chr10.2591_chr10_60725732_60735243_-_2.R 

fitVsDatCorrelation=0.718707754405206
cont.fitVsDatCorrelation=0.275586980088761

fstatistic=10591.1078599601,61,899
cont.fstatistic=5534.18621548061,61,899

residuals=-0.514696349478289,-0.0903946192106008,-0.00606905010534582,0.0703641146297792,1.71499957248489
cont.residuals=-0.507605118427394,-0.141011020850659,-0.0298423950325984,0.122175991517586,1.80937670266148

predictedValues:
Include	Exclude	Both
chr10.2591_chr10_60725732_60735243_-_2.R.tl.Lung	64.2845217960303	67.0556876088991	66.5250998350095
chr10.2591_chr10_60725732_60735243_-_2.R.tl.cerebhem	64.3634513340927	60.976884766735	67.0223893804653
chr10.2591_chr10_60725732_60735243_-_2.R.tl.cortex	60.3712126443376	66.120971439173	63.4447079521477
chr10.2591_chr10_60725732_60735243_-_2.R.tl.heart	62.3535343176457	62.7934061414443	64.995290700535
chr10.2591_chr10_60725732_60735243_-_2.R.tl.kidney	74.4622988258942	65.794014986614	79.3967444451104
chr10.2591_chr10_60725732_60735243_-_2.R.tl.liver	60.5047946054023	61.9471789138776	59.3544858576482
chr10.2591_chr10_60725732_60735243_-_2.R.tl.stomach	65.6974806769762	72.3520203648568	66.86008820104
chr10.2591_chr10_60725732_60735243_-_2.R.tl.testicle	62.7418295767568	62.9855511921514	66.2765968158377


diffExp=-2.7711658128688,3.38656656735773,-5.7497587948353,-0.439871823798619,8.66828383928015,-1.44238430847533,-6.6545396878806,-0.24372161539457
diffExpScore=4.69956964655969
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=0,0,0,0,0,0,0,0
diffExp1.2Score=0

cont.predictedValues:
Include	Exclude	Both
Lung	68.5081570075756	65.3232142024457	67.2788888970051
cerebhem	71.0868691503682	68.6123170130045	65.7323322425511
cortex	70.6842807449353	68.6525716284671	65.9636295919138
heart	69.685350636332	76.1812372669764	67.0211109647266
kidney	68.2071590123747	68.4236488185526	65.9500745781772
liver	68.093442252327	65.6574723037116	65.6956570933632
stomach	67.0114426465347	69.9814684074041	65.8387169547441
testicle	71.4273832530244	65.3888870257272	64.9668817960677
cont.diffExp=3.18494280512985,2.47455213736379,2.03170911646818,-6.49588663064445,-0.216489806177904,2.43596994861534,-2.97002576086945,6.03849622729719
cont.diffExpScore=3.45411554205106

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=0.282619408520881
cont.tran.correlation=-0.0152833914703640

tran.covariance=0.00109773980065968
cont.tran.covariance=-1.98877927450844e-05

tran.mean=64.6753024494304
cont.tran.mean=68.93280633561

weightedLogRatios:
wLogRatio
Lung	-0.176601607027454
cerebhem	0.223637495925527
cortex	-0.377176409887751
heart	-0.0290772559030676
kidney	0.525800602702064
liver	-0.096935442410321
stomach	-0.408440822473466
testicle	-0.0160544885307437

cont.weightedLogRatios:
wLogRatio
Lung	0.200092411416565
cerebhem	0.150444844236494
cortex	0.123764171178096
heart	-0.382217616038212
kidney	-0.0133861862469343
liver	0.153101018927105
stomach	-0.183292996250861
testicle	0.373147320233025

varWeightedLogRatios=0.0945020232244848
cont.varWeightedLogRatios=0.0569012791815935

