chr16.9461_chr16_13653994_13667182_+_2.R 

fitVsDatCorrelation=0.890074280040119
cont.fitVsDatCorrelation=0.280287183063736

fstatistic=8713.31239645246,52,692
cont.fstatistic=1954.38659997227,52,692

residuals=-0.759078284241315,-0.0919035241520207,-4.6061845070593e-05,0.0902153108214074,0.88361794573636
cont.residuals=-0.657044094296919,-0.256567066165909,-0.0736978186698148,0.206829844843444,1.15250768030552

predictedValues:
Include	Exclude	Both
chr16.9461_chr16_13653994_13667182_+_2.R.tl.Lung	83.9142679910488	51.0272467508133	76.1276849162062
chr16.9461_chr16_13653994_13667182_+_2.R.tl.cerebhem	65.9953262393905	48.3002565767807	64.201523466603
chr16.9461_chr16_13653994_13667182_+_2.R.tl.cortex	79.2631412431831	50.507244228946	72.5675382561424
chr16.9461_chr16_13653994_13667182_+_2.R.tl.heart	99.1464941162716	52.6622512025097	84.0005682191172
chr16.9461_chr16_13653994_13667182_+_2.R.tl.kidney	67.261800210865	52.179329021727	67.12751473747
chr16.9461_chr16_13653994_13667182_+_2.R.tl.liver	70.6912346058415	52.9670897837072	71.2196558645281
chr16.9461_chr16_13653994_13667182_+_2.R.tl.stomach	74.5326051113021	53.7448811640094	68.0133093405108
chr16.9461_chr16_13653994_13667182_+_2.R.tl.testicle	72.8931979364728	49.6259109778384	70.2008066782953


diffExp=32.8870212402355,17.6950696626099,28.7558970142371,46.4842429137619,15.0824711891381,17.7241448221343,20.7877239472927,23.2672869586344
diffExpScore=0.995090430773179
diffExp1.5=1,0,1,1,0,0,0,0
diffExp1.5Score=0.75
diffExp1.4=1,0,1,1,0,0,0,1
diffExp1.4Score=0.8
diffExp1.3=1,1,1,1,0,1,1,1
diffExp1.3Score=0.875
diffExp1.2=1,1,1,1,1,1,1,1
diffExp1.2Score=0.888888888888889

cont.predictedValues:
Include	Exclude	Both
Lung	71.4306175011104	56.3899224719218	81.5199575859438
cerebhem	72.7416292315825	71.5004613883804	75.181208764291
cortex	71.7935440121542	64.910168478921	60.8248922916054
heart	70.5186900756515	64.5244429893642	84.0537360204954
kidney	75.9284796901692	70.7691523665065	70.1706440714387
liver	69.2967329980711	61.3553092642046	76.8880005891843
stomach	70.0486358375216	88.8638786355962	70.9976409784393
testicle	65.8830704956972	62.6726937732998	60.0608214045366
cont.diffExp=15.0406950291886,1.24116784320211,6.88337553323326,5.99424708628729,5.15932732366269,7.94142373386653,-18.8152427980746,3.21037672239742
cont.diffExpScore=2.32453425749262

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

tran.correlation=0.296111192794447
cont.tran.correlation=0.163097255101683

tran.covariance=0.00148085342138941
cont.tran.covariance=0.00106271328757110

tran.mean=64.0445173225442
cont.tran.mean=69.2892143256345

weightedLogRatios:
wLogRatio
Lung	2.07981822534569
cerebhem	1.25904838091179
cortex	1.86907281032972
heart	2.70811160560733
kidney	1.03635295002431
liver	1.18750833724077
stomach	1.35626309546899
testicle	1.57512920262669

cont.weightedLogRatios:
wLogRatio
Lung	0.981330178191162
cerebhem	0.0736294161041791
cortex	0.425677547599483
heart	0.374119528037270
kidney	0.302205672618858
liver	0.508473652874739
stomach	-1.03925201346016
testicle	0.207960621023956

varWeightedLogRatios=0.312208267699654
cont.varWeightedLogRatios=0.334607470551101

