chr8.22775_chr8_70875729_70876660_+_1.R 

fitVsDatCorrelation=0.934421546112796
cont.fitVsDatCorrelation=0.271310802264761

fstatistic=8853.76533727557,39,393
cont.fstatistic=1203.70382348228,39,393

residuals=-0.577128997141688,-0.0972093927282725,-0.00551638560514611,0.0849065849782209,0.631608537695654
cont.residuals=-0.954722634852378,-0.334633049353917,0.0374702431000408,0.298418066050181,1.08827814997352

predictedValues:
Include	Exclude	Both
chr8.22775_chr8_70875729_70876660_+_1.R.tl.Lung	96.5565657176854	124.570647152021	61.0972637368806
chr8.22775_chr8_70875729_70876660_+_1.R.tl.cerebhem	75.6379284454135	86.2169792850057	52.3662197231924
chr8.22775_chr8_70875729_70876660_+_1.R.tl.cortex	81.8830015304011	106.353901603046	62.5493703631979
chr8.22775_chr8_70875729_70876660_+_1.R.tl.heart	91.682992370095	114.492005998139	59.6208615102239
chr8.22775_chr8_70875729_70876660_+_1.R.tl.kidney	92.750218245602	126.877274838353	57.9636729505731
chr8.22775_chr8_70875729_70876660_+_1.R.tl.liver	103.276980776040	128.585535500286	58.18129926419
chr8.22775_chr8_70875729_70876660_+_1.R.tl.stomach	90.3771598374619	132.697977693861	60.5767242751677
chr8.22775_chr8_70875729_70876660_+_1.R.tl.testicle	99.2939811793849	111.940680381356	53.6512597680696


diffExp=-28.0140814343356,-10.5790508395921,-24.4709000726453,-22.8090136280437,-34.1270565927509,-25.3085547242462,-42.3208178563990,-12.6466992019708
diffExpScore=0.995031702071895
diffExp1.5=0,0,0,0,0,0,0,0
diffExp1.5Score=0
diffExp1.4=0,0,0,0,0,0,-1,0
diffExp1.4Score=0.5
diffExp1.3=0,0,0,0,-1,0,-1,0
diffExp1.3Score=0.666666666666667
diffExp1.2=-1,0,-1,-1,-1,-1,-1,0
diffExp1.2Score=0.857142857142857

cont.predictedValues:
Include	Exclude	Both
Lung	96.9372323949472	89.6305418423496	80.1177920433367
cerebhem	86.6066833228839	81.3750441604522	103.593711951711
cortex	82.8499444093979	112.341880642904	87.006934753512
heart	86.2113301533554	82.152076264451	73.1821606156356
kidney	80.6492647794038	97.434238947002	87.6956145831931
liver	94.7725308543453	96.5657141758446	86.6191462428748
stomach	96.337665116938	85.1757554165718	84.1530225534059
testicle	88.9290603196144	95.6592373390476	100.777235271511
cont.diffExp=7.30669055259763,5.23163916243168,-29.4919362335057,4.05925388890439,-16.7849741675982,-1.79318332149936,11.1619097003663,-6.73017701943324
cont.diffExpScore=2.94427514464095

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,-1,0,0,0,0,0
cont.diffExp1.3Score=0.5
cont.diffExp1.2=0,0,-1,0,-1,0,0,0
cont.diffExp1.2Score=0.666666666666667

tran.correlation=0.746538858328973
cont.tran.correlation=-0.360347719446573

tran.covariance=0.0112684013016419
cont.tran.covariance=-0.00261843086530319

tran.mean=103.949614409634
cont.tran.mean=90.8517625087193

weightedLogRatios:
wLogRatio
Lung	-1.19666015922911
cerebhem	-0.574876636259612
cortex	-1.18608527343182
heart	-1.02850971266716
kidney	-1.46834878627693
liver	-1.04044755588145
stomach	-1.80367196098576
testicle	-0.558423539727217

cont.weightedLogRatios:
wLogRatio
Lung	0.355387426218224
cerebhem	0.276039847223539
cortex	-1.39142000099672
heart	0.213786231633437
kidney	-0.847902566000893
liver	-0.085489261341036
stomach	0.554915728853039
testicle	-0.330063703959240

varWeightedLogRatios=0.174985631463715
cont.varWeightedLogRatios=0.447224935407892

