chr9.25212_chr9_59935047_59944552_-_2.R 

fitVsDatCorrelation=0.87002373694892
cont.fitVsDatCorrelation=0.319852038553143

fstatistic=9335.63026712605,55,761
cont.fstatistic=2517.61363326109,55,761

residuals=-0.646283272130076,-0.0969737174494493,0.00378120162760844,0.093084738199803,0.717035118134944
cont.residuals=-0.845245969535714,-0.208628186639884,-0.0470909703039535,0.171797198508749,1.24521671676066

predictedValues:
Include	Exclude	Both
chr9.25212_chr9_59935047_59944552_-_2.R.tl.Lung	79.8578156521531	75.5618852466473	110.704711626971
chr9.25212_chr9_59935047_59944552_-_2.R.tl.cerebhem	82.3936316476526	68.6883612365043	102.911084007178
chr9.25212_chr9_59935047_59944552_-_2.R.tl.cortex	86.9163078237253	64.78234507521	130.834022949784
chr9.25212_chr9_59935047_59944552_-_2.R.tl.heart	80.5292985275445	66.077959383439	125.118279615293
chr9.25212_chr9_59935047_59944552_-_2.R.tl.kidney	72.4483895876917	77.7395072879256	92.8454430902207
chr9.25212_chr9_59935047_59944552_-_2.R.tl.liver	67.9543451382625	76.8317059587584	92.5309938887187
chr9.25212_chr9_59935047_59944552_-_2.R.tl.stomach	73.5375419883394	68.2869235794094	108.306140944188
chr9.25212_chr9_59935047_59944552_-_2.R.tl.testicle	77.9070141825149	67.6069813821995	112.397600841819


diffExp=4.29593040550587,13.7052704111483,22.1339627485154,14.4513391441055,-5.29111770023395,-8.87736082049591,5.25061840892992,10.3000328003154
diffExpScore=1.47985944645151
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=0,0,1,0,0,0,0,0
diffExp1.3Score=0.5
diffExp1.2=0,0,1,1,0,0,0,0
diffExp1.2Score=0.666666666666667

cont.predictedValues:
Include	Exclude	Both
Lung	80.140438789429	70.6328336620409	69.7070146394434
cerebhem	76.057725674192	86.0996950098073	77.1754834365304
cortex	83.012448127672	77.2778423115954	73.0663489198402
heart	75.0078932981342	70.2274867989858	78.2800229259952
kidney	74.9868140725264	79.3308795657345	75.3526104043565
liver	75.16055442897	80.1009979384769	63.287592448269
stomach	72.4458646861987	70.8003593667959	74.2682550865872
testicle	75.0115946693966	79.9647542539709	84.7707964821582
cont.diffExp=9.50760512738819,-10.0419693356153,5.7346058160766,4.78040649914844,-4.34406549320811,-4.94044350950695,1.64550531940286,-4.95315958457432
cont.diffExpScore=12.7225717290402

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=-0.699239347013045
cont.tran.correlation=-0.0166988069196424

tran.covariance=-0.00401947003854672
cont.tran.covariance=-8.9265512017324e-06

tran.mean=74.1950008561236
cont.tran.mean=76.6411364158704

weightedLogRatios:
wLogRatio
Lung	0.240680302157590
cerebhem	0.786029552606747
cortex	1.26911132981283
heart	0.848447433456244
kidney	-0.304380525293855
liver	-0.525531838812869
stomach	0.315627341094881
testicle	0.607578508018198

cont.weightedLogRatios:
wLogRatio
Lung	0.545633815310424
cerebhem	-0.544852201695587
cortex	0.313763864738458
heart	0.282160580062302
kidney	-0.244715869986185
liver	-0.277021445150594
stomach	0.098136497310922
testicle	-0.278128557454428

varWeightedLogRatios=0.361950501924593
cont.varWeightedLogRatios=0.142125257913764