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.01700967225346	0.0921430946693626	43.595341427024	1.19076035493299e-200	***
df.mm.trans1	0.354854104979672	0.0827579739289978	4.28785394485514	2.0604227466588e-05	***
df.mm.trans2	-0.0679189998052685	0.0761071965912535	-0.892412319035202	0.372482185122135	   
df.mm.exp2	-0.124749546005379	0.104248011328352	-1.19666115847959	0.231848464495626	   
df.mm.exp3	-0.0193710985330671	0.104248011328353	-0.185817439452667	0.852642381341374	   
df.mm.exp4	0.0999304311930493	0.104248011328352	0.9585835731513	0.338103357080943	   
df.mm.exp5	-0.0730585273487107	0.104248011328353	-0.700814590300399	0.483654305138005	   
df.mm.exp6	-0.0675198908627401	0.104248011328353	-0.647685169264966	0.517403414662263	   
df.mm.exp7	0.0460382954999628	0.104248011328352	0.441622769713609	0.658900166990826	   
df.mm.exp8	-0.0875947814822808	0.104248011328353	-0.840253740729705	0.401056348168880	   
df.mm.trans1:exp2	-0.115462187533105	0.0998098719754545	-1.15682131684829	0.247744548302253	   
df.mm.trans2:exp2	0.0698266787189231	0.0868733427736271	0.803775663391658	0.421802561479686	   
df.mm.trans1:exp3	-0.0376513408556712	0.0998098719754545	-0.377230629700942	0.706117877032046	   
df.mm.trans2:exp3	0.00912813452499942	0.0868733427736271	0.105074056477662	0.916347493445222	   
df.mm.trans1:exp4	0.0668724052527293	0.0998098719754545	0.669997906311058	0.503082632767618	   
df.mm.trans2:exp4	-0.0683912683553605	0.0868733427736271	-0.787252638977795	0.431403532263096	   
df.mm.trans1:exp5	-0.148144660245214	0.0998098719754546	-1.48426861304507	0.138193011273175	   
df.mm.trans2:exp5	0.0953852079783815	0.0868733427736271	1.09798017358368	0.272595036508087	   
df.mm.trans1:exp6	-0.103954182539050	0.0998098719754545	-1.04152205069069	0.297997059424735	   
df.mm.trans2:exp6	0.104830923990368	0.0868733427736271	1.20670991403582	0.227956227722932	   
df.mm.trans1:exp7	-0.164597271858422	0.0998098719754545	-1.64910813530450	0.099579334113393	.  
df.mm.trans2:exp7	0.00585039273524216	0.0868733427736271	0.0673439348418651	0.946327353742839	   
df.mm.trans1:exp8	-0.053205549129125	0.0998098719754545	-0.533069004859654	0.594156961980499	   
df.mm.trans2:exp8	0.0597481374776398	0.0868733427736271	0.687761464795137	0.491833462376004	   
df.mm.trans1:probe2	-0.191878147523114	0.0499049359877273	-3.84487313179404	0.000131734021208679	***
df.mm.trans1:probe3	0.110418692293976	0.0499049359877273	2.21258058163085	0.0272522912639870	*  
df.mm.trans1:probe4	-0.149248150093721	0.0499049359877273	-2.99064906386062	0.00288269928407011	** 
