chr18.11435_chr18_4238946_4240265_+_1.R 

fitVsDatCorrelation=0.855187221134873
cont.fitVsDatCorrelation=0.272031860926134

fstatistic=10238.7320514696,49,623
cont.fstatistic=2961.48053437045,49,623

residuals=-0.77632418008662,-0.0800056565425449,-0.00116032687105083,0.0756252100283812,0.76469294464266
cont.residuals=-0.642621843681028,-0.177330871787903,-0.048373345850155,0.101986151155577,1.33249014827859

predictedValues:
Include	Exclude	Both
chr18.11435_chr18_4238946_4240265_+_1.R.tl.Lung	46.0338566314591	44.4384002217023	60.7316511612495
chr18.11435_chr18_4238946_4240265_+_1.R.tl.cerebhem	71.127714710893	49.1399932685559	56.8023463458698
chr18.11435_chr18_4238946_4240265_+_1.R.tl.cortex	48.1071681574614	47.4698104232615	66.4476794225545
chr18.11435_chr18_4238946_4240265_+_1.R.tl.heart	45.3487631948263	47.65077734772	64.1638705874103
chr18.11435_chr18_4238946_4240265_+_1.R.tl.kidney	47.7272494339603	45.1995111440444	58.8627580928762
chr18.11435_chr18_4238946_4240265_+_1.R.tl.liver	49.3422476698594	51.12341712499	58.0204229882182
chr18.11435_chr18_4238946_4240265_+_1.R.tl.stomach	49.4654682756051	47.0573470378156	60.9075846236627
chr18.11435_chr18_4238946_4240265_+_1.R.tl.testicle	51.9476858175753	48.3377712989557	62.7940138564681


diffExp=1.59545640975676,21.9877214423370,0.637357734199895,-2.30201415289368,2.52773828991584,-1.78116945513064,2.40812123778955,3.60991451861961
diffExpScore=1.24142899269136
diffExp1.5=0,0,0,0,0,0,0,0
diffExp1.5Score=0
diffExp1.4=0,1,0,0,0,0,0,0
diffExp1.4Score=0.5
diffExp1.3=0,1,0,0,0,0,0,0
diffExp1.3Score=0.5
diffExp1.2=0,1,0,0,0,0,0,0
diffExp1.2Score=0.5

cont.predictedValues:
Include	Exclude	Both
Lung	51.7629289316285	53.3904736410433	58.1665099074232
cerebhem	54.9441529053977	55.5927136016524	57.0421885220238
cortex	60.971910335272	50.3788536127006	56.8790653622504
heart	50.5978324341221	48.293908040387	59.4098474080238
kidney	59.1586050379711	63.6814178976225	52.1406997616767
liver	50.8421185202285	55.0754326573299	57.2189545004815
stomach	55.6541723636862	53.9433059621779	56.2135144626481
testicle	50.8964601464645	55.617846837076	57.9776379283196
cont.diffExp=-1.62754470941483,-0.648560696254762,10.5930567225714,2.30392439373513,-4.52281285965133,-4.23331413710143,1.71086640150821,-4.72138669061142
cont.diffExpScore=14.1494402113837

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

tran.correlation=0.407712311433916
cont.tran.correlation=0.289103968110187

tran.covariance=0.00275233663409636
cont.tran.covariance=0.00167062566670424

tran.mean=49.3448238599178
cont.tran.mean=54.4251333077975

weightedLogRatios:
wLogRatio
Lung	0.134452187672297
cerebhem	1.50864251248414
cortex	0.0515719914293384
heart	-0.190099055223535
kidney	0.208865511529938
liver	-0.138887290311814
stomach	0.193458679276528
testicle	0.281917768537907

cont.weightedLogRatios:
wLogRatio
Lung	-0.122660731445926
cerebhem	-0.047082484769621
cortex	0.766228198580175
heart	0.181781212442678
kidney	-0.303306705497869
liver	-0.317412353643897
stomach	0.125004475523357
testicle	-0.352549757081333

varWeightedLogRatios=0.283917518599061
cont.varWeightedLogRatios=0.13849471704552

