chrX.25822_chrX_113294719_113297016_+_2.R 

fitVsDatCorrelation=0.864916727342643
cont.fitVsDatCorrelation=0.270321885531488

fstatistic=12308.9992549027,52,692
cont.fstatistic=3335.63672908406,52,692

residuals=-0.482764172673524,-0.0821196525462268,-0.00533066729852403,0.0668864959294417,0.970847697464316
cont.residuals=-0.504522270724914,-0.183125060971317,-0.0557654485494442,0.132841616613811,1.21173490229823

predictedValues:
Include	Exclude	Both
chrX.25822_chrX_113294719_113297016_+_2.R.tl.Lung	54.029667970698	45.1567731484339	60.6158023255912
chrX.25822_chrX_113294719_113297016_+_2.R.tl.cerebhem	51.5731514468881	47.5567481246504	54.0716349290939
chrX.25822_chrX_113294719_113297016_+_2.R.tl.cortex	48.7404903419191	40.5712114344125	53.6101396975372
chrX.25822_chrX_113294719_113297016_+_2.R.tl.heart	52.3018142655337	42.6544487655000	66.725538521235
chrX.25822_chrX_113294719_113297016_+_2.R.tl.kidney	54.9783285378701	41.2716824296725	59.3911954650041
chrX.25822_chrX_113294719_113297016_+_2.R.tl.liver	55.8940967500635	46.0321763059785	59.8422178808955
chrX.25822_chrX_113294719_113297016_+_2.R.tl.stomach	53.1112455795284	43.9531797068439	61.9165815441788
chrX.25822_chrX_113294719_113297016_+_2.R.tl.testicle	52.3478449574789	43.7154047283738	52.9978917879323


diffExp=8.87289482226412,4.01640332223769,8.16927890750661,9.64736550003364,13.7066461081977,9.86192044408499,9.15806587268453,8.63244022910505
diffExpScore=0.986313559270753
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=0,0,0,0,1,0,0,0
diffExp1.3Score=0.5
diffExp1.2=0,0,1,1,1,1,1,0
diffExp1.2Score=0.833333333333333

cont.predictedValues:
Include	Exclude	Both
Lung	55.3612607933652	57.2592035543377	52.5861165763593
cerebhem	52.4499404830342	48.560082010957	59.0858274156803
cortex	52.8378531412435	50.409489970332	48.4618778166972
heart	51.3248637922914	46.9569531600641	55.4205285345196
kidney	52.2183802419355	52.4670077766732	46.9330826349245
liver	52.4281099111661	48.5631646696209	57.7869969287871
stomach	53.5937387452792	50.2387771744399	49.1314600413521
testicle	50.0333827029728	53.9546667335378	45.0543401086465
cont.diffExp=-1.89794276097255,3.88985847207723,2.42836317091143,4.36791063222731,-0.248627534737700,3.86494524154516,3.35496157083935,-3.92128403056491
cont.diffExpScore=1.86738965512448

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=0.332781903484371
cont.tran.correlation=0.386826250869624

tran.covariance=0.000801175770196816
cont.tran.covariance=0.000697237701765024

tran.mean=48.3680165308653
cont.tran.mean=51.7910546788282

weightedLogRatios:
wLogRatio
Lung	0.699603498149489
cerebhem	0.316401981548851
cortex	0.696158406468259
heart	0.786049087303663
kidney	1.10792339707290
liver	0.762185383665043
stomach	0.733918671705111
testicle	0.697018107063086

cont.weightedLogRatios:
wLogRatio
Lung	-0.135869218224303
cerebhem	0.302167395025288
cortex	0.185544942820528
heart	0.346321613555360
kidney	-0.0187996108983384
liver	0.300272497117411
stomach	0.255290452671641
testicle	-0.298073809574914

varWeightedLogRatios=0.0458616703809035
cont.varWeightedLogRatios=0.0569939121900178