df.mm.trans1:probe5	-0.302852216295911	0.0499049359877273	-6.0685824017566	2.12523068590658e-09	***
df.mm.trans1:probe6	0.288857578242816	0.0499049359877273	5.78815647241492	1.07899042110758e-08	***
df.mm.trans1:probe7	-0.0198688633567202	0.0499049359877273	-0.398134231884525	0.690654041030392	   
df.mm.trans1:probe8	-0.321681358534605	0.0499049359877273	-6.44588259994389	2.15500183763786e-10	***
df.mm.trans1:probe9	0.0961056305727215	0.0499049359877273	1.92577404760836	0.0545419976029262	.  
df.mm.trans1:probe10	0.465511909000113	0.0499049359877273	9.32797327131314	1.43048231474829e-19	***
df.mm.trans1:probe11	-0.255061847895844	0.0499049359877273	-5.11095431439026	4.15185312288002e-07	***
df.mm.trans1:probe12	-0.184217691977952	0.0499049359877273	-3.69137217254932	0.000240596592001685	***
df.mm.trans1:probe13	-0.203896863601347	0.0499049359877273	-4.08570534288412	4.90898681322253e-05	***
df.mm.trans1:probe14	-0.28357229271352	0.0499049359877273	-5.6822494028097	1.95914788285885e-08	***
df.mm.trans1:probe15	-0.127226083193605	0.0499049359877273	-2.54936872827355	0.0110064152165212	*  
df.mm.trans1:probe16	-0.159137397204422	0.0499049359877273	-3.18881076700624	0.00149299093348774	** 
df.mm.trans1:probe17	0.426648897402345	0.0499049359877273	8.54923243478895	7.91080779403455e-17	***
df.mm.trans1:probe18	0.509814440845125	0.0499049359877273	10.2157117478419	6.48126674351726e-23	***
df.mm.trans1:probe19	0.19549068894721	0.0499049359877273	3.91726159102349	9.84429464558452e-05	***
df.mm.trans1:probe20	0.397572310802675	0.0499049359877273	7.96659294183739	6.71762334670571e-15	***
df.mm.trans1:probe21	0.590928987945655	0.0499049359877273	11.8410929951093	1.3844160711465e-29	***
df.mm.trans1:probe22	0.565588808078655	0.0499049359877273	11.3333239865841	1.98070795943335e-27	***
df.mm.trans2:probe2	0.0372652858722526	0.0499049359877273	0.746725451795329	0.455482923568275	   
df.mm.trans2:probe3	-0.127916458603063	0.0499049359877273	-2.56320253841265	0.0105814129528846	*  
df.mm.trans2:probe4	-0.103408660412902	0.0499049359877273	-2.07211287553465	0.0386247446190183	*  
df.mm.trans2:probe5	0.0473892027111288	0.0499049359877273	0.94958948996113	0.342652392760355	   
df.mm.trans2:probe6	-0.00390776088523574	0.0499049359877273	-0.0783040957350741	0.937608795708017	   
df.mm.trans3:probe2	0.197185928435544	0.0499049359877273	3.95123096609193	8.57276366895746e-05	***
df.mm.trans3:probe3	-0.111784274989174	0.0499049359877273	-2.23994426155891	0.0254112561299433	*  