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.75425038641855	0.0745651808337277	50.3485721410657	7.32218902366758e-264	***
df.mm.trans1	0.307689241407687	0.0641395067877103	4.7971875185458	1.88308147859965e-06	***
df.mm.trans2	0.446990183161174	0.056418612781649	7.92274324239614	6.8461104561403e-15	***
df.mm.exp2	-0.101248931812716	0.0720137308810309	-1.40596703675834	0.16007943636536	   
df.mm.exp3	-0.0294334399211965	0.0720137308810309	-0.408719831080847	0.68284261890579	   
df.mm.exp4	-0.0729073892826258	0.0720137308810309	-1.01240955565920	0.311614669398871	   
df.mm.exp5	-0.0488985462825385	0.0720137308810309	-0.679016983071194	0.497301869247908	   
df.mm.exp6	-0.0257860194455253	0.0720137308810309	-0.358070872457986	0.720374332993875	   
df.mm.exp7	0.0927387576460468	0.0720137308810309	1.28779270996603	0.198149522186005	   
df.mm.exp8	-0.0831661267884058	0.0720137308810309	-1.15486485384015	0.248452646081505	   
df.mm.trans1:exp2	0.102475994253662	0.0662429977660692	1.54697096613209	0.122222179592731	   
df.mm.trans2:exp2	0.00622035340861327	0.0475452864826287	0.130830075256481	0.895939000722005	   
df.mm.trans1:exp3	-0.0333730642997514	0.0662429977660692	-0.503797615222746	0.614527033284643	   
df.mm.trans2:exp3	0.0153959723166738	0.0475452864826287	0.32381700596754	0.746151897105317	   
df.mm.trans1:exp4	0.0424088612312914	0.0662429977660692	0.640201419945609	0.522204787152678	   
df.mm.trans2:exp4	0.00723402698450116	0.0475452864826287	0.152150244948975	0.879102574199052	   
df.mm.trans1:exp5	0.195872603852746	0.0662429977660692	2.95688013009995	0.003188978232843	** 
df.mm.trans2:exp5	0.0299039903147444	0.0475452864826287	0.628958042469051	0.529536301955395	   
df.mm.trans1:exp6	-0.034810252679172	0.0662429977660692	-0.525493317831132	0.599369879904511	   
df.mm.trans2:exp6	-0.0534553441414966	0.0475452864826287	-1.12430375534759	0.261184292975032	   
df.mm.trans1:exp7	-0.0709970624485097	0.0662429977660693	-1.07176705225855	0.284112305059388	   
df.mm.trans2:exp7	-0.016718812593291	0.0475452864826287	-0.351639748756156	0.725190852482236	   
df.mm.trans1:exp8	0.0588756066771965	0.0662429977660693	0.888782341721763	0.374357812096232	   
df.mm.trans2:exp8	0.0205480482078347	0.0475452864826287	0.432178449809998	0.665715359971875	   
df.mm.trans1:probe2	0.446383198486295	0.0468408729265129	9.52977966885055	1.41937049616978e-20	***
df.mm.trans1:probe3	0.147871371463796	0.0468408729265129	3.15688761171864	0.00164776648290869	** 
df.mm.trans1:probe4	0.367656905494131	0.0468408729265129	7.84906178138346	1.18887515555764e-14	***
df.mm.trans1:probe5	0.109401455892985	0.0468408729265129	2.33559814447996	0.0197312969634371	*  
df.mm.trans1:probe6	0.270531420221243	0.0468408729265129	5.77554181463849	1.05630299840575e-08	***
df.mm.trans1:probe7	0.0686495121658638	0.0468408729265129	1.46558994051127	0.143109684283143	   
df.mm.trans1:probe8	0.103563738563476	0.0468408729265129	2.21096943957372	0.0272889860605292	*  
df.mm.trans1:probe9	-0.105452156679914	0.0468408729265129	-2.25128504426794	0.0246082889916891	*  
df.mm.trans1:probe10	0.334927352393143	0.0468408729265129	7.15032260219829	1.79270484550598e-12	***
df.mm.trans1:probe11	0.107140830091371	0.0468408729265129	2.28733632397204	0.0224077522525349	*  
df.mm.trans1:probe12	0.0646657655930555	0.0468408729265129	1.38054142787876	0.167763142888750	   
df.mm.trans1:probe13	0.0556327667921458	0.0468408729265129	1.18769705422498	0.235266483271870	   
df.mm.trans1:probe14	-0.00654904440670818	0.0468408729265129	-0.139814738657470	0.888837684542327	   
df.mm.trans1:probe15	-0.00230269941030189	0.0468408729265129	-0.0491600447736002	0.960802671716171	   
df.mm.trans1:probe16	0.278000967689212	0.0468408729265129	5.9350082592474	4.19169452685371e-09	***
df.mm.trans1:probe17	0.234071222007387	0.0468408729265129	4.99715755457021	6.99178403917864e-07	***
df.mm.trans1:probe18	0.311108180382038	0.0468408729265129	6.64181004632695	5.35875956094442e-11	***
