chr9.24641_chr9_13781475_13782424_-_1.R 

fitVsDatCorrelation=0.74926970014969
cont.fitVsDatCorrelation=0.259387190408365

fstatistic=10597.9621597876,39,393
cont.fstatistic=4978.17310663137,39,393

residuals=-0.35372855365987,-0.0884519431870157,0.00191011002876008,0.081092835382048,0.392477544706714
cont.residuals=-0.476574210417311,-0.128930167430346,-0.00681283142899388,0.116638737191418,0.53123880298656

predictedValues:
Include	Exclude	Both
chr9.24641_chr9_13781475_13782424_-_1.R.tl.Lung	59.788400094877	52.0522571735942	56.4593357476491
chr9.24641_chr9_13781475_13782424_-_1.R.tl.cerebhem	49.699085937709	47.3920233248089	54.1645015005104
chr9.24641_chr9_13781475_13782424_-_1.R.tl.cortex	65.9813132595229	51.0289283693957	59.7495797016707
chr9.24641_chr9_13781475_13782424_-_1.R.tl.heart	60.9968623085082	55.1627021490891	58.4146138828054
chr9.24641_chr9_13781475_13782424_-_1.R.tl.kidney	63.2292365940473	55.8862358514773	58.7939850108713
chr9.24641_chr9_13781475_13782424_-_1.R.tl.liver	54.2002853381663	54.6841279785096	50.7472363486651
chr9.24641_chr9_13781475_13782424_-_1.R.tl.stomach	52.8300305031312	48.0662087431428	55.8140542211961
chr9.24641_chr9_13781475_13782424_-_1.R.tl.testicle	53.9772296267688	52.082635807653	53.874280103256


diffExp=7.7361429212828,2.30706261290017,14.9523848901272,5.83416015941914,7.34300074256993,-0.483842640343305,4.7638217599884,1.89459381911587
diffExpScore=0.999287395235836
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=0,0,1,0,0,0,0,0
diffExp1.2Score=0.5

cont.predictedValues:
Include	Exclude	Both
Lung	57.2848097253877	53.7992623518863	52.0050799647876
cerebhem	55.9351417719754	52.0814740023984	52.3262798301244
cortex	53.0614971948411	54.8519878629598	55.0693628819685
heart	57.5280735441109	55.3192048383188	54.3655909005567
kidney	55.2179980262341	54.5159379139307	55.1022347592744
liver	51.6773245682302	57.03379113835	57.9851944532018
stomach	55.3068697532183	53.2148297482852	53.7681640811865
testicle	52.8180769382776	51.3881739013945	49.7352225878113
cont.diffExp=3.4855473735014,3.85366776957703,-1.79049066811869,2.20886870579211,0.7020601123034,-5.35646657011985,2.09204000493315,1.42990303688309
cont.diffExpScore=2.74343452329563

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=0.556864239407597
cont.tran.correlation=-0.185711827005326

tran.covariance=0.00355382244530244
cont.tran.covariance=-0.000240313387865848

tran.mean=54.8160976912751
cont.tran.mean=54.4396533299874

weightedLogRatios:
wLogRatio
Lung	0.557237276754761
cerebhem	0.184532064614987
cortex	1.04356092046067
heart	0.408229437726227
kidney	0.504293086124648
liver	-0.03552380960878
stomach	0.370425383745540
testicle	0.141875688804979

cont.weightedLogRatios:
wLogRatio
Lung	0.252147993267181
cerebhem	0.284713312493558
cortex	-0.132350701112344
heart	0.15789189314592
kidney	0.0512459993729988
liver	-0.393940266457845
stomach	0.153993938627619
testicle	0.108495382564058

varWeightedLogRatios=0.107624110653108
cont.varWeightedLogRatios=0.0501889994434006

