chr1.1307_chr1_10149457_10161894_+_2.R 

fitVsDatCorrelation=0.744919378158477
cont.fitVsDatCorrelation=0.273853241174145

fstatistic=12864.3967311573,55,761
cont.fstatistic=6182.9327447472,55,761

residuals=-0.368428807978007,-0.0779632292324172,-0.00789934744804378,0.067405831135838,0.80331634704555
cont.residuals=-0.481371551923773,-0.125246810079605,-0.0280885650844375,0.083904824418616,0.890021901139275

predictedValues:
Include	Exclude	Both
chr1.1307_chr1_10149457_10161894_+_2.R.tl.Lung	45.9602983743470	43.5977917355715	60.1252511984495
chr1.1307_chr1_10149457_10161894_+_2.R.tl.cerebhem	50.3997744952821	54.227642212192	58.5552181808172
chr1.1307_chr1_10149457_10161894_+_2.R.tl.cortex	46.3881399930227	45.9727854951935	63.2704225183195
chr1.1307_chr1_10149457_10161894_+_2.R.tl.heart	46.6506949475461	44.837086822082	59.7816235133666
chr1.1307_chr1_10149457_10161894_+_2.R.tl.kidney	46.3164330780547	43.1996007595596	63.5960166170163
chr1.1307_chr1_10149457_10161894_+_2.R.tl.liver	48.7612967690906	49.1610979564905	60.9805897340664
chr1.1307_chr1_10149457_10161894_+_2.R.tl.stomach	47.8727530160958	43.3208574613509	59.9021316237482
chr1.1307_chr1_10149457_10161894_+_2.R.tl.testicle	48.1624656599794	47.8022190225882	59.2708250744358


diffExp=2.3625066387755,-3.82786771690984,0.415354497829206,1.81360812546409,3.11683231849504,-0.399801187399895,4.55189555474493,0.360246637391199
diffExpScore=1.79373112983993
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	51.2228991181489	51.3051006985922	47.815384776144
cerebhem	52.0833460075983	55.474463989636	48.6646748437836
cortex	50.9475458091848	47.6299926392702	46.6645492875781
heart	49.6260188811065	53.4239134179101	51.5646360207147
kidney	50.6173317556357	48.4213899017431	48.8448684895287
liver	50.9439604083344	47.0345529356631	52.1419581867552
stomach	49.6116899743708	52.1842770970694	50.4494505049031
testicle	51.3157612078053	49.8899058266502	49.1447539473217
cont.diffExp=-0.0822015804432539,-3.3911179820377,3.31755316991454,-3.7978945368036,2.19594185389255,3.90940747267125,-2.57258712269866,1.42585538115511
cont.diffExpScore=10.3207014681850

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=0.883425198629807
cont.tran.correlation=0.0251104390552597

tran.covariance=0.00218909203581195
cont.tran.covariance=6.17643652811307e-06

tran.mean=47.0394336124029
cont.tran.mean=50.7332593542949

weightedLogRatios:
wLogRatio
Lung	0.200604957565615
cerebhem	-0.289638445037159
cortex	0.0344707729591301
heart	0.151584864076869
kidney	0.264775429239369
liver	-0.0317729763084632
stomach	0.381524132307465
testicle	0.0290619120125663

cont.weightedLogRatios:
wLogRatio
Lung	-0.00631294360390271
cerebhem	-0.251325235468556
cortex	0.262409051906945
heart	-0.290650448973252
kidney	0.173068238029844
liver	0.310656264887885
stomach	-0.198655099611722
testicle	0.110572780736679

varWeightedLogRatios=0.0424652535703128
cont.varWeightedLogRatios=0.0561692229800034