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	5.4406179285331	0.08488849629563	64.0913453053259	3.57105100733403e-210	***
df.mm.trans1	-0.737606379549295	0.0722264121936283	-10.2124189357749	7.13379123235329e-22	***
df.mm.trans2	-0.761310195723955	0.0700513529937686	-10.8678871026471	3.11812912080684e-24	***
df.mm.exp2	-0.457971661552376	0.0958929558846867	-4.77586343363006	2.53015167557081e-06	***
df.mm.exp3	-0.346427455522627	0.0958929558846867	-3.61264758528471	0.00034240010956953	***
df.mm.exp4	-0.111698572502464	0.0958929558846867	-1.16482562740879	0.244796142175472	   
df.mm.exp5	0.0307789180434764	0.0958929558846867	0.320971626742727	0.748402507846668	   
df.mm.exp6	0.147909926908658	0.0958929558846867	1.54244830127588	0.123769489947579	   
df.mm.exp7	0.00562161891427782	0.0958929558846867	0.0586238985169874	0.9532814717737	   
df.mm.exp8	0.051014168215466	0.0958929558846867	0.531990778100653	0.595032983880875	   
df.mm.trans1:exp2	0.213800508089621	0.0841036859537196	2.54210627828215	0.0114016949972259	*  
df.mm.trans2:exp2	0.089965793317232	0.0797877621687794	1.12756381269251	0.26019211105398	   
df.mm.trans1:exp3	0.181589863465722	0.0841036859537196	2.15911896614908	0.0314454913685581	*  
df.mm.trans2:exp3	0.188326680965600	0.0797877621687794	2.36034544454603	0.0187456921584071	*  
df.mm.trans1:exp4	0.0599064543733341	0.0841036859537196	0.712292852495184	0.476706097133276	   
df.mm.trans2:exp4	0.0273305744775486	0.0797877621687794	0.34254093277782	0.732126972039286	   
df.mm.trans1:exp5	-0.0709978733591632	0.0841036859537197	-0.84417077033023	0.39908754707218	   
df.mm.trans2:exp5	-0.0124316406195186	0.0797877621687794	-0.155808864437397	0.876263682115229	   
df.mm.trans1:exp6	-0.0806244240349388	0.0841036859537196	-0.958631279006066	0.338333898019416	   
df.mm.trans2:exp6	-0.116188600072516	0.0797877621687794	-1.45622081525154	0.146130001856632	   
df.mm.trans1:exp7	-0.0717590499572404	0.0841036859537197	-0.853221225009422	0.394056348709948	   
df.mm.trans2:exp7	0.0575810806514396	0.0797877621687794	0.721678100579325	0.470921549752625	   
df.mm.trans1:exp8	-0.0230582213477331	0.0841036859537197	-0.274164218681468	0.78410247157395	   
df.mm.trans2:exp8	-0.157918078652846	0.0797877621687794	-1.97922681825307	0.0484879687689939	*  
df.mm.trans1:probe2	-0.0892175377902242	0.0460654859661826	-1.93675451195110	0.0534918165325134	.  
df.mm.trans1:probe3	0.188125673400341	0.0460654859661826	4.0838747156265	5.3690023205254e-05	***
df.mm.trans1:probe4	0.0188794398383496	0.0460654859661826	0.409839154898078	0.682147246852841	   
df.mm.trans1:probe5	0.0317731293831279	0.0460654859661826	0.689738287064919	0.490765853298685	   
df.mm.trans1:probe6	-0.350512210609475	0.0460654859661826	-7.60899843468039	2.06526101995832e-13	***
df.mm.trans1:probe7	-0.790139585178388	0.0460654859661826	-17.1525290270126	1.25134021162257e-49	***
df.mm.trans1:probe8	-0.603499378616042	0.0460654859661826	-13.1009011618607	8.64055669129722e-33	***
df.mm.trans2:probe2	0.240162793367628	0.0460654859661826	5.21350829868451	3.00154924364805e-07	***
df.mm.trans2:probe3	0.497612103911028	0.0460654859661826	10.8022762264211	5.41503172396902e-24	***
df.mm.trans2:probe4	0.57528024573953	0.0460654859661826	12.4883138356958	2.25791394348628e-30	***
df.mm.trans2:probe5	0.0464026326361126	0.0460654859661826	1.00731885625124	0.314401365748612	   
df.mm.trans2:probe6	0.096194915972048	0.0460654859661826	2.08822101741564	0.0374211072061392	*  
df.mm.trans3:probe2	0.239981156137574	0.0460654859661826	5.20956527656622	3.06182068726689e-07	***
df.mm.trans3:probe3	0.217093715344140	0.0460654859661826	4.71271952940021	3.39815976410738e-06	***