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.52598894380562	0.072467264235693	48.656299930651	2.2223618950841e-214	***
df.mm.trans1	0.276009119713319	0.057269716506022	4.81946020606364	1.81038607279325e-06	***
df.mm.trans2	0.27173009323007	0.057269716506022	4.74474311744668	2.59122217819371e-06	***
df.mm.exp2	0.602556549310043	0.075916953802658	7.93704856594163	9.66298158810356e-15	***
df.mm.exp3	0.0200941618008465	0.075916953802658	0.264686091766540	0.791338836498582	   
df.mm.exp4	-0.000174548338192597	0.075916953802658	-0.00229920102756397	0.99816624062422	   
df.mm.exp5	0.0843640659879157	0.075916953802658	1.11126779674558	0.266881772111074	   
df.mm.exp6	0.255212141830114	0.075916953802658	3.36172790195882	0.00082196636737332	***
df.mm.exp7	0.126268007085604	0.0759169538026579	1.66323858849541	0.0967676155504598	.  
df.mm.exp8	0.171574584650325	0.075916953802658	2.26002988866392	0.0241638631816844	*  
df.mm.trans1:exp2	-0.167456628915914	0.0577641404977462	-2.8989720520891	0.00387566229225106	** 
df.mm.trans2:exp2	-0.501987284713948	0.0577641404977462	-8.69029263464128	3.18499452339000e-17	***
df.mm.trans1:exp3	0.0239598908590333	0.0577641404977462	0.41478832113789	0.678439661824085	   
df.mm.trans2:exp3	0.0458958116309896	0.0577641404977462	0.794538120631784	0.427184784124223	   
df.mm.trans1:exp4	-0.0148196874041875	0.0577641404977462	-0.256555144359254	0.797606903600344	   
df.mm.trans2:exp4	0.0699695264916495	0.0577641404977462	1.2112969376629	0.226240882713265	   
df.mm.trans1:exp5	-0.0482387037363306	0.0577641404977462	-0.835097749584151	0.403982800904791	   
df.mm.trans2:exp5	-0.0673817601173681	0.0577641404977462	-1.16649809962977	0.243859447282225	   
df.mm.trans1:exp6	-0.185808616538196	0.0577641404977462	-3.21667759508073	0.00136402628526565	** 
df.mm.trans2:exp6	-0.115073454338741	0.0577641404977462	-1.99212614170604	0.0467932257215177	*  
df.mm.trans1:exp7	-0.0543703310394834	0.0577641404977462	-0.941247122712833	0.346943052097700	   
df.mm.trans2:exp7	-0.069004964924435	0.0577641404977462	-1.19459866155418	0.232698467346389	   
df.mm.trans1:exp8	-0.0507145539296871	0.0577641404977462	-0.877959119493275	0.380304359148166	   
df.mm.trans2:exp8	-0.0874652806465676	0.0577641404977462	-1.51417955660537	0.130487449142140	   
df.mm.trans1:probe2	0.0499994410238446	0.0426616450740434	1.17199983584941	0.241645110608751	   