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.02603917111595	0.0646071472414287	46.8375295972755	2.28591804366805e-226	***
df.mm.trans1	0.71264423642361	0.0565729942905205	12.5968979609591	3.48917912281163e-33	***
df.mm.trans2	0.738754193774184	0.0507329442589134	14.5616266622332	1.48905031833404e-42	***
df.mm.exp2	0.336852734094306	0.0669046256044088	5.03481980582384	5.97585684826642e-07	***
df.mm.exp3	0.0113209240931952	0.0669046256044088	0.169209886325844	0.86567652044763	   
df.mm.exp4	0.0486705979657412	0.0669046256044088	0.72746237686941	0.467166495387284	   
df.mm.exp5	-0.0575772982887024	0.0669046256044088	-0.860587706284217	0.389736205351345	   
df.mm.exp6	0.165129366079986	0.0669046256044088	2.46813078450445	0.0138008991151335	*  
df.mm.exp7	0.0381140972900755	0.0669046256044088	0.569678059562505	0.569064194710084	   
df.mm.exp8	0.153180322669267	0.0669046256044088	2.28953261879061	0.022321375986503	*  
df.mm.trans1:exp2	-0.244643978330536	0.062762102068001	-3.89795705162124	0.000105562996753183	***
df.mm.trans2:exp2	-0.118668452547134	0.0500668373204162	-2.37020069367841	0.0180268012723718	*  
df.mm.trans1:exp3	-0.00205504613538754	0.062762102068001	-0.0327434242588139	0.97388778068683	   
df.mm.trans2:exp3	0.0417221768446068	0.0500668373204162	0.833329586560351	0.404920342384675	   
df.mm.trans1:exp4	-0.0337607187334284	0.062762102068001	-0.537915678745903	0.590792537883781	   
df.mm.trans2:exp4	-0.0206414709633422	0.0500668373204162	-0.41227830771977	0.680251556563136	   
df.mm.trans1:exp5	0.065296177497832	0.062762102068001	1.04037588522904	0.298495718630376	   
df.mm.trans2:exp5	0.0484020510135875	0.0500668373204162	0.966748722389343	0.333976788904125	   
df.mm.trans1:exp6	-0.10597041188791	0.062762102068001	-1.68844586774825	0.091735362543865	.  
df.mm.trans2:exp6	-0.0450332480903372	0.0500668373204162	-0.89946260839555	0.368690824410537	   
df.mm.trans1:exp7	0.00265446971605181	0.062762102068001	0.0422941493128411	0.9662753033822	   
df.mm.trans2:exp7	-0.0444863824987988	0.0500668373204162	-0.888539897459394	0.374531164757276	   
df.mm.trans1:exp8	-0.106378270724114	0.062762102068001	-1.6949443568486	0.0904950740796457	.  
df.mm.trans2:exp8	-0.0611147619882891	0.0500668373204162	-1.22066352218673	0.222591527404237	   
df.mm.trans1:probe2	0.142395360710517	0.0384337813127698	3.70495319083281	0.000226737304544187	***
df.mm.trans1:probe3	0.186847392511723	0.0384337813127698	4.86154071053222	1.41531616669476e-06	***
df.mm.trans1:probe4	0.237513244013798	0.0384337813127698	6.17980422173248	1.04543367550734e-09	***
df.mm.trans1:probe5	0.064826815723304	0.0384337813127699	1.68671448681436	0.0920681092988006	.  
df.mm.trans1:probe6	0.095269941760066	0.0384337813127698	2.47880740603610	0.0133979063868062	*  
df.mm.trans1:probe7	0.124762309014868	0.0384337813127698	3.24616274416422	0.00122091902594371	** 
df.mm.trans1:probe8	0.0569448071787509	0.0384337813127698	1.48163426115532	0.138851571124530	   
df.mm.trans1:probe9	0.123543685436115	0.0384337813127698	3.21445564855381	0.00136205887642798	** 
df.mm.trans1:probe10	0.271822136208735	0.0384337813127699	7.07247965004215	3.45255758465836e-12	***
df.mm.trans1:probe11	0.0260536794667257	0.0384337813127699	0.677884886077269	0.498050733709577	   
df.mm.trans1:probe12	0.00844808855731935	0.0384337813127698	0.219808935492705	0.826078908059493	   
df.mm.trans1:probe13	0.0704460652922691	0.0384337813127698	1.83292049041407	0.0672049121617277	.  
df.mm.trans1:probe14	0.0481026523797975	0.0384337813127699	1.25157220384701	0.211110364138454	   
