chr17.10695_chr17_64698226_64708018_-_2.R 

fitVsDatCorrelation=0.850186147909288
cont.fitVsDatCorrelation=0.278173744839213

fstatistic=9174.9781151599,52,692
cont.fstatistic=2747.13874538070,52,692

residuals=-0.943526714887924,-0.088011569629641,-0.00709291503479784,0.0856579604793603,0.755267909629469
cont.residuals=-0.540079373278024,-0.173867592599936,-0.0612995759432335,0.0935897844850843,1.81758245708007

predictedValues:
Include	Exclude	Both
chr17.10695_chr17_64698226_64708018_-_2.R.tl.Lung	52.443141051643	61.1411326996783	106.764949742303
chr17.10695_chr17_64698226_64708018_-_2.R.tl.cerebhem	58.4840183104426	69.3446247490102	77.1159749701985
chr17.10695_chr17_64698226_64708018_-_2.R.tl.cortex	51.5043795384249	73.7994387915337	178.506323758531
chr17.10695_chr17_64698226_64708018_-_2.R.tl.heart	53.2557753726782	56.7504858203839	121.843916808496
chr17.10695_chr17_64698226_64708018_-_2.R.tl.kidney	51.2510139186851	57.565857905851	82.8945048629626
chr17.10695_chr17_64698226_64708018_-_2.R.tl.liver	50.4909020194475	52.0604973322852	77.8504364213716
chr17.10695_chr17_64698226_64708018_-_2.R.tl.stomach	52.73920473472	58.1775829255436	132.025359084352
chr17.10695_chr17_64698226_64708018_-_2.R.tl.testicle	51.7857336142121	60.9214425149234	115.550453771947


diffExp=-8.69799164803537,-10.8606064385677,-22.2950592531088,-3.49471044770571,-6.31484398716594,-1.56959531283768,-5.43837819082358,-9.13570890071136
diffExpScore=0.985466572616995
diffExp1.5=0,0,0,0,0,0,0,0
diffExp1.5Score=0
diffExp1.4=0,0,-1,0,0,0,0,0
diffExp1.4Score=0.5
diffExp1.3=0,0,-1,0,0,0,0,0
diffExp1.3Score=0.5
diffExp1.2=0,0,-1,0,0,0,0,0
diffExp1.2Score=0.5

cont.predictedValues:
Include	Exclude	Both
Lung	60.6808717795447	59.5675380602262	54.4631455175826
cerebhem	56.6684122034487	66.5462194363853	53.5344998705761
cortex	56.4844592846759	62.6711494140156	59.5516721200724
heart	57.1733218410419	57.7486566381832	52.7114522853694
kidney	60.6105319592797	53.6429603314167	68.3733546549385
liver	62.7876440131325	57.9957538947516	59.6650031154748
stomach	55.9089800312537	61.0423624220416	53.4427916938414
testicle	60.157912893728	51.5519273768056	74.1948244344134
cont.diffExp=1.11333371931844,-9.87780723293662,-6.18669012933969,-0.575334797141323,6.96757162786297,4.7918901183809,-5.13338239078793,8.60598551692237
cont.diffExpScore=33.4138395443844

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=0.44887727179338
cont.tran.correlation=-0.597460638785596

tran.covariance=0.00237134532516380
cont.tran.covariance=-0.00211514637725031

tran.mean=56.9822019562164
cont.tran.mean=58.8274188487457

weightedLogRatios:
wLogRatio
Lung	-0.619415699962234
cerebhem	-0.707558600346823
cortex	-1.48244200631115
heart	-0.254669454105312
kidney	-0.464176551743322
liver	-0.120527580365672
stomach	-0.393980158328389
testicle	-0.654488063621489

cont.weightedLogRatios:
wLogRatio
Lung	0.0758555855663514
cerebhem	-0.661608143639576
cortex	-0.424674187661614
heart	-0.0405623832241918
kidney	0.493774802528433
liver	0.325497949576224
stomach	-0.357314590170000
testicle	0.620587455704063

varWeightedLogRatios=0.171762621428243
cont.varWeightedLogRatios=0.212559054144199