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.85209718674948	0.0671758009432019	57.3435244933884	5.10760973205876e-193	***
df.mm.trans1	0.259121206448971	0.0548488117912059	4.72428112819241	3.22028012095182e-06	***
df.mm.trans2	0.132603525508612	0.0548488117912059	2.41761892697688	0.0160766070374137	*  
df.mm.exp2	-0.237124434066834	0.0745248599295621	-3.18181656820226	0.00157985936500735	** 
df.mm.exp3	0.0220629421904104	0.0745248599295621	0.296048086655425	0.767349729791637	   
df.mm.exp4	0.0440042025820673	0.0745248599295621	0.590463405414761	0.555219391048644	   
df.mm.exp5	0.0865062007112717	0.0745248599295621	1.16076971889694	0.246440156942781	   
df.mm.exp6	0.0578633494603345	0.074524859929562	0.776430167262637	0.437962071749014	   
df.mm.exp7	-0.191905673236097	0.0745248599295621	-2.57505580577378	0.0103872640217841	*  
df.mm.exp8	-0.0547984602131136	0.0745248599295621	-0.735304437538117	0.462592679119777	   
df.mm.trans1:exp2	0.0522993116110831	0.0608492933266051	0.859489219215243	0.390594627595951	   
df.mm.trans2:exp2	0.143330204712101	0.0608492933266051	2.35549497580496	0.0189886699007114	*  
df.mm.trans1:exp3	0.0764969635211766	0.0608492933266051	1.25715450975878	0.209444289382657	   
df.mm.trans2:exp3	-0.0419184069545171	0.0608492933266052	-0.688888969170478	0.491299625287244	   
df.mm.trans1:exp4	-0.0239934410909665	0.0608492933266051	-0.394309280835573	0.69356659084398	   
df.mm.trans2:exp4	0.0140346770099672	0.0608492933266052	0.230646507834311	0.817709452140188	   
df.mm.trans1:exp5	-0.0305510659844806	0.0608492933266051	-0.502077580761694	0.615894127243453	   
df.mm.trans2:exp5	-0.0154362385364043	0.0608492933266051	-0.253679832460029	0.799875575800815	   
df.mm.trans1:exp6	-0.155988840280593	0.0608492933266051	-2.56352755722785	0.0107326331389342	*  
df.mm.trans2:exp6	-0.00853800667272518	0.0608492933266051	-0.140313982397428	0.888483802560725	   
df.mm.trans1:exp7	0.0681737980010999	0.0608492933266051	1.12037123644445	0.263239787231761	   
df.mm.trans2:exp7	0.112236922924124	0.0608492933266051	1.84450659634942	0.0658617209840285	.  
df.mm.trans1:exp8	-0.0474509195240853	0.0608492933266051	-0.779810527451736	0.435971778085064	   
df.mm.trans2:exp8	0.0553819079711294	0.0608492933266051	0.910148745259376	0.363301933477597	   
df.mm.trans1:probe2	-0.106026087241418	0.0372624299647810	-2.84538843391668	0.00466794020433139	** 
df.mm.trans1:probe3	-0.0948601284130757	0.0372624299647810	-2.54573114267464	0.0112859287949786	*  
df.mm.trans1:probe4	0.00663829835147403	0.0372624299647810	0.178149904816951	0.85869704330538	   
df.mm.trans1:probe5	-0.0740536731796943	0.0372624299647810	-1.98735491082269	0.0475767907639621	*  
df.mm.trans1:probe6	0.0234208376270407	0.0372624299647810	0.62853758193379	0.530016951174493	   
df.mm.trans2:probe2	-0.184591613455567	0.0372624299647810	-4.95382651185218	1.08304547166794e-06	***
df.mm.trans2:probe3	0.179821734835852	0.0372624299647810	4.82581879404569	1.99902229769803e-06	***
df.mm.trans2:probe4	-0.0909866431866565	0.0372624299647810	-2.44177964970758	0.0150551874563377	*  
df.mm.trans2:probe5	-0.152492096720356	0.0372624299647810	-4.09238197467221	5.18350462704892e-05	***
df.mm.trans2:probe6	-0.141182013278709	0.0372624299647810	-3.78885685695079	0.00017504863569868	***
df.mm.trans3:probe2	-0.307386262304308	0.0372624299647810	-8.24922750864173	2.42934156928584e-15	***
df.mm.trans3:probe3	-0.214967467420087	0.0372624299647810	-5.76901365861716	1.61752691067229e-08	***
df.mm.trans3:probe4	-0.28948220379044	0.0372624299647810	-7.76874197587348	6.97825338576382e-14	***
df.mm.trans3:probe5	-0.376621246544428	0.0372624299647810	-10.107264794604	1.67594318351458e-21	***
df.mm.trans3:probe6	-0.0733503856942438	0.0372624299647810	-1.96848100790989	0.0497151416250504	*  