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.21217317854150	0.0842343459166085	50.0054120763462	1.06680449511910e-242	***
df.mm.trans1	0.370773049133413	0.0737594735888643	5.02678545674152	6.22320422596184e-07	***
df.mm.trans2	0.187538203649493	0.0661452572747713	2.83524792821423	0.00470046573132805	** 
df.mm.exp2	0.00888922455806383	0.0872297821094496	0.101905843888390	0.918858255799807	   
df.mm.exp3	-0.236284069946184	0.0872297821094496	-2.70875455873216	0.00690539930953353	** 
df.mm.exp4	-0.248136544781183	0.0872297821094496	-2.84463102830911	0.0045656523680664	** 
df.mm.exp5	0.106968478893087	0.0872297821094496	1.22628391710151	0.220471155825980	   
df.mm.exp6	0.0345764794022602	0.0872297821094496	0.396383879061810	0.691932976416765	   
df.mm.exp7	-0.161780852478249	0.0872297821094496	-1.85465157158433	0.0640326213866414	.  
df.mm.exp8	-0.151148690893301	0.0872297821094496	-1.73276474201954	0.0835426128812816	.  
df.mm.trans1:exp2	0.0223711744946633	0.0818287889464252	0.273390023031016	0.784627599512868	   
df.mm.trans2:exp2	-0.104261446646039	0.0652767917153007	-1.59722075650971	0.110631633857205	   
df.mm.trans1:exp3	0.320981997517804	0.0818287889464253	3.9226047660067	9.55150865036532e-05	***
df.mm.trans2:exp3	0.0823651909911703	0.0652767917153007	1.2617836879974	0.207413231468586	   
df.mm.trans1:exp4	0.25650987080684	0.0818287889464253	3.13471425044285	0.00178628915135768	** 
df.mm.trans2:exp4	0.114019799867604	0.0652767917153007	1.74671268105350	0.0810906234135833	.  
df.mm.trans1:exp5	-0.204341787333133	0.0818287889464253	-2.49718699206118	0.0127285259130720	*  
df.mm.trans2:exp5	-0.0785568840853032	0.0652767917153007	-1.20344278603523	0.22917924913049	   
df.mm.trans1:exp6	-0.195988143784814	0.0818287889464253	-2.39510013906146	0.0168566268577680	*  
df.mm.trans2:exp6	-0.0179110790863794	0.0652767917153007	-0.274386632916904	0.783861980514078	   
df.mm.trans1:exp7	0.0793291546007329	0.0818287889464253	0.969452873763915	0.332627292354655	   
df.mm.trans2:exp7	0.0605471524312302	0.0652767917153007	0.927544856911804	0.353937794133737	   
df.mm.trans1:exp8	0.126416931523883	0.0818287889464253	1.54489554533980	0.122787257066658	   
df.mm.trans2:exp8	0.0399079508460948	0.0652767917153007	0.611365077808205	0.541140494463067	   
df.mm.trans1:probe2	-0.285469700494477	0.0501096947971595	-5.69689561371382	1.74373875659578e-08	***
df.mm.trans1:probe3	-0.117460009408175	0.0501096947971595	-2.34405756977058	0.0193315634317090	*  
df.mm.trans1:probe4	-0.189888989172729	0.0501096947971595	-3.78946608917469	0.000162899396435269	***
df.mm.trans1:probe5	-0.527663691942055	0.0501096947971596	-10.5301717377845	2.67486712445207e-24	***
df.mm.trans1:probe6	-0.0923929254492344	0.0501096947971595	-1.84381337430280	0.0655989378532715	.  
df.mm.trans1:probe7	0.284801815485615	0.0501096947971595	5.68356715478855	1.87929930689177e-08	***
df.mm.trans1:probe8	-0.728877014621798	0.0501096947971595	-14.5456286966472	1.78804593512095e-42	***
df.mm.trans1:probe9	-0.364975930482824	0.0501096947971595	-7.28353928237282	8.13600808564508e-13	***
df.mm.trans1:probe10	-0.523249685809429	0.0501096947971596	-10.442084868557	6.03856311574666e-24	***
df.mm.trans1:probe11	-0.285914741415145	0.0501096947971595	-5.70577694740523	1.65874268154440e-08	***
df.mm.trans1:probe12	-0.149941376359752	0.0501096947971595	-2.99226281394656	0.00285869162480562	** 
df.mm.trans1:probe13	-0.440601816773612	0.0501096947971595	-8.79274596576843	9.64217031107079e-18	***
df.mm.trans1:probe14	-0.367120737758414	0.0501096947971596	-7.32634152421987	6.04266743429673e-13	***