df.mm.trans3:probe4	0.225988042749441	0.0460654859661826	4.90579960266439	1.36513785859165e-06	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.70554048688479	0.229398053182659	20.5125563255674	4.28312756749017e-64	***
df.mm.trans1	-0.191069921432411	0.195180726112586	-0.978938470196062	0.328212605757232	   
df.mm.trans2	-0.141479593819957	0.189302964486709	-0.747371253290071	0.455286420551917	   
df.mm.exp2	-0.466292860826867	0.259135905968571	-1.79941432309045	0.0727201164194418	.  
df.mm.exp3	-0.0136718835130957	0.259135905968571	-0.0527595103503484	0.957950329847674	   
df.mm.exp4	-0.113839814990811	0.259135905968571	-0.439305446944192	0.660681674420352	   
df.mm.exp5	-0.190846385644412	0.259135905968571	-0.73647218022947	0.4618827732309	   
df.mm.exp6	-0.0260793772500155	0.259135905968571	-0.100639767200685	0.91988774825246	   
df.mm.exp7	-0.106322499800017	0.259135905968571	-0.410296286045795	0.681812200410668	   
df.mm.exp8	-0.250543105850567	0.259135905968571	-0.966840565432696	0.334218333947141	   
df.mm.trans1:exp2	0.353606167707354	0.227277224420127	1.55583635188058	0.120551910370670	   
df.mm.trans2:exp2	0.369665373359276	0.215614106834653	1.71447675101685	0.087229588589472	.  
df.mm.trans1:exp3	-0.143360723932697	0.227277224420127	-0.63077470388186	0.528554483899819	   
df.mm.trans2:exp3	0.239522480437427	0.215614106834653	1.11088501561314	0.267297027736337	   
df.mm.trans1:exp4	-0.00342225602215054	0.227277224420127	-0.0150576285454121	0.987993845727653	   
df.mm.trans2:exp4	0.0267158025452960	0.215614106834653	0.123905633715253	0.901453338620479	   
df.mm.trans1:exp5	0.00689239364641844	0.227277224420127	0.0303259319714222	0.975822509749136	   
df.mm.trans2:exp5	0.274327933076432	0.215614106834653	1.27230976258341	0.204015348525517	   
df.mm.trans1:exp6	0.00349530526311853	0.227277224420127	0.0153790388457815	0.987737590247497	   
df.mm.trans2:exp6	0.100606999077009	0.215614106834653	0.466606756644919	0.641039800854373	   
df.mm.trans1:exp7	0.100118184505948	0.227277224420127	0.440511295231578	0.65980902925535	   
df.mm.trans2:exp7	0.0553432016272647	0.215614106834653	0.256677090565811	0.79756237566385	   
df.mm.trans1:exp8	0.164318402349728	0.227277224420127	0.722986664277375	0.470118123424852	   
df.mm.trans2:exp8	0.315639240775103	0.215614106834653	1.46390811533104	0.144018443047167	   
df.mm.trans1:probe2	0.195715922066219	0.124484862622068	1.57220659559554	0.116707345121484	   
df.mm.trans1:probe3	0.0270959861367764	0.124484862622068	0.21766490773291	0.827803173719665	   
df.mm.trans1:probe4	0.0770484436437041	0.124484862622068	0.618938254987844	0.536315631627405	   
df.mm.trans1:probe5	0.200451371404987	0.124484862622068	1.61024695840772	0.108146825112034	   
df.mm.trans1:probe6	0.0513672618761039	0.124484862622068	0.412638619621193	0.680096416350114	   
df.mm.trans1:probe7	0.00467763976882263	0.124484862622068	0.0375759724539666	0.970044845014235	   
df.mm.trans1:probe8	0.158760753745616	0.124484862622068	1.27534183997623	0.202941644051307	   
df.mm.trans2:probe2	-0.109799684906722	0.124484862622068	-0.882032422207596	0.378298426081181	   
df.mm.trans2:probe3	-0.204503354188582	0.124484862622068	-1.64279696246641	0.101224764769632	   
df.mm.trans2:probe4	-0.0923499594707702	0.124484862622068	-0.741856941684081	0.458617126118238	   
df.mm.trans2:probe5	-0.0881317150344302	0.124484862622068	-0.707971340274483	0.479382715474896	   
df.mm.trans2:probe6	-0.188862910302404	0.124484862622068	-1.51715563100861	0.130031351437841	   
df.mm.trans3:probe2	-0.088688165840577	0.124484862622068	-0.712441368151175	0.476614257135579	   
df.mm.trans3:probe3	0.0735564531739493	0.124484862622068	0.590886728109782	0.554935981660994	   
df.mm.trans3:probe4	0.0571596353240281	0.124484862622068	0.459169365013986	0.64636641021042