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.51818591775566	0.0858120191826699	40.9987546181197	2.43500397556014e-187	***
df.mm.trans1	0.421251731276184	0.0770717422916699	5.46570920483413	6.44068456178665e-08	***
df.mm.trans2	0.53334149998028	0.0708779343396569	7.52478899038498	1.64737639402090e-13	***
df.mm.exp2	0.560246992203675	0.0970852170742015	5.77067249873358	1.19142213555762e-08	***
df.mm.exp3	-0.343890768051648	0.0970852170742016	-3.54215377392435	0.000423511870482852	***
df.mm.exp4	-0.191255092772955	0.0970852170742015	-1.96997131527017	0.0492400171227695	*  
df.mm.exp5	0.169811564257720	0.0970852170742016	1.74909805401098	0.0807174031993868	.  
df.mm.exp6	0.117125399407218	0.0970852170742016	1.20641847375899	0.228068451817729	   
df.mm.exp7	-0.256419520140471	0.0970852170742015	-2.64117986103375	0.00844827350338383	** 
df.mm.exp8	-0.095292069710994	0.0970852170742016	-0.98153017094418	0.326674393322364	   
df.mm.trans1:exp2	-0.451223021675542	0.092952018589249	-4.85436495649951	1.49390300134791e-06	***
df.mm.trans2:exp2	-0.434343200092166	0.0809043475618346	-5.36860148041118	1.08419842391827e-07	***
df.mm.trans1:exp3	0.325828056575086	0.092952018589249	3.50533599506759	0.000485439691218491	***
df.mm.trans2:exp3	0.532057052559574	0.0809043475618347	6.57637158686592	9.50445890607303e-11	***
df.mm.trans1:exp4	0.206631794072597	0.092952018589249	2.22299415557282	0.0265384950796105	*  
df.mm.trans2:exp4	0.116734467190611	0.0809043475618347	1.44287013873255	0.149509538378583	   
df.mm.trans1:exp5	-0.192805717767334	0.0929520185892491	-2.07424992693633	0.0384252992725015	*  
df.mm.trans2:exp5	-0.230066759607368	0.0809043475618346	-2.84368846101292	0.00459104209496245	** 
df.mm.trans1:exp6	-0.155061792498712	0.092952018589249	-1.66819177089551	0.0957300449107934	.  
df.mm.trans2:exp6	-0.277903789445608	0.0809043475618347	-3.43496731412620	0.000627997247233434	***
df.mm.trans1:exp7	0.262049066862639	0.092952018589249	2.81918640218695	0.00495219303740177	** 
df.mm.trans2:exp7	0.206734784960202	0.0809043475618346	2.55529883362814	0.0108224034437638	*  
df.mm.trans1:exp8	0.0826772130786406	0.092952018589249	0.889461190122052	0.374064367553523	   
df.mm.trans2:exp8	0.0916924336786392	0.0809043475618346	1.13334371318623	0.257462436903694	   
df.mm.trans1:probe2	0.0563780679543224	0.0464760092946245	1.21305742059147	0.225521786002045	   
df.mm.trans1:probe3	0.0398055392639972	0.0464760092946245	0.856474982859622	0.39203158594751	   
df.mm.trans1:probe4	-0.0165254535636256	0.0464760092946245	-0.355569546835790	0.722271311390113	   
df.mm.trans1:probe5	-0.0451239491864612	0.0464760092946245	-0.970908429344865	0.331933114109902	   
df.mm.trans1:probe6	0.0524801756967475	0.0464760092946245	1.12918851022817	0.259209601904774	   
df.mm.trans1:probe7	-0.0993781354643498	0.0464760092946245	-2.13826739801096	0.0328446367715696	*  
df.mm.trans1:probe8	-0.0375729187072141	0.0464760092946245	-0.808436853281201	0.419116979853324	   
df.mm.trans1:probe9	0.269866231307791	0.0464760092946245	5.80657064587953	9.7175952343162e-09	***
df.mm.trans1:probe10	0.0349536352384369	0.0464760092946245	0.75207909992564	0.452259218129464	   
df.mm.trans1:probe11	0.158763085993032	0.0464760092946245	3.41602233932324	0.000672567641320997	***
df.mm.trans1:probe12	0.0446516622343092	0.0464760092946245	0.960746477849458	0.337015225453071	   
