chr2.13697_chr2_157398799_157402651_+_2.R 

fitVsDatCorrelation=0.751784204488347
cont.fitVsDatCorrelation=0.298748467442389

fstatistic=9566.22143469733,53,715
cont.fstatistic=4560.16654614266,53,715

residuals=-0.631796009548603,-0.0850269635462234,-0.00595164311366421,0.0689375428579011,1.71324618822956
cont.residuals=-0.549170912359697,-0.160387794979881,-0.0210149017327648,0.113727091888426,1.47718407377874

predictedValues:
Include	Exclude	Both
chr2.13697_chr2_157398799_157402651_+_2.R.tl.Lung	56.2130818378253	75.7793251765736	75.1598278902454
chr2.13697_chr2_157398799_157402651_+_2.R.tl.cerebhem	57.2599632052451	78.826688849247	58.4288284917105
chr2.13697_chr2_157398799_157402651_+_2.R.tl.cortex	56.6841193444109	94.4413765457853	71.1983124281692
chr2.13697_chr2_157398799_157402651_+_2.R.tl.heart	59.5550486699802	74.540903847301	64.4965007888465
chr2.13697_chr2_157398799_157402651_+_2.R.tl.kidney	56.830992841892	74.7976727849209	76.208173944222
chr2.13697_chr2_157398799_157402651_+_2.R.tl.liver	56.7027965907735	67.0355546897426	63.2648809978793
chr2.13697_chr2_157398799_157402651_+_2.R.tl.stomach	59.4117990588003	82.8732850046824	63.5346584672452
chr2.13697_chr2_157398799_157402651_+_2.R.tl.testicle	58.2555859498159	77.7075699081045	63.9494228062425


diffExp=-19.5662433387482,-21.5667256440018,-37.7572572013745,-14.9858551773209,-17.9666799430289,-10.3327580989692,-23.4614859458821,-19.4519839582886
diffExpScore=0.993979131282761
diffExp1.5=0,0,-1,0,0,0,0,0
diffExp1.5Score=0.5
diffExp1.4=0,0,-1,0,0,0,0,0
diffExp1.4Score=0.5
diffExp1.3=-1,-1,-1,0,-1,0,-1,-1
diffExp1.3Score=0.857142857142857
diffExp1.2=-1,-1,-1,-1,-1,0,-1,-1
diffExp1.2Score=0.875

cont.predictedValues:
Include	Exclude	Both
Lung	62.7889525232851	64.2866305884157	63.6332157692097
cerebhem	65.4383526116792	63.7907930390321	63.1233735058312
cortex	66.4329463171036	66.0293923464363	61.4845990098833
heart	60.6466069997205	58.6641024477696	61.09355977109
kidney	67.8789170938559	68.2160691403995	61.959616334318
liver	66.0932749106527	69.3312559162873	56.514957809663
stomach	63.4567069533	62.6077598666977	72.707869083478
testicle	62.0398644732737	63.2015739848251	58.3184886404821
cont.diffExp=-1.49767806513055,1.64755957264710,0.403553970667332,1.98250455195089,-0.337152046543579,-3.23798100563458,0.848947086602315,-1.16170951155139
cont.diffExpScore=4.72674166721295

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=0.0261296185361211
cont.tran.correlation=0.862923794716059

tran.covariance=0.000119712024006342
cont.tran.covariance=0.00176277543737722

tran.mean=67.9322352690688
cont.tran.mean=64.4314499507959

weightedLogRatios:
wLogRatio
Lung	-1.2480139238009
cerebhem	-1.34490381486427
cortex	-2.19137854103577
heart	-0.942480684805572
kidney	-1.14756186536427
liver	-0.689941414132849
stomach	-1.41478508934869
testicle	-1.21263645833801

cont.weightedLogRatios:
wLogRatio
Lung	-0.0978629602407482
cerebhem	0.106291678380701
cortex	0.0255493929984404
heart	0.135882329846084
kidney	-0.0209097038481706
liver	-0.201597618118093
stomach	0.0558090700212248
testicle	-0.0767508251607552

varWeightedLogRatios=0.191126859877991
cont.varWeightedLogRatios=0.0127713006873829