df.mm.trans1:probe15	-0.000103312808491893	0.0384337813127698	-0.00268807296506022	0.997855935136507	   
df.mm.trans1:probe16	0.0875711963449233	0.0384337813127698	2.27849546294388	0.0229733780357345	*  
df.mm.trans1:probe17	0.168869255416715	0.0384337813127698	4.39377156367924	1.2720924340619e-05	***
df.mm.trans1:probe18	0.195636238090418	0.0384337813127699	5.09021572710612	4.51117474463665e-07	***
df.mm.trans1:probe19	0.111737948433636	0.0384337813127698	2.90728480563296	0.00375180858862352	** 
df.mm.trans1:probe20	0.0771609179023491	0.0384337813127698	2.00763274564171	0.0450353912452739	*  
df.mm.trans1:probe21	0.122651071051469	0.0384337813127698	3.19123091359001	0.00147484818735204	** 
df.mm.trans1:probe22	0.274147556307737	0.0384337813127699	7.13298423792221	2.28966287639014e-12	***
df.mm.trans2:probe2	0.0269281370144348	0.0384337813127698	0.70063720234282	0.483743611535906	   
df.mm.trans2:probe3	0.0256313827236868	0.0384337813127698	0.666897241130177	0.505039919916584	   
df.mm.trans2:probe4	0.143590578799478	0.0384337813127699	3.73605130421475	0.000200910980399093	***
df.mm.trans2:probe5	-0.0742752455314235	0.0384337813127698	-1.93255107861961	0.0536623044893902	.  
df.mm.trans2:probe6	0.00068277797957874	0.0384337813127698	0.0177650482533157	0.985830944302308	   
df.mm.trans3:probe2	-0.574691788289227	0.0384337813127698	-14.9527777038759	1.64063917107114e-44	***
df.mm.trans3:probe3	-0.400292141849249	0.0384337813127698	-10.4151121273162	7.74075203220445e-24	***
df.mm.trans3:probe4	-0.323746347271434	0.0384337813127698	-8.42348413851925	1.81391256719818e-16	***
df.mm.trans3:probe5	-0.532330245878001	0.0384337813127698	-13.8505821622379	4.53852697496967e-39	***
df.mm.trans3:probe6	-0.45498564647063	0.0384337813127698	-11.8381702484075	8.51463872080297e-30	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.06405694278405	0.0931378156860088	43.6348749737165	2.64741272927358e-209	***
df.mm.trans1	-0.0959552651535962	0.0815557618624798	-1.17656022042191	0.239738975065531	   
df.mm.trans2	-0.134124833519698	0.0731367319770044	-1.83389153294229	0.0670604440248633	.  
df.mm.exp2	0.0771852339707678	0.096449865907236	0.80026274007476	0.423808331394322	   
df.mm.exp3	-0.0553549218974278	0.096449865907236	-0.573924301259966	0.566188610228411	   
df.mm.exp4	-0.0666917677099884	0.096449865907236	-0.691465634323759	0.489483855230162	   
df.mm.exp5	-0.0910430395862602	0.096449865907236	-0.943941587993746	0.345499081438278	   
df.mm.exp6	-0.178990662177280	0.096449865907236	-1.85578964256343	0.0638699597506119	.  
df.mm.exp7	-0.0685935310198141	0.096449865907236	-0.711183269925812	0.477188596088291	   
df.mm.exp8	-0.0535828816416061	0.096449865907236	-0.555551644759582	0.578680673129484	   
df.mm.trans1:exp2	-0.0605266711375632	0.0904779942168312	-0.668965660230162	0.503720264384959	   
df.mm.trans2:exp2	0.000947396548007222	0.0721764706450331	0.0131261135317430	0.989530617715852	   
df.mm.trans1:exp3	0.0499648313322991	0.0904779942168312	0.55223186328112	0.580951650087285	   
df.mm.trans2:exp3	-0.0189725938860624	0.0721764706450331	-0.262863973764669	0.792726573069234	   
df.mm.trans1:exp4	0.0350203576547178	0.0904779942168312	0.387059394473215	0.698820452909663	   
df.mm.trans2:exp4	0.107160054194443	0.0721764706450331	1.48469512622002	0.138038687381559	   
df.mm.trans1:exp5	0.0791504015853285	0.0904779942168312	0.874802787909334	0.381957221632158	   
df.mm.trans2:exp5	0.0331945197442679	0.0721764706450331	0.459907771156058	0.645713787000958	   
df.mm.trans1:exp6	0.17353019477732	0.0904779942168312	1.91792707474763	0.0554942251499147	.  
