	.  
df.mm.trans1:probe8	0.23310786862643	0.0732217110699606	3.18358947394311	0.00151978051459107	** 
df.mm.trans1:probe9	0.079698776332387	0.0732217110699606	1.0884582614607	0.276771781832917	   
df.mm.trans1:probe10	0.125279027984876	0.0732217110699606	1.7109546629575	0.0875376373784023	.  
df.mm.trans1:probe11	0.192503183499981	0.0732217110699607	2.62904513821114	0.00875260332740331	** 
df.mm.trans1:probe12	0.157413825373885	0.0732217110699606	2.14982445880678	0.0319150475513966	*  
df.mm.trans1:probe13	0.0692014548607127	0.0732217110699606	0.945094751945817	0.344940371595842	   
df.mm.trans1:probe14	0.0325498869680833	0.0732217110699606	0.444538737110132	0.656792122916408	   
df.mm.trans1:probe15	0.0751184241290959	0.0732217110699606	1.02590369757029	0.305295452902918	   
df.mm.trans1:probe16	0.122393950396290	0.0732217110699606	1.67155272128709	0.095064649508154	.  
df.mm.trans1:probe17	0.185306384310298	0.0732217110699606	2.53075736147773	0.0116021516696976	*  
df.mm.trans1:probe18	0.0317553194798069	0.0732217110699606	0.433687208558481	0.664650761564635	   
df.mm.trans1:probe19	0.146926909947826	0.0732217110699606	2.00660306623322	0.0451805151508151	*  
df.mm.trans1:probe20	0.22527998224412	0.0732217110699606	3.07668284382037	0.00217573804884982	** 
df.mm.trans2:probe2	-0.0692362076459305	0.0732217110699606	-0.945569375998027	0.344698311258362	   
df.mm.trans2:probe3	0.00734240673572575	0.0732217110699607	0.100276361046935	0.920153967947407	   
df.mm.trans2:probe4	0.100236841814040	0.0732217110699607	1.36894973293190	0.171459165318314	   
df.mm.trans2:probe5	0.00311449076851883	0.0732217110699607	0.0425350722211756	0.966084423169063	   
df.mm.trans2:probe6	-0.0182411101213379	0.0732217110699607	-0.249121604163405	0.803340640902356	   
df.mm.trans3:probe2	-0.064445436745567	0.0732217110699607	-0.880141092086632	0.379088431360977	   
df.mm.trans3:probe3	-0.0456693984840014	0.0732217110699607	-0.623713893279084	0.53302103110772	   
df.mm.trans3:probe4	-0.137581048041915	0.0732217110699607	-1.87896521443567	0.0606693283414739	.  
df.mm.trans3:probe5	-0.0561997161755567	0.0732217110699607	-0.767528037167282	0.443029430333357