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.12731715269679	0.0821846875652061	50.2200260775115	1.16040423681806e-236	***
df.mm.trans1	-0.324544530034840	0.0729815931016646	-4.44693677188909	1.00963064899985e-05	***
df.mm.trans2	0.342706633791632	0.0663862494847149	5.16231352805281	3.16451888919677e-07	***
df.mm.exp2	0.309685762375028	0.0894985294943986	3.46023296834626	0.000571748762510376	***
df.mm.exp3	0.282646142482746	0.0894985294943987	3.15810934637128	0.00165474968979543	** 
df.mm.exp4	0.194280002597639	0.0894985294943987	2.17076195212568	0.0302775153717126	*  
df.mm.exp5	-0.0159582476272667	0.0894985294943986	-0.178307372393928	0.858532096596265	   
df.mm.exp6	0.058358186511314	0.0894985294943987	0.652057490117381	0.514573638728832	   
df.mm.exp7	0.312861920278742	0.0894985294943987	3.49572134923538	0.000501963297647703	***
df.mm.exp8	0.222342040531080	0.0894985294943986	2.48430942706154	0.0132081423883320	*  
df.mm.trans1:exp2	-0.291233608023439	0.0849830526008883	-3.42696101293489	0.00064530441679591	***
df.mm.trans2:exp2	-0.270259632391211	0.0715621344700786	-3.77657310521127	0.000172217528725412	***
df.mm.trans1:exp3	-0.27430155581579	0.0849830526008883	-3.22772067395615	0.00130475450831559	** 
df.mm.trans2:exp3	-0.0624923550125343	0.0715621344700786	-0.873260076369906	0.382814517995559	   
df.mm.trans1:exp4	-0.136528432764944	0.0849830526008883	-1.60653716931224	0.108597442431039	   
df.mm.trans2:exp4	-0.210757483469396	0.0715621344700786	-2.94509778153022	0.0033333396976561	** 
df.mm.trans1:exp5	0.0268905704398762	0.0849830526008883	0.316422740968887	0.751774013002328	   
df.mm.trans2:exp5	0.00291951910517111	0.0715621344700786	0.040796981906567	0.967469134477097	   
df.mm.trans1:exp6	-0.0496841571326045	0.0849830526008883	-0.584636061097259	0.558976881768861	   
df.mm.trans2:exp6	-0.180960541380674	0.0715621344700786	-2.52871917139835	0.0116619644758369	*  
df.mm.trans1:exp7	-0.257518579009665	0.0849830526008883	-3.03023451298069	0.00253177333261182	** 
df.mm.trans2:exp7	-0.223374666263788	0.0715621344700786	-3.12140866001118	0.00187235351837881	** 
df.mm.trans1:exp8	-0.186651559276107	0.0849830526008883	-2.19633860591819	0.0283880665702525	*  
df.mm.trans2:exp8	-0.197214863605703	0.0715621344700786	-2.75585496528422	0.00600297998927359	** 
df.mm.trans1:probe2	0.646360931963621	0.0465471349151546	13.8861593337978	5.46273266252292e-39	***
df.mm.trans1:probe3	0.395558548175156	0.0465471349151546	8.49802139049319	1.11769868264389e-16	***
df.mm.trans1:probe4	0.235777791503894	0.0465471349151546	5.06535562142903	5.19342748987905e-07	***
df.mm.trans1:probe5	0.0510544260835389	0.0465471349151546	1.09683283786639	0.273083739058647	   
df.mm.trans1:probe6	0.141709049783516	0.0465471349151546	3.04442045771068	0.00241684042261863	** 
df.mm.trans1:probe7	0.156368169559361	0.0465471349151546	3.35935111461503	0.00082270894584236	***
df.mm.trans1:probe8	0.0326708680786507	0.0465471349151546	0.701887842038026	0.482977517034696	   
df.mm.trans1:probe9	0.301552521298504	0.0465471349151546	6.47843356735424	1.72464872237568e-10	***
df.mm.trans1:probe10	0.146557242330864	0.0465471349151546	3.14857708423959	0.00170890691540569	** 
df.mm.trans1:probe11	0.236965821752531	0.0465471349151546	5.0908787873726	4.56206127950446e-07	***
df.mm.trans1:probe12	0.348290641681139	0.0465471349151546	7.4825366226299	2.14539145853241e-13	***