df.mm.trans1:probe19	0.155699067000139	0.0468408729265129	3.3240001151219	0.00092322816179267	***
df.mm.trans1:probe20	0.169598059242377	0.0468408729265129	3.62072798063465	0.000310200048218867	***
df.mm.trans1:probe21	0.0382439243778758	0.0468408729265129	0.816464809224957	0.414450632506029	   
df.mm.trans1:probe22	0.298052862262438	0.0468408729265129	6.36309367526185	3.14611502897649e-10	***
df.mm.trans2:probe2	0.334639048034921	0.0468408729265129	7.14416762812951	1.87036856520594e-12	***
df.mm.trans2:probe3	0.0965786854375088	0.0468408729265129	2.06184640472068	0.0395093085800987	*  
df.mm.trans2:probe4	-0.148480192793332	0.0468408729265129	-3.16988526294713	0.00157659318407363	** 
df.mm.trans2:probe5	-0.11269749607489	0.0468408729265129	-2.40596489847013	0.0163307851825528	*  
df.mm.trans2:probe6	-0.0929485118245705	0.0468408729265129	-1.98434627745718	0.0475209343618056	*  
df.mm.trans3:probe2	-0.457949907974599	0.0468408729265129	-9.77671591844715	1.60049119392501e-21	***
df.mm.trans3:probe3	-0.283285281765866	0.0468408729265129	-6.04782242658677	2.15106335278142e-09	***
df.mm.trans3:probe4	-0.173739903014932	0.0468408729265129	-3.70915169082155	0.000220709238581340	***
df.mm.trans3:probe5	-0.53669319510729	0.0468408729265129	-11.4577966117175	1.82160667199608e-28	***
df.mm.trans3:probe6	-0.329430542718077	0.0468408729265129	-7.03297189262271	4.00248622559593e-12	***
df.mm.trans3:probe7	-0.413958618971357	0.0468408729265129	-8.83755133301642	5.07140808101753e-18	***
df.mm.trans3:probe8	-0.468476210389821	0.0468408729265129	-10.0014406461809	2.11397788287890e-22	***
df.mm.trans3:probe9	-0.244418301902836	0.0468408729265129	-5.21805608290639	2.24584021732465e-07	***
df.mm.trans3:probe10	-0.368944772063609	0.0468408729265129	-7.87655628541412	9.6808713491167e-15	***
df.mm.trans3:probe11	-0.100674494796140	0.0468408729265129	-2.14928733190102	0.0318779716371898	*  
df.mm.trans3:probe12	-0.358634692222958	0.0468408729265129	-7.65644766666941	4.92945579317689e-14	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.1857196203047	0.103087104104882	40.6037171831512	1.49084260235994e-205	***
df.mm.trans1	0.0962054207670548	0.0886735060457297	1.08493985472325	0.278239147898739	   
df.mm.trans2	-0.00755096722260743	0.0779992932927233	-0.0968081492003964	0.922900329034499	   
df.mm.exp2	0.109329989851385	0.0995596991693457	1.09813499602304	0.272439565781084	   
df.mm.exp3	0.100724487356349	0.0995596991693456	1.01169939440076	0.311954014531390	   
df.mm.exp4	0.174643870212762	0.0995596991693456	1.75416229327594	0.0797434025201129	.  
df.mm.exp5	0.0619162364655018	0.0995596991693457	0.621900598154536	0.534164911009278	   
df.mm.exp6	0.0228457144522118	0.0995596991693456	0.229467491794572	0.818557779601006	   
df.mm.exp7	0.0684320001103284	0.0995596991693457	0.687346392981052	0.492041748059918	   
df.mm.exp8	0.0777022148467497	0.0995596991693457	0.780458513786612	0.43532647996201	   
df.mm.trans1:exp2	-0.072380170108559	0.0915816032439824	-0.790335258880882	0.429540425059124	   
df.mm.trans2:exp2	-0.0602053968519831	0.0657318314329656	-0.915924530619256	0.359952097687024	   
df.mm.trans1:exp3	-0.0694540951681968	0.0915816032439823	-0.758384792447499	0.448419425220238	   
df.mm.trans2:exp3	-0.0510133697618368	0.0657318314329656	-0.776083195154255	0.437903985402689	   
df.mm.trans1:exp4	-0.157606570501927	0.0915816032439824	-1.72094137817229	0.0856055288524058	.  
df.mm.trans2:exp4	-0.0208761418420393	0.0657318314329656	-0.317595621283262	0.750865445900775	   
df.mm.trans1:exp5	-0.0663195248504406	0.0915816032439824	-0.72415771837668	0.469157315586804	   
df.mm.trans2:exp5	-0.0155452025411242	0.0657318314329656	-0.236494285983458	0.813103013282875	   
df.mm.trans1:exp6	-0.0289176203689526	0.0915816032439824	-0.315757961693608	0.752259516788185	   
df.mm.trans2:exp6	-0.0177417737750596	0.0657318314329656	-0.269911447593742	0.78729032385531	   
df.mm.trans1:exp7	-0.0905214281136517	0.0915816032439824	-0.988423710736901	0.323211128382907	   