df.mm.trans1:probe13	-0.0121747220220648	0.0464760092946245	-0.26195713028814	0.793432448465661	   
df.mm.trans1:probe14	0.0808298659058526	0.0464760092946245	1.73917397669514	0.0824488451860014	.  
df.mm.trans1:probe15	0.0367922115040984	0.0464760092946245	0.791638784450321	0.428842588727431	   
df.mm.trans1:probe16	-0.0509723898179889	0.0464760092946245	-1.09674626956158	0.273133832354247	   
df.mm.trans1:probe17	-0.0474866988427843	0.0464760092946245	-1.02174647874246	0.307257963455330	   
df.mm.trans1:probe18	-0.0119307401033708	0.0464760092946245	-0.256707498867609	0.797480884290362	   
df.mm.trans1:probe19	-0.0783018481042396	0.0464760092946245	-1.68477993899739	0.0924819298556034	.  
df.mm.trans1:probe20	-0.0266767063665076	0.0464760092946245	-0.57398874755783	0.566161928022147	   
df.mm.trans1:probe21	0.113763195616265	0.0464760092946245	2.44778321854375	0.0146212117571604	*  
df.mm.trans1:probe22	0.0451575434590222	0.0464760092946245	0.971631259748569	0.331573518713628	   
df.mm.trans2:probe2	0.0590043237238062	0.0464760092946245	1.26956519329706	0.204666334442745	   
df.mm.trans2:probe3	0.146672109555148	0.0464760092946245	3.15586711899794	0.00166960254606576	** 
df.mm.trans2:probe4	0.101455515229788	0.0464760092946245	2.18296529262298	0.0293737114769295	*  
df.mm.trans2:probe5	0.190149472674829	0.0464760092946245	4.09134681657838	4.79382561767944e-05	***
df.mm.trans2:probe6	0.0576354027623459	0.0464760092946245	1.24011083647434	0.215354732007928	   
df.mm.trans3:probe2	0.411216793356175	0.0464760092946245	8.84793680863078	7.37846251492756e-18	***
df.mm.trans3:probe3	0.182334819790883	0.0464760092946245	3.92320301502246	9.60972982720659e-05	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.24264375503749	0.156558159345124	27.0994739129808	8.63889535560228e-111	***
df.mm.trans1	-0.0650961348959035	0.140612122003795	-0.462948243496009	0.643547014090855	   
df.mm.trans2	-0.181648161119107	0.129311943059863	-1.40472841735134	0.160550757093021	   
df.mm.exp2	0.0595723437994615	0.177125337796834	0.336328751947347	0.73672493057506	   
df.mm.exp3	-0.110192666048006	0.177125337796834	-0.622116899923141	0.534069920950672	   
df.mm.exp4	-0.0578603067885933	0.177125337796834	-0.326663070954648	0.744021510076385	   
df.mm.exp5	-0.333379318047476	0.177125337796834	-1.88216616659254	0.0602328482781556	.  
df.mm.exp6	-0.0838325737428901	0.177125337796834	-0.473295208837075	0.63615184306036	   
df.mm.exp7	-0.0385337063333432	0.177125337796834	-0.217550503009016	0.827843483146439	   
df.mm.exp8	-0.462346851070439	0.177125337796834	-2.61028070191041	0.0092425528882384	** 
df.mm.trans1:exp2	-0.127983913513538	0.169584599877187	-0.754690659447987	0.450691365691332	   
df.mm.trans2:exp2	0.0512136292907627	0.147604448164029	0.346965351842585	0.728722836070813	   
df.mm.trans1:exp3	0.0385296882448425	0.169584599877187	0.227200395983748	0.820335024218577	   
df.mm.trans2:exp3	0.160983108584564	0.147604448164029	1.09063859922208	0.275811548669743	   
df.mm.trans1:exp4	-0.00168082621707356	0.169584599877187	-0.00991143192418893	0.992094807591739	   
df.mm.trans2:exp4	0.0268496319741784	0.147604448164029	0.181902593777805	0.855712439185376	   
df.mm.trans1:exp5	0.332219469517701	0.169584599877188	1.95901909582764	0.0505117278781239	.  
df.mm.trans2:exp5	0.228618801546837	0.147604448164029	1.54886119212871	0.121872282137167	   