df.mm.trans1:probe13	0.307282997705103	0.0465471349151546	6.6015448268774	7.93226858169827e-11	***
df.mm.trans1:probe14	0.242701077833961	0.0465471349151546	5.21409273151512	2.42085187761666e-07	***
df.mm.trans1:probe15	0.413260142710348	0.0465471349151546	8.87831535632929	5.41677565142725e-18	***
df.mm.trans1:probe16	0.308410979366369	0.0465471349151546	6.62577793302501	6.79785246462821e-11	***
df.mm.trans1:probe17	0.216333641133499	0.0465471349151546	4.64762528408739	3.99816073723499e-06	***
df.mm.trans1:probe18	0.440478287247816	0.0465471349151546	9.46305907013854	4.22638065217125e-20	***
df.mm.trans1:probe19	0.319729039569116	0.0465471349151546	6.86893060447036	1.40741697899391e-11	***
df.mm.trans1:probe20	0.606193612919784	0.0465471349151546	13.0232207422593	6.05904137712838e-35	***
df.mm.trans1:probe21	0.209398249128982	0.0465471349151546	4.49862810054088	7.980133484882e-06	***
df.mm.trans1:probe22	0.129144841872691	0.0465471349151546	2.77449604810466	0.0056730244543309	** 
df.mm.trans2:probe2	-0.282997027933379	0.0465471349151546	-6.07979478112288	1.9579986413346e-09	***
df.mm.trans2:probe3	-0.375620731911587	0.0465471349151546	-8.06968533286233	2.9797915762177e-15	***
df.mm.trans2:probe4	-0.231005477037807	0.0465471349151546	-4.96282912920162	8.6918900769159e-07	***
df.mm.trans2:probe5	-0.130516760160466	0.0465471349151546	-2.80396979101657	0.00518466845640895	** 
df.mm.trans2:probe6	-0.401842862666334	0.0465471349151546	-8.63303108556105	3.86190658830979e-17	***
df.mm.trans3:probe2	0.0966712805760639	0.0465471349151546	2.07684706593166	0.03817223438764	*  
df.mm.trans3:probe3	0.302716050756435	0.0465471349151546	6.50343036812516	1.47451172627513e-10	***
df.mm.trans3:probe4	0.297163181377408	0.0465471349151546	6.38413474683399	3.10044791530342e-10	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.28355404018381	0.118942026532915	36.0137973519260	8.6606118411413e-163	***
df.mm.trans1	-0.0102093230399424	0.105622821480283	-0.0966583063855018	0.923024837523528	   
df.mm.trans2	-0.088186614177121	0.0960776913748907	-0.91786774760252	0.358997616763463	   
df.mm.exp2	0.0416310317031486	0.129527005396641	0.321408123160611	0.747995059551862	   
df.mm.exp3	0.117511138095224	0.129527005396641	0.907232725217255	0.364589393554702	   
df.mm.exp4	-0.0855100043078725	0.129527005396641	-0.660171244182028	0.509356553384329	   
df.mm.exp5	0.163927583175227	0.129527005396641	1.26558614300735	0.20607353518029	   
df.mm.exp6	0.245462249838931	0.129527005396641	1.89506619941742	0.0584867645117223	.  
df.mm.exp7	-0.149197731528793	0.129527005396641	-1.15186582961535	0.249761212318442	   
df.mm.exp8	0.0581919516130511	0.129527005396641	0.449265011839456	0.65337664003425	   
df.mm.trans1:exp2	-0.000301656517375284	0.122991968416053	-0.00245265216306524	0.998043752797151	   
df.mm.trans2:exp2	-0.0493738487041426	0.103568505874514	-0.476726474783417	0.633702567847471	   
df.mm.trans1:exp3	-0.0610971679596563	0.122991968416053	-0.496757379741895	0.619512848813232	   
df.mm.trans2:exp3	-0.0907628438252742	0.103568505874514	-0.876355635903878	0.381131055065464	   
df.mm.trans1:exp4	0.0507945514228191	0.122991968416053	0.412990799943888	0.679737172355134	   
df.mm.trans2:exp4	-0.00601368575902238	0.103568505874514	-0.0580648113849273	0.953713226875521	   
df.mm.trans1:exp5	-0.0859812389445324	0.122991968416053	-0.699080111098622	0.484729306335432	   
df.mm.trans2:exp5	-0.104599115389974	0.103568505874514	-1.00995099337157	0.31286042073616	   