df.mm.trans2:exp7	0.000450995942813077	0.0657318314329656	0.00686114981100155	0.994527159631665	   
df.mm.trans1:exp8	-0.0359737186571193	0.0915816032439824	-0.392805076378515	0.694556574632557	   
df.mm.trans2:exp8	-0.076697367886288	0.0657318314329656	-1.16682231750237	0.243591545108985	   
df.mm.trans1:probe2	-0.125547592828334	0.0647579726857559	-1.93872024742908	0.0528480939828253	.  
df.mm.trans1:probe3	-0.102722815186946	0.0647579726857559	-1.58625742787562	0.113032575676389	   
df.mm.trans1:probe4	-0.0441800136736595	0.0647579726857558	-0.682232809974568	0.495267487817705	   
df.mm.trans1:probe5	-0.0490881793191977	0.0647579726857559	-0.758025263659854	0.4486345158799	   
df.mm.trans1:probe6	-0.106827235212944	0.0647579726857558	-1.64963835003503	0.0993662571828488	.  
df.mm.trans1:probe7	-0.190984272501833	0.0647579726857558	-2.94920091814181	0.00326851204536979	** 
df.mm.trans1:probe8	-0.0333502765107695	0.0647579726857558	-0.514998773550322	0.606680474632605	   
df.mm.trans1:probe9	-0.0541043157221679	0.0647579726857559	-0.835485014095704	0.403666666326106	   
df.mm.trans1:probe10	-0.011970077868999	0.0647579726857559	-0.184843307666300	0.853393628664382	   
df.mm.trans1:probe11	-0.105199322648201	0.0647579726857559	-1.62449993854333	0.104619676155256	   
df.mm.trans1:probe12	-0.0283307652505329	0.0647579726857558	-0.437486908183655	0.66186334593671	   
df.mm.trans1:probe13	-0.0278808861435421	0.0647579726857559	-0.430539823703820	0.666906200539913	   
df.mm.trans1:probe14	-0.11832224793545	0.0647579726857558	-1.82714564752698	0.0680092758905928	.  
df.mm.trans1:probe15	-0.124913642707730	0.0647579726857558	-1.92893071736334	0.0540539831156265	.  
df.mm.trans1:probe16	-0.120014575554832	0.0647579726857558	-1.85327876363910	0.0641698908548658	.  
df.mm.trans1:probe17	-0.185985398809898	0.0647579726857559	-2.87200774045860	0.00417461941995853	** 
df.mm.trans1:probe18	-0.175004224831051	0.0647579726857558	-2.70243519945066	0.00701310293395958	** 
df.mm.trans1:probe19	-0.0762643274738874	0.0647579726857559	-1.17768244296293	0.239234895373	   
df.mm.trans1:probe20	-0.0478300806305207	0.0647579726857558	-0.738597560220433	0.460344180715187	   
df.mm.trans1:probe21	-0.0770008668437528	0.0647579726857559	-1.18905616791629	0.234731535893371	   
df.mm.trans1:probe22	-0.0635344470234382	0.0647579726857559	-0.981106177176748	0.326804335304849	   
df.mm.trans2:probe2	0.00187242829396677	0.0647579726857558	0.0289142512699232	0.97693939681764	   
df.mm.trans2:probe3	0.115308781935955	0.0647579726857558	1.78061136187047	0.0753135869655008	.  
df.mm.trans2:probe4	-0.0543238192522268	0.0647579726857558	-0.838874612641725	0.401762672361182	   
df.mm.trans2:probe5	0.000715151422507193	0.0647579726857558	0.0110434498309194	0.991191231123743	   
df.mm.trans2:probe6	-0.0423537674961113	0.0647579726857558	-0.65403170821355	0.513258640469376	   
df.mm.trans3:probe2	-0.150848634215385	0.0647579726857558	-2.3294218141663	0.0200574273581643	*  
df.mm.trans3:probe3	-0.116691143359909	0.0647579726857558	-1.80195794464049	0.0718869181593719	.  
df.mm.trans3:probe4	-0.0840987481846775	0.0647579726857558	-1.29866246111154	0.194392801379241	   
df.mm.trans3:probe5	-0.085235153520846	0.0647579726857558	-1.31621096192213	0.188438734099257	   
df.mm.trans3:probe6	-0.106931472365742	0.0647579726857559	-1.65124799203701	0.099037189641503	.  
df.mm.trans3:probe7	-0.0520765923328402	0.0647579726857558	-0.804172678251476	0.421509850311523	   
df.mm.trans3:probe8	-0.110952973508013	0.0647579726857558	-1.71334847133685	0.0869932065123875	.  
df.mm.trans3:probe9	-0.0745292440971957	0.0647579726857558	-1.15088908757005	0.250083881616885	   
df.mm.trans3:probe10	-0.0540801284255737	0.0647579726857558	-0.835111511103083	0.403876799687842	   
df.mm.trans3:probe11	-0.031858250184145	0.0647579726857558	-0.491958732845763	0.622868618728682	   
df.mm.trans3:probe12	0.0809714650183659	0.0647579726857558	1.25037059778396	0.211489554586063