df.mm.trans1:probe3	0.133002089443071	0.0426616450740434	3.11760339321734	0.00190729012654452	** 
df.mm.trans1:probe4	0.0347201543060189	0.0426616450740434	0.813849401394595	0.416042174360135	   
df.mm.trans1:probe5	0.173322692987041	0.0426616450740434	4.06272877396601	5.46993755879734e-05	***
df.mm.trans1:probe6	0.211295293629651	0.0426616450740434	4.952816359119	9.4329207888526e-07	***
df.mm.trans2:probe2	-0.0145335781409033	0.0426616450740434	-0.340670832446308	0.733466244955376	   
df.mm.trans2:probe3	-0.0546216061132762	0.0426616450740434	-1.28034458161365	0.200900465535205	   
df.mm.trans2:probe4	-0.0145428074478399	0.0426616450740434	-0.340887169789150	0.733303449688961	   
df.mm.trans2:probe5	0.0421059898849098	0.0426616450740434	0.98697529858098	0.324037900596404	   
df.mm.trans2:probe6	-0.03793957170614	0.0426616450740434	-0.889313378335322	0.374177937868599	   
df.mm.trans3:probe2	-0.225165069803250	0.0426616450740434	-5.27792750168105	1.80641667185933e-07	***
df.mm.trans3:probe3	0.142606243703703	0.0426616450740434	3.34272725433340	0.000879283788362425	***
df.mm.trans3:probe4	0.367601601767322	0.0426616450740434	8.61667666892155	5.66616210767777e-17	***
df.mm.trans3:probe5	0.767867558733621	0.0426616450740434	17.9990142761938	1.23364745242464e-58	***
df.mm.trans3:probe6	-0.26636316985429	0.0426616450740434	-6.24362162762339	7.91244566056753e-10	***
df.mm.trans3:probe7	-0.0671991287157369	0.0426616450740434	-1.57516496607447	0.115726016774789	   
df.mm.trans3:probe8	0.0585827361073726	0.0426616450740434	1.37319449368857	0.170185894364374	   
df.mm.trans3:probe9	0.0438260989695834	0.0426616450740434	1.02729510063475	0.304680213219235	   
df.mm.trans3:probe10	-0.104770219242578	0.0426616450740434	-2.45584105021593	0.0143274829959199	*  
df.mm.trans3:probe11	0.313112043361854	0.0426616450740434	7.33942731974864	6.72376510216343e-13	***
df.mm.trans3:probe12	-0.162482027258031	0.0426616450740434	-3.80862076406167	0.000153578673224373	***
df.mm.trans3:probe13	-0.143828370303335	0.0426616450740434	-3.37137421807592	0.000794215717303003	***
df.mm.trans3:probe14	-0.0388895698617399	0.0426616450740434	-0.911581580931614	0.362341606598312	   
df.mm.trans3:probe15	-0.154238912172500	0.0426616450740434	-3.61540001340322	0.000324131964678375	***
df.mm.trans3:probe16	-0.0068703538178043	0.0426616450740434	-0.161042871316381	0.872111842343632	   