df.mm.trans2:exp6	0.092082986570527	0.0721764706450331	1.27580339891368	0.202414298621064	   
df.mm.trans1:exp7	0.0366333414874566	0.0904779942168312	0.404886755111575	0.685674537665276	   
df.mm.trans2:exp7	0.0855845994436095	0.0721764706450331	1.18576869551461	0.236083763759170	   
df.mm.trans1:exp8	0.0553941421895518	0.0904779942168312	0.612238839609987	0.540562631219422	   
df.mm.trans2:exp8	0.0256113998365447	0.0721764706450331	0.354844170235237	0.722804650238507	   
df.mm.trans1:probe2	0.00806308390502937	0.0554062296954309	0.145526666393875	0.884333599253401	   
df.mm.trans1:probe3	-0.109780281362402	0.0554062296954309	-1.98137072249576	0.0479092174220386	*  
df.mm.trans1:probe4	-0.0266080775754831	0.0554062296954309	-0.48023620668196	0.631197454020504	   
df.mm.trans1:probe5	0.0700966099120396	0.0554062296954309	1.26513950321763	0.206208573384832	   
df.mm.trans1:probe6	-0.0413389978378437	0.0554062296954309	-0.746107397400706	0.455832950725760	   
df.mm.trans1:probe7	-0.0167481842102895	0.0554062296954309	-0.302279803234303	0.762521369453451	   
df.mm.trans1:probe8	-0.058325321259706	0.0554062296954309	-1.05268525904616	0.292819397661627	   
df.mm.trans1:probe9	-0.0983507856125849	0.0554062296954309	-1.77508533161742	0.0762834451132281	.  
df.mm.trans1:probe10	-0.12397530281404	0.055406229695431	-2.23756973711322	0.0255377932032367	*  
df.mm.trans1:probe11	-0.0526383340064153	0.055406229695431	-0.950043601518623	0.342391712352464	   
df.mm.trans1:probe12	-0.0345516156035592	0.0554062296954309	-0.623605247884399	0.533073727541125	   
df.mm.trans1:probe13	-0.0122107084955458	0.0554062296954309	-0.220385118472567	0.825630343329895	   
df.mm.trans1:probe14	-0.115491900536603	0.0554062296954309	-2.08445694954276	0.0374515359324698	*  
df.mm.trans1:probe15	-0.0424699549658762	0.0554062296954309	-0.766519490666201	0.443604967927155	   
df.mm.trans1:probe16	-0.0579161934653462	0.0554062296954309	-1.04530111115145	0.296215734587711	   
df.mm.trans1:probe17	-0.00599975352432363	0.0554062296954309	-0.108286623314822	0.913796877267205	   
df.mm.trans1:probe18	-0.0343754324647514	0.0554062296954309	-0.620425404394304	0.5351635003114	   
df.mm.trans1:probe19	-0.0627756777581	0.0554062296954309	-1.13300757158859	0.257567905081402	   
df.mm.trans1:probe20	0.0337920943040208	0.0554062296954309	0.609897018616437	0.542112092522236	   
df.mm.trans1:probe21	-0.0849075003495233	0.0554062296954309	-1.53245403659952	0.125826023648315	   
df.mm.trans1:probe22	-0.0271076870217519	0.0554062296954309	-0.489253413754435	0.624803280452381	   
df.mm.trans2:probe2	0.0482579980020173	0.0554062296954309	0.87098505470039	0.384036987161677	   
df.mm.trans2:probe3	-0.0244445079564479	0.0554062296954309	-0.441186994509819	0.659202943735997	   
df.mm.trans2:probe4	-0.0132265589670548	0.0554062296954309	-0.2387197078697	0.811387203242302	   
df.mm.trans2:probe5	0.00219150723977235	0.0554062296954309	0.0395534446544930	0.968459518179741	   
df.mm.trans2:probe6	0.0815183631871326	0.0554062296954309	1.47128515394822	0.141627393215214	   
df.mm.trans3:probe2	0.0340836493265662	0.0554062296954309	0.615159152931442	0.538633525024091	   
df.mm.trans3:probe3	0.0061559809249023	0.0554062296954309	0.111106295424573	0.911561352726142	   
df.mm.trans3:probe4	0.033507725092631	0.0554062296954309	0.604764577500104	0.545515706771125	   
df.mm.trans3:probe5	0.0772702853197306	0.0554062296954309	1.394613669699	0.163539327653362	   
df.mm.trans3:probe6	0.0492059643955845	0.0554062296954309	0.888094437504061	0.374770560979926