df.mm.trans1:probe15	-0.320207887919450	0.0501096947971595	-6.39013845954617	2.88640640265601e-10	***
df.mm.trans1:probe16	-0.365599704937556	0.0501096947971595	-7.29598746145786	7.46287768614908e-13	***
df.mm.trans1:probe17	-0.105011750552243	0.0501096947971595	-2.09563740065316	0.0364442105057468	*  
df.mm.trans1:probe18	-0.139560325073952	0.0501096947971595	-2.78509629002696	0.00548418332931447	** 
df.mm.trans1:probe19	-0.357500457197094	0.0501096947971595	-7.13435710682794	2.26834699424266e-12	***
df.mm.trans1:probe20	-0.275150071803661	0.0501096947971595	-5.49095485249808	5.45131766346815e-08	***
df.mm.trans1:probe21	-0.412202332108689	0.0501096947971595	-8.2259996549022	8.35922221830756e-16	***
df.mm.trans1:probe22	0.0884299322002295	0.0501096947971595	1.76472701656211	0.0780107394460149	.  
df.mm.trans2:probe2	-0.0377916945620009	0.0501096947971595	-0.75417930033258	0.450974862100971	   
df.mm.trans2:probe3	-0.350538581961967	0.0501096947971595	-6.99542440601409	5.80032167998907e-12	***
df.mm.trans2:probe4	-0.12329680472012	0.0501096947971595	-2.46053793021922	0.0140939835173364	*  
df.mm.trans2:probe5	-0.0706379462552894	0.0501096947971595	-1.40966626400793	0.159046784705948	   
df.mm.trans2:probe6	-0.314847647013497	0.0501096947971595	-6.28316832277622	5.57981425059177e-10	***
df.mm.trans3:probe2	-0.326957404835904	0.0501096947971595	-6.52483329143001	1.24176202131322e-10	***
df.mm.trans3:probe3	-0.348940428019674	0.0501096947971595	-6.96353129732998	7.17959595859308e-12	***
df.mm.trans3:probe4	-0.419947811581239	0.0501096947971595	-8.38057013280878	2.53446150518318e-16	***
df.mm.trans3:probe5	0.255501080462103	0.0501096947971595	5.09883529517299	4.31702923275763e-07	***
df.mm.trans3:probe6	0.458636381616932	0.0501096947971595	9.1526476757334	5.01861460053548e-19	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.39145185961834	0.161881771834643	27.1275252911370	6.61333902784438e-114	***
df.mm.trans1	-0.0425219390323338	0.141751136596665	-0.299976000568691	0.76427738112414	   
df.mm.trans2	-0.171073457349390	0.1271181170766	-1.34578344364795	0.178773162893488	   
df.mm.exp2	0.0439419051909666	0.167638408430299	0.262123135159905	0.793297446636995	   
df.mm.exp3	0.078055195687819	0.167638408430299	0.465616420596553	0.641623310213195	   
df.mm.exp4	-0.187934028936115	0.167638408430299	-1.12106784295948	0.262612622878967	   
df.mm.exp5	-0.0282135575481205	0.167638408430299	-0.168300079989432	0.866391929365254	   
df.mm.exp6	0.158250836010703	0.167638408430299	0.944001064508441	0.345468707156843	   
df.mm.exp7	-0.161954671514424	0.167638408430299	-0.966095258424988	0.334303427663079	   
df.mm.exp8	-0.137697169180116	0.167638408430299	-0.821393918431094	0.411679174188541	   
df.mm.trans1:exp2	-0.0962298853499877	0.157258766570639	-0.611920641681763	0.540773035943567	   
df.mm.trans2:exp2	0.154068861533704	0.125449097842051	1.22813845762117	0.219774699970555	   
df.mm.trans1:exp3	-0.0428452021740033	0.157258766570639	-0.272450325716868	0.78534968828923	   
df.mm.trans2:exp3	0.0118569710039732	0.125449097842051	0.0945161918892545	0.92472400086857	   
df.mm.trans1:exp4	0.121746800430668	0.157258766570639	0.774181326011998	0.439064015844654	   
df.mm.trans2:exp4	0.182178710751268	0.125449097842051	1.45221220307732	0.146854767204761	   
df.mm.trans1:exp5	-0.0382547372178104	0.157258766570639	-0.24325980708126	0.807869694106521	   
df.mm.trans2:exp5	0.144345909805387	0.125449097842051	1.15063330297622	0.250244547700871	   
df.mm.trans1:exp6	-0.222404865322353	0.157258766570639	-1.4142605221468	0.157694408414970	   