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.82727780254294	0.134539475770117	28.4472477734525	1.02684382586492e-114	***
df.mm.trans1	0.106974371853643	0.106324389605264	1.00611319990448	0.314751725297611	   
df.mm.trans2	0.144204963281537	0.106324389605264	1.35627360586698	0.175503449755605	   
df.mm.exp2	0.119581455196648	0.140944014851372	0.848432303583437	0.396523119380574	   
df.mm.exp3	0.128060622835980	0.140944014851372	0.908592131216225	0.363916800435808	   
df.mm.exp4	-0.144242641843880	0.140944014851372	-1.02340381034261	0.306514172972200	   
df.mm.exp5	0.419172224898852	0.140944014851372	2.97403352203977	0.00305280119837923	** 
df.mm.exp6	0.0295468371410734	0.140944014851372	0.20963527378038	0.834020880132604	   
df.mm.exp7	0.116936645381082	0.140944014851372	0.82966733638455	0.407044679366265	   
df.mm.exp8	0.0272433174533006	0.140944014851372	0.193291765400816	0.846793473876948	   
df.mm.trans1:exp2	-0.0599384229234424	0.107242315034854	-0.558906462472041	0.576426395702155	   
df.mm.trans2:exp2	-0.0791616466744525	0.107242315034854	-0.738156824092475	0.460697113797795	   
df.mm.trans1:exp3	0.0356784136042783	0.107242315034854	0.332689699888358	0.739480441246766	   
df.mm.trans2:exp3	-0.18612144079635	0.107242315034854	-1.73552240769756	0.0831427790889233	.  
df.mm.trans1:exp4	0.121477144579785	0.107242315034854	1.13273519450140	0.257761224044302	   
df.mm.trans2:exp4	0.0439157332029378	0.107242315034854	0.409500048452564	0.682313484990143	   
df.mm.trans1:exp5	-0.285624402183776	0.107242315034854	-2.66335543102504	0.00793657719408107	** 
df.mm.trans2:exp5	-0.24291175144686	0.107242315034854	-2.26507373855099	0.0238507598620133	*  
df.mm.trans1:exp6	-0.0474959567148519	0.107242315034854	-0.442884478010528	0.658003029701232	   
df.mm.trans2:exp6	0.00152457748444679	0.107242315034854	0.0142161933370358	0.98866205214031	   
df.mm.trans1:exp7	-0.0444538307509774	0.107242315034854	-0.414517634541271	0.678637742496676	   
df.mm.trans2:exp7	-0.106635373742741	0.107242315034854	-0.994340468201235	0.320443177524251	   
df.mm.trans1:exp8	-0.0441241769409642	0.107242315034854	-0.411443719082563	0.680888704019852	   
df.mm.trans2:exp8	0.0136284847600024	0.107242315034854	0.127081224939737	0.89891711621653	   
df.mm.trans1:probe2	0.0837917653148867	0.0792036987222929	1.05792742847377	0.290498552199124	   
df.mm.trans1:probe3	0.081187217441884	0.0792036987222929	1.02504325873146	0.30574061125641	   
df.mm.trans1:probe4	0.0368379389523418	0.0792036987222929	0.465103770993125	0.642019667544503	   
df.mm.trans1:probe5	0.0486153056073927	0.0792036987222929	0.61380095111277	0.539570918112879	   
df.mm.trans1:probe6	0.0228531140912168	0.0792036987222929	0.288535945415192	0.773032492780078	   
df.mm.trans2:probe2	0.0559311849718536	0.0792036987222929	0.70616885163358	0.480347129299252	   
df.mm.trans2:probe3	0.00152134443235571	0.0792036987222929	0.0192079973144929	0.984681327728973	   
df.mm.trans2:probe4	0.00337377357325820	0.0792036987222929	0.0425961618924826	0.966037103060542	   
df.mm.trans2:probe5	-0.0378272902725123	0.0792036987222929	-0.477594997237993	0.633106005056218	   
df.mm.trans2:probe6	0.112291483119701	0.0792036987222929	1.41775554590477	0.156762098294619	   
df.mm.trans3:probe2	0.0630935761584014	0.0792036987222929	0.79659886061158	0.425987494023913	   
df.mm.trans3:probe3	-0.0465524348373068	0.0792036987222929	-0.587755819340342	0.556909176506691	   
df.mm.trans3:probe4	0.0789876257064764	0.0792036987222929	0.997271932759428	0.319019713331628	   
df.mm.trans3:probe5	-0.0155967852956194	0.0792036987222929	-0.196919910903472	0.843954435600995	   
df.mm.trans3:probe6	-0.0916403977388599	0.0792036987222929	-1.15702169491065	0.247706927914784	   
df.mm.trans3:probe7	-0.0248294485690762	0.0792036987222929	-0.313488498259837	0.754014485450098	   
df.mm.trans3:probe8	0.0214584683165685	0.0792036987222929	0.270927603921718	0.786536451864274	   
df.mm.trans3:probe9	0.0413989950619931	0.0792036987222929	0.522690173941849	0.601375699929815	   
df.mm.trans3:probe10	-0.0216747879707358	0.0792036987222929	-0.273658785137457	0.784437554414696	   
df.mm.trans3:probe11	-0.0824842784504782	0.0792036987222929	-1.04141952687952	0.298084849143530	   
df.mm.trans3:probe12	-0.100549681612947	0.0792036987222929	-1.26950739971750	0.204734167212584	   
df.mm.trans3:probe13	-0.0601110216655381	0.0792036987222929	-0.758942102897262	0.448174229237843	   
df.mm.trans3:probe14	0.0353628259253021	0.0792036987222929	0.446479476284216	0.655406082963659	   
df.mm.trans3:probe15	-0.0738328150997278	0.0792036987222929	-0.932188979691508	0.351599978613054	   
df.mm.trans3:probe16	0.0346120847323954	0.0792036987222929	0.437000863479288	0.662262149118758