chr3.15264_chr3_116839452_116842086_-_0.R 

fitVsDatCorrelation=0.92139612216291
cont.fitVsDatCorrelation=0.364061140086490

fstatistic=7843.20565258602,43,485
cont.fstatistic=1356.22737096685,43,485

residuals=-0.54447781793137,-0.0955252902391343,-0.0130544613522936,0.105952226337391,0.795690976452097
cont.residuals=-0.931837749301652,-0.335488885794357,-0.0446178468127267,0.265832339365568,1.32288300121371

predictedValues:
Include	Exclude	Both
chr3.15264_chr3_116839452_116842086_-_0.R.tl.Lung	128.582478169545	57.0406793902777	74.4406814216277
chr3.15264_chr3_116839452_116842086_-_0.R.tl.cerebhem	155.010684187598	60.0581185060502	74.2279430385208
chr3.15264_chr3_116839452_116842086_-_0.R.tl.cortex	141.637569541072	59.3522505733293	105.332132840332
chr3.15264_chr3_116839452_116842086_-_0.R.tl.heart	127.752701899901	56.002259737226	81.1385627181645
chr3.15264_chr3_116839452_116842086_-_0.R.tl.kidney	110.649414068581	55.0823911809246	77.2835015933936
chr3.15264_chr3_116839452_116842086_-_0.R.tl.liver	131.521668805264	59.9449520085754	71.2317744331336
chr3.15264_chr3_116839452_116842086_-_0.R.tl.stomach	179.289269838951	59.0056680425826	78.9469691741445
chr3.15264_chr3_116839452_116842086_-_0.R.tl.testicle	155.67573800032	56.9037083559268	79.8437210473147


diffExp=71.541798779267,94.9525656815478,82.2853189677425,71.7504421626755,55.5670228876561,71.5767167966887,120.283601796368,98.7720296443932
diffExpScore=0.998502387561254
diffExp1.5=1,1,1,1,1,1,1,1
diffExp1.5Score=0.888888888888889
diffExp1.4=1,1,1,1,1,1,1,1
diffExp1.4Score=0.888888888888889
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	76.857247401508	78.9444509636544	104.346918295581
cerebhem	80.3856722195664	83.7677431779898	83.990572839231
cortex	105.077492197272	78.1640514173465	88.2043603626517
heart	72.6087480176879	96.6733127199742	72.9742359912005
kidney	85.5251418910118	71.964019154266	88.0593259967442
liver	79.5401241524853	102.350582023979	88.7266522149652
stomach	97.0242210663243	91.787114396973	84.7216202767479
testicle	119.389681453335	110.356900472106	76.0866574495712
cont.diffExp=-2.08720356214634,-3.38207095842343,26.9134407799255,-24.0645647022863,13.5611227367458,-22.8104578714935,5.23710666935129,9.03278098122887
cont.diffExpScore=31.4952634396976

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=0,0,1,-1,0,0,0,0
cont.diffExp1.3Score=2
cont.diffExp1.2=0,0,1,-1,0,-1,0,0
cont.diffExp1.2Score=1.5

tran.correlation=0.561894842193455
cont.tran.correlation=0.312178314883889

tran.covariance=0.00296337413668013
cont.tran.covariance=0.00622368842315384

tran.mean=99.5943470191327
cont.tran.mean=89.4010314203425

weightedLogRatios:
wLogRatio
Lung	3.61712234164359
cerebhem	4.33262289051979
cortex	3.93000366006302
heart	3.6598268942441
kidney	3.03958476380919
liver	3.52508687785256
stomach	5.14931575936046
testicle	4.57372004894008

cont.weightedLogRatios:
wLogRatio
Lung	-0.116700138395440
cerebhem	-0.181639732621737
cortex	1.33349557950500
heart	-1.26758425178056
kidney	0.753158000560221
liver	-1.13522898886798
stomach	0.25231924452322
testicle	0.373151002207214

varWeightedLogRatios=0.453163277394809
cont.varWeightedLogRatios=0.78435366695645