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.41946003235975	0.0720183571236487	47.480394845565	6.95127878016665e-220	***
df.mm.trans1	0.415875576191641	0.0646830165910402	6.42944003092842	2.38681911477630e-10	***
df.mm.trans2	0.378389111996098	0.0594848185146867	6.36110391599622	3.64087050619411e-10	***
df.mm.exp2	0.119497209387303	0.0814794699072744	1.46659286717616	0.142941119098434	   
df.mm.exp3	-0.0872873400217342	0.0814794699072744	-1.07128016567940	0.284416956016136	   
df.mm.exp4	-0.185543090774232	0.0814794699072744	-2.27717596819646	0.0230800674660443	*  
df.mm.exp5	-0.0521482625493204	0.0814794699072744	-0.640017204440166	0.522373254573956	   
df.mm.exp6	0.0659700492048431	0.0814794699072744	0.809652410354641	0.418418287542861	   
df.mm.exp7	-0.0653923253639699	0.0814794699072744	-0.802561988171842	0.422503485883279	   
df.mm.exp8	0.0702412241758252	0.0814794699072744	0.86207267003285	0.388946164080418	   
df.mm.trans1:exp2	-0.166029295670726	0.0780106532148413	-2.12829003256109	0.0336657568272247	*  
df.mm.trans2:exp2	-0.067713797042385	0.067899558256062	-0.99726417640338	0.318984882344195	   
df.mm.trans1:exp3	-0.0157358540327289	0.0780106532148413	-0.201714168312274	0.840199466474716	   
df.mm.trans2:exp3	-0.0197942058291367	0.067899558256062	-0.291521864611976	0.770739587797171	   
df.mm.trans1:exp4	0.153040848272636	0.0780106532148413	1.96179421612022	0.050186915507047	.  
df.mm.trans2:exp4	0.128534384985400	0.067899558256062	1.89300767614235	0.058773846157739	.  
df.mm.trans1:exp5	0.0695540409883817	0.0780106532148413	0.891596700220287	0.37291904496648	   
df.mm.trans2:exp5	-0.037815411082506	0.067899558256062	-0.556931621556314	0.577754221677543	   
df.mm.trans1:exp6	-0.0320445809361887	0.0780106532148413	-0.410771857632546	0.681366993469381	   
df.mm.trans2:exp6	-0.046769695302457	0.067899558256062	-0.688807063016223	0.491175555718625	   
df.mm.trans1:exp7	0.0482477097877683	0.0780106532148413	0.618475910654077	0.536465186674763	   
df.mm.trans2:exp7	0.0383770121829966	0.067899558256062	0.565202678318902	0.572119104611468	   
df.mm.trans1:exp8	-0.101863756313231	0.0780106532148413	-1.3057672524892	0.192065634865938	   
df.mm.trans2:exp8	-0.102680956218633	0.067899558256062	-1.51224777974849	0.130927369987410	   
df.mm.trans1:probe2	-0.0125000857390666	0.0390053266074207	-0.320471249090591	0.748707868269619	   
df.mm.trans1:probe3	0.082061474172887	0.0390053266074206	2.1038530198405	0.0357515315339825	*  
df.mm.trans1:probe4	0.20726493760064	0.0390053266074207	5.31375982790024	1.44977182589448e-07	***
df.mm.trans1:probe5	0.0102781361622668	0.0390053266074206	0.263505963319159	0.79223907163418	   
df.mm.trans1:probe6	0.094807816328431	0.0390053266074207	2.43063767373746	0.0153253414291180	*  
df.mm.trans1:probe7	0.497061248609519	0.0390053266074207	12.7434197286007	1.45375139702045e-33	***
df.mm.trans1:probe8	0.0523235651681725	0.0390053266074207	1.34144666175460	0.180215536509153	   
df.mm.trans1:probe9	-0.0255023860226579	0.0390053266074206	-0.653818035657882	0.513446246077519	   
df.mm.trans1:probe10	-0.0116753210265993	0.0390053266074206	-0.299326323917465	0.764780947416433	   
df.mm.trans1:probe11	0.032147535355587	0.0390053266074207	0.824183211671173	0.41011944069695	   
df.mm.trans1:probe12	0.0766513316952298	0.0390053266074207	1.96515036181359	0.0497964431151643	*  