df.mm.trans1:exp6	-0.194174392062109	0.122991968416053	-1.5787566827556	0.114834158422486	   
df.mm.trans2:exp6	-0.169918109205519	0.103568505874514	-1.64063493791631	0.101313021878492	   
df.mm.trans1:exp7	0.159776481881653	0.122991968416053	1.29908061428179	0.194335017062834	   
df.mm.trans2:exp7	0.122735274279361	0.103568505874514	1.18506367590231	0.236386001415040	   
df.mm.trans1:exp8	-0.0701939405776213	0.122991968416053	-0.570719710251094	0.568368971718466	   
df.mm.trans2:exp8	-0.0752144331522256	0.103568505874514	-0.726228813644925	0.467936172434016	   
df.mm.trans1:probe2	-0.187951724743012	0.067365475493435	-2.79003040305607	0.00541067996658434	** 
df.mm.trans1:probe3	-0.185421319311060	0.067365475493435	-2.75246805508157	0.00606475711913877	** 
df.mm.trans1:probe4	-0.102538404025579	0.067365475493435	-1.52212098667026	0.128420885899134	   
df.mm.trans1:probe5	-0.212060261495138	0.067365475493435	-3.14790714296678	0.0017127739415621	** 
df.mm.trans1:probe6	-0.109168725993217	0.067365475493435	-1.62054413174677	0.105556349845188	   
df.mm.trans1:probe7	-0.175330993558402	0.067365475493435	-2.60268323312715	0.00944140635917525	** 
df.mm.trans1:probe8	-0.217076727072688	0.067365475493435	-3.22237355978944	0.00132899341002907	** 
df.mm.trans1:probe9	-0.164143208514246	0.067365475493435	-2.43660728751543	0.0150687586375914	*  
df.mm.trans1:probe10	-0.197218126864200	0.067365475493435	-2.92758457384332	0.00352455654680791	** 
df.mm.trans1:probe11	-0.102396380518541	0.067365475493435	-1.52001273305819	0.128949841537056	   
df.mm.trans1:probe12	-0.226016427559946	0.067365475493435	-3.35507804115437	0.000835320909932201	***
df.mm.trans1:probe13	-0.0912154778672292	0.067365475493435	-1.35403895243222	0.176151750182669	   
df.mm.trans1:probe14	-0.166352860631210	0.067365475493435	-2.46940824528763	0.0137662209699443	*  
df.mm.trans1:probe15	-0.211435048389110	0.067365475493435	-3.13862623013349	0.00176718140235879	** 
df.mm.trans1:probe16	-0.278625278207680	0.067365475493435	-4.13602481340509	3.95373513685962e-05	***
df.mm.trans1:probe17	-0.136719151860950	0.067365475493435	-2.02951364715407	0.0427758817406399	*  
df.mm.trans1:probe18	-0.147527722935157	0.067365475493435	-2.18996038927289	0.0288494630323414	*  
df.mm.trans1:probe19	-0.101995555635849	0.067365475493435	-1.51406272855283	0.130451833662479	   
df.mm.trans1:probe20	-0.0814362751983825	0.067365475493435	-1.20887256568565	0.227111416589815	   
df.mm.trans1:probe21	-0.1570765007484	0.067365475493435	-2.33170625751328	0.0199930237043429	*  
df.mm.trans1:probe22	-0.220998762722233	0.067365475493435	-3.28059382203529	0.00108614115355258	** 
df.mm.trans2:probe2	-0.0862366752151396	0.067365475493435	-1.28013161910426	0.200914010949999	   
df.mm.trans2:probe3	-0.070526635118441	0.067365475493435	-1.04692551491474	0.295487711060015	   
df.mm.trans2:probe4	-0.0507156528610364	0.067365475493435	-0.752843388836153	0.451791823526607	   
df.mm.trans2:probe5	-0.0472839194864152	0.067365475493435	-0.701901369211343	0.482969085558184	   
df.mm.trans2:probe6	-0.0653945053354011	0.067365475493435	-0.970742132470719	0.332004972842997	   
df.mm.trans3:probe2	-0.058671480309649	0.067365475493435	-0.870942866206989	0.384077673472228	   
df.mm.trans3:probe3	-0.0893291723417774	0.067365475493435	-1.32603788049389	0.185250598004054	   
df.mm.trans3:probe4	0.0199106189351468	0.067365475493435	0.295561172682396	0.767651040136524