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.9836452738302	0.0886731655280416	56.2024062652211	1.61328449582930e-214	***
df.mm.trans1	-0.138244461062292	0.0709875062166149	-1.94744777539369	0.0520577686584141	.  
df.mm.trans2	-0.943627258006279	0.0709875062166149	-13.2928638896941	1.33399101594088e-34	***
df.mm.exp2	0.241333456852895	0.0950571069773876	2.53882602286964	0.0114339673648232	*  
df.mm.exp3	-0.210689672382281	0.0950571069773876	-2.21645365698327	0.0271236883247167	*  
df.mm.exp4	-0.111002575880092	0.0950571069773875	-1.16774620446315	0.243482896162144	   
df.mm.exp5	-0.222616295433585	0.0950571069773876	-2.34192163544954	0.0195884622205904	*  
df.mm.exp6	0.116326652407125	0.0950571069773876	1.22375544665794	0.221638673791742	   
df.mm.exp7	0.307525038115399	0.0950571069773876	3.23516092477498	0.00129876254722049	** 
df.mm.exp8	0.118731863401407	0.0950571069773876	1.24905824695097	0.212246280879384	   
df.mm.trans1:exp2	-0.0544099639615952	0.0745689297512886	-0.729659982288467	0.465950294374793	   
df.mm.trans2:exp2	-0.189785408498352	0.0745689297512886	-2.54510034046817	0.0112332598882017	*  
df.mm.trans1:exp3	0.307390588173148	0.0745689297512886	4.12223414226803	4.41345132563729e-05	***
df.mm.trans2:exp3	0.250415026274027	0.0745689297512887	3.3581684370319	0.000846355460992147	***
df.mm.trans1:exp4	0.104528402746697	0.0745689297512886	1.40176884790130	0.161623919863996	   
df.mm.trans2:exp4	0.092629931447215	0.0745689297512886	1.24220545683257	0.214761077883394	   
df.mm.trans1:exp5	0.072412514663178	0.0745689297512886	0.97108158726023	0.331991687837248	   
df.mm.trans2:exp5	0.187681694418193	0.0745689297512886	2.51688866990811	0.0121610455686068	*  
df.mm.trans1:exp6	-0.0937255844701878	0.0745689297512886	-1.25689861424581	0.209395323887029	   
df.mm.trans2:exp6	-0.066664664643133	0.0745689297512886	-0.894000555800937	0.371765004492184	   
df.mm.trans1:exp7	0.0249049440746304	0.0745689297512886	0.333985537377248	0.738534832909666	   
df.mm.trans2:exp7	-0.273656217109967	0.0745689297512886	-3.66984235958191	0.00026961707366517	***
df.mm.trans1:exp8	0.0724728260985685	0.0745689297512886	0.971890388400218	0.331589531760857	   
df.mm.trans2:exp8	-0.121136037982274	0.0745689297512886	-1.62448406308500	0.104922201832696	   
df.mm.trans1:probe2	-0.35514366509528	0.0510538561422486	-6.95625545121925	1.13648234636702e-11	***
df.mm.trans1:probe3	0.17140327080125	0.0510538561422486	3.35730312561853	0.000848947958392093	***
df.mm.trans1:probe4	0.110178414503560	0.0510538561422486	2.15808212795084	0.0314115770524365	*  
df.mm.trans1:probe5	0.143999532120954	0.0510538561422486	2.82054173772370	0.00499073136912436	** 
df.mm.trans1:probe6	0.108278273702026	0.0510538561422486	2.12086376786772	0.0344400636032042	*  
df.mm.trans2:probe2	0.0257749443286373	0.0510538561422486	0.504857933881075	0.61388793219875	   
df.mm.trans2:probe3	-0.0190247331922171	0.0510538561422486	-0.372640474780388	0.709578831766149	   
df.mm.trans2:probe4	0.0313837656417933	0.0510538561422486	0.614718808983799	0.53902871657214	   
df.mm.trans2:probe5	0.0452050915490006	0.0510538561422486	0.885439317708893	0.376358511694348	   
df.mm.trans2:probe6	-0.0233923326402472	0.0510538561422486	-0.458189339803645	0.647021640472557	   
df.mm.trans3:probe2	0.630414666571555	0.0510538561422486	12.3480323369711	1.19377925469233e-30	***
df.mm.trans3:probe3	0.589405579052582	0.0510538561422486	11.5447808175420	2.08205664571768e-27	***
df.mm.trans3:probe4	0.539480595456909	0.0510538561422486	10.5668922236507	1.25228150232463e-23	***
df.mm.trans3:probe5	0.576617550436459	0.0510538561422486	11.2942996671958	2.01768933056739e-26	***
df.mm.trans3:probe6	0.0347450331025119	0.0510538561422486	0.680556489321858	0.4964769198548	   
df.mm.trans3:probe7	0.595835344712547	0.0510538561422486	11.6707216601309	6.57684478028768e-28	***
df.mm.trans3:probe8	0.652019274094935	0.0510538561422486	12.7712052205861	2.11060041270011e-32	***
df.mm.trans3:probe9	0.142392375136380	0.0510538561422486	2.78906209826032	0.00549400003691238	** 