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.97782293934749	0.194047037127547	20.4992717138622	2.43651965519188e-73	***
df.mm.trans1	0.359969008859392	0.174282616589177	2.0654326627877	0.0392538857052011	*  
df.mm.trans2	-0.0331647620718707	0.160276535703624	-0.206922129470015	0.836131544684031	   
df.mm.exp2	0.336547235398875	0.219539161315274	1.53297130854740	0.125739953114480	   
df.mm.exp3	0.438630540177917	0.219539161315274	1.99796035272273	0.0461118869604931	*  
df.mm.exp4	0.091296398075045	0.219539161315274	0.415854727366553	0.677645237171234	   
df.mm.exp5	0.438115808960021	0.219539161315274	1.99561575408797	0.0463673263112254	*  
df.mm.exp6	0.112560456690997	0.219539161315274	0.512712429147675	0.608316110596103	   
df.mm.exp7	0.573479704714958	0.219539161315274	2.61219775678837	0.0091913962169436	** 
df.mm.exp8	0.330280431224817	0.219539161315274	1.50442604064844	0.132927959978161	   
df.mm.trans1:exp2	-0.318359992048421	0.210192744257335	-1.51460980812287	0.130327850041531	   
df.mm.trans2:exp2	-0.0991337956984535	0.182949301096062	-0.541864850559889	0.588086038534195	   
df.mm.trans1:exp3	-0.433562578403874	0.210192744257335	-2.06269050787534	0.0395146545289551	*  
df.mm.trans2:exp3	-0.297916712521695	0.182949301096062	-1.62841131798185	0.103892847598665	   
df.mm.trans1:exp4	-0.104145209907708	0.210192744257335	-0.495474809445397	0.620422300607148	   
df.mm.trans2:exp4	0.0434572520514219	0.182949301096062	0.237537130730025	0.812310425933213	   
df.mm.trans1:exp5	-0.377050562426011	0.210192744257335	-1.79383243583515	0.0732763864161045	.  
df.mm.trans2:exp5	-0.21098306720673	0.182949301096062	-1.15323243074839	0.249213171037849	   
df.mm.trans1:exp6	-0.142889288463541	0.210192744257335	-0.679801241324508	0.496857660196677	   
df.mm.trans2:exp6	-0.0281692116677272	0.182949301096062	-0.153972775511923	0.877676093254693	   
df.mm.trans1:exp7	-0.593016500219045	0.210192744257335	-2.82129862433799	0.00492007468229716	** 
df.mm.trans2:exp7	-0.118664422286512	0.182949301096062	-0.648619161568725	0.516799746152651	   
df.mm.trans1:exp8	-0.411125513570821	0.210192744257335	-1.95594531592151	0.0508735552846643	.  
df.mm.trans2:exp8	-0.224645047417266	0.182949301096062	-1.22790874887962	0.219898720047412	   
df.mm.trans1:probe2	-0.0633106459051015	0.105096372128667	-0.602405626595669	0.547101506013856	   
df.mm.trans1:probe3	-0.216694704364179	0.105096372128667	-2.06186664653737	0.0395932880184419	*  
df.mm.trans1:probe4	-0.0818427771817935	0.105096372128667	-0.778740269755411	0.43639893344806	   
df.mm.trans1:probe5	-0.134076777643259	0.105096372128667	-1.27575076977073	0.202471697249803	   
df.mm.trans1:probe6	-0.0575288093330085	0.105096372128667	-0.547391010439229	0.584286622611054	   
df.mm.trans1:probe7	-0.14945272852409	0.105096372128667	-1.42205411563701	0.155461097666843	   
df.mm.trans1:probe8	0.0300351910187094	0.105096372128667	0.285787134325987	0.775126687885428	   
df.mm.trans1:probe9	-0.0322102994004772	0.105096372128667	-0.306483456546366	0.75932876522681	   
df.mm.trans1:probe10	-0.0796368686778787	0.105096372128667	-0.757750882022653	0.448858088856228	   
df.mm.trans1:probe11	-0.119879911041641	0.105096372128667	-1.14066650078914	0.254403343426181	   
df.mm.trans1:probe12	-0.0417449587478575	0.105096372128667	-0.397206467762274	0.691337692880076	   
df.mm.trans1:probe13	-0.0287139367213165	0.105096372128667	-0.273215298870285	0.784769238176221	   
df.mm.trans1:probe14	-0.193116218202905	0.105096372128667	-1.83751555159750	0.0665624029663545	.  
df.mm.trans1:probe15	-0.0247649201704650	0.105096372128667	-0.235640105066098	0.813781669572548	   
df.mm.trans1:probe16	0.0559700570547414	0.105096372128667	0.532559363573638	0.594509596806777	   
df.mm.trans1:probe17	-0.172970270634903	0.105096372128667	-1.64582532328651	0.100253804411001	   
df.mm.trans1:probe18	-0.128642645066171	0.105096372128667	-1.22404458365771	0.221351987025171	   
df.mm.trans1:probe19	-0.0326864095146852	0.105096372128667	-0.311013680611809	0.755883883495122	   
df.mm.trans1:probe20	-0.066204784618256	0.105096372128667	-0.62994357728355	0.528939440327805	   
df.mm.trans1:probe21	-0.142046900552901	0.105096372128667	-1.35158709740234	0.176949081833877	   
df.mm.trans1:probe22	-0.0471145376404223	0.105096372128667	-0.448298420641398	0.65407816708205	   
df.mm.trans2:probe2	0.163752456437132	0.105096372128667	1.55811711784545	0.119662684074362	   
df.mm.trans2:probe3	0.134908223324597	0.105096372128667	1.28366203887068	0.199689896393269	   
df.mm.trans2:probe4	0.186364975416787	0.105096372128667	1.77327696134576	0.0766226041711397	.  
df.mm.trans2:probe5	0.225090674374673	0.105096372128667	2.1417549418271	0.0325616998678104	*  
df.mm.trans2:probe6	0.0785742416338345	0.105096372128667	0.747639904616666	0.454931368558204	   
df.mm.trans3:probe2	0.142865870935714	0.105096372128667	1.35937966308491	0.174469118697314	   
df.mm.trans3:probe3	0.145796176531698	0.105096372128667	1.38726174442256	0.165808576338304