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.07999758542019	0.097961697469134	41.6489065709148	3.27384353505668e-146	***
df.mm.trans1	-0.0308971719635882	0.0799853910454243	-0.386285189829747	0.699494512397696	   
df.mm.trans2	-0.0893137873603702	0.0799853910454243	-1.11662625128192	0.264836385012081	   
df.mm.exp2	-0.0624504287416902	0.108678745617370	-0.574633323074614	0.565868093549912	   
df.mm.exp3	-0.114457379270575	0.108678745617370	-1.05317169995271	0.292909388866717	   
df.mm.exp4	-0.0122920749820513	0.108678745617370	-0.113104682173353	0.91000531549962	   
df.mm.exp5	-0.0813620630346354	0.108678745617370	-0.748647424778806	0.454517551277221	   
df.mm.exp6	-0.153478540295930	0.108678745617370	-1.41222222822011	0.158675875338109	   
df.mm.exp7	-0.0794011448281712	0.108678745617370	-0.730604170825839	0.46545628246948	   
df.mm.exp8	-0.0824056599462996	0.108678745617370	-0.75825000995529	0.448755786484634	   
df.mm.trans1:exp2	0.0386077778250210	0.088735824216097	0.435086710086785	0.663738318419393	   
df.mm.trans2:exp2	0.0299999727566482	0.088735824216097	0.338081862896655	0.735482024023117	   
df.mm.trans1:exp3	0.0378734568008538	0.088735824216097	0.426811348578012	0.669750453702706	   
df.mm.trans2:exp3	0.133836050997024	0.088735824216097	1.5082527511222	0.132293307122405	   
df.mm.trans1:exp4	0.0165296513902177	0.088735824216097	0.186279346997029	0.852321817362615	   
df.mm.trans2:exp4	0.0401524516776328	0.088735824216097	0.452494266350083	0.651162624311329	   
df.mm.trans1:exp5	0.0446155265777397	0.088735824216097	0.502790467907169	0.615393232941021	   
df.mm.trans2:exp5	0.0945954045974039	0.088735824216097	1.06603398833640	0.287062744289542	   
df.mm.trans1:exp6	0.0504621414429151	0.088735824216097	0.568678342582646	0.569899263157164	   
df.mm.trans2:exp6	0.211862703324314	0.088735824216097	2.38756674878421	0.0174320904366961	*  
df.mm.trans1:exp7	0.0442627848463678	0.088735824216097	0.498815278241798	0.618188600869142	   
df.mm.trans2:exp7	0.0684785008556587	0.088735824216097	0.771712005389098	0.440748790492143	   
df.mm.trans1:exp8	0.00122367052312223	0.088735824216097	0.0137900395238595	0.989004486793735	   
df.mm.trans2:exp8	0.0365539700533472	0.088735824216097	0.411941517152395	0.680606878004634	   
df.mm.trans1:probe2	-0.027713598243203	0.0543393728086852	-0.510009534721266	0.610331080499849	   
df.mm.trans1:probe3	-0.0310061425390013	0.0543393728086852	-0.570601774300301	0.568595711302732	   
df.mm.trans1:probe4	0.0444082520561173	0.0543393728086852	0.817238951440738	0.41428703616341	   
df.mm.trans1:probe5	-0.0102990976153503	0.0543393728086852	-0.189532876126686	0.849773039684872	   
df.mm.trans1:probe6	0.0118314784225981	0.0543393728086852	0.217733069247112	0.827750099180466	   
df.mm.trans2:probe2	-0.0428825110704993	0.0543393728086852	-0.78916095004404	0.430493767275442	   
df.mm.trans2:probe3	0.013506363003032	0.0543393728086852	0.248555739695127	0.803834281831808	   
df.mm.trans2:probe4	-0.0192575240560706	0.0543393728086852	-0.354393565120291	0.723234033423642	   
df.mm.trans2:probe5	0.0494886500938763	0.0543393728086852	0.910732817401352	0.362994416961029	   
df.mm.trans2:probe6	-0.065943480981026	0.0543393728086852	-1.21354880582070	0.225648895981307	   
df.mm.trans3:probe2	-0.0282310902361424	0.0543393728086852	-0.519532868653761	0.603681673610333	   
df.mm.trans3:probe3	-0.0202750379125294	0.0543393728086852	-0.373118732597678	0.709261243660101	   
df.mm.trans3:probe4	0.0130277054063065	0.0543393728086852	0.239747069812044	0.810651335684908	   
df.mm.trans3:probe5	-0.037333266392345	0.0543393728086852	-0.687038963879574	0.492463383167578	   
df.mm.trans3:probe6	0.0221403651999763	0.0543393728086852	0.407446093975482	0.683902225187182