df.mm.trans1:exp6	0.117962355263632	0.169584599877187	0.695595917017583	0.48691545034012	   
df.mm.trans2:exp6	0.0570916106289624	0.147604448164029	0.386787873530194	0.699032165934912	   
df.mm.trans1:exp7	-0.0433698030533833	0.169584599877187	-0.255741400367672	0.798226523443621	   
df.mm.trans2:exp7	0.0629910331426004	0.147604448164029	0.426755656256377	0.669690040207806	   
df.mm.trans1:exp8	0.453691316307186	0.169584599877187	2.67530964861047	0.00764252806501018	** 
df.mm.trans2:exp8	0.317825687058029	0.147604448164029	2.15322567179574	0.0316458138027908	*  
df.mm.trans1:probe2	-0.116781840828293	0.0847922999385937	-1.37726940904853	0.168874283474572	   
df.mm.trans1:probe3	-0.135411181892899	0.0847922999385937	-1.59697498465029	0.110727802235102	   
df.mm.trans1:probe4	-0.155333772285195	0.0847922999385937	-1.83193252686491	0.0673911246759669	.  
df.mm.trans1:probe5	-0.0937590307877705	0.0847922999385937	-1.10574935289726	0.269219295304137	   
df.mm.trans1:probe6	-0.161911907326501	0.0847922999385937	-1.90951191846143	0.0566093249992736	.  
df.mm.trans1:probe7	-0.00841522736932913	0.0847922999385937	-0.0992451835299125	0.9209723429909	   
df.mm.trans1:probe8	-0.145228910520185	0.0847922999385937	-1.71276060002334	0.0872045399403668	.  
df.mm.trans1:probe9	-0.0266893206859405	0.0847922999385937	-0.314761136391733	0.753037903516991	   
df.mm.trans1:probe10	-0.0931629367814006	0.0847922999385937	-1.098719304098	0.272272637451562	   
df.mm.trans1:probe11	-0.109957821668004	0.0847922999385937	-1.29679017726415	0.195135620541527	   
df.mm.trans1:probe12	-0.0362637003329824	0.0847922999385937	-0.427676809795753	0.669019490792333	   
df.mm.trans1:probe13	-0.0523646743425709	0.0847922999385937	-0.617564028579166	0.537065925622607	   
df.mm.trans1:probe14	-0.156388145291792	0.0847922999385937	-1.84436729992048	0.0655568712978598	.  
df.mm.trans1:probe15	-0.0892834585739532	0.0847922999385937	-1.05296658586466	0.292723834032242	   
df.mm.trans1:probe16	-0.118317058407423	0.0847922999385937	-1.39537503397252	0.16335038848861	   
df.mm.trans1:probe17	-0.091041770273048	0.0847922999385937	-1.07370327658266	0.283329949695396	   
df.mm.trans1:probe18	-0.145895312567332	0.0847922999385937	-1.72061982836872	0.085766857973136	.  
df.mm.trans1:probe19	0.0456224117870898	0.0847922999385937	0.538048995252274	0.590716226010902	   
df.mm.trans1:probe20	-0.0647742531525652	0.0847922999385937	-0.763916690542355	0.445177271375182	   
df.mm.trans1:probe21	-0.0870296550555436	0.0847922999385937	-1.02638630062600	0.305068170116766	   
df.mm.trans1:probe22	0.0444100931178818	0.0847922999385937	0.523751486279337	0.60061909126751	   
df.mm.trans2:probe2	0.00580400890807004	0.0847922999385937	0.068449716687403	0.945447419721836	   
df.mm.trans2:probe3	0.0967363556248992	0.0847922999385937	1.14086250396504	0.254321812854238	   
df.mm.trans2:probe4	0.0179787772647395	0.0847922999385937	0.212033136001260	0.83214361889627	   
df.mm.trans2:probe5	0.0284162356010553	0.0847922999385937	0.335127548393359	0.737630435827023	   
df.mm.trans2:probe6	0.0861011377593031	0.0847922999385937	1.01543580987492	0.310253024356709	   
df.mm.trans3:probe2	-0.0224718571951793	0.0847922999385937	-0.265022380705009	0.791071143463014	   
df.mm.trans3:probe3	0.0273462117918379	0.0847922999385937	0.32250819722595	0.747165141838708