df.mm.trans1:probe13	-0.046984377031263	0.0390053266074207	-1.20456309734692	0.228783821368492	   
df.mm.trans1:probe14	-0.0145972102415487	0.0390053266074206	-0.374236329013884	0.708343140110482	   
df.mm.trans1:probe15	-0.0118468091357848	0.0390053266074206	-0.303722854445500	0.761430341780598	   
df.mm.trans1:probe16	0.000548716519256357	0.0390053266074206	0.0140677329734746	0.988779998000095	   
df.mm.trans1:probe17	0.662340817859827	0.0390053266074206	16.9807786645688	2.54265083300048e-54	***
df.mm.trans1:probe18	0.306132161014129	0.0390053266074207	7.8484706485162	1.60328185349342e-14	***
df.mm.trans1:probe19	0.315911781763257	0.0390053266074207	8.09919591092864	2.49873076248077e-15	***
df.mm.trans1:probe20	0.492983548827039	0.0390053266074206	12.6388776022517	4.29299777663679e-33	***
df.mm.trans1:probe21	0.738404401516458	0.0390053266074206	18.9308606218921	1.01211866410448e-64	***
df.mm.trans1:probe22	0.409131066828436	0.0390053266074207	10.4891075761586	5.46519431857244e-24	***
df.mm.trans2:probe2	0.0569801669620594	0.0390053266074207	1.46083040235892	0.144515870816920	   
df.mm.trans2:probe3	0.0138786341890056	0.0390053266074207	0.35581381816619	0.722088440007033	   
df.mm.trans2:probe4	-0.0116398757994771	0.0390053266074207	-0.298417596053732	0.765474043844579	   
df.mm.trans2:probe5	0.0436417730966195	0.0390053266074207	1.11886700849511	0.263585109588035	   
df.mm.trans2:probe6	0.00775954911280146	0.0390053266074207	0.198935627200343	0.842371534305787	   
df.mm.trans3:probe2	-0.0960418740290986	0.0390053266074207	-2.46227585774982	0.0140484819532586	*  
df.mm.trans3:probe3	-0.231465417020877	0.0390053266074207	-5.93420020169351	4.66760342314755e-09	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.00267624613338	0.138145025659954	28.9744507774463	1.72243831122669e-121	***
df.mm.trans1	-0.0394128861933889	0.124074435235878	-0.317655173029490	0.750842343993472	   
df.mm.trans2	0.0612505999590172	0.114103294052936	0.536799576799254	0.591578600713755	   
df.mm.exp2	-0.335346387395605	0.156293255090148	-2.14562290101502	0.0322503500695314	*  
df.mm.exp3	-0.0923864989425024	0.156293255090149	-0.591109954739983	0.554639772243333	   
df.mm.exp4	-0.326559809687681	0.156293255090149	-2.08940436680600	0.0370359902742423	*  
df.mm.exp5	-0.032120054170144	0.156293255090149	-0.205511454423401	0.837232989610955	   
df.mm.exp6	-0.313471906934262	0.156293255090149	-2.00566497097686	0.0452808327980856	*  
df.mm.exp7	-0.0952964689707897	0.156293255090149	-0.609728608671075	0.542241749606293	   
df.mm.exp8	-0.00605102777020952	0.156293255090149	-0.0387158599180709	0.969128094801639	   
df.mm.trans1:exp2	0.281325502003369	0.149639399182784	1.88002293206036	0.0605248088830428	.  
df.mm.trans2:exp2	0.170559832715502	0.130244379241790	1.30953699275474	0.190787122134758	   
df.mm.trans1:exp3	0.0457342628506682	0.149639399182784	0.305629821426935	0.759978426656626	   
df.mm.trans2:exp3	-0.0350224409311229	0.130244379241790	-0.268897906650589	0.788088396547191	   
df.mm.trans1:exp4	0.250855033264714	0.149639399182784	1.67639695584646	0.0941121340514333	.  
df.mm.trans2:exp4	0.128202711214443	0.130244379241790	0.984324329086348	0.325300093265622	   
df.mm.trans1:exp5	-0.0263254865003484	0.149639399182785	-0.175926170808744	0.86040341310849	   
df.mm.trans2:exp5	-0.0552837873626064	0.130244379241790	-0.424461982040511	0.671360855558831	   