df.mm.trans3:probe10	0.32137989085498	0.0510538561422486	6.29491903529357	6.89076716207087e-10	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.10185168949995	0.212513425610424	19.3016120168304	5.1929000604497e-62	***
df.mm.trans1	0.243118173518756	0.170128110706315	1.42902999692062	0.153639347967463	   
df.mm.trans2	0.296051223023672	0.170128110706315	1.74016640633089	0.0824640883225307	.  
df.mm.exp2	0.321206273319889	0.227813130523607	1.40995504772542	0.159193907152275	   
df.mm.exp3	0.470878417755324	0.227813130523607	2.06695029682027	0.0392689003197088	*  
df.mm.exp4	0.503343224254609	0.227813130523607	2.209456597597	0.0276094009703268	*  
df.mm.exp5	0.183992780243922	0.227813130523607	0.807647828819314	0.419689270683562	   
df.mm.exp6	0.456132155816607	0.227813130523607	2.00222065676474	0.0458172925839056	*  
df.mm.exp7	0.592088621158856	0.227813130523607	2.59901007373015	0.00963403608509504	** 
df.mm.exp8	1.09126639683509	0.227813130523607	4.79018217399025	2.21769445767559e-06	***
df.mm.trans1:exp2	-0.2763200905881	0.178711322768082	-1.5461812173294	0.122713040359485	   
df.mm.trans2:exp2	-0.26190271899261	0.178711322768082	-1.46550713707429	0.143430581197894	   
df.mm.trans1:exp3	-0.158130090223141	0.178711322768082	-0.8848353186236	0.376683907024505	   
df.mm.trans2:exp3	-0.480813029224839	0.178711322768082	-2.69044524866957	0.00738187077988025	** 
df.mm.trans1:exp4	-0.560207584919745	0.178711322768082	-3.13470672278970	0.00182456153252039	** 
df.mm.trans2:exp4	-0.300750292810584	0.178711322768082	-1.68288325637249	0.09304126340819	.  
df.mm.trans1:exp5	-0.0771321616709527	0.178711322768082	-0.431601985124631	0.666222506260476	   
df.mm.trans2:exp5	-0.276570972885680	0.178711322768082	-1.54758505841621	0.122374474745444	   
df.mm.trans1:exp6	-0.421820326505364	0.178711322768082	-2.36034471667344	0.0186528810314292	*  
df.mm.trans2:exp6	-0.196472609901547	0.178711322768082	-1.09938534871971	0.272145429871978	   
df.mm.trans1:exp7	-0.35907774345929	0.178711322768082	-2.00926129300310	0.0450629692287802	*  
df.mm.trans2:exp7	-0.441361152211342	0.178711322768082	-2.46968768052883	0.0138663159311950	*  
df.mm.trans1:exp8	-0.65082339094805	0.178711322768082	-3.64175800876724	0.000299925745131155	***
df.mm.trans2:exp8	-0.756291186270834	0.178711322768082	-4.23191532890332	2.77169523225468e-05	***
df.mm.trans1:probe2	-0.00260087682641151	0.122355278452081	-0.0212567603074853	0.983049578523054	   
df.mm.trans1:probe3	-0.222196265565809	0.122355278452081	-1.81599248006966	0.0699888159330914	.  
df.mm.trans1:probe4	0.041301726989073	0.122355278452081	0.337555743500255	0.73584404132994	   
df.mm.trans1:probe5	0.143064716452030	0.122355278452081	1.16925659654364	0.242874592899371	   
df.mm.trans1:probe6	-0.00789076777679543	0.122355278452081	-0.0644906200747665	0.94860614750669	   
df.mm.trans2:probe2	-0.0812886504370727	0.122355278452081	-0.664365701794454	0.506772009365042	   
df.mm.trans2:probe3	-0.0949014315035196	0.122355278452081	-0.77562188329036	0.438350302013549	   
df.mm.trans2:probe4	-0.177000657976894	0.122355278452081	-1.44661235883022	0.148651481005908	   
df.mm.trans2:probe5	-0.0517873887529181	0.122355278452081	-0.423254226610256	0.672297405294904	   
df.mm.trans2:probe6	-0.0615572272172171	0.122355278452081	-0.503102342587739	0.615120712810218	   
df.mm.trans3:probe2	0.073245590948071	0.122355278452081	0.598630413617641	0.54969884460463	   
df.mm.trans3:probe3	0.00382466394072188	0.122355278452081	0.0312586754662960	0.975076105530885	   
df.mm.trans3:probe4	-0.0445691818398783	0.122355278452081	-0.364260393206765	0.715822326029069	   
df.mm.trans3:probe5	0.0834073749116454	0.122355278452081	0.681681869117815	0.495765516077914	   
df.mm.trans3:probe6	0.217077602975835	0.122355278452081	1.77415805613038	0.0766643145700564	.  
df.mm.trans3:probe7	-0.120668200538586	0.122355278452081	-0.986211645833033	0.3245209973731	   
df.mm.trans3:probe8	-0.00247565988633334	0.122355278452081	-0.0202333721736647	0.98386552733232	   
df.mm.trans3:probe9	-0.0935736572703052	0.122355278452081	-0.76477008964474	0.444780406027215	   
df.mm.trans3:probe10	-0.0492683866480317	0.122355278452081	-0.402666621917149	0.6873709132111