df.mm.trans2:exp6	-0.0324576257804373	0.125449097842051	-0.258731440391095	0.795912423314993	   
df.mm.trans1:exp7	0.0610136799976614	0.157258766570639	0.387982694562563	0.698137342242705	   
df.mm.trans2:exp7	0.164323645579339	0.125449097842051	1.30988303946381	0.190630677249303	   
df.mm.trans1:exp8	0.0715592858805914	0.157258766570639	0.45504163259762	0.649209073754732	   
df.mm.trans2:exp8	0.261788032524273	0.125449097842051	2.08680681668896	0.0372378713777644	*  
df.mm.trans1:probe2	0.256618998364337	0.0963009339193783	2.66476126367761	0.00786750412158495	** 
df.mm.trans1:probe3	0.048746821679612	0.0963009339193783	0.506192616163225	0.612868044096376	   
df.mm.trans1:probe4	-0.021203529451906	0.0963009339193783	-0.220179894305670	0.825790106049254	   
df.mm.trans1:probe5	0.136748975042248	0.0963009339193783	1.42001712212607	0.156012225674062	   
df.mm.trans1:probe6	0.157956631450943	0.0963009339193783	1.64023987122681	0.101368547806689	   
df.mm.trans1:probe7	-0.0224813305430944	0.0963009339193783	-0.233448728149567	0.815475759077883	   
df.mm.trans1:probe8	0.106001231384875	0.0963009339193783	1.1007290071934	0.271362663249555	   
df.mm.trans1:probe9	0.00285036675195768	0.0963009339193783	0.0295985369606484	0.97639499254248	   
df.mm.trans1:probe10	-0.0565315367849121	0.0963009339193783	-0.58702999528789	0.557357671907031	   
df.mm.trans1:probe11	0.156839820441155	0.0963009339193783	1.62864277694813	0.103802537242209	   
df.mm.trans1:probe12	-0.0164949220670349	0.0963009339193783	-0.171285172383107	0.864045072253114	   
df.mm.trans1:probe13	-0.0181609695450689	0.0963009339193783	-0.188585601467510	0.850467897501192	   
df.mm.trans1:probe14	-0.0182034514504430	0.0963009339193783	-0.189026738470498	0.850122260627128	   
df.mm.trans1:probe15	-0.00947362128663949	0.0963009339193783	-0.0983751756194874	0.921660291434345	   
df.mm.trans1:probe16	0.0431712037587194	0.0963009339193783	0.448294756880153	0.654068088718834	   
df.mm.trans1:probe17	0.0325185724519146	0.0963009339193783	0.337676605287532	0.73570002868086	   
df.mm.trans1:probe18	0.0559110482445994	0.0963009339193783	0.580586770751437	0.561690871748572	   
df.mm.trans1:probe19	-0.0262264486045787	0.0963009339193783	-0.272338465860935	0.785435656762606	   
df.mm.trans1:probe20	-0.115646825523018	0.0963009339193783	-1.20088996872902	0.230167530792901	   
df.mm.trans1:probe21	0.200660568323804	0.0963009339193783	2.08368247489473	0.0375221846606399	*  
df.mm.trans1:probe22	0.0822168743697545	0.0963009339193783	0.85374950193718	0.393512451771682	   
df.mm.trans2:probe2	0.199616768506924	0.0963009339193783	2.07284353726039	0.0385229132721599	*  
df.mm.trans2:probe3	-0.103657701420896	0.0963009339193783	-1.07639352187048	0.282092414664042	   
df.mm.trans2:probe4	0.0669607626602106	0.0963009339193783	0.695328279124158	0.487061858291665	   
df.mm.trans2:probe5	0.200529458465725	0.0963009339193783	2.08232101501326	0.0376466545250377	*  
df.mm.trans2:probe6	0.0819511136495889	0.0963009339193783	0.850989811980403	0.395042696290674	   
df.mm.trans3:probe2	0.107647550259890	0.0963009339193783	1.11782457218962	0.263994683110210	   
df.mm.trans3:probe3	0.0624918318443737	0.0963009339193783	0.648922386325879	0.516584324749714	   
df.mm.trans3:probe4	-0.0610470644268273	0.0963009339193783	-0.633919754900144	0.52632368160659	   
df.mm.trans3:probe5	0.107495771349841	0.0963009339193783	1.11624848248964	0.264668116309885	   
df.mm.trans3:probe6	0.18207885047517	0.0963009339193783	1.89072777453647	0.0590403313631296	.