df.mm.trans1:exp6	0.259034717613771	0.149639399182784	1.73105959412040	0.0838868697325942	.  
df.mm.trans2:exp6	0.148748831570807	0.130244379241790	1.14207486293642	0.253817918151738	   
df.mm.trans1:exp7	0.0628486302737548	0.149639399182784	0.420000552107171	0.67461542204489	   
df.mm.trans2:exp7	-0.0355047391343951	0.130244379241790	-0.272600931733743	0.785241318347676	   
df.mm.trans1:exp8	-0.0951386210207095	0.149639399182784	-0.635785906253858	0.525126175353984	   
df.mm.trans2:exp8	-0.053393173469791	0.130244379241790	-0.409946085816648	0.681972373964561	   
df.mm.trans1:probe2	0.0457286870470806	0.0748196995913923	0.611185119651851	0.5412777425554	   
df.mm.trans1:probe3	0.0604445923827808	0.0748196995913922	0.807870022372219	0.419443024637474	   
df.mm.trans1:probe4	0.173467965478353	0.0748196995913923	2.31847984455567	0.0207137888421117	*  
df.mm.trans1:probe5	0.128980552409777	0.0748196995913922	1.72388492755478	0.0851752478253665	.  
df.mm.trans1:probe6	0.0267327532678491	0.0748196995913923	0.357295650929405	0.720979421583999	   
df.mm.trans1:probe7	0.0849043590902855	0.0748196995913923	1.13478615329877	0.256857843804226	   
df.mm.trans1:probe8	0.0411414171655462	0.0748196995913923	0.549874129276502	0.582583132762895	   
df.mm.trans1:probe9	0.0156811932903608	0.0748196995913922	0.209586424110220	0.834052162418711	   
df.mm.trans1:probe10	0.140598774937842	0.0748196995913922	1.87916786228340	0.0606416177243331	.  
df.mm.trans1:probe11	0.0254168262523332	0.0748196995913923	0.339707675801164	0.734179758559295	   
df.mm.trans1:probe12	0.0728844188644375	0.0748196995913923	0.974134075149676	0.330330362830133	   
df.mm.trans1:probe13	-0.00466015954451077	0.0748196995913923	-0.0622851945404884	0.95035370398967	   
df.mm.trans1:probe14	0.0151933202536255	0.0748196995913922	0.203065774610160	0.839143315442214	   
df.mm.trans1:probe15	0.0040477967497065	0.0748196995913922	0.0541006816628837	0.956870563785285	   
df.mm.trans1:probe16	0.00255485761810557	0.0748196995913922	0.0341468574728079	0.972769890279435	   
df.mm.trans1:probe17	0.0640950236958733	0.0748196995913922	0.856659730604522	0.391929516912223	   
df.mm.trans1:probe18	0.105674177570152	0.0748196995913923	1.412384414095	0.158286353712354	   
df.mm.trans1:probe19	0.0241600851261905	0.0748196995913923	0.322910747545557	0.746860381573654	   
df.mm.trans1:probe20	0.156481302260715	0.0748196995913922	2.09144520915341	0.0368522082600773	*  
df.mm.trans1:probe21	0.0536539212327335	0.0748196995913922	0.717109551705635	0.473548475884513	   
df.mm.trans1:probe22	0.0282362737686849	0.0748196995913923	0.377390900028866	0.705998840305154	   
df.mm.trans2:probe2	0.0263958620799878	0.0748196995913923	0.352792943892341	0.724351104014099	   
df.mm.trans2:probe3	-0.0689000081864661	0.0748196995913923	-0.920880577745501	0.357433695486348	   
df.mm.trans2:probe4	-0.049090039354113	0.0748196995913923	-0.656111152840831	0.511970702213201	   
df.mm.trans2:probe5	0.0215391622448622	0.0748196995913923	0.287880897176714	0.773524105709485	   
df.mm.trans2:probe6	-0.0769910790222209	0.0748196995913923	-1.02902149357304	0.303829106201574	   
df.mm.trans3:probe2	-0.117546598639424	0.0748196995913923	-1.57106483027028	0.116624687339111	   
df.mm.trans3:probe3	-0.0686388565898504	0.0748196995913923	-0.917390165